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Sample records for connectivity predicts individual

  1. Predicting individual brain maturity using dynamic functional connectivity

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    Jian eQin

    2015-07-01

    Full Text Available Neuroimaging-based functional connectivity (FC analyses have revealed significant developmental trends in specific intrinsic connectivity networks linked to cognitive and behavioral maturation. However, knowledge of how brain functional maturation is associated with FC dynamics at rest is limited. Here, we examined age-related differences in the temporal variability of FC dynamics with data publicly released by the Nathan Kline Institute (NKI (n=183, ages 7-30 and showed that dynamic inter-region interactions can be used to accurately predict individual brain maturity across development. Furthermore, we identified a significant age-dependent trend underlying dynamic inter-network FC, including increasing variability of the connections between the visual network, default mode network (DMN and cerebellum as well as within the cerebellum and DMN and decreasing variability within the cerebellum and between the cerebellum and DMN as well as the cingulo-opercular network. Overall, the results suggested significant developmental changes in dynamic inter-network interaction, which may shed new light on the functional organization of typical developmental brains.

  2. Thalamic functional connectivity predicts seizure laterality in individual TLE patients: Application of a biomarker development strategy

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    Daniel S. Barron

    2015-01-01

    No significant differences in functional connection strength in patient and control groups were observed with Mann-Whitney Tests (corrected for multiple comparisons. Notwithstanding the lack of group differences, individual patient difference scores (from control mean connection strength successfully predicted seizure onset zone as shown in ROC curves: discriminant analysis (two-dimensional predicted seizure onset zone with 85% sensitivity and 91% specificity; logistic regression (four-dimensional achieved 86% sensitivity and 100% specificity. The strongest markers in both analyses were left thalamo-hippocampal and right thalamo-entorhinal cortex functional connection strength. Thus, this study shows that thalamic functional connections are sensitive and specific markers of seizure onset laterality in individual temporal lobe epilepsy patients. This study also advances an overall strategy for the programmatic development of neuroimaging biomarkers in clinical and genetic populations: a disease model informed by coordinate-based meta-analysis was used to anatomically constrain individual patient analyses.

  3. Thalamic functional connectivity predicts seizure laterality in individual TLE patients: application of a biomarker development strategy.

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    Barron, Daniel S; Fox, Peter T; Pardoe, Heath; Lancaster, Jack; Price, Larry R; Blackmon, Karen; Berry, Kristen; Cavazos, Jose E; Kuzniecky, Ruben; Devinsky, Orrin; Thesen, Thomas

    2015-01-01

    Noninvasive markers of brain function could yield biomarkers in many neurological disorders. Disease models constrained by coordinate-based meta-analysis are likely to increase this yield. Here, we evaluate a thalamic model of temporal lobe epilepsy that we proposed in a coordinate-based meta-analysis and extended in a diffusion tractography study of an independent patient population. Specifically, we evaluated whether thalamic functional connectivity (resting-state fMRI-BOLD) with temporal lobe areas can predict seizure onset laterality, as established with intracranial EEG. Twenty-four lesional and non-lesional temporal lobe epilepsy patients were studied. No significant differences in functional connection strength in patient and control groups were observed with Mann-Whitney Tests (corrected for multiple comparisons). Notwithstanding the lack of group differences, individual patient difference scores (from control mean connection strength) successfully predicted seizure onset zone as shown in ROC curves: discriminant analysis (two-dimensional) predicted seizure onset zone with 85% sensitivity and 91% specificity; logistic regression (four-dimensional) achieved 86% sensitivity and 100% specificity. The strongest markers in both analyses were left thalamo-hippocampal and right thalamo-entorhinal cortex functional connection strength. Thus, this study shows that thalamic functional connections are sensitive and specific markers of seizure onset laterality in individual temporal lobe epilepsy patients. This study also advances an overall strategy for the programmatic development of neuroimaging biomarkers in clinical and genetic populations: a disease model informed by coordinate-based meta-analysis was used to anatomically constrain individual patient analyses.

  4. Chronnectome fingerprinting: Identifying individuals and predicting higher cognitive functions using dynamic brain connectivity patterns.

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    Liu, Jin; Liao, Xuhong; Xia, Mingrui; He, Yong

    2018-02-01

    The human brain is a large, interacting dynamic network, and its architecture of coupling among brain regions varies across time (termed the "chronnectome"). However, very little is known about whether and how the dynamic properties of the chronnectome can characterize individual uniqueness, such as identifying individuals as a "fingerprint" of the brain. Here, we employed multiband resting-state functional magnetic resonance imaging data from the Human Connectome Project (N = 105) and a sliding time-window dynamic network analysis approach to systematically examine individual time-varying properties of the chronnectome. We revealed stable and remarkable individual variability in three dynamic characteristics of brain connectivity (i.e., strength, stability, and variability), which was mainly distributed in three higher order cognitive systems (i.e., default mode, dorsal attention, and fronto-parietal) and in two primary systems (i.e., visual and sensorimotor). Intriguingly, the spatial patterns of these dynamic characteristics of brain connectivity could successfully identify individuals with high accuracy and could further significantly predict individual higher cognitive performance (e.g., fluid intelligence and executive function), which was primarily contributed by the higher order cognitive systems. Together, our findings highlight that the chronnectome captures inherent functional dynamics of individual brain networks and provides implications for individualized characterization of health and disease. © 2017 Wiley Periodicals, Inc.

  5. Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity.

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    Finn, Emily S; Shen, Xilin; Scheinost, Dustin; Rosenberg, Monica D; Huang, Jessica; Chun, Marvin M; Papademetris, Xenophon; Constable, R Todd

    2015-11-01

    Functional magnetic resonance imaging (fMRI) studies typically collapse data from many subjects, but brain functional organization varies between individuals. Here we establish that this individual variability is both robust and reliable, using data from the Human Connectome Project to demonstrate that functional connectivity profiles act as a 'fingerprint' that can accurately identify subjects from a large group. Identification was successful across scan sessions and even between task and rest conditions, indicating that an individual's connectivity profile is intrinsic, and can be used to distinguish that individual regardless of how the brain is engaged during imaging. Characteristic connectivity patterns were distributed throughout the brain, but the frontoparietal network emerged as most distinctive. Furthermore, we show that connectivity profiles predict levels of fluid intelligence: the same networks that were most discriminating of individuals were also most predictive of cognitive behavior. Results indicate the potential to draw inferences about single subjects on the basis of functional connectivity fMRI.

  6. Functional connectivity between somatosensory and motor brain areas predicts individual differences in motor learning by observing.

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    McGregor, Heather R; Gribble, Paul L

    2017-08-01

    Action observation can facilitate the acquisition of novel motor skills; however, there is considerable individual variability in the extent to which observation promotes motor learning. Here we tested the hypothesis that individual differences in brain function or structure can predict subsequent observation-related gains in motor learning. Subjects underwent an anatomical MRI scan and resting-state fMRI scans to assess preobservation gray matter volume and preobservation resting-state functional connectivity (FC), respectively. On the following day, subjects observed a video of a tutor adapting her reaches to a novel force field. After observation, subjects performed reaches in a force field as a behavioral assessment of gains in motor learning resulting from observation. We found that individual differences in resting-state FC, but not gray matter volume, predicted postobservation gains in motor learning. Preobservation resting-state FC between left primary somatosensory cortex and bilateral dorsal premotor cortex, primary motor cortex, and primary somatosensory cortex and left superior parietal lobule was positively correlated with behavioral measures of postobservation motor learning. Sensory-motor resting-state FC can thus predict the extent to which observation will promote subsequent motor learning. NEW & NOTEWORTHY We show that individual differences in preobservation brain function can predict subsequent observation-related gains in motor learning. Preobservation resting-state functional connectivity within a sensory-motor network may be used as a biomarker for the extent to which observation promotes motor learning. This kind of information may be useful if observation is to be used as a way to boost neuroplasticity and sensory-motor recovery for patients undergoing rehabilitation for diseases that impair movement such as stroke. Copyright © 2017 the American Physiological Society.

  7. Brain network connectivity in individuals with schizophrenia and their siblings.

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    Repovs, Grega; Csernansky, John G; Barch, Deanna M

    2011-05-15

    Research on brain activity in schizophrenia has shown that changes in the function of any single region cannot explain the range of cognitive and affective impairments in this illness. Rather, neural circuits that support sensory, cognitive, and emotional processes are now being investigated as substrates for cognitive and affective impairments in schizophrenia, a shift in focus consistent with long-standing hypotheses about schizophrenia as a disconnection syndrome. Our goal was to further examine alterations in functional connectivity within and between the default mode network and three cognitive control networks (frontal-parietal, cingulo-opercular, and cerebellar) as a basis for such impairments. Resting state functional magnetic resonance imaging was collected from 40 individuals with DSM-IV-TR schizophrenia, 31 siblings of individuals with schizophrenia, 15 healthy control subjects, and 18 siblings of healthy control subjects while they rested quietly with their eyes closed. Connectivity metrics were compared between patients and control subjects for both within- and between-network connections and were used to predict clinical symptoms and cognitive function. Individuals with schizophrenia showed reduced distal and somewhat enhanced local connectivity between the cognitive control networks compared with control subjects. Additionally, greater connectivity between the frontal-parietal and cerebellar regions was robustly predictive of better cognitive performance across groups and predictive of fewer disorganization symptoms among patients. These results are consistent with the hypothesis that impairments of executive function and cognitive control result from disruption in the coordination of activity across brain networks and additionally suggest that these might reflect impairments in normal pattern of brain connectivity development. Copyright © 2011 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

  8. Connectivity patterns in cognitive control networks predict naturalistic multitasking ability.

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    Wen, Tanya; Liu, De-Cyuan; Hsieh, Shulan

    2018-06-01

    Multitasking is a fundamental aspect of everyday life activities. To achieve a complex, multi-component goal, the tasks must be subdivided into sub-tasks and component steps, a critical function of prefrontal networks. The prefrontal cortex is considered to be organized in a cascade of executive processes from the sensorimotor to anterior prefrontal cortex, which includes execution of specific goal-directed action, to encoding and maintaining task rules, and finally monitoring distal goals. In the current study, we used a virtual multitasking paradigm to tap into real-world performance and relate it to each individual's resting-state functional connectivity in fMRI. While did not find any correlation between global connectivity of any of the major networks with multitasking ability, global connectivity of the lateral prefrontal cortex (LPFC) was predictive of multitasking ability. Further analysis showed that multivariate connectivity patterns within the sensorimotor network (SMN), and between-network connectivity of the frontoparietal network (FPN) and dorsal attention network (DAN), predicted individual multitasking ability and could be generalized to novel individuals. Together, these results support previous research that prefrontal networks underlie multitasking abilities and show that connectivity patterns in the cascade of prefrontal networks may explain individual differences in performance. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

  9. Functional connectivity patterns reflect individual differences in conflict adaptation.

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    Wang, Xiangpeng; Wang, Ting; Chen, Zhencai; Hitchman, Glenn; Liu, Yijun; Chen, Antao

    2015-04-01

    Individuals differ in the ability to utilize previous conflict information to optimize current conflict resolution, which is termed the conflict adaptation effect. Previous studies have linked individual differences in conflict adaptation to distinct brain regions. However, the network-based neural mechanisms subserving the individual differences of the conflict adaptation effect have not been studied. The present study employed a psychophysiological interaction (PPI) analysis with a color-naming Stroop task to examine this issue. The main results were as follows: (1) the anterior cingulate cortex (ACC)-seeded PPI revealed the involvement of the salience network (SN) in conflict adaptation, while the posterior parietal cortex (PPC)-seeded PPI revealed the engagement of the central executive network (CEN). (2) Participants with high conflict adaptation effect showed higher intra-CEN connectivity and lower intra-SN connectivity; while those with low conflict adaptation effect showed higher intra-SN connectivity and lower intra-CEN connectivity. (3) The PPC-centered intra-CEN connectivity positively predicted the conflict adaptation effect; while the ACC-centered intra-SN connectivity had a negative correlation with this effect. In conclusion, our data demonstrated that conflict adaptation is likely supported by the CEN and the SN, providing a new perspective on studying individual differences in conflict adaptation on the basis of large-scale networks. Copyright © 2015 Elsevier Ltd. All rights reserved.

  10. Baseline frontostriatal-limbic connectivity predicts reward-based memory formation.

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    Hamann, Janne M; Dayan, Eran; Hummel, Friedhelm C; Cohen, Leonardo G

    2014-12-01

    Reward mediates the acquisition and long-term retention of procedural skills in humans. Yet, learning under rewarded conditions is highly variable across individuals and the mechanisms that determine interindividual variability in rewarded learning are not known. We postulated that baseline functional connectivity in a large-scale frontostriatal-limbic network could predict subsequent interindividual variability in rewarded learning. Resting-state functional MRI was acquired in two groups of subjects (n = 30) who then trained on a visuomotor procedural learning task with or without reward feedback. We then tested whether baseline functional connectivity within the frontostriatal-limbic network predicted memory strength measured immediately, 24 h and 1 month after training in both groups. We found that connectivity in the frontostriatal-limbic network predicted interindividual variability in the rewarded but not in the unrewarded learning group. Prediction was strongest for long-term memory. Similar links between connectivity and reward-based memory were absent in two control networks, a fronto-parieto-temporal language network and the dorsal attention network. The results indicate that baseline functional connectivity within the frontostriatal-limbic network successfully predicts long-term retention of rewarded learning. © 2014 Wiley Periodicals, Inc.

  11. Cingulate cortex functional connectivity predicts future relapse in alcohol dependent individuals

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    Yasmin Zakiniaeiz

    2017-01-01

    Full Text Available Alcohol dependence is a chronic relapsing illness. Alcohol and stress cues have consistently been shown to increase craving and relapse risk in recovering alcohol dependent (AUD patients. However, differences in functional connectivity in response to these cues have not been studied using data-driven approaches. Here, voxel-wise connectivity is used in a whole-brain investigation of functional connectivity differences associated with alcohol and stress cues and to examine whether these differences are related to subsequent relapse. In Study 1, 45, 4- to 8-week abstinent, recovering AUD patients underwent functional magnetic resonance imaging during individualized imagery of alcohol, stress, and neutral cues. Relapse measures were collected prospectively for 90 days post-discharge from inpatient treatment. AUD patients showed blunted anterior (ACC, mid (MCC and posterior cingulate cortex (PCC, voxel-wise connectivity responses to stress compared to neutral cues and blunted PCC response to alcohol compared to neutral cues. Using Cox proportional hazard regression, weaker connectivity in ACC and MCC during neutral exposure was associated with longer time to relapse (better recovery outcome. Similarly, greater connectivity in PCC during alcohol-cue compared to stress cue was associated with longer time to relapse. In Study 2, a sub-group of 30 AUD patients were demographically-matched to 30 healthy control (HC participants for group comparisons. AUD compared to HC participants showed reduced cingulate connectivity during alcohol and stress cues. Using novel data-driven approaches, the cingulate cortex emerged as a key region in the disruption of functional connectivity during alcohol and stress-cue processing in AUD patients and as a marker of subsequent alcohol relapse.

  12. Medial prefrontal-hippocampal connectivity during emotional memory encoding predicts individual differences in the loss of associative memory specificity.

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    Berkers, Ruud M W J; Klumpers, Floris; Fernández, Guillén

    2016-10-01

    Emotionally charged items are often remembered better, whereas a paradoxical loss of specificity is found for associative emotional information (specific memory). The balance between specific and generalized emotional memories appears to show large individual differences, potentially related to differences in (the risk for) affective disorders that are characterized by 'overgeneralized' emotional memories. Here, we investigate the neural underpinnings of individual differences in emotional associative memory. A large group of healthy male participants were scanned while encoding associations of face-photographs and written occupational identities that were of either neutral ('driver') or negative ('murderer') valence. Subsequently, memory was tested by prompting participants to retrieve the occupational identities corresponding to each face. Whereas in both valence categories a similar amount of faces was labeled correctly with 'neutral' and 'negative' identities, (gist memory), specific associations were found to be less accurately remembered when the occupational identity was negative compared to neutral (specific memory). This pattern of results suggests reduced memory specificity for associations containing a negatively valenced component. The encoding of these negative associations was paired with a selective increase in medial prefrontal cortex activity and medial prefrontal-hippocampal connectivity. Individual differences in valence-specific neural connectivity were predictive of valence-specific reduction of memory specificity. The relationship between loss of emotional memory specificity and medial prefrontal-hippocampal connectivity is in line with the hypothesized role of a medial prefrontal-hippocampal circuit in regulating memory specificity, and warrants further investigations in individuals displaying 'overgeneralized' emotional memories. Copyright © 2016 Elsevier Inc. All rights reserved.

  13. Resting-State Functional Connectivity Predicts Cognitive Impairment Related to Alzheimer's Disease

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    Qi Lin

    2018-04-01

    Full Text Available Resting-state functional connectivity (rs-FC is a promising neuromarker for cognitive decline in aging population, based on its ability to reveal functional differences associated with cognitive impairment across individuals, and because rs-fMRI may be less taxing for participants than task-based fMRI or neuropsychological tests. Here, we employ an approach that uses rs-FC to predict the Alzheimer's Disease Assessment Scale (11 items; ADAS11 scores, which measure overall cognitive functioning, in novel individuals. We applied this technique, connectome-based predictive modeling, to a heterogeneous sample of 59 subjects from the Alzheimer's Disease Neuroimaging Initiative, including normal aging, mild cognitive impairment, and AD subjects. First, we built linear regression models to predict ADAS11 scores from rs-FC measured with Pearson's r correlation. The positive network model tested with leave-one-out cross validation (LOOCV significantly predicted individual differences in cognitive function from rs-FC. In a second analysis, we considered other functional connectivity features, accordance and discordance, which disentangle the correlation and anticorrelation components of activity timecourses between brain areas. Using partial least square regression and LOOCV, we again built models to successfully predict ADAS11 scores in novel individuals. Our study provides promising evidence that rs-FC can reveal cognitive impairment in an aging population, although more development is needed for clinical application.

  14. Structural integrity of frontostriatal connections predicts longitudinal changes in self-esteem.

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    Chavez, Robert S; Heatherton, Todd F

    2017-06-01

    Diverse neurological and psychiatric conditions are marked by a diminished sense of positive self-regard, and reductions in self-esteem are associated with risk for these disorders. Recent evidence has shown that the connectivity of frontostriatal circuitry reflects individual differences in self-esteem. However, it remains an open question as to whether the integrity of these connections can predict self-esteem changes over larger timescales. Using diffusion magnetic resonance imaging and probabilistic tractography, we demonstrate that the integrity of white matter pathways linking the medial prefrontal cortex to the ventral striatum predicts changes in self-esteem 8 months after initial scanning in a sample of 30 young adults. Individuals with greater integrity of this pathway during the scanning session at Time 1 showed increased levels of self-esteem at follow-up, whereas individuals with lower integrity showed stifled or decreased levels of self-esteem. These results provide evidence that frontostriatal white matter integrity predicts the trajectory of self-esteem development in early adulthood, which may contribute to blunted levels of positive self-regard seen in multiple psychiatric conditions, including depression and anxiety.

  15. Social network models predict movement and connectivity in ecological landscapes

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    Fletcher, Robert J.; Acevedo, M.A.; Reichert, Brian E.; Pias, Kyle E.; Kitchens, Wiley M.

    2011-01-01

    Network analysis is on the rise across scientific disciplines because of its ability to reveal complex, and often emergent, patterns and dynamics. Nonetheless, a growing concern in network analysis is the use of limited data for constructing networks. This concern is strikingly relevant to ecology and conservation biology, where network analysis is used to infer connectivity across landscapes. In this context, movement among patches is the crucial parameter for interpreting connectivity but because of the difficulty of collecting reliable movement data, most network analysis proceeds with only indirect information on movement across landscapes rather than using observed movement to construct networks. Statistical models developed for social networks provide promising alternatives for landscape network construction because they can leverage limited movement information to predict linkages. Using two mark-recapture datasets on individual movement and connectivity across landscapes, we test whether commonly used network constructions for interpreting connectivity can predict actual linkages and network structure, and we contrast these approaches to social network models. We find that currently applied network constructions for assessing connectivity consistently, and substantially, overpredict actual connectivity, resulting in considerable overestimation of metapopulation lifetime. Furthermore, social network models provide accurate predictions of network structure, and can do so with remarkably limited data on movement. Social network models offer a flexible and powerful way for not only understanding the factors influencing connectivity but also for providing more reliable estimates of connectivity and metapopulation persistence in the face of limited data.

  16. Social network models predict movement and connectivity in ecological landscapes.

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    Fletcher, Robert J; Acevedo, Miguel A; Reichert, Brian E; Pias, Kyle E; Kitchens, Wiley M

    2011-11-29

    Network analysis is on the rise across scientific disciplines because of its ability to reveal complex, and often emergent, patterns and dynamics. Nonetheless, a growing concern in network analysis is the use of limited data for constructing networks. This concern is strikingly relevant to ecology and conservation biology, where network analysis is used to infer connectivity across landscapes. In this context, movement among patches is the crucial parameter for interpreting connectivity but because of the difficulty of collecting reliable movement data, most network analysis proceeds with only indirect information on movement across landscapes rather than using observed movement to construct networks. Statistical models developed for social networks provide promising alternatives for landscape network construction because they can leverage limited movement information to predict linkages. Using two mark-recapture datasets on individual movement and connectivity across landscapes, we test whether commonly used network constructions for interpreting connectivity can predict actual linkages and network structure, and we contrast these approaches to social network models. We find that currently applied network constructions for assessing connectivity consistently, and substantially, overpredict actual connectivity, resulting in considerable overestimation of metapopulation lifetime. Furthermore, social network models provide accurate predictions of network structure, and can do so with remarkably limited data on movement. Social network models offer a flexible and powerful way for not only understanding the factors influencing connectivity but also for providing more reliable estimates of connectivity and metapopulation persistence in the face of limited data.

  17. Organization of intrinsic functional brain connectivity predicts decisions to reciprocate social behavior.

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    Cáceda, Ricardo; James, G Andrew; Gutman, David A; Kilts, Clinton D

    2015-10-01

    Reciprocation of trust exchanges is central to the development of interpersonal relationships and societal well-being. Understanding how humans make pro-social and self-centered decisions in dyadic interactions and how to predict these choices has been an area of great interest in social neuroscience. A functional magnetic resonance imaging (fMRI) based technology with potential clinical application is the study of resting state brain connectivity. We tested if resting state connectivity may predict choice behavior in a social context. Twenty-nine healthy adults underwent resting state fMRI before performing the Trust Game, a two person monetary exchange game. We assessed the ability of patterns of resting-state functional brain organization, demographic characteristics and a measure of moral development, the Defining Issues Test (DIT-2), to predict individuals' decisions to reciprocate money during the Trust Game. Subjects reciprocated in 74.9% of the trials. Independent component analysis identified canonical resting-state networks. Increased functional connectivity between the salience (bilateral insula/anterior cingulate) and central executive (dorsolateral prefrontal cortex/ posterior parietal cortex) networks significantly predicted the choice to reciprocate pro-social behavior (R(2) = 0.20, p = 0.015). Stepwise linear regression analysis showed that functional connectivity between these two networks (p = 0.002), age (p = 0.007) and DIT-2 personal interest schema score (p = 0.032) significantly predicted reciprocity behavior (R(2) = 0.498, p = 0.001). Intrinsic functional connectivity between neural networks in conjunction with other individual characteristics may be a valuable tool for predicting performance during social interactions. Future replication and temporal extension of these findings may bolster the understanding of decision making in clinical, financial and marketing settings. Copyright © 2015 Elsevier B.V. All rights reserved.

  18. Corticostriatal connectivity underlies individual differences in the balance between habitual and goal-directed action control

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    Wit, S. de; Watson, A.J.P.; Harsay, H.A.; Cohen, M.X.; Vijver, I. van de; Ridderinkhof, K.R.

    2012-01-01

    Why are some individuals more susceptible to the formation of inflexible habits than others? In the present study, we used diffusion tensor imaging to demonstrate that brain connectivity predicts individual differences in relative goal-directed and habitual behavioral control in humans.

  19. Resting state functional connectivity predicts neurofeedback response

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    Dustin eScheinost

    2014-09-01

    Full Text Available Tailoring treatments to the specific needs and biology of individual patients – personalized medicine – requires delineation of reliable predictors of response. Unfortunately, these have been slow to emerge, especially in neuropsychiatric disorders. We have recently described a real-time functional magnetic resonance imaging (rt-fMRI neurofeedback protocol that can reduce contamination-related anxiety, a prominent symptom of many cases of obsessive-compulsive disorder (OCD. Individual response to this intervention is variable. Here we used patterns of brain functional connectivity, as measured by baseline resting-state fMRI (rs-fMRI, to predict improvements in contamination anxiety after neurofeedback training. Activity of a region of the orbitofrontal cortex (OFC and anterior prefrontal cortex, Brodmann area (BA 10, associated with contamination anxiety in each subject was measured in real time and presented as a neurofeedback signal, permitting subjects to learn to modulate this target brain region. We have previously reported both enhanced OFC/BA 10 control and improved anxiety in a group of subclinically anxious subjects after neurofeedback. Five individuals with contamination-related OCD who underwent the same protocol also showed improved clinical symptomatology. In both groups, these behavioral improvements were strongly correlated with baseline whole-brain connectivity in the OFC/BA 10, computed from rs-fMRI collected several days prior to neurofeedback training. These pilot data suggest that rs-fMRI can be used to identify individuals likely to benefit from rt-fMRI neurofeedback training to control contamination anxiety.

  20. Investigation into Methods for Predicting Connection Temperatures

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    K. Anderson

    2009-01-01

    Full Text Available The mechanical response of connections in fire is largely based on material strength degradation and the interactions between the various components of the connection. In order to predict connection performance in fire, temperature profiles must initially be established in order to evaluate the material strength degradation over time. This paper examines two current methods for predicting connection temperatures: The percentage method, where connection temperatures are calculated as a percentage of the adjacent beam lower-flange, mid-span temperatures; and the lumped capacitance method, based on the lumped mass of the connection. Results from the percentage method do not correlate well with experimental results, whereas the lumped capacitance method shows much better agreement with average connection temperatures. A 3D finite element heat transfer model was also created in Abaqus, and showed good correlation with experimental results. 

  1. Spatial Topography of Individual-Specific Cortical Networks Predicts Human Cognition, Personality, and Emotion.

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    Kong, Ru; Li, Jingwei; Orban, Csaba; Sabuncu, Mert R; Liu, Hesheng; Schaefer, Alexander; Sun, Nanbo; Zuo, Xi-Nian; Holmes, Avram J; Eickhoff, Simon B; Yeo, B T Thomas

    2018-06-06

    Resting-state functional magnetic resonance imaging (rs-fMRI) offers the opportunity to delineate individual-specific brain networks. A major question is whether individual-specific network topography (i.e., location and spatial arrangement) is behaviorally relevant. Here, we propose a multi-session hierarchical Bayesian model (MS-HBM) for estimating individual-specific cortical networks and investigate whether individual-specific network topography can predict human behavior. The multiple layers of the MS-HBM explicitly differentiate intra-subject (within-subject) from inter-subject (between-subject) network variability. By ignoring intra-subject variability, previous network mappings might confuse intra-subject variability for inter-subject differences. Compared with other approaches, MS-HBM parcellations generalized better to new rs-fMRI and task-fMRI data from the same subjects. More specifically, MS-HBM parcellations estimated from a single rs-fMRI session (10 min) showed comparable generalizability as parcellations estimated by 2 state-of-the-art methods using 5 sessions (50 min). We also showed that behavioral phenotypes across cognition, personality, and emotion could be predicted by individual-specific network topography with modest accuracy, comparable to previous reports predicting phenotypes based on connectivity strength. Network topography estimated by MS-HBM was more effective for behavioral prediction than network size, as well as network topography estimated by other parcellation approaches. Thus, similar to connectivity strength, individual-specific network topography might also serve as a fingerprint of human behavior.

  2. Soft Computing Methods for Disulfide Connectivity Prediction.

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    Márquez-Chamorro, Alfonso E; Aguilar-Ruiz, Jesús S

    2015-01-01

    The problem of protein structure prediction (PSP) is one of the main challenges in structural bioinformatics. To tackle this problem, PSP can be divided into several subproblems. One of these subproblems is the prediction of disulfide bonds. The disulfide connectivity prediction problem consists in identifying which nonadjacent cysteines would be cross-linked from all possible candidates. Determining the disulfide bond connectivity between the cysteines of a protein is desirable as a previous step of the 3D PSP, as the protein conformational search space is highly reduced. The most representative soft computing approaches for the disulfide bonds connectivity prediction problem of the last decade are summarized in this paper. Certain aspects, such as the different methodologies based on soft computing approaches (artificial neural network or support vector machine) or features of the algorithms, are used for the classification of these methods.

  3. Fronto-Temporal Connectivity Predicts ECT Outcome in Major Depression

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    Amber M. Leaver

    2018-03-01

    MRI metrics can successfully predict ECT outcome, particularly for individuals who will not respond to treatment. Notably, connectivity with networks highly relevant to ECT and depression were consistently selected as important predictive features. These included the left DLPFC and the sgACC, which are both targets of other neurostimulation therapies for depression, as well as connectivity between motor and right temporal cortices near electrode sites. Future studies that probe additional functional and structural MRI metrics and other patient characteristics may further improve the predictive power of these and similar models.

  4. Understanding spatial connectivity of individuals with non-uniform population density.

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    Wang, Pu; González, Marta C

    2009-08-28

    We construct a two-dimensional geometric graph connecting individuals placed in space within a given contact distance. The individuals are distributed using a measured country's density of population. We observe that while large clusters (group of individuals connected) emerge within some regions, they are trapped in detached urban areas owing to the low population density of the regions bordering them. To understand the emergence of a giant cluster that connects the entire population, we compare the empirical geometric graph with the one generated by placing the same number of individuals randomly in space. We find that, for small contact distances, the empirical distribution of population dominates the growth of connected components, but no critical percolation transition is observed in contrast to the graph generated by a random distribution of population. Our results show that contact distances from real-world situations as for WIFI and Bluetooth connections drop in a zone where a fully connected cluster is not observed, hinting that human mobility must play a crucial role in contact-based diseases and wireless viruses' large-scale spreading.

  5. Quantifying Individual Brain Connectivity with Functional Principal Component Analysis for Networks.

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    Petersen, Alexander; Zhao, Jianyang; Carmichael, Owen; Müller, Hans-Georg

    2016-09-01

    In typical functional connectivity studies, connections between voxels or regions in the brain are represented as edges in a network. Networks for different subjects are constructed at a given graph density and are summarized by some network measure such as path length. Examining these summary measures for many density values yields samples of connectivity curves, one for each individual. This has led to the adoption of basic tools of functional data analysis, most commonly to compare control and disease groups through the average curves in each group. Such group differences, however, neglect the variability in the sample of connectivity curves. In this article, the use of functional principal component analysis (FPCA) is demonstrated to enrich functional connectivity studies by providing increased power and flexibility for statistical inference. Specifically, individual connectivity curves are related to individual characteristics such as age and measures of cognitive function, thus providing a tool to relate brain connectivity with these variables at the individual level. This individual level analysis opens a new perspective that goes beyond previous group level comparisons. Using a large data set of resting-state functional magnetic resonance imaging scans, relationships between connectivity and two measures of cognitive function-episodic memory and executive function-were investigated. The group-based approach was implemented by dichotomizing the continuous cognitive variable and testing for group differences, resulting in no statistically significant findings. To demonstrate the new approach, FPCA was implemented, followed by linear regression models with cognitive scores as responses, identifying significant associations of connectivity in the right middle temporal region with both cognitive scores.

  6. An experiment on individual 'parochial altruism' revealing no connection between individual 'altruism' and individual 'parochialism'.

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    Corr, Philip J; Hargreaves Heap, Shaun P; Seger, Charles R; Tsutsui, Kei

    2015-01-01

    Is parochial altruism an attribute of individual behavior? This is the question we address with an experiment. We examine whether the individual pro-sociality that is revealed in the public goods and trust games when interacting with fellow group members helps predict individual parochialism, as measured by the in-group bias (i.e., the difference in these games in pro-sociality when interacting with own group members as compared with members of another group). We find that it is not. An examination of the Big-5 personality predictors of each behavior reinforces this result: they are different. In short, knowing how pro-social individuals are with respect to fellow group members does not help predict their parochialism.

  7. Electroconvulsive therapy-induced brain functional connectivity predicts therapeutic efficacy in patients with schizophrenia: a multivariate pattern recognition study.

    Science.gov (United States)

    Li, Peng; Jing, Ri-Xing; Zhao, Rong-Jiang; Ding, Zeng-Bo; Shi, Le; Sun, Hong-Qiang; Lin, Xiao; Fan, Teng-Teng; Dong, Wen-Tian; Fan, Yong; Lu, Lin

    2017-05-11

    Previous studies suggested that electroconvulsive therapy can influence regional metabolism and dopamine signaling, thereby alleviating symptoms of schizophrenia. It remains unclear what patients may benefit more from the treatment. The present study sought to identify biomarkers that predict the electroconvulsive therapy response in individual patients. Thirty-four schizophrenia patients and 34 controls were included in this study. Patients were scanned prior to treatment and after 6 weeks of treatment with antipsychotics only (n = 16) or a combination of antipsychotics and electroconvulsive therapy (n = 13). Subject-specific intrinsic connectivity networks were computed for each subject using a group information-guided independent component analysis technique. Classifiers were built to distinguish patients from controls and quantify brain states based on intrinsic connectivity networks. A general linear model was built on the classification scores of first scan (referred to as baseline classification scores) to predict treatment response. Classifiers built on the default mode network, the temporal lobe network, the language network, the corticostriatal network, the frontal-parietal network, and the cerebellum achieved a cross-validated classification accuracy of 83.82%, with specificity of 91.18% and sensitivity of 76.47%. After the electroconvulsive therapy, psychosis symptoms of the patients were relieved and classification scores of the patients were decreased. Moreover, the baseline classification scores were predictive for the treatment outcome. Schizophrenia patients exhibited functional deviations in multiple intrinsic connectivity networks which were able to distinguish patients from healthy controls at an individual level. Patients with lower classification scores prior to treatment had better treatment outcome, indicating that the baseline classification scores before treatment is a good predictor for treatment outcome. CONNECTIVITY NETWORKS

  8. Increased Default Mode Network Connectivity in Individuals at High Familial Risk for Depression.

    Science.gov (United States)

    Posner, Jonathan; Cha, Jiook; Wang, Zhishun; Talati, Ardesheer; Warner, Virginia; Gerber, Andrew; Peterson, Bradley S; Weissman, Myrna

    2016-06-01

    Research into the pathophysiology of major depressive disorder (MDD) has focused largely on individuals already affected by MDD. Studies have thus been limited in their ability to disentangle effects that arise as a result of MDD from precursors of the disorder. By studying individuals at high familial risk for MDD, we aimed to identify potential biomarkers indexing risk for developing MDD, a critical step toward advancing prevention and early intervention. Using resting-state functional connectivity MRI (rs-fcMRI) and diffusion MRI (tractography), we examined connectivity within the default mode network (DMN) and between the DMN and the central executive network (CEN) in 111 individuals, aged 11-60 years, at high and low familial risk for depression. Study participants were part of a three-generation longitudinal, cohort study of familial depression. Based on rs-fcMRI, individuals at high vs low familial risk for depression showed increased DMN connectivity, as well as decreased DMN-CEN-negative connectivity. These findings remained significant after excluding individuals with a current or lifetime history of depression. Diffusion MRI measures based on tractography supported the findings of decreased DMN-CEN-negative connectivity. Path analyses indicated that decreased DMN-CEN-negative connectivity mediated a relationship between familial risk and a neuropsychological measure of impulsivity. Our findings suggest that DMN and DMN-CEN connectivity differ in those at high vs low risk for depression and thus suggest potential biomarkers for identifying individuals at risk for developing MDD.

  9. Advancement in Watershed Modelling Using Dynamic Lateral and Longitudinal Sediment (Dis)connectivity Prediction

    Science.gov (United States)

    Mahoney, D. T.; al Aamery, N. M. H.; Fox, J.

    2017-12-01

    The authors find that sediment (dis)connectivity has seldom taken precedence within watershed models, and the present study advances this modeling framework and applies the modeling within a bedrock-controlled system. Sediment (dis)connectivity, defined as the detachment and transport of sediment from source to sink between geomorphic zones, is a major control on sediment transport. Given the availability of high resolution geospatial data, coupling sediment connectivity concepts within sediment prediction models offers an approach to simulate sediment sources and pathways within a watershed's sediment cascade. Bedrock controlled catchments are potentially unique due to the presence of rock outcrops causing longitudinal impedance to sediment transport pathways in turn impacting the longitudinal distribution of the energy gradient responsible for conveying sediment. Therefore, the authors were motivated by the need to formulate a sediment transport model that couples sediment (dis)connectivity knowledge to predict sediment flux for bedrock controlled catchments. A watershed-scale sediment transport model was formulated that incorporates sediment (dis)connectivity knowledge collected via field reconnaissance and predicts sediment flux through coupling with the Partheniades equation and sediment continuity model. Sediment (dis)connectivity was formulated by coupling probabilistic upland lateral connectivity prediction with instream longitudinal connectivity assessments via discretization of fluid and sediment pathways. Flux predictions from the upland lateral connectivity model served as an input to the instream longitudinal connectivity model. Disconnectivity in the instream model was simulated via the discretization of stream reaches due to barriers such as bedrock outcroppings and man-made check dams. The model was tested for a bedrock controlled catchment in Kentucky, USA for which extensive historic water and sediment flux data was available. Predicted sediment

  10. Distributed patterns of occipito-parietal functional connectivity predict the precision of visual working memory.

    Science.gov (United States)

    Galeano Weber, Elena M; Hahn, Tim; Hilger, Kirsten; Fiebach, Christian J

    2017-02-01

    Limitations in visual working memory (WM) quality (i.e., WM precision) may depend on perceptual and attentional limitations during stimulus encoding, thereby affecting WM capacity. WM encoding relies on the interaction between sensory processing systems and fronto-parietal 'control' regions, and differences in the quality of this interaction are a plausible source of individual differences in WM capacity. Accordingly, we hypothesized that the coupling between perceptual and attentional systems affects the quality of WM encoding. We combined fMRI connectivity analysis with behavioral modeling by fitting a variable precision and fixed capacity model to the performance data obtained while participants performed a visual delayed continuous response WM task. We quantified functional connectivity during WM encoding between occipital and parietal brain regions activated during both perception and WM encoding, as determined using a conjunction of two independent experiments. The multivariate pattern of voxel-wise inter-areal functional connectivity significantly predicted WM performance, most specifically the mean of WM precision but not the individual number of items that could be stored in memory. In particular, higher occipito-parietal connectivity was associated with higher behavioral mean precision. These results are consistent with a network perspective of WM capacity, suggesting that the efficiency of information flow between perceptual and attentional neural systems is a critical determinant of limitations in WM quality. Copyright © 2016 Elsevier Inc. All rights reserved.

  11. Never forget a name: white matter connectivity predicts person memory

    Science.gov (United States)

    Metoki, Athanasia; Alm, Kylie H.; Wang, Yin; Ngo, Chi T.; Olson, Ingrid R.

    2018-01-01

    Through learning and practice, we can acquire numerous skills, ranging from the simple (whistling) to the complex (memorizing operettas in a foreign language). It has been proposed that complex learning requires a network of brain regions that interact with one another via white matter pathways. One candidate white matter pathway, the uncinate fasciculus (UF), has exhibited mixed results for this hypothesis: some studies have shown UF involvement across a range of memory tasks, while other studies report null results. Here, we tested the hypothesis that the UF supports associative memory processes and that this tract can be parcellated into subtracts that support specific types of memory. Healthy young adults performed behavioral tasks (two face-name learning tasks, one word pair memory task) and underwent a diffusion-weighted imaging scan. Our results revealed that variation in UF microstructure was significantly associated with individual differences in performance on both face-name tasks, as well as the word association memory task. A UF sub-tract, functionally defined by its connectivity between face-selective regions in the anterior temporal lobe and orbitofrontal cortex, selectively predicted face-name learning. In contrast, connectivity between the fusiform face patch and both anterior face patches had no predictive validity. These findings suggest that there is a robust and replicable relationship between the UF and associative learning and memory. Moreover, this large white matter pathway can be subdivided to reveal discrete functional profiles. PMID:28646241

  12. The relationship between default mode network connectivity and social functioning in individuals at familial high-risk for schizophrenia.

    Science.gov (United States)

    Dodell-Feder, David; Delisi, Lynn E; Hooker, Christine I

    2014-06-01

    Unaffected first-degree relatives of individuals with schizophrenia (i.e., those at familial high-risk [FHR]), demonstrate social dysfunction qualitatively similar though less severe than that of their affected relatives. These social difficulties may be the consequence of genetically conferred disruption to aspects of the default mode network (DMN), such as the dMPFC subsystem, which overlaps with the network of brain regions recruited during social cognitive processes. In the present study, we investigate this possibility, testing DMN connectivity and its relationship to social functioning in FHR using resting-state fMRI. Twenty FHR individuals and 17 controls underwent fMRI during a resting-state scan. Hypothesis-driven functional connectivity analyses examined ROI-to-ROI correlations between the DMN's hubs, and regions of the dMPFC subsystem and MTL subsystem. Connectivity values were examined in relationship to a measure of social functioning and empathy/perspective-taking. Results demonstrate that FHR exhibit reduced connectivity specifically within the dMPFC subsystem of the DMN. Certain ROI-to-ROI correlations predicted aspects of social functioning and empathy/perspective-taking across all participants. Together, the data indicate that disruption to the dMPFC subsystem of the DMN may be associated with familial risk for schizophrenia, and that these intrinsic connections may carry measurable consequences for social functioning. Copyright © 2014 Elsevier B.V. All rights reserved.

  13. Resting-state functional connectivity predicts longitudinal pain symptom change in urologic chronic pelvic pain syndrome: a MAPP network study.

    Science.gov (United States)

    Kutch, Jason J; Labus, Jennifer S; Harris, Richard E; Martucci, Katherine T; Farmer, Melissa A; Fenske, Sonja; Fling, Connor; Ichesco, Eric; Peltier, Scott; Petre, Bogdan; Guo, Wensheng; Hou, Xiaoling; Stephens, Alisa J; Mullins, Chris; Clauw, Daniel J; Mackey, Sean C; Apkarian, A Vania; Landis, J Richard; Mayer, Emeran A

    2017-06-01

    Chronic pain symptoms often change over time, even in individuals who have had symptoms for years. Studying biological factors that predict trends in symptom change in chronic pain may uncover novel pathophysiological mechanisms and potential therapeutic targets. In this study, we investigated whether brain functional connectivity measures obtained from resting-state functional magnetic resonance imaging at baseline can predict longitudinal symptom change (3, 6, and 12 months after scan) in urologic chronic pelvic pain syndrome. We studied 52 individuals with urologic chronic pelvic pain syndrome (34 women, 18 men) who had baseline neuroimaging followed by symptom tracking every 2 weeks for 1 year as part of the Multidisciplinary Approach to the Study of Chronic Pelvic Pain (MAPP) Research Network study. We found that brain functional connectivity can make a significant prediction of short-term (3 month) pain reduction with 73.1% accuracy (69.2% sensitivity and 75.0% precision). In addition, we found that the brain regions with greatest contribution to the classification were preferentially aligned with the left frontoparietal network. Resting-state functional magnetic resonance imaging measures seemed to be less informative about 6- or 12-month symptom change. Our study provides the first evidence that future trends in symptom change in patients in a state of chronic pain may be linked to functional connectivity within specific brain networks.

  14. Quantifying Individual Brain Connectivity with Functional Principal Component Analysis for Networks

    OpenAIRE

    Petersen, Alexander; Zhao, Jianyang; Carmichael, Owen; Müller, Hans-Georg

    2016-01-01

    In typical functional connectivity studies, connections between voxels or regions in the brain are represented as edges in a network. Networks for different subjects are constructed at a given graph density and are summarized by some network measure such as path length. Examining these summary measures for many density values yields samples of connectivity curves, one for each individual. This has led to the adoption of basic tools of functional data analysis, most commonly to compare control...

  15. Childhood maltreatment and combat posttraumatic stress differentially predict fear-related fronto-subcortical connectivity.

    Science.gov (United States)

    Birn, Rasmus M; Patriat, Rémi; Phillips, Mary L; Germain, Anne; Herringa, Ryan J

    2014-10-01

    Adult posttraumatic stress disorder (PTSD) has been characterized by altered fear-network connectivity. Childhood trauma is a major risk factor for adult PTSD, yet its contribution to fear-network connectivity in PTSD remains unexplored. We examined, within a single model, the contribution of childhood maltreatment, combat exposure, and combat-related posttraumatic stress symptoms (PTSS) to resting-state connectivity (rs-FC) of the amygdala and hippocampus in military veterans. Medication-free male veterans (n = 27, average 26.6 years) with a range of PTSS completed resting-state fMRI. Measures including the Clinician-Administered PTSD Scale (CAPS), Childhood Trauma Questionnaire (CTQ), and Combat Exposure Scale (CES) were used to predict rs-FC using multilinear regression. Fear-network seeds included the amygdala and hippocampus. Amygdala: CTQ predicted lower connectivity to ventromedial prefrontal cortex (vmPFC), but greater anticorrelation with dorsal/lateral PFC. CAPS positively predicted connectivity to insula, and loss of anticorrelation with dorsomedial/dorsolateral (dm/dl)PFC. Hippocampus: CTQ predicted lower connectivity to vmPFC, but greater anticorrelation with dm/dlPFC. CES predicted greater anticorrelation, whereas CAPS predicted less anticorrelation with dmPFC. Childhood trauma, combat exposure, and PTSS differentially predict fear-network rs-FC. Childhood maltreatment may weaken ventral prefrontal-subcortical circuitry important in automatic fear regulation, but, in a compensatory manner, may also strengthen dorsal prefrontal-subcortical pathways involved in more effortful emotion regulation. PTSD symptoms, in turn, appear to emerge with the loss of connectivity in the latter pathway. These findings suggest potential mechanisms by which developmental trauma exposure leads to adult PTSD, and which brain mechanisms are associated with the emergence of PTSD symptoms. © 2014 Wiley Periodicals, Inc.

  16. Observed connection and individuation: relation to symptoms in families of adolescents with bulimia nervosa.

    Science.gov (United States)

    Thomas, Sarah A; Hoste, Renee Rienecke; Le Grange, Daniel

    2012-11-01

    To examine the relation between observed familial connection and individuation and adolescent bulimia nervosa (BN) symptoms. As part of a treatment study for adolescent BN, adolescents (n = 54) and their parents participated in a videotaped semi-structured interview. Participants were rated on observed connection and individuation from these interviews using the Scale of Intergenerational Relationship Quality and two measures of connection. There was a significant negative relation between individuation from parents and adolescent BN symptoms. Connection both to and from mothers and adolescents was negatively associated with BN symptoms. Increased eating concern was significantly associated with a greater likelihood of expressing a desire for more connection with the family. Investigating and understanding family factors present at the time of adolescent BN may assist in providing treatment specific to the needs of the family to best aid the adolescent's recovery process. Copyright © 2012 Wiley Periodicals, Inc.

  17. An experiment on individual ‘parochial altruism’ revealing no connection between individual ‘altruism’ and individual ‘parochialism’

    Science.gov (United States)

    Corr, Philip J.; Hargreaves Heap, Shaun P.; Seger, Charles R.; Tsutsui, Kei

    2015-01-01

    Is parochial altruism an attribute of individual behavior? This is the question we address with an experiment. We examine whether the individual pro-sociality that is revealed in the public goods and trust games when interacting with fellow group members helps predict individual parochialism, as measured by the in-group bias (i.e., the difference in these games in pro-sociality when interacting with own group members as compared with members of another group). We find that it is not. An examination of the Big-5 personality predictors of each behavior reinforces this result: they are different. In short, knowing how pro-social individuals are with respect to fellow group members does not help predict their parochialism. PMID:26347703

  18. Intrinsic spontaneous brain activity predicts individual variability in associative memory in older adults.

    Science.gov (United States)

    Zheng, Zhiwei; Li, Rui; Xiao, Fengqiu; He, Rongqiao; Zhang, Shouzi; Li, Juan

    2018-04-19

    Older adults demonstrate notable individual differences in associative memory. Here, resting-state functional magnetic resonance imaging (rsfMRI) was used to investigate whether intrinsic brain activity at rest could predict individual differences in associative memory among cognitively healthy older adults. Regional amplitude of low-frequency fluctuations (ALFF) analysis and a correlation-based resting-state functional connectivity (RSFC) approach were used to analyze data acquired from 102 cognitively normal elderly who completed the paired-associative learning test (PALT) and underwent fMRI scans. Participants were divided into two groups based on the retrospective self-reports on whether or not they utilized encoding strategies during the PALT. The behavioral results revealed better associative memory performance in the participants who reported utilizing memory strategies compared with participants who reported not doing so. The fMRI results showed that higher associative memory performance was associated with greater functional connectivity between the right superior frontal gyrus and the right posterior cerebellum lobe in the strategy group. The regional ALFF values in the right superior frontal gyrus were linked to associative memory performance in the no-strategy group. These findings suggest that the regional spontaneous fluctuations and functional connectivity during rest may subserve the individual differences in the associative memory in older adults, and that this is modulated by self-initiated memory strategy use. © 2018 The Institute of Psychology, Chinese Academy of Sciences and John Wiley & Sons Australia, Ltd.

  19. An experiment on individual ‘parochial altruism’ revealing no connection between individual ‘altruism’ and individual ‘parochialism’

    Directory of Open Access Journals (Sweden)

    Shaun eHargreaves Heap

    2015-08-01

    Full Text Available Is parochial altruism an attribute of individual behaviour? This is the question we address with an experiment. We examine whether the individual pro-sociality that is revealed in the public goods and trust games when interacting with fellow group members helps predict individual parochialism, as measured by the in-group bias (i.e., the difference in these games in pro-sociality when interacting with own group members as compared with members of another group. We find that it is not. An examination of the Big 5 personality predictors of each behaviour reinforces this result: they are different. In short, knowing how ‘nice’ individuals are with respect to fellow group members does not help predict their parochialism.

  20. Protein complex prediction based on k-connected subgraphs in protein interaction network

    Directory of Open Access Journals (Sweden)

    Habibi Mahnaz

    2010-09-01

    Full Text Available Abstract Background Protein complexes play an important role in cellular mechanisms. Recently, several methods have been presented to predict protein complexes in a protein interaction network. In these methods, a protein complex is predicted as a dense subgraph of protein interactions. However, interactions data are incomplete and a protein complex does not have to be a complete or dense subgraph. Results We propose a more appropriate protein complex prediction method, CFA, that is based on connectivity number on subgraphs. We evaluate CFA using several protein interaction networks on reference protein complexes in two benchmark data sets (MIPS and Aloy, containing 1142 and 61 known complexes respectively. We compare CFA to some existing protein complex prediction methods (CMC, MCL, PCP and RNSC in terms of recall and precision. We show that CFA predicts more complexes correctly at a competitive level of precision. Conclusions Many real complexes with different connectivity level in protein interaction network can be predicted based on connectivity number. Our CFA program and results are freely available from http://www.bioinf.cs.ipm.ir/softwares/cfa/CFA.rar.

  1. Structural and Functional Brain Connectivity of People with Obesity and Prediction of Body Mass Index Using Connectivity.

    Directory of Open Access Journals (Sweden)

    Bo-yong Park

    Full Text Available Obesity is a medical condition affecting billions of people. Various neuroimaging methods including magnetic resonance imaging (MRI have been used to obtain information about obesity. We adopted a multi-modal approach combining diffusion tensor imaging (DTI and resting state functional MRI (rs-fMRI to incorporate complementary information and thus better investigate the brains of non-healthy weight subjects. The objective of this study was to explore multi-modal neuroimaging and use it to predict a practical clinical score, body mass index (BMI. Connectivity analysis was applied to DTI and rs-fMRI. Significant regions and associated imaging features were identified based on group-wise differences between healthy weight and non-healthy weight subjects. Six DTI-driven connections and 10 rs-fMRI-driven connectivities were identified. DTI-driven connections better reflected group-wise differences than did rs-fMRI-driven connectivity. We predicted BMI values using multi-modal imaging features in a partial least-square regression framework (percent error 15.0%. Our study identified brain regions and imaging features that can adequately explain BMI. We identified potentially good imaging biomarker candidates for obesity-related diseases.

  2. Blunted amygdala functional connectivity during a stress task in alcohol dependent individuals: A pilot study

    Directory of Open Access Journals (Sweden)

    Natasha E. Wade, M.S.

    2017-12-01

    Full Text Available Background: Scant research has been conducted on neural mechanisms underlying stress processing in individuals with alcohol dependence (AD. We examined neural substrates of stress in AD individuals compared with controls using an fMRI task previously shown to induce stress, assessing amygdala functional connectivity to medial prefrontal cortex (mPFC. Materials and methods: For this novel pilot study, 10 abstinent AD individuals and 11 controls completed a modified Trier stress task while undergoing fMRI acquisition. The amygdala was used as a seed region for whole-brain seed-based functional connectivity analysis. Results: After controlling for family-wise error (p = 0.05, there was significantly decreased left and right amygdala connectivity with frontal (specifically mPFC, temporal, parietal, and cerebellar regions. Subjective stress, but not craving, increased from pre-to post-task. Conclusions: This study demonstrated decreased connectivity between the amygdala and regions important for stress and emotional processing in long-term abstinent individuals with AD. These results suggest aberrant stress processing in individuals with AD even after lengthy periods of abstinence. Keywords: Alcohol dependence, fMRI, Stress task, Functional connectivity, Amygdala

  3. Subjective Cognitive Decline Is Associated With Altered Default Mode Network Connectivity in Individuals With a Family History of Alzheimer's Disease.

    Science.gov (United States)

    Verfaillie, Sander C J; Pichet Binette, Alexa; Vachon-Presseau, Etienne; Tabrizi, Shirin; Savard, Mélissa; Bellec, Pierre; Ossenkoppele, Rik; Scheltens, Philip; van der Flier, Wiesje M; Breitner, John C S; Villeneuve, Sylvia

    2018-05-01

    Both subjective cognitive decline (SCD) and a family history of Alzheimer's disease (AD) portend risk of brain abnormalities and progression to dementia. Posterior default mode network (pDMN) connectivity is altered early in the course of AD. It is unclear whether SCD predicts similar outcomes in cognitively normal individuals with a family history of AD. We studied 124 asymptomatic individuals with a family history of AD (age 64 ± 5 years). Participants were categorized as having SCD if they reported that their memory was becoming worse (SCD + ). We used extensive neuropsychological assessment to investigate five different cognitive domain performances at baseline (n = 124) and 1 year later (n = 59). We assessed interconnectivity among three a priori defined ROIs: pDMN, anterior ventral DMN, medial temporal memory system (MTMS), and the connectivity of each with the rest of brain. Sixty-eight (55%) participants reported SCD. Baseline cognitive performance was comparable between groups (all false discovery rate-adjusted p values > .05). At follow-up, immediate and delayed memory improved across groups, but the improvement in immediate memory was reduced in SCD + compared with SCD - (all false discovery rate-adjusted p values < .05). When compared with SCD - , SCD + subjects showed increased pDMN-MTMS connectivity (false discovery rate-adjusted p < .05). Higher connectivity between the MTMS and the rest of the brain was associated with better baseline immediate memory, attention, and global cognition, whereas higher MTMS and pDMN-MTMS connectivity were associated with lower immediate memory over time (all false discovery rate-adjusted p values < .05). SCD in cognitively normal individuals is associated with diminished immediate memory practice effects and a brain connectivity pattern that mirrors early AD-related connectivity failure. Copyright © 2017 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

  4. Prediction of movement intention using connectivity within motor-related network: An electrocorticography study.

    Science.gov (United States)

    Kang, Byeong Keun; Kim, June Sic; Ryun, Seokyun; Chung, Chun Kee

    2018-01-01

    Most brain-machine interface (BMI) studies have focused only on the active state of which a BMI user performs specific movement tasks. Therefore, models developed for predicting movements were optimized only for the active state. The models may not be suitable in the idle state during resting. This potential maladaptation could lead to a sudden accident or unintended movement resulting from prediction error. Prediction of movement intention is important to develop a more efficient and reasonable BMI system which could be selectively operated depending on the user's intention. Physical movement is performed through the serial change of brain states: idle, planning, execution, and recovery. The motor networks in the primary motor cortex and the dorsolateral prefrontal cortex are involved in these movement states. Neuronal communication differs between the states. Therefore, connectivity may change depending on the states. In this study, we investigated the temporal dynamics of connectivity in dorsolateral prefrontal cortex and primary motor cortex to predict movement intention. Movement intention was successfully predicted by connectivity dynamics which may reflect changes in movement states. Furthermore, dorsolateral prefrontal cortex is crucial in predicting movement intention to which primary motor cortex contributes. These results suggest that brain connectivity is an excellent approach in predicting movement intention.

  5. An individual-based modelling approach to estimate landscape connectivity for bighorn sheep (Ovis canadensis

    Directory of Open Access Journals (Sweden)

    Corrie H. Allen

    2016-05-01

    Full Text Available Background. Preserving connectivity, or the ability of a landscape to support species movement, is among the most commonly recommended strategies to reduce the negative effects of climate change and human land use development on species. Connectivity analyses have traditionally used a corridor-based approach and rely heavily on least cost path modeling and circuit theory to delineate corridors. Individual-based models are gaining popularity as a potentially more ecologically realistic method of estimating landscape connectivity. However, this remains a relatively unexplored approach. We sought to explore the utility of a simple, individual-based model as a land-use management support tool in identifying and implementing landscape connectivity. Methods. We created an individual-based model of bighorn sheep (Ovis canadensis that simulates a bighorn sheep traversing a landscape by following simple movement rules. The model was calibrated for bighorn sheep in the Okanagan Valley, British Columbia, Canada, a region containing isolated herds that are vital to conservation of the species in its northern range. Simulations were run to determine baseline connectivity between subpopulations in the study area. We then applied the model to explore two land management scenarios on simulated connectivity: restoring natural fire regimes and identifying appropriate sites for interventions that would increase road permeability for bighorn sheep. Results. This model suggests there are no continuous areas of good habitat between current subpopulations of sheep in the study area; however, a series of stepping-stones or circuitous routes could facilitate movement between subpopulations and into currently unoccupied, yet suitable, bighorn habitat. Restoring natural fire regimes or mimicking fire with prescribed burns and tree removal could considerably increase bighorn connectivity in this area. Moreover, several key road crossing sites that could benefit from

  6. Resting state connectivity of the medial prefrontal cortex covaries with individual differences in high-frequency heart rate variability.

    Science.gov (United States)

    Jennings, J Richard; Sheu, Lei K; Kuan, Dora C-H; Manuck, Stephen B; Gianaros, Peter J

    2016-04-01

    Resting high-frequency heart rate variability (HF-HRV) relates to cardiac vagal control and predicts individual differences in health and longevity, but its functional neural correlates are not well defined. The medial prefrontal cortex (mPFC) encompasses visceral control regions that are components of intrinsic networks of the brain, particularly the default mode network (DMN) and the salience network (SN). Might individual differences in resting HF-HRV covary with resting state neural activity in the DMN and SN, particularly within the mPFC? This question was addressed using fMRI data from an eyes-open, 5-min rest period during which echoplanar brain imaging yielded BOLD time series. Independent component analysis yielded functional connectivity estimates defining the DMN and SN. HF-HRV was measured in a rest period outside of the scanner. Midlife (52% female) adults were assessed in two studies (Study 1, N = 107; Study 2, N = 112). Neither overall DMN nor SN connectivity strength was related to HF-HRV. However, HF-HRV related to connectivity of one region within mPFC shared by the DMN and SN, namely, the perigenual anterior cingulate cortex, an area with connectivity to other regions involved in autonomic control. In sum, HF-HRV does not seem directly related to global resting state activity of intrinsic brain networks, but rather to more localized connectivity. A mPFC region was of particular interest as connectivity related to HF-HRV was shared by the DMN and SN. These findings may indicate a functional basis for the coordination of autonomic cardiac control with engagement and disengagement from the environment. © 2015 Society for Psychophysiological Research.

  7. Blunted amygdala functional connectivity during a stress task in alcohol dependent individuals: A pilot study.

    Science.gov (United States)

    Wade, Natasha E; Padula, Claudia B; Anthenelli, Robert M; Nelson, Erik; Eliassen, James; Lisdahl, Krista M

    2017-12-01

    Scant research has been conducted on neural mechanisms underlying stress processing in individuals with alcohol dependence (AD). We examined neural substrates of stress in AD individuals compared with controls using an fMRI task previously shown to induce stress, assessing amygdala functional connectivity to medial prefrontal cortex (mPFC). For this novel pilot study, 10 abstinent AD individuals and 11 controls completed a modified Trier stress task while undergoing fMRI acquisition. The amygdala was used as a seed region for whole-brain seed-based functional connectivity analysis. After controlling for family-wise error (p = 0.05), there was significantly decreased left and right amygdala connectivity with frontal (specifically mPFC), temporal, parietal, and cerebellar regions. Subjective stress, but not craving, increased from pre-to post-task. This study demonstrated decreased connectivity between the amygdala and regions important for stress and emotional processing in long-term abstinent individuals with AD. These results suggest aberrant stress processing in individuals with AD even after lengthy periods of abstinence.

  8. Model Predictive Control for Connected Hybrid Electric Vehicles

    Directory of Open Access Journals (Sweden)

    Kaijiang Yu

    2015-01-01

    Full Text Available This paper presents a new model predictive control system for connected hybrid electric vehicles to improve fuel economy. The new features of this study are as follows. First, the battery charge and discharge profile and the driving velocity profile are simultaneously optimized. One is energy management for HEV for Pbatt; the other is for the energy consumption minimizing problem of acc control of two vehicles. Second, a system for connected hybrid electric vehicles has been developed considering varying drag coefficients and the road gradients. Third, the fuel model of a typical hybrid electric vehicle is developed using the maps of the engine efficiency characteristics. Fourth, simulations and analysis (under different parameters, i.e., road conditions, vehicle state of charge, etc. are conducted to verify the effectiveness of the method to achieve higher fuel efficiency. The model predictive control problem is solved using numerical computation method: continuation and generalized minimum residual method. Computer simulation results reveal improvements in fuel economy using the proposed control method.

  9. Influence of ROI selection on Resting Functional Connectivity: An Individualized Approach for Resting fMRI Analysis

    Directory of Open Access Journals (Sweden)

    William Seunghyun Sohn

    2015-08-01

    Full Text Available The differences in how our brain is connected are often thought to reflect the differences in our individual personalities and cognitive abilities. Individual differences in brain connectivity has long been recognized in the neuroscience community however it has yet to manifest itself in the methodology of resting state analysis. This is evident as previous studies use the same region of interest (ROIs for all subjects. In this paper we demonstrate that the use of ROIs which are standardized across individuals leads to inaccurate calculations of functional connectivity. We also show that this problem can be addressed by taking an individualized approach by using subject-specific ROIs. Finally we show that ROI selection can affect the way we interpret our data by showing different changes in functional connectivity with ageing.

  10. Connectivity and dynamics of neuronal networks as defined by the shape of individual neurons

    International Nuclear Information System (INIS)

    Ahnert, Sebastian E; A N Travencolo, Bruno; Costa, Luciano da Fontoura

    2009-01-01

    Biological neuronal networks constitute a special class of dynamical systems, as they are formed by individual geometrical components, namely the neurons. In the existing literature, relatively little attention has been given to the influence of neuron shape on the overall connectivity and dynamics of the emerging networks. The current work addresses this issue by considering simplified neuronal shapes consisting of circular regions (soma/axons) with spokes (dendrites). Networks are grown by placing these patterns randomly in the two-dimensional (2D) plane and establishing connections whenever a piece of dendrite falls inside an axon. Several topological and dynamical properties of the resulting graph are measured, including the degree distribution, clustering coefficients, symmetry of connections, size of the largest connected component, as well as three hierarchical measurements of the local topology. By varying the number of processes of the individual basic patterns, we can quantify relationships between the individual neuronal shape and the topological and dynamical features of the networks. Integrate-and-fire dynamics on these networks is also investigated with respect to transient activation from a source node, indicating that long-range connections play an important role in the propagation of avalanches.

  11. An IoT Based Predictive Connected Car Maintenance Approach

    Directory of Open Access Journals (Sweden)

    Rohit Dhall

    2017-03-01

    Full Text Available Internet of Things (IoT is fast emerging and becoming an almost basic necessity in general life. The concepts of using technology in our daily life is not new, but with the advancements in technology, the impact of technology in daily activities of a person can be seen in almost all the aspects of life. Today, all aspects of our daily life, be it health of a person, his location, movement, etc. can be monitored and analyzed using information captured from various connected devices. This paper discusses one such use case, which can be implemented by the automobile industry, using technological advancements in the areas of IoT and Analytics. ‘Connected Car’ is a terminology, often associated with cars and other passenger vehicles, which are capable of internet connectivity and sharing of various kinds of data with backend applications. The data being shared can be about the location and speed of the car, status of various parts/lubricants of the car, and if the car needs urgent service or not. Once data are transmitted to the backend services, various workflows can be created to take necessary actions, e.g. scheduling a service with the car service provider, or if large numbers of care are in the same location, then the traffic management system can take necessary action. ’Connected cars’ can also communicate with each other, and can send alerts to each other in certain scenarios like possible crash etc. This paper talks about how the concept of ‘connected cars’ can be used to perform ‘predictive car maintenance’. It also discusses how certain technology components, i.e., Eclipse Mosquito and Eclipse Paho can be used to implement a predictive connected car use case.

  12. Evaluation and comparison of predictive individual-level general surrogates.

    Science.gov (United States)

    Gabriel, Erin E; Sachs, Michael C; Halloran, M Elizabeth

    2018-07-01

    An intermediate response measure that accurately predicts efficacy in a new setting at the individual level could be used both for prediction and personalized medical decisions. In this article, we define a predictive individual-level general surrogate (PIGS), which is an individual-level intermediate response that can be used to accurately predict individual efficacy in a new setting. While methods for evaluating trial-level general surrogates, which are predictors of trial-level efficacy, have been developed previously, few, if any, methods have been developed to evaluate individual-level general surrogates, and no methods have formalized the use of cross-validation to quantify the expected prediction error. Our proposed method uses existing methods of individual-level surrogate evaluation within a given clinical trial setting in combination with cross-validation over a set of clinical trials to evaluate surrogate quality and to estimate the absolute prediction error that is expected in a new trial setting when using a PIGS. Simulations show that our method performs well across a variety of scenarios. We use our method to evaluate and to compare candidate individual-level general surrogates over a set of multi-national trials of a pentavalent rotavirus vaccine.

  13. High Quality Model Predictive Control for Single Phase Grid Connected Photovoltaic Inverters

    DEFF Research Database (Denmark)

    Zangeneh Bighash, Esmaeil; Sadeghzadeh, Seyed Mohammad; Ebrahimzadeh, Esmaeil

    2018-01-01

    Single phase grid-connected inverters with LCL filter are widely used to connect the photovoltaic systems to the utility grid. Among the presented control schemes, predictive control methods are faster and more accurate but are more complex to implement. Recently, the model-predictive control...... algorithm for single-phase inverter has been presented, where the algorithm implementation is straightforward. In the proposed approach, all switching states are tested in each switching period to achieve the control objectives. However, since the number of the switching states in single-phase inverter...... is low, the inverter output current has a high total harmonic distortions. In order to reduce the total harmonic distortions of the injected current, this paper presents a high-quality model-predictive control for one of the newest structure of the grid connected photovoltaic inverter, i.e., HERIC...

  14. Methylphenidate Modulates Functional Network Connectivity to Enhance Attention

    Science.gov (United States)

    Zhang, Sheng; Hsu, Wei-Ting; Scheinost, Dustin; Finn, Emily S.; Shen, Xilin; Constable, R. Todd; Li, Chiang-Shan R.; Chun, Marvin M.

    2016-01-01

    Recent work has demonstrated that human whole-brain functional connectivity patterns measured with fMRI contain information about cognitive abilities, including sustained attention. To derive behavioral predictions from connectivity patterns, our group developed a connectome-based predictive modeling (CPM) approach (Finn et al., 2015; Rosenberg et al., 2016). Previously using CPM, we defined a high-attention network, comprising connections positively correlated with performance on a sustained attention task, and a low-attention network, comprising connections negatively correlated with performance. Validating the networks as generalizable biomarkers of attention, models based on network strength at rest predicted attention-deficit/hyperactivity disorder (ADHD) symptoms in an independent group of individuals (Rosenberg et al., 2016). To investigate whether these networks play a causal role in attention, here we examined their strength in healthy adults given methylphenidate (Ritalin), a common ADHD treatment, compared with unmedicated controls. As predicted, individuals given methylphenidate showed patterns of connectivity associated with better sustained attention: higher high-attention and lower low-attention network strength than controls. There was significant overlap between the high-attention network and a network with greater strength in the methylphenidate group, and between the low-attention network and a network with greater strength in the control group. Network strength also predicted behavior on a stop-signal task, such that participants with higher go response rates showed higher high-attention and lower low-attention network strength. These results suggest that methylphenidate acts by modulating functional brain networks related to sustained attention, and that changing whole-brain connectivity patterns may help improve attention. SIGNIFICANCE STATEMENT Recent work identified a promising neuromarker of sustained attention based on whole

  15. Group-regularized individual prediction: theory and application to pain.

    Science.gov (United States)

    Lindquist, Martin A; Krishnan, Anjali; López-Solà, Marina; Jepma, Marieke; Woo, Choong-Wan; Koban, Leonie; Roy, Mathieu; Atlas, Lauren Y; Schmidt, Liane; Chang, Luke J; Reynolds Losin, Elizabeth A; Eisenbarth, Hedwig; Ashar, Yoni K; Delk, Elizabeth; Wager, Tor D

    2017-01-15

    Multivariate pattern analysis (MVPA) has become an important tool for identifying brain representations of psychological processes and clinical outcomes using fMRI and related methods. Such methods can be used to predict or 'decode' psychological states in individual subjects. Single-subject MVPA approaches, however, are limited by the amount and quality of individual-subject data. In spite of higher spatial resolution, predictive accuracy from single-subject data often does not exceed what can be accomplished using coarser, group-level maps, because single-subject patterns are trained on limited amounts of often-noisy data. Here, we present a method that combines population-level priors, in the form of biomarker patterns developed on prior samples, with single-subject MVPA maps to improve single-subject prediction. Theoretical results and simulations motivate a weighting based on the relative variances of biomarker-based prediction-based on population-level predictive maps from prior groups-and individual-subject, cross-validated prediction. Empirical results predicting pain using brain activity on a trial-by-trial basis (single-trial prediction) across 6 studies (N=180 participants) confirm the theoretical predictions. Regularization based on a population-level biomarker-in this case, the Neurologic Pain Signature (NPS)-improved single-subject prediction accuracy compared with idiographic maps based on the individuals' data alone. The regularization scheme that we propose, which we term group-regularized individual prediction (GRIP), can be applied broadly to within-person MVPA-based prediction. We also show how GRIP can be used to evaluate data quality and provide benchmarks for the appropriateness of population-level maps like the NPS for a given individual or study. Copyright © 2015 Elsevier Inc. All rights reserved.

  16. How do dispersal costs and habitat selection influence realized population connectivity?

    Science.gov (United States)

    Burgess, Scott C; Treml, Eric A; Marshall, Dustin J

    2012-06-01

    Despite the importance of dispersal for population connectivity, dispersal is often costly to the individual. A major impediment to understanding connectivity has been a lack of data combining the movement of individuals and their survival to reproduction in the new habitat (realized connectivity). Although mortality often occurs during dispersal (an immediate cost), in many organisms costs are paid after dispersal (deferred costs). It is unclear how such deferred costs influence the mismatch between dispersal and realized connectivity. Through a series of experiments in the field and laboratory, we estimated both direct and indirect deferred costs in a marine bryozoan (Bugula neritina). We then used the empirical data to parameterize a theoretical model in order to formalize predictions about how dispersal costs influence realized connectivity. Individuals were more likely to colonize poor-quality habitat after prolonged dispersal durations. Individuals that colonized poor-quality habitat performed poorly after colonization because of some property of the habitat (an indirect deferred cost) rather than from prolonged dispersal per se (a direct deferred cost). Our theoretical model predicted that indirect deferred costs could result in nonlinear mismatches between spatial patterns of potential and realized connectivity. The deferred costs of dispersal are likely to be crucial for determining how well patterns of dispersal reflect realized connectivity. Ignoring these deferred costs could lead to inaccurate predictions of spatial population dynamics.

  17. Dynamic functional connectivity and individual differences in emotions during social stress.

    Science.gov (United States)

    Tobia, Michael J; Hayashi, Koby; Ballard, Grey; Gotlib, Ian H; Waugh, Christian E

    2017-12-01

    Exposure to acute stress induces multiple emotional responses, each with their own unique temporal dynamics. Dynamic functional connectivity (dFC) measures the temporal variability of network synchrony and captures individual differences in network neurodynamics. This study investigated the relationship between dFC and individual differences in emotions induced by an acute psychosocial stressor. Sixteen healthy adult women underwent fMRI scanning during a social evaluative threat (SET) task, and retrospectively completed questionnaires that assessed individual differences in subjectively experienced positive and negative emotions about stress and stress relief during the task. Group dFC was decomposed with parallel factor analysis (PARAFAC) into 10 components, each with a temporal signature, spatial network of functionally connected regions, and vector of participant loadings that captures individual differences in dFC. Participant loadings of two networks were positively correlated with stress-related emotions, indicating the existence of networks for positive and negative emotions. The emotion-related networks involved the ventromedial prefrontal cortex, cingulate cortex, anterior insula, and amygdala, among other distributed brain regions, and time signatures for these emotion-related networks were uncorrelated. These findings demonstrate that individual differences in stress-induced positive and negative emotions are each uniquely associated with large-scale brain networks, and suggest that dFC is a mechanism that generates individual differences in the emotional components of the stress response. Hum Brain Mapp 38:6185-6205, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  18. Disturbance estimator based predictive current control of grid-connected inverters

    OpenAIRE

    Al-Khafaji, Ahmed Samawi Ghthwan

    2013-01-01

    ABSTRACT: The work presented in my thesis considers one of the modern discrete-time control approaches based on digital signal processing methods, that have been developed to improve the performance control of grid-connected three-phase inverters. Disturbance estimator based predictive current control of grid-connected inverters is proposed. For inverter modeling with respect to the design of current controllers, we choose the d-q synchronous reference frame to make it easier to understand an...

  19. Structural Connectivity of the Developing Human Amygdala

    Science.gov (United States)

    Saygin, Zeynep M.; Osher, David E.; Koldewyn, Kami; Martin, Rebecca E.; Finn, Amy; Saxe, Rebecca; Gabrieli, John D.E.; Sheridan, Margaret

    2015-01-01

    A large corpus of research suggests that there are changes in the manner and degree to which the amygdala supports cognitive and emotional function across development. One possible basis for these developmental differences could be the maturation of amygdalar connections with the rest of the brain. Recent functional connectivity studies support this conclusion, but the structural connectivity of the developing amygdala and its different nuclei remains largely unstudied. We examined age related changes in the DWI connectivity fingerprints of the amygdala to the rest of the brain in 166 individuals of ages 5-30. We also developed a model to predict age based on individual-subject amygdala connectivity, and identified the connections that were most predictive of age. Finally, we segmented the amygdala into its four main nucleus groups, and examined the developmental changes in connectivity for each nucleus. We observed that with age, amygdalar connectivity becomes increasingly sparse and localized. Age related changes were largely localized to the subregions of the amygdala that are implicated in social inference and contextual memory (the basal and lateral nuclei). The central nucleus’ connectivity also showed differences with age but these differences affected fewer target regions than the basal and lateral nuclei. The medial nucleus did not exhibit any age related changes. These findings demonstrate increasing specificity in the connectivity patterns of amygdalar nuclei across age. PMID:25875758

  20. Connected Traveler

    Energy Technology Data Exchange (ETDEWEB)

    2016-06-01

    The Connected Traveler framework seeks to boost the energy efficiency of personal travel and the overall transportation system by maximizing the accuracy of predicted traveler behavior in response to real-time feedback and incentives. It is anticipated that this approach will establish a feedback loop that 'learns' traveler preferences and customizes incentives to meet or exceed energy efficiency targets by empowering individual travelers with information needed to make energy-efficient choices and reducing the complexity required to validate transportation system energy savings. This handout provides an overview of NREL's Connected Traveler project, including graphics, milestones, and contact information.

  1. Aberrant default-mode functional and structural connectivity in heroin-dependent individuals.

    Directory of Open Access Journals (Sweden)

    Xiaofen Ma

    Full Text Available Little is known about connectivity within the default mode network (DMN in heroin-dependent individuals (HDIs. In the current study, diffusion-tensor imaging (DTI and resting-state functional MRI (rs-fMRI were combined to investigate both structural and functional connectivity within the DMN in HDIs.Fourteen HDIs and 14 controls participated in the study. Structural (path length, tracts count, (fractional anisotropy FA and (mean diffusivity MD derived from DTI tractographyand functional (temporal correlation coefficient derived from rs-fMRI DMN connectivity changes were examined in HDIs. Pearson correlation analysis was performed to compare the structural/functional indices and duration of heroin use/Iowa gambling task(IGT performance in HDIs.HDIs had lower FA and higher MD in the tract connecting the posterior cingulate cortex/precuneus (PCC/PCUN to right parahippocampal gyrus (PHG, compared to the controls. HDIs also had decreased FA and track count in the tract connecting the PCC/PCUN and medial prefrontal cortex (MPFC, as well as decreased functional connectivity between the PCC/PCUN and bilateral PHG and MPFC, compared to controls. FA values for the tract connecting PCC/PCUN to the right PHG and connecting PCC/PCUN to the MPFC were negatively correlated to the duration of heroin use. The temporal correlation coefficients between the PCC/PCUN and the MPFC, and the FA values for the tract connecting the PCC/PCUN to the MPFC were positively correlated to IGT performance in HDIs.Structural and functional connectivity within the DMN are both disturbed in HDIs. This disturbance progresses as duration of heroin use increases and is related to deficits in decision making in HDIs.

  2. A Novel Model Predictive Control for Single-Phase Grid-Connected Photovoltaic Inverters

    DEFF Research Database (Denmark)

    Zangeneh Bighash, Esmaeil; Sadeghzadeh, Seyed Mohammad; Ebrahimzadeh, Esmaeil

    2017-01-01

    Single-phase grid-connected inverters with LCL filter are widely used to connect photovoltaic systems to the utility grid. Among the existing control schemes, predictive control methods are faster and more accurate but also more complicated to implement. Recently, the Model Predictive Control (MPC......) algorithm for single-phase inverter has been presented, where the algorithm implementation is straightforward. In the MPC approach, all switching states are considered in each switching period to achieve the control objectives. However, since the number of switching states in single-phase inverters is small......, the inverter output current has a high Total Harmonic Distortions (THD). In order to reduce this, this paper presents an improved MPC for single-phase grid-connected inverters. In the proposed approach, the switching algorithm is changed and the number of the switching states is increased by means of virtual...

  3. Frontoparietal Structural Connectivity in Childhood Predicts Development of Functional Connectivity and Reasoning Ability: A Large-Scale Longitudinal Investigation.

    Science.gov (United States)

    Wendelken, Carter; Ferrer, Emilio; Ghetti, Simona; Bailey, Stephen K; Cutting, Laurie; Bunge, Silvia A

    2017-08-30

    Prior research points to a positive concurrent relationship between reasoning ability and both frontoparietal structural connectivity (SC) as measured by diffusion tensor imaging (Tamnes et al., 2010) and frontoparietal functional connectivity (FC) as measured by fMRI (Cocchi et al., 2014). Further, recent research demonstrates a link between reasoning ability and FC of two brain regions in particular: rostrolateral prefrontal cortex (RLPFC) and the inferior parietal lobe (IPL) (Wendelken et al., 2016). Here, we sought to investigate the concurrent and dynamic, lead-lag relationships among frontoparietal SC, FC, and reasoning ability in humans. To this end, we combined three longitudinal developmental datasets with behavioral and neuroimaging data from 523 male and female participants between 6 and 22 years of age. Cross-sectionally, reasoning ability was most strongly related to FC between RLPFC and IPL in adolescents and adults, but to frontoparietal SC in children. Longitudinal analysis revealed that RLPFC-IPL SC, but not FC, was a positive predictor of future changes in reasoning ability. Moreover, we found that RLPFC-IPL SC at one time point positively predicted future changes in RLPFC-IPL FC, whereas, in contrast, FC did not predict future changes in SC. Our results demonstrate the importance of strong white matter connectivity between RLPFC and IPL during middle childhood for the subsequent development of both robust FC and good reasoning ability. SIGNIFICANCE STATEMENT The human capacity for reasoning develops substantially during childhood and has a profound impact on achievement in school and in cognitively challenging careers. Reasoning ability depends on communication between lateral prefrontal and parietal cortices. Therefore, to understand how this capacity develops, we examined the dynamic relationships over time among white matter tracts connecting frontoparietal cortices (i.e., structural connectivity, SC), coordinated frontoparietal activation

  4. Diurnal variation of connective tissue metabolites in early and long-standing rheumatoid arthritis and in healthy individuals

    DEFF Research Database (Denmark)

    Lottenburger, T; Junker, P; Hørslev-Petersen, K

    2011-01-01

    To study the circadian variability of circulating connective tissue metabolites in patients with very early (VERA) and long-standing rheumatoid arthritis (LRA) and in healthy control individuals.......To study the circadian variability of circulating connective tissue metabolites in patients with very early (VERA) and long-standing rheumatoid arthritis (LRA) and in healthy control individuals....

  5. Genetic influences on functional connectivity associated with feedback processing and prediction error: Phase coupling of theta-band oscillations in twins.

    Science.gov (United States)

    Demiral, Şükrü Barış; Golosheykin, Simon; Anokhin, Andrey P

    2017-05-01

    Detection and evaluation of the mismatch between the intended and actually obtained result of an action (reward prediction error) is an integral component of adaptive self-regulation of behavior. Extensive human and animal research has shown that evaluation of action outcome is supported by a distributed network of brain regions in which the anterior cingulate cortex (ACC) plays a central role, and the integration of distant brain regions into a unified feedback-processing network is enabled by long-range phase synchronization of cortical oscillations in the theta band. Neural correlates of feedback processing are associated with individual differences in normal and abnormal behavior, however, little is known about the role of genetic factors in the cerebral mechanisms of feedback processing. Here we examined genetic influences on functional cortical connectivity related to prediction error in young adult twins (age 18, n=399) using event-related EEG phase coherence analysis in a monetary gambling task. To identify prediction error-specific connectivity pattern, we compared responses to loss and gain feedback. Monetary loss produced a significant increase of theta-band synchronization between the frontal midline region and widespread areas of the scalp, particularly parietal areas, whereas gain resulted in increased synchrony primarily within the posterior regions. Genetic analyses showed significant heritability of frontoparietal theta phase synchronization (24 to 46%), suggesting that individual differences in large-scale network dynamics are under substantial genetic control. We conclude that theta-band synchronization of brain oscillations related to negative feedback reflects genetically transmitted differences in the neural mechanisms of feedback processing. To our knowledge, this is the first evidence for genetic influences on task-related functional brain connectivity assessed using direct real-time measures of neuronal synchronization. Copyright © 2016

  6. Different Roles of Direct and Indirect Frontoparietal Pathways for Individual Working Memory Capacity.

    Science.gov (United States)

    Ekman, Matthias; Fiebach, Christian J; Melzer, Corina; Tittgemeyer, Marc; Derrfuss, Jan

    2016-03-09

    The ability to temporarily store and manipulate information in working memory is a hallmark of human intelligence and differs considerably across individuals, but the structural brain correlates underlying these differences in working memory capacity (WMC) are only poorly understood. In two separate studies, diffusion MRI data and WMC scores were collected for 70 and 109 healthy individuals. Using a combination of probabilistic tractography and network analysis of the white matter tracts, we examined whether structural brain network properties were predictive of individual WMC. Converging evidence from both studies showed that lateral prefrontal cortex and posterior parietal cortex of high-capacity individuals are more densely connected compared with low-capacity individuals. Importantly, our network approach was further able to dissociate putative functional roles associated with two different pathways connecting frontal and parietal regions: a corticocortical pathway and a subcortical pathway. In Study 1, where participants were required to maintain and update working memory items, the connectivity of the direct and indirect pathway was predictive of WMC. In contrast, in Study 2, where participants were required to maintain working memory items without updating, only the connectivity of the direct pathway was predictive of individual WMC. Our results suggest an important dissociation in the circuitry connecting frontal and parietal regions, where direct frontoparietal connections might support storage and maintenance, whereas subcortically mediated connections support the flexible updating of working memory content. Copyright © 2016 the authors 0270-6474/16/362894-10$15.00/0.

  7. Multimodal frontostriatal connectivity underlies individual differences in self-esteem.

    Science.gov (United States)

    Chavez, Robert S; Heatherton, Todd F

    2015-03-01

    A heightened sense of self-esteem is associated with a reduced risk for several types of affective and psychiatric disorders, including depression, anxiety and eating disorders. However, little is known about how brain systems integrate self-referential processing and positive evaluation to give rise to these feelings. To address this, we combined diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) to test how frontostriatal connectivity reflects long-term trait and short-term state aspects of self-esteem. Using DTI, we found individual variability in white matter structural integrity between the medial prefrontal cortex and the ventral striatum was related to trait measures of self-esteem, reflecting long-term stability of self-esteem maintenance. Using fMRI, we found that functional connectivity of these regions during positive self-evaluation was related to current feelings of self-esteem, reflecting short-term state self-esteem. These results provide convergent anatomical and functional evidence that self-esteem is related to the connectivity of frontostriatal circuits and suggest that feelings of self-worth may emerge from neural systems integrating information about the self with positive affect and reward. This information could potentially inform the etiology of diminished self-esteem underlying multiple psychiatric conditions and inform future studies of evaluative self-referential processing. © The Author (2014). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  8. A Dyadic Perspective on Speech Accommodation and Social Connection: Both Partners’ Rejection Sensitivity Matter

    Science.gov (United States)

    Aguilar, Lauren; Downey, Geraldine; Krauss, Robert; Pardo, Jennifer; Lane, Sean; Bolger, Niall

    2014-01-01

    Objective Findings from confederate paradigms predict that mimicry is an adaptive route to social connection for rejection sensitive individuals (Lakin et al., 2008). However, dyadic perspectives predict that whether mimicry leads to perceived connection depends on the rejection sensitivity (RS) of both partners in an interaction. Method We investigated these predictions in 50 college women who completed a dyadic cooperative task in which members were matched or mismatched in being dispositionally high or low in RS. We used a psycholinguistics paradigm to assess, through independent listeners’ judgments (N = 162), how much interacting individuals accommodate phonetic aspects of their speech toward each other. Results Results confirmed predictions from confederate paradigms in matched RS dyads. However, mismatched dyads showed an asymmetry in levels of accommodation and perceived connection: Those high in RS accommodated more than their low RS partner but emerged feeling less connected. Meditational analyses indicated that low RS individuals’ nonaccommodation in mismatched dyads helped explain their high RS partners’ relatively low perceived connection to them. Conclusions Establishing whether mimicry is an adaptive route to social connection requires analyzing mimicry as a dyadic process influenced by the needs of each dyad member. PMID:25393028

  9. Individual laboratory-measured discount rates predict field behavior.

    Science.gov (United States)

    Chabris, Christopher F; Laibson, David; Morris, Carrie L; Schuldt, Jonathon P; Taubinsky, Dmitry

    2008-12-01

    We estimate discount rates of 555 subjects using a laboratory task and find that these individual discount rates predict inter-individual variation in field behaviors (e.g., exercise, BMI, smoking). The correlation between the discount rate and each field behavior is small: none exceeds 0.28 and many are near 0. However, the discount rate has at least as much predictive power as any variable in our dataset (e.g., sex, age, education). The correlation between the discount rate and field behavior rises when field behaviors are aggregated: these correlations range from 0.09-0.38. We present a model that explains why specific intertemporal choice behaviors are only weakly correlated with discount rates, even though discount rates robustly predict aggregates of intertemporal decisions.

  10. Predicting the cumulative effect of multiple disturbances on seagrass connectivity.

    Science.gov (United States)

    Grech, Alana; Hanert, Emmanuel; McKenzie, Len; Rasheed, Michael; Thomas, Christopher; Tol, Samantha; Wang, Mingzhu; Waycott, Michelle; Wolter, Jolan; Coles, Rob

    2018-03-15

    The rate of exchange, or connectivity, among populations effects their ability to recover after disturbance events. However, there is limited information on the extent to which populations are connected or how multiple disturbances affect connectivity, especially in coastal and marine ecosystems. We used network analysis and the outputs of a biophysical model to measure potential functional connectivity and predict the impact of multiple disturbances on seagrasses in the central Great Barrier Reef World Heritage Area (GBRWHA), Australia. The seagrass networks were densely connected, indicating that seagrasses are resilient to the random loss of meadows. Our analysis identified discrete meadows that are important sources of seagrass propagules and that serve as stepping stones connecting various different parts of the network. Several of these meadows were close to urban areas or ports and likely to be at risk from coastal development. Deep water meadows were highly connected to coastal meadows and may function as a refuge, but only for non-foundation species. We evaluated changes to the structure and functioning of the seagrass networks when one or more discrete meadows were removed due to multiple disturbance events. The scale of disturbance required to disconnect the seagrass networks into two or more components was on average >245 km, about half the length of the metapopulation. The densely connected seagrass meadows of the central GBRWHA are not limited by the supply of propagules; therefore, management should focus on improving environmental conditions that support natural seagrass recruitment and recovery processes. Our study provides a new framework for assessing the impact of global change on the connectivity and persistence of coastal and marine ecosystems. Without this knowledge, management actions, including coastal restoration, may prove unnecessary and be unsuccessful. © 2018 John Wiley & Sons Ltd.

  11. Gray matter deficits and altered resting-state connectivity in the superior temporal gyrus among individuals with problematic hypersexual behavior.

    Science.gov (United States)

    Seok, Ji-Woo; Sohn, Jin-Hun

    2018-04-01

    Neuroimaging studies on the characteristics of hypersexual disorder have been accumulating, yet alternations in brain structures and functional connectivity in individuals with problematic hypersexual behavior (PHB) has only recently been studied. This study aimed to investigate gray matter deficits and resting-state abnormalities in individuals with PHB using voxel-based morphometry and resting-state connectivity analysis. Seventeen individuals with PHB and 19 age-matched healthy controls participated in this study. Gray matter volume of the brain and resting-state connectivity were measured using 3T magnetic resonance imaging. Compared to healthy subjects, individuals with PHB had significant reductions in gray matter volume in the left superior temporal gyrus (STG) and right middle temporal gyrus. Individuals with PHB also exhibited a decrease in resting-state functional connectivity between the left STG and left precuneus and between the left STG and right caudate. The gray matter volume of the left STG and its resting-state functional connectivity with the right caudate both showed significant negative correlations with the severity of PHB. The findings suggest that structural deficits and resting-state functional impairments in the left STG might be linked to PHB and provide new insights into the underlying neural mechanisms of PHB. Copyright © 2018 Elsevier B.V. All rights reserved.

  12. Predictive assessment of models for dynamic functional connectivity

    DEFF Research Database (Denmark)

    Nielsen, Søren Føns Vind; Schmidt, Mikkel Nørgaard; Madsen, Kristoffer Hougaard

    2018-01-01

    represent functional brain networks as a meta-stable process with a discrete number of states; however, there is a lack of consensus on how to perform model selection and learn the number of states, as well as a lack of understanding of how different modeling assumptions influence the estimated state......In neuroimaging, it has become evident that models of dynamic functional connectivity (dFC), which characterize how intrinsic brain organization changes over time, can provide a more detailed representation of brain function than traditional static analyses. Many dFC models in the literature...... dynamics. To address these issues, we consider a predictive likelihood approach to model assessment, where models are evaluated based on their predictive performance on held-out test data. Examining several prominent models of dFC (in their probabilistic formulations) we demonstrate our framework...

  13. Combination antiretroviral therapy improves cognitive performance and functional connectivity in treatment-naïve HIV-infected individuals.

    Science.gov (United States)

    Zhuang, Yuchuan; Qiu, Xing; Wang, Lu; Ma, Qing; Mapstone, Mark; Luque, Amneris; Weber, Miriam; Tivarus, Madalina; Miller, Eric; Arduino, Roberto C; Zhong, Jianhui; Schifitto, Giovanni

    2017-10-01

    Our study aimed to investigate the short-term effect of combination antiretroviral therapy (cART) on cognitive performance and functional and structural connectivity and their relationship to plasma levels of antiretroviral (ARV) drugs. Seventeen ARV treatment-naïve HIV-infected individuals (baseline mean CD4 cell count, 479 ± 48 cells/mm 3 ) were age matched with 17 HIV-uninfected individuals. All subjects underwent a detailed neurocognitive and functional assessment and magnetic resonance imaging. HIV-infected subjects were scanned before starting cART and 12 weeks after initiation of treatment. Uninfected subjects were assessed once at baseline. Functional connectivity (FC) was assessed within the default mode network while structural connectivity was assessed by voxel-wise analysis using tract-based spatial statistics (TBSS) and probabilistic tractography within the DMN. Tenofovir and emtricitabine blood concentration were measured at week 12 of cART. Prior to cART, HIV-infected individuals had significantly lower cognitive performance than control subjects as measured by the total Z-score from the neuropsychological tests assessing six cognitive domains (p = 0.020). After 12 weeks of cART treatment, there remained only a weak cognitive difference between HIV-infected and HIV-uninfected subjects (p = 0.057). Mean FC was lower in HIV-infected individuals compared with those uninfected (p = 0.008), but FC differences became non-significant after treatment (p = 0.197). There were no differences in DTI metrics between HIV-infected and HIV-uninfected individuals using the TBSS approach and limited evidence of decreased structural connectivity within the DMN in HIV-infected individuals. Tenofovir and emtricitabine plasma concentrations did not correlate with either cognitive performance or imaging metrics. Twelve weeks of cART improves cognitive performance and functional connectivity in ARV treatment-naïve HIV-infected individuals with relatively

  14. Neighborhood Integration and Connectivity Predict Cognitive Performance and Decline

    Directory of Open Access Journals (Sweden)

    Amber Watts PhD

    2015-08-01

    Full Text Available Objective: Neighborhood characteristics may be important for promoting walking, but little research has focused on older adults, especially those with cognitive impairment. We evaluated the role of neighborhood characteristics on cognitive function and decline over a 2-year period adjusting for measures of walking. Method: In a study of 64 older adults with and without mild Alzheimer’s disease (AD, we evaluated neighborhood integration and connectivity using geographical information systems data and space syntax analysis. In multiple regression analyses, we used these characteristics to predict 2-year declines in factor analytically derived cognitive scores (attention, verbal memory, mental status adjusting for age, sex, education, and self-reported walking. Results : Neighborhood integration and connectivity predicted cognitive performance at baseline, and changes in cognitive performance over 2 years. The relationships between neighborhood characteristics and cognitive performance were not fully explained by self-reported walking. Discussion : Clearer definitions of specific neighborhood characteristics associated with walkability are needed to better understand the mechanisms by which neighborhoods may impact cognitive outcomes. These results have implications for measuring neighborhood characteristics, design and maintenance of living spaces, and interventions to increase walking among older adults. We offer suggestions for future research measuring neighborhood characteristics and cognitive function.

  15. Conservatism and the neural circuitry of threat: economic conservatism predicts greater amygdala–BNST connectivity during periods of threat vs safety

    Science.gov (United States)

    Muftuler, L Tugan; Larson, Christine L

    2018-01-01

    Abstract Political conservatism is associated with an increased negativity bias, including increased attention and reactivity toward negative and threatening stimuli. Although the human amygdala has been implicated in the response to threatening stimuli, no studies to date have investigated whether conservatism is associated with altered amygdala function toward threat. Furthermore, although an influential theory posits that connectivity between the amygdala and bed nucleus of the stria terminalis (BNST) is important in initiating the response to sustained or uncertain threat, whether individual differences in conservatism modulate this connectivity is unknown. To test whether conservatism is associated with increased reactivity in neural threat circuitry, we measured participants’ self-reported social and economic conservatism and asked them to complete high-resolution fMRI scans while under threat of an unpredictable shock and while safe. We found that economic conservatism predicted greater connectivity between the BNST and a cluster of voxels in the left amygdala during threat vs safety. These results suggest that increased amygdala–BNST connectivity during threat may be a key neural correlate of the enhanced negativity bias found in conservatism. PMID:29126127

  16. Amygdala-frontal connectivity predicts internalizing symptom recovery among inpatient adolescents.

    Science.gov (United States)

    Venta, Amanda; Sharp, Carla; Patriquin, Michelle; Salas, Ramiro; Newlin, Elizabeth; Curtis, Kaylah; Baldwin, Philip; Fowler, Christopher; Frueh, B Christopher

    2018-01-01

    The possibility of using biological measures to predict the trajectory of symptoms among adolescent psychiatric inpatients has important implications. This study aimed to examine emotion regulation ability (measured via self-report) and a hypothesized proxy in resting-state functional connectivity [RSFC] between the amygdala and frontal brain regions as baseline predictors of internalizing symptom recovery during inpatient care. 196 adolescents (61% female; Mage = 15.20; SD = 1.48) completed the Achenbach Brief Problem Monitor (BPM) each week during their inpatient care. RSFC (n = 45) and self-report data of emotion regulation (n = 196) were collected at baseline. The average internalizing symptom score at admission was high (α 0 = 66.52), exceeding the BPM's clinical cut off score of 65. On average, internalizing symptom scores declined significantly, by 0.40 points per week (p = 0.004). While self-reported emotion regulation was associated with admission levels of internalizing problems, it did not predict change in symptoms. RSFC between left amygdala and left superior frontal gyrus was significantly associated with the intercept-higher connectivity was associated with higher internalizing at admission-and the slope- higher connectivity was associated with a more positive slope (i.e., less decline in symptoms). RSFC between the right amygdala and the left superior frontal gyrus was significantly, positively correlated with the slope parameter. Results indicate the potential of biologically-based measures that can be developed further for personalized care in adolescent psychiatry. Copyright © 2017 Elsevier B.V. All rights reserved.

  17. Intrinsic default mode network connectivity predicts spontaneous verbal descriptions of autobiographical memories during social processing

    Directory of Open Access Journals (Sweden)

    Xiao-Fei eYang

    2013-01-01

    Full Text Available Neural systems activated in a coordinated way during rest, known as the default mode network (DMN, also support autobiographical memory (AM retrieval and social processing/mentalizing. However, little is known about how individual variability in reliance on personal memories during social processing relates to individual differences in DMN functioning during rest (intrinsic functional connectivity. Here we examined 18 participants’ spontaneous descriptions of autobiographical memories during a two-hour, private, open-ended interview in which they reacted to a series of true stories about real people’s social situations and responded to the prompt, how does this person’s story make you feel? We classified these descriptions as either containing factual information (semantic AMs or more elaborate descriptions of emotionally meaningful events (episodic AMs. We also collected resting state fMRI scans from the participants and related individual differences in frequency of described AMs to participants’ intrinsic functional connectivity within regions of the DMN. We found that producing more descriptions of either memory type correlated with stronger intrinsic connectivity in the parahippocampal and middle temporal gyri. Additionally, episodic AM descriptions correlated with connectivity in the bilateral hippocampi and medial prefrontal cortex, and semantic memory descriptions correlated with connectivity in right inferior lateral parietal cortex. These findings suggest that in individuals who naturally invoke more memories during social processing, brain regions involved in memory retrieval and self/social processing are more strongly coupled to the DMN during rest.

  18. Rapprochement in Late Adolescent Separation-Individuation: A Structural Equations Approach.

    Science.gov (United States)

    Quintana, Stephen M.; Lapsley, Daniel K.

    1990-01-01

    Attempted to integrate indices of connection and individuality into a single, positively related construct. College students (n=101) responded to measures of parenting style, individuation, and ego identity. Results suggest that parental control restricts successful individuation but that adjustment on individuation indices predicts advanced…

  19. Predicting muscle forces of individuals with hemiparesis following stroke

    Directory of Open Access Journals (Sweden)

    Maladen Ryan

    2008-02-01

    Full Text Available Abstract Background Functional electrical stimulation (FES has been used to improve function in individuals with hemiparesis following stroke. An ideal functional electrical stimulation (FES system needs an accurate mathematical model capable of designing subject and task-specific stimulation patterns. Such a model was previously developed in our laboratory and shown to predict the isometric forces produced by the quadriceps femoris muscles of able-bodied individuals and individuals with spinal cord injury in response to a wide range of clinically relevant stimulation frequencies and patterns. The aim of this study was to test our isometric muscle force model on the quadriceps femoris, ankle dorsiflexor, and ankle plantar-flexor muscles of individuals with post-stroke hemiparesis. Methods Subjects were seated on a force dynamometer and isometric forces were measured in response to a range of stimulation frequencies (10 to 80-Hz and 3 different patterns. Subject-specific model parameter values were obtained by fitting the measured force responses from 2 stimulation trains. The model parameters thus obtained were then used to obtain predicted forces for a range of frequencies and patterns. Predicted and measured forces were compared using intra-class correlation coefficients, r2 values, and model error relative to the physiological error (variability of measured forces. Results Results showed excellent agreement between measured and predicted force-time responses (r2 >0.80, peak forces (ICCs>0.84, and force-time integrals (ICCs>0.82 for the quadriceps, dorsiflexor, and plantar-fexor muscles. The model error was within or below the +95% confidence interval of the physiological error for >88% comparisons between measured and predicted forces. Conclusion Our results show that the model has potential to be incorporated as a feed-forward controller for predicting subject-specific stimulation patterns during FES.

  20. Stress responsiveness predicts individual variation in mate selectivity.

    Science.gov (United States)

    Vitousek, Maren N; Romero, L Michael

    2013-06-15

    Steroid hormones, including glucocorticoids, mediate a variety of behavioral and physiological processes. Circulating hormone concentrations vary substantially within populations, and although hormone titers predict reproductive success in several species, little is known about how individual variation in circulating hormone concentrations is linked with most reproductive behaviors in free-living organisms. Mate choice is an important and often costly component of reproduction that also varies substantially within populations. We examined whether energetically costly mate selection behavior in female Galápagos marine iguanas (Amblyrhynchus cristatus) was associated with individual variation in the concentrations of hormones previously shown to differ between reproductive and non-reproductive females during the breeding season (corticosterone and testosterone). Stress-induced corticosterone levels - which are suppressed in female marine iguanas during reproduction - were individually repeatable throughout the seven-week breeding period. Mate selectivity was strongly predicted by individual variation in stress-induced corticosterone: reproductive females that secreted less corticosterone in response to a standardized stressor assessed more displaying males. Neither baseline corticosterone nor testosterone predicted variation in mate selectivity. Scaled body mass was not significantly associated with mate selectivity, but females that began the breeding period in lower body condition showed a trend towards being less selective about potential mates. These results provide the first evidence that individual variation in the corticosterone stress response is associated with how selective females are in their choice of a mate, an important contributor to fitness in many species. Future research is needed to determine the functional basis of this association, and whether transient acute increases in circulating corticosterone directly mediate mate choice behaviors

  1. Conservatism and the neural circuitry of threat: economic conservatism predicts greater amygdala-BNST connectivity during periods of threat vs safety.

    Science.gov (United States)

    Pedersen, Walker S; Muftuler, L Tugan; Larson, Christine L

    2018-01-01

    Political conservatism is associated with an increased negativity bias, including increased attention and reactivity toward negative and threatening stimuli. Although the human amygdala has been implicated in the response to threatening stimuli, no studies to date have investigated whether conservatism is associated with altered amygdala function toward threat. Furthermore, although an influential theory posits that connectivity between the amygdala and bed nucleus of the stria terminalis (BNST) is important in initiating the response to sustained or uncertain threat, whether individual differences in conservatism modulate this connectivity is unknown. To test whether conservatism is associated with increased reactivity in neural threat circuitry, we measured participants' self-reported social and economic conservatism and asked them to complete high-resolution fMRI scans while under threat of an unpredictable shock and while safe. We found that economic conservatism predicted greater connectivity between the BNST and a cluster of voxels in the left amygdala during threat vs safety. These results suggest that increased amygdala-BNST connectivity during threat may be a key neural correlate of the enhanced negativity bias found in conservatism. © The Author (2017). Published by Oxford University Press.

  2. Preschool anxiety disorders predict different patterns of amygdala-prefrontal connectivity at school-age.

    Directory of Open Access Journals (Sweden)

    Kimberly L H Carpenter

    Full Text Available In this prospective, longitudinal study of young children, we examined whether a history of preschool generalized anxiety, separation anxiety, and/or social phobia is associated with amygdala-prefrontal dysregulation at school-age. As an exploratory analysis, we investigated whether distinct anxiety disorders differ in the patterns of this amygdala-prefrontal dysregulation.Participants were children taking part in a 5-year study of early childhood brain development and anxiety disorders. Preschool symptoms of generalized anxiety, separation anxiety, and social phobia were assessed with the Preschool Age Psychiatric Assessment (PAPA in the first wave of the study when the children were between 2 and 5 years old. The PAPA was repeated at age 6. We conducted functional MRIs when the children were 5.5 to 9.5 year old to assess neural responses to viewing of angry and fearful faces.A history of preschool social phobia predicted less school-age functional connectivity between the amygdala and the ventral prefrontal cortices to angry faces. Preschool generalized anxiety predicted less functional connectivity between the amygdala and dorsal prefrontal cortices in response to fearful faces. Finally, a history of preschool separation anxiety predicted less school-age functional connectivity between the amygdala and the ventral prefrontal cortices to angry faces and greater school-age functional connectivity between the amygdala and dorsal prefrontal cortices to angry faces.Our results suggest that there are enduring neurobiological effects associated with a history of preschool anxiety, which occur over-and-above the effect of subsequent emotional symptoms. Our results also provide preliminary evidence for the neurobiological differentiation of specific preschool anxiety disorders.

  3. Consistency of test behaviour and individual difference in prescision of prediction

    NARCIS (Netherlands)

    Meijer, R.R.

    1998-01-01

    Ghiselli ((1956, 1960) argued that the precision of prediction on the basis of a test may vary for different individuals. To quantify the individual precision of prediction he compared the observed criterion scores with the expected criterion scores estimated on the basis of the total scores on a

  4. Individualized performance prediction during total sleep deprivation: accounting for trait vulnerability to sleep loss.

    Science.gov (United States)

    Ramakrishnan, Sridhar; Laxminarayan, Srinivas; Thorsley, David; Wesensten, Nancy J; Balkin, Thomas J; Reifman, Jaques

    2012-01-01

    Individual differences in vulnerability to sleep loss can be considerable, and thus, recent efforts have focused on developing individualized models for predicting the effects of sleep loss on performance. Individualized models constructed using a Bayesian formulation, which combines an individual's available performance data with a priori performance predictions from a group-average model, typically need at least 40 h of individual data before showing significant improvement over the group-average model predictions. Here, we improve upon the basic Bayesian formulation for developing individualized models by observing that individuals may be classified into three sleep-loss phenotypes: resilient, average, and vulnerable. For each phenotype, we developed a phenotype-specific group-average model and used these models to identify each individual's phenotype. We then used the phenotype-specific models within the Bayesian formulation to make individualized predictions. Results on psychomotor vigilance test data from 48 individuals indicated that, on average, ∼85% of individual phenotypes were accurately identified within 30 h of wakefulness. The percentage improvement of the proposed approach in 10-h-ahead predictions was 16% for resilient subjects and 6% for vulnerable subjects. The trade-off for these improvements was a slight decrease in prediction accuracy for average subjects.

  5. Can the theory of planned behaviour predict the physical activity behaviour of individuals?

    Science.gov (United States)

    Hobbs, Nicola; Dixon, Diane; Johnston, Marie; Howie, Kate

    2013-01-01

    The theory of planned behaviour (TPB) can identify cognitions that predict differences in behaviour between individuals. However, it is not clear whether the TPB can predict the behaviour of an individual person. This study employs a series of n-of-1 studies and time series analyses to examine the ability of the TPB to predict physical activity (PA) behaviours of six individuals. Six n-of-1 studies were conducted, in which TPB cognitions and up to three PA behaviours (walking, gym workout and a personally defined PA) were measured twice daily for six weeks. Walking was measured by pedometer step count, gym attendance by self-report with objective validation of gym entry and the personally defined PA behaviour by self-report. Intra-individual variability in TPB cognitions and PA behaviour was observed in all participants. The TPB showed variable predictive utility within individuals and across behaviours. The TPB predicted at least one PA behaviour for five participants but had no predictive utility for one participant. Thus, n-of-1 designs and time series analyses can be used to test theory in an individual.

  6. Knowing what from where: Hippocampal connectivity with temporoparietal cortex at rest is linked to individual differences in semantic and topographic memory.

    Science.gov (United States)

    Sormaz, Mladen; Jefferies, Elizabeth; Bernhardt, Boris C; Karapanagiotidis, Theodoros; Mollo, Giovanna; Bernasconi, Neda; Bernasconi, Andrea; Hartley, Tom; Smallwood, Jonathan

    2017-05-15

    The hippocampus contributes to episodic, spatial and semantic aspects of memory, yet individual differences within and between these functions are not well-understood. In 136 healthy individuals, we investigated whether these differences reflect variation in the strength of connections between functionally-specialised segments of the hippocampus and diverse cortical regions that participate in different aspects of memory. Better topographical memory was associated with stronger connectivity between lingual gyrus and left anterior, rather than posterior, hippocampus. Better semantic memory was associated with increased connectivity between the intracalcarine/cuneus and left, rather than right, posterior hippocampus. Notably, we observed a double dissociation between semantic and topographical memory: better semantic memory was associated with stronger connectivity between left temporoparietal cortex and left anterior hippocampus, while better topographic memory was linked to stronger connectivity with right anterior hippocampus. Together these data support a division-of-labour account of hippocampal functioning: at the population level, differences in connectivity across the hippocampus reflect functional specialisation for different facets of memory, while variation in these connectivity patterns across individuals is associated with differences in the capacity to retrieve different types of information. In particular, within-hemisphere connectivity between hippocampus and left temporoparietal cortex supports conceptual processing at the expense of spatial ability. Copyright © 2017. Published by Elsevier Inc.

  7. Self-esteem modulates amygdala-ventrolateral prefrontal cortex connectivity in response to mortality threats.

    Science.gov (United States)

    Yanagisawa, Kuniaki; Abe, Nobuhito; Kashima, Emiko S; Nomura, Michio

    2016-03-01

    Reminders of death often elicit defensive responses in individuals, especially among those with low self-esteem. Although empirical evidence indicates that self-esteem serves as a buffer against mortality threats, the precise neural mechanism underlying this effect remains unknown. We used functional magnetic resonance imaging (fMRI) to test the hypothesis that self-esteem modulates neural responses to death-related stimuli, especially functional connectivity within the limbic-frontal circuitry, thereby affecting subsequent defensive reactions. As predicted, individuals with high self-esteem subjected to a mortality threat exhibited increased amygdala-ventrolateral prefrontal cortex (VLPFC) connectivity during the processing of death-related stimuli compared with individuals who have low self-esteem. Further analysis revealed that stronger functional connectivity between the amygdala and the VLPFC predicted a subsequent decline in responding defensively to those who threaten one's beliefs. These results suggest that the amygdala-VLPFC interaction, which is modulated by self-esteem, can reduce the defensiveness caused by death-related stimuli, thereby providing a neural explanation for why individuals with high self-esteem exhibit less defensive reactions to mortality threats. (c) 2016 APA, all rights reserved).

  8. Developmental dyslexia: predicting individual risk.

    Science.gov (United States)

    Thompson, Paul A; Hulme, Charles; Nash, Hannah M; Gooch, Debbie; Hayiou-Thomas, Emma; Snowling, Margaret J

    2015-09-01

    Causal theories of dyslexia suggest that it is a heritable disorder, which is the outcome of multiple risk factors. However, whether early screening for dyslexia is viable is not yet known. The study followed children at high risk of dyslexia from preschool through the early primary years assessing them from age 3 years and 6 months (T1) at approximately annual intervals on tasks tapping cognitive, language, and executive-motor skills. The children were recruited to three groups: children at family risk of dyslexia, children with concerns regarding speech, and language development at 3;06 years and controls considered to be typically developing. At 8 years, children were classified as 'dyslexic' or not. Logistic regression models were used to predict the individual risk of dyslexia and to investigate how risk factors accumulate to predict poor literacy outcomes. Family-risk status was a stronger predictor of dyslexia at 8 years than low language in preschool. Additional predictors in the preschool years include letter knowledge, phonological awareness, rapid automatized naming, and executive skills. At the time of school entry, language skills become significant predictors, and motor skills add a small but significant increase to the prediction probability. We present classification accuracy using different probability cutoffs for logistic regression models and ROC curves to highlight the accumulation of risk factors at the individual level. Dyslexia is the outcome of multiple risk factors and children with language difficulties at school entry are at high risk. Family history of dyslexia is a predictor of literacy outcome from the preschool years. However, screening does not reach an acceptable clinical level until close to school entry when letter knowledge, phonological awareness, and RAN, rather than family risk, together provide good sensitivity and specificity as a screening battery. © 2015 The Authors. Journal of Child Psychology and Psychiatry published by

  9. Using the Change Manager Model for the Hippocampal System to Predict Connectivity and Neurophysiological Parameters in the Perirhinal Cortex

    Science.gov (United States)

    Coward, L. Andrew; Gedeon, Tamas D.

    2016-01-01

    Theoretical arguments demonstrate that practical considerations, including the needs to limit physiological resources and to learn without interference with prior learning, severely constrain the anatomical architecture of the brain. These arguments identify the hippocampal system as the change manager for the cortex, with the role of selecting the most appropriate locations for cortical receptive field changes at each point in time and driving those changes. This role results in the hippocampal system recording the identities of groups of cortical receptive fields that changed at the same time. These types of records can also be used to reactivate the receptive fields active during individual unique past events, providing mechanisms for episodic memory retrieval. Our theoretical arguments identify the perirhinal cortex as one important focal point both for driving changes and for recording and retrieving episodic memories. The retrieval of episodic memories must not drive unnecessary receptive field changes, and this consideration places strong constraints on neuron properties and connectivity within and between the perirhinal cortex and regular cortex. Hence the model predicts a number of such properties and connectivity. Experimental test of these falsifiable predictions would clarify how change is managed in the cortex and how episodic memories are retrieved. PMID:26819594

  10. Network connectivity and individual responses to brain stimulation in the human motor system.

    Science.gov (United States)

    Cárdenas-Morales, Lizbeth; Volz, Lukas J; Michely, Jochen; Rehme, Anne K; Pool, Eva-Maria; Nettekoven, Charlotte; Eickhoff, Simon B; Fink, Gereon R; Grefkes, Christian

    2014-07-01

    The mechanisms driving cortical plasticity in response to brain stimulation are still incompletely understood. We here explored whether neural activity and connectivity in the motor system relate to the magnitude of cortical plasticity induced by repetitive transcranial magnetic stimulation (rTMS). Twelve right-handed volunteers underwent functional magnetic resonance imaging during rest and while performing a simple hand motor task. Resting-state functional connectivity, task-induced activation, and task-related effective connectivity were assessed for a network of key motor areas. We then investigated the effects of intermittent theta-burst stimulation (iTBS) on motor-evoked potentials (MEP) for up to 25 min after stimulation over left primary motor cortex (M1) or parieto-occipital vertex (for control). ITBS-induced increases in MEP amplitudes correlated negatively with movement-related fMRI activity in left M1. Control iTBS had no effect on M1 excitability. Subjects with better response to M1-iTBS featured stronger preinterventional effective connectivity between left premotor areas and left M1. In contrast, resting-state connectivity did not predict iTBS aftereffects. Plasticity-related changes in M1 following brain stimulation seem to depend not only on local factors but also on interconnected brain regions. Predominantly activity-dependent properties of the cortical motor system are indicative of excitability changes following induction of cortical plasticity with rTMS. © The Author 2013. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  11. Periodontitis in older Swedish individuals fails to predict mortality.

    Science.gov (United States)

    Renvert, Stefan; Wallin-Bengtsson, Viveca; Berglund, Johan; Persson, Rutger G

    2015-03-01

    This study aims to assess mortality risk and its association to health aspects in dentate individuals 60 years of age and older. Medical and periodontal data from 870 dentate individuals (age range 60–96) participating in the Swedish National Study on Aging and Care in Blekinge (SNACBlekinge)with survival statistics over 6 years were studied. During 6 years of follow-up, 42/474 of the individuals(8.9 %), who at baseline were between age 60 and 75, and 134/396 individuals of the individuals (33.9 %), who at baseline were ≥75 years, died. Surviving dentate individuals had more teeth (mean 19.3, S.D.±7.9) than those who died (mean 15.9,S.D.±7.3; mean diff 3,3; S.E. mean diff 0.7; 95 % CI 2.0, 4.6;p=0.001). A self-reported history of high blood pressure (F=15.0, pheart failure (F=24.5, pheart disease, diabetes, any form of cancer,or periodontitis failed to predict mortality. A self-reported history of angina pectoris, chronic heart failure, elevated serum HbA1c, and few remaining teeth were associated with mortality risk. A professional diagnosis of cardiovascular disease, diabetes, cancer, or periodontitis was not predictive of mortality. Self-health reports are important to observe in the assessment of disease and survival in older individual.

  12. A Note on the Use of Mixture Models for Individual Prediction.

    Science.gov (United States)

    Cole, Veronica T; Bauer, Daniel J

    Mixture models capture heterogeneity in data by decomposing the population into latent subgroups, each of which is governed by its own subgroup-specific set of parameters. Despite the flexibility and widespread use of these models, most applications have focused solely on making inferences for whole or sub-populations, rather than individual cases. The current article presents a general framework for computing marginal and conditional predicted values for individuals using mixture model results. These predicted values can be used to characterize covariate effects, examine the fit of the model for specific individuals, or forecast future observations from previous ones. Two empirical examples are provided to demonstrate the usefulness of individual predicted values in applications of mixture models. The first example examines the relative timing of initiation of substance use using a multiple event process survival mixture model whereas the second example evaluates changes in depressive symptoms over adolescence using a growth mixture model.

  13. Repetitive model predictive approach to individual pitch control of wind turbines

    DEFF Research Database (Denmark)

    Adegas, Fabiano Daher; Stoustrup, Jakob; Odgaard, Peter Fogh

    2011-01-01

    prediction. As a consequence, individual pitch feed-forward control action is generated by the controller, taking ”future” wind disturbance into account. Information about the estimated wind spatial distribution one blade experience can be used in the prediction model to better control the next passing blade......Wind turbines are inherently exposed to nonuniform wind fields with of wind shear, tower shadow, and possible wake contributions. Asymmetrical aerodynamic rotor loads are a consequence of such periodic, repetitive wind disturbances experienced by the blades. A controller may estimate and use...... this peculiar disturbance pattern to better attenuate loads and regulate power by controlling the blade pitch angles individually. A novel model predictive (MPC) approach for individual pitch control of wind turbines is proposed in this paper. A repetitive wind disturbance model is incorporated into the MPC...

  14. Days on radiosensitivity: individual variability and predictive tests

    International Nuclear Information System (INIS)

    2008-01-01

    The radiosensitivity is a part of usual clinical observations. It is already included in the therapy protocols. however, some questions stay on its individual variability and on the difficulty to evaluate it. The point will be stocked on its origin and its usefulness in predictive medicine. Through examples on the use of predictive tests and ethical and legal questions that they raise, concrete cases will be presented by specialists such radio biologists, geneticists, immunologists, jurists and occupational physicians. (N.C.)

  15. Auditory working memory predicts individual differences in absolute pitch learning.

    Science.gov (United States)

    Van Hedger, Stephen C; Heald, Shannon L M; Koch, Rachelle; Nusbaum, Howard C

    2015-07-01

    Absolute pitch (AP) is typically defined as the ability to label an isolated tone as a musical note in the absence of a reference tone. At first glance the acquisition of AP note categories seems like a perceptual learning task, since individuals must assign a category label to a stimulus based on a single perceptual dimension (pitch) while ignoring other perceptual dimensions (e.g., loudness, octave, instrument). AP, however, is rarely discussed in terms of domain-general perceptual learning mechanisms. This is because AP is typically assumed to depend on a critical period of development, in which early exposure to pitches and musical labels is thought to be necessary for the development of AP precluding the possibility of adult acquisition of AP. Despite this view of AP, several previous studies have found evidence that absolute pitch category learning is, to an extent, trainable in a post-critical period adult population, even if the performance typically achieved by this population is below the performance of a "true" AP possessor. The current studies attempt to understand the individual differences in learning to categorize notes using absolute pitch cues by testing a specific prediction regarding cognitive capacity related to categorization - to what extent does an individual's general auditory working memory capacity (WMC) predict the success of absolute pitch category acquisition. Since WMC has been shown to predict performance on a wide variety of other perceptual and category learning tasks, we predict that individuals with higher WMC should be better at learning absolute pitch note categories than individuals with lower WMC. Across two studies, we demonstrate that auditory WMC predicts the efficacy of learning absolute pitch note categories. These results suggest that a higher general auditory WMC might underlie the formation of absolute pitch categories for post-critical period adults. Implications for understanding the mechanisms that underlie the

  16. Wind Power Grid Connected Capacity Prediction Using LSSVM Optimized by the Bat Algorithm

    Directory of Open Access Journals (Sweden)

    Qunli Wu

    2015-12-01

    Full Text Available Given the stochastic nature of wind, wind power grid-connected capacity prediction plays an essential role in coping with the challenge of balancing supply and demand. Accurate forecasting methods make enormous contribution to mapping wind power strategy, power dispatching and sustainable development of wind power industry. This study proposes a bat algorithm (BA–least squares support vector machine (LSSVM hybrid model to improve prediction performance. In order to select input of LSSVM effectively, Stationarity, Cointegration and Granger causality tests are conducted to examine the influence of installed capacity with different lags, and partial autocorrelation analysis is employed to investigate the inner relationship of grid-connected capacity. The parameters in LSSVM are optimized by BA to validate the learning ability and generalization of LSSVM. Multiple model sufficiency evaluation methods are utilized. The research results reveal that the accuracy improvement of the present approach can reach about 20% compared to other single or hybrid models.

  17. Intrinsic Functional Connectivity in the Adult Brain and Success in Second-Language Learning.

    Science.gov (United States)

    Chai, Xiaoqian J; Berken, Jonathan A; Barbeau, Elise B; Soles, Jennika; Callahan, Megan; Chen, Jen-Kai; Klein, Denise

    2016-01-20

    There is considerable variability in an individual's ability to acquire a second language (L2) during adulthood. Using resting-state fMRI data acquired before training in English speakers who underwent a 12 week intensive French immersion training course, we investigated whether individual differences in intrinsic resting-state functional connectivity relate to a person's ability to acquire an L2. We focused on two key aspects of language processing--lexical retrieval in spontaneous speech and reading speed--and computed whole-brain functional connectivity from two regions of interest in the language network, namely the left anterior insula/frontal operculum (AI/FO) and the visual word form area (VWFA). Connectivity between the left AI/FO and left posterior superior temporal gyrus (STG) and between the left AI/FO and dorsal anterior cingulate cortex correlated positively with improvement in L2 lexical retrieval in spontaneous speech. Connectivity between the VWFA and left mid-STG correlated positively with improvement in L2 reading speed. These findings are consistent with the different language functions subserved by subcomponents of the language network and suggest that the human capacity to learn an L2 can be predicted by an individual's intrinsic functional connectivity within the language network. Significance statement: There is considerable variability in second-language learning abilities during adulthood. We investigated whether individual differences in intrinsic functional connectivity in the adult brain relate to success in second-language learning, using resting-state functional magnetic resonance imaging in English speakers who underwent a 12 week intensive French immersion training course. We found that pretraining functional connectivity within two different language subnetworks correlated strongly with learning outcome in two different language skills: lexical retrieval in spontaneous speech and reading speed. Our results suggest that the human

  18. Amygdala subnuclei connectivity in response to violence reveals unique influences of individual differences in psychopathic traits in a nonforensic sample.

    Science.gov (United States)

    Yoder, Keith J; Porges, Eric C; Decety, Jean

    2015-04-01

    Atypical amygdala function and connectivity have reliably been associated with psychopathy. However, the amygdala is not a unitary structure. To examine how psychopathic traits in a nonforensic sample are linked to amygdala response to violence, this study used probabilistic tractography to classify amygdala subnuclei based on anatomical projections to and from amygdala subnuclei in a group of 43 male participants. The segmentation identified the basolateral complex (BLA; lateral, basal, and accessory basal subnuclei) and the central subnucleus (CE), which were used as seeds in a functional connectivity analysis to identify differences in neuronal coupling specific to observed violence. While a full amygdala seed showed significant connectivity only to right middle occipital gyrus, subnuclei seeds revealed unique connectivity patterns. BLA showed enhanced coupling with anterior cingulate and prefrontal regions, while CE showed increased connectivity with the brainstem, but reduced connectivity with superior parietal and precentral gyrus. Further, psychopathic personality factors were related to specific patterns of connectivity. Fearless Dominance scores on the psychopathic personality inventory predicted increased coupling between the BLA seed and sensory integration cortices, and increased connectivity between the CE seed and posterior insula. Conversely, Self-Centered Impulsivity scores were negatively correlated with coupling between BLA and ventrolateral prefrontal cortex, and Coldheartedness scores predicted increased functional connectivity between BLA and dorsal anterior cingulate cortex. Taken together, these findings demonstrate how subnuclei segmentations reveal important functional connectivity differences that are otherwise inaccessible. Such an approach yields a better understanding of amygdala dysfunction in psychopathy. © 2014 Wiley Periodicals, Inc.

  19. Altered interhemispheric connectivity in individuals with Tourette's disorder

    DEFF Research Database (Denmark)

    Plessen, Kerstin J; Wentzel-Larsen, Tore; Hugdahl, Kenneth

    2004-01-01

    OBJECTIVE: The corpus callosum is the major commissure connecting the cerebral hemispheres. Prior evidence suggests involvement of the corpus callosum in the pathophysiology of Tourette's disorder. The authors assessed corpus callosum size and anatomical connectivity across the cerebral hemispheres...

  20. Optimal Predictions in Everyday Cognition: The Wisdom of Individuals or Crowds?

    Science.gov (United States)

    Mozer, Michael C.; Pashler, Harold; Homaei, Hadjar

    2008-01-01

    Griffiths and Tenenbaum (2006) asked individuals to make predictions about the duration or extent of everyday events (e.g., cake baking times), and reported that predictions were optimal, employing Bayesian inference based on veridical prior distributions. Although the predictions conformed strikingly to statistics of the world, they reflect…

  1. Connecting Representations: Using Predict, Check, Explain

    Science.gov (United States)

    Roy, George J.; Fueyo, Vivian; Vahey, Philip; Knudsen, Jennifer; Rafanan, Ken; Lara-Meloy, Teresa

    2016-01-01

    Although educators agree that making connections with the real world, as advocated by "Principles to Actions: Ensuring Mathematical Success for All" (NCTM 2014), is important, making such connections while addressing important mathematics is elusive. The authors have found that math content coupled with the instructional strategy of…

  2. Posttraumatic stress disorder symptom severity is associated with reduced default mode network connectivity in individuals with elevated genetic risk for psychopathology.

    Science.gov (United States)

    Miller, Danielle R; Logue, Mark W; Wolf, Erika J; Maniates, Hannah; Robinson, Meghan E; Hayes, Jasmeet P; Stone, Annjanette; Schichman, Steven; McGlinchey, Regina E; Milberg, William P; Miller, Mark W

    2017-07-01

    Accumulating evidence suggests that posttraumatic stress disorder (PTSD) is associated with disrupted default mode network (DMN) connectivity, but findings across studies have not been uniform. Individual differences in relevant genes may account for some of the reported variability in the relationship between DMN connectivity and PTSD. In this study, we investigated this possibility using genome-wide association study (GWAS) derived polygenic risk scores (PRSs) for relevant psychiatric traits. We hypothesized that the association between PTSD and DMN connectivity would be moderated by genetic risk for one or more psychiatric traits such that individuals with elevated polygenic risk for psychopathology and severe PTSD would exhibit disrupted DMN connectivity. Participants were 156 white, non-Hispanic veterans of the wars in Iraq and Afghanistan who were genotyped and underwent resting state functional magnetic resonance imaging and clinical assessment. PRSs for neuroticism, anxiety, major depressive disorder, and cross-disorder risk (based on five psychiatric disorders) were calculated using summary statistics from published large-scale consortia-based GWASs. Cross-disorder polygenic risk influenced the relationship between DMN connectivity and PTSD symptom severity such that individuals at greater genetic risk showed a significant negative association between PTSD symptom severity and connectivity between the posterior cingulate cortex and right middle temporal gyrus. Polygenic risk for neuroticism, anxiety, and major depressive disorder did not influence DMN connectivity directly or through an interaction with PTSD. Findings illustrate the potential power of genome-wide PRSs to advance understanding of the relationship between PTSD and DMN connectivity, a putative neural endophenotype of the disorder. © 2017 Wiley Periodicals, Inc.

  3. Amygdala functional connectivity as a longitudinal biomarker of symptom changes in generalized anxiety.

    Science.gov (United States)

    Makovac, Elena; Watson, David R; Meeten, Frances; Garfinkel, Sarah N; Cercignani, Mara; Critchley, Hugo D; Ottaviani, Cristina

    2016-11-01

    Generalized anxiety disorder (GAD) is characterized by excessive worry, autonomic dysregulation and functional amygdala dysconnectivity, yet these illness markers have rarely been considered together, nor their interrelationship tested longitudinally. We hypothesized that an individual's capacity for emotion regulation predicts longer-term changes in amygdala functional connectivity, supporting the modification of GAD core symptoms. Sixteen patients with GAD (14 women) and individually matched controls were studied at two time points separated by 1 year. Resting-state fMRI data and concurrent measurement of vagally mediated heart rate variability were obtained before and after the induction of perseverative cognition. A greater rise in levels of worry following the induction predicted a stronger reduction in connectivity between right amygdala and ventromedial prefrontal cortex, and enhanced coupling between left amygdala and ventral tegmental area at follow-up. Similarly, amplified physiological responses to the induction predicted increased connectivity between right amygdala and thalamus. Longitudinal shifts in a distinct set of functional connectivity scores were associated with concomitant changes in GAD symptomatology over the course of the year. Results highlight the prognostic value of indices of emotional dysregulation and emphasize the integral role of the amygdala as a critical hub in functional neural circuitry underlying the progression of GAD symptomatology. © The Author (2016). Published by Oxford University Press.

  4. Individual differences in self-reported self-control predict successful emotion regulation.

    Science.gov (United States)

    Paschke, Lena M; Dörfel, Denise; Steimke, Rosa; Trempler, Ima; Magrabi, Amadeus; Ludwig, Vera U; Schubert, Torsten; Stelzel, Christine; Walter, Henrik

    2016-08-01

    Both self-control and emotion regulation enable individuals to adapt to external circumstances and social contexts, and both are assumed to rely on the overlapping neural resources. Here, we tested whether high self-reported self-control is related to successful emotion regulation on the behavioral and neural level. One hundred eight participants completed three self-control questionnaires and regulated their negative emotions during functional magnetic resonance imaging using reappraisal (distancing). Trait self-control correlated positively with successful emotion regulation both subjectively and neurally, as indicated by online ratings of negative emotions and functional connectivity strength between the amygdala and prefrontal areas, respectively. This stronger overall connectivity of the left amygdala was related to more successful subjective emotion regulation. Comparing amygdala activity over time showed that high self-controllers successfully maintained down-regulation of the left amygdala over time, while low self-controllers failed to down-regulate towards the end of the experiment. This indicates that high self-controllers are better at maintaining a motivated state supporting emotion regulation over time. Our results support assumptions concerning a close relation of self-control and emotion regulation as two domains of behavioral control. They further indicate that individual differences in functional connectivity between task-related brain areas directly relate to differences in trait self-control. © The Author (2016). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  5. Structural brain connectivity and cognitive ability differences: A multivariate distance matrix regression analysis.

    Science.gov (United States)

    Ponsoda, Vicente; Martínez, Kenia; Pineda-Pardo, José A; Abad, Francisco J; Olea, Julio; Román, Francisco J; Barbey, Aron K; Colom, Roberto

    2017-02-01

    Neuroimaging research involves analyses of huge amounts of biological data that might or might not be related with cognition. This relationship is usually approached using univariate methods, and, therefore, correction methods are mandatory for reducing false positives. Nevertheless, the probability of false negatives is also increased. Multivariate frameworks have been proposed for helping to alleviate this balance. Here we apply multivariate distance matrix regression for the simultaneous analysis of biological and cognitive data, namely, structural connections among 82 brain regions and several latent factors estimating cognitive performance. We tested whether cognitive differences predict distances among individuals regarding their connectivity pattern. Beginning with 3,321 connections among regions, the 36 edges better predicted by the individuals' cognitive scores were selected. Cognitive scores were related to connectivity distances in both the full (3,321) and reduced (36) connectivity patterns. The selected edges connect regions distributed across the entire brain and the network defined by these edges supports high-order cognitive processes such as (a) (fluid) executive control, (b) (crystallized) recognition, learning, and language processing, and (c) visuospatial processing. This multivariate study suggests that one widespread, but limited number, of regions in the human brain, supports high-level cognitive ability differences. Hum Brain Mapp 38:803-816, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  6. Fronto-temporal connectivity predicts cognitive empathy deficits and experiential negative symptoms in schizophrenia.

    Science.gov (United States)

    Abram, Samantha V; Wisner, Krista M; Fox, Jaclyn M; Barch, Deanna M; Wang, Lei; Csernansky, John G; MacDonald, Angus W; Smith, Matthew J

    2017-03-01

    Impaired cognitive empathy is a core social cognitive deficit in schizophrenia associated with negative symptoms and social functioning. Cognitive empathy and negative symptoms have also been linked to medial prefrontal and temporal brain networks. While shared behavioral and neural underpinnings are suspected for cognitive empathy and negative symptoms, research is needed to test these hypotheses. In two studies, we evaluated whether resting-state functional connectivity between data-driven networks, or components (referred to as, inter-component connectivity), predicted cognitive empathy and experiential and expressive negative symptoms in schizophrenia subjects. Study 1: We examined associations between cognitive empathy and medial prefrontal and temporal inter-component connectivity at rest using a group-matched schizophrenia and control sample. We then assessed whether inter-component connectivity metrics associated with cognitive empathy were also related to negative symptoms. Study 2: We sought to replicate the connectivity-symptom associations observed in Study 1 using an independent schizophrenia sample. Study 1 results revealed that while the groups did not differ in average inter-component connectivity, a medial-fronto-temporal metric and an orbito-fronto-temporal metric were related to cognitive empathy. Moreover, the medial-fronto-temporal metric was associated with experiential negative symptoms in both schizophrenia samples. These findings support recent models that link social cognition and negative symptoms in schizophrenia. Hum Brain Mapp 38:1111-1124, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  7. Predicting clinical symptoms of attention deficit hyperactivity disorder based on temporal patterns between and within intrinsic connectivity networks.

    Science.gov (United States)

    Wang, Xun-Heng; Jiao, Yun; Li, Lihua

    2017-10-24

    Attention deficit hyperactivity disorder (ADHD) is a common brain disorder with high prevalence in school-age children. Previously developed machine learning-based methods have discriminated patients with ADHD from normal controls by providing label information of the disease for individuals. Inattention and impulsivity are the two most significant clinical symptoms of ADHD. However, predicting clinical symptoms (i.e., inattention and impulsivity) is a challenging task based on neuroimaging data. The goal of this study is twofold: to build predictive models for clinical symptoms of ADHD based on resting-state fMRI and to mine brain networks for predictive patterns of inattention and impulsivity. To achieve this goal, a cohort of 74 boys with ADHD and a cohort of 69 age-matched normal controls were recruited from the ADHD-200 Consortium. Both structural and resting-state fMRI images were obtained for each participant. Temporal patterns between and within intrinsic connectivity networks (ICNs) were applied as raw features in the predictive models. Specifically, sample entropy was taken asan intra-ICN feature, and phase synchronization (PS) was used asan inter-ICN feature. The predictive models were based on the least absolute shrinkage and selectionator operator (LASSO) algorithm. The performance of the predictive model for inattention is r=0.79 (p<10 -8 ), and the performance of the predictive model for impulsivity is r=0.48 (p<10 -8 ). The ICN-related predictive patterns may provide valuable information for investigating the brain network mechanisms of ADHD. In summary, the predictive models for clinical symptoms could be beneficial for personalizing ADHD medications. Copyright © 2017 IBRO. Published by Elsevier Ltd. All rights reserved.

  8. Model Predictive Current Control for High-Power Grid-Connected Converters with Output LCL Filter

    DEFF Research Database (Denmark)

    Delpino, Hernan Anres Miranda; Teodorescu, Remus; Rodriguez, Pedro

    2009-01-01

    A model predictive control strategy for a highpower, grid connected 3-level neutral clamped point converter is presented. Power losses constraints set a limit on commutation losses so reduced switching frequency is required, thus producing low frequency current harmonics. To reduce these harmonics...

  9. Illusory conjunctions in visual short-term memory: Individual differences in corpus callosum connectivity and splitting attention between the two hemifields.

    Science.gov (United States)

    Qin, Shuo; Ray, Nicholas R; Ramakrishnan, Nithya; Nashiro, Kaoru; O'Connell, Margaret A; Basak, Chandramallika

    2016-11-01

    Overloading the capacity of visual attention can result in mistakenly combining the various features of an object, that is, illusory conjunctions. We hypothesize that if the two hemispheres separately process visual information by splitting attention, connectivity of corpus callosum-a brain structure integrating the two hemispheres-would predict the degree of illusory conjunctions. In the current study, we assessed two types of illusory conjunctions using a memory-scanning paradigm; the features were either presented across the two opposite hemifields or within the same hemifield. Four objects, each with two visual features, were briefly presented together followed by a probe-recognition and a confidence rating for the recognition accuracy. MRI scans were also obtained. Results indicated that successful recollection during probe recognition was better for across hemifields conjunctions compared to within hemifield conjunctions, lending support to the bilateral advantage of the two hemispheres in visual short-term memory. Age-related differences regarding the underlying mechanisms of the bilateral advantage indicated greater reliance on recollection-based processing in young and on familiarity-based processing in old. Moreover, the integrity of the posterior corpus callosum was more predictive of opposite hemifield illusory conjunctions compared to within hemifield illusory conjunctions, even after controlling for age. That is, individuals with lesser posterior corpus callosum connectivity had better recognition for objects when their features were recombined from the opposite hemifields than from the same hemifield. This study is the first to investigate the role of the corpus callosum in splitting attention between versus within hemifields. © 2016 Society for Psychophysiological Research.

  10. Individualized prediction of perineural invasion in colorectal cancer: development and validation of a radiomics prediction model.

    Science.gov (United States)

    Huang, Yanqi; He, Lan; Dong, Di; Yang, Caiyun; Liang, Cuishan; Chen, Xin; Ma, Zelan; Huang, Xiaomei; Yao, Su; Liang, Changhong; Tian, Jie; Liu, Zaiyi

    2018-02-01

    To develop and validate a radiomics prediction model for individualized prediction of perineural invasion (PNI) in colorectal cancer (CRC). After computed tomography (CT) radiomics features extraction, a radiomics signature was constructed in derivation cohort (346 CRC patients). A prediction model was developed to integrate the radiomics signature and clinical candidate predictors [age, sex, tumor location, and carcinoembryonic antigen (CEA) level]. Apparent prediction performance was assessed. After internal validation, independent temporal validation (separate from the cohort used to build the model) was then conducted in 217 CRC patients. The final model was converted to an easy-to-use nomogram. The developed radiomics nomogram that integrated the radiomics signature and CEA level showed good calibration and discrimination performance [Harrell's concordance index (c-index): 0.817; 95% confidence interval (95% CI): 0.811-0.823]. Application of the nomogram in validation cohort gave a comparable calibration and discrimination (c-index: 0.803; 95% CI: 0.794-0.812). Integrating the radiomics signature and CEA level into a radiomics prediction model enables easy and effective risk assessment of PNI in CRC. This stratification of patients according to their PNI status may provide a basis for individualized auxiliary treatment.

  11. Stimulus-Elicited Connectivity Influences Resting-State Connectivity Years Later in Human Development: A Prospective Study.

    Science.gov (United States)

    Gabard-Durnam, Laurel Joy; Gee, Dylan Grace; Goff, Bonnie; Flannery, Jessica; Telzer, Eva; Humphreys, Kathryn Leigh; Lumian, Daniel Stephen; Fareri, Dominic Stephen; Caldera, Christina; Tottenham, Nim

    2016-04-27

    Although the functional architecture of the brain is indexed by resting-state connectivity networks, little is currently known about the mechanisms through which these networks assemble into stable mature patterns. The current study posits and tests the long-term phasic molding hypothesis that resting-state networks are gradually shaped by recurring stimulus-elicited connectivity across development by examining how both stimulus-elicited and resting-state functional connections of the human brain emerge over development at the systems level. Using a sequential design following 4- to 18-year-olds over a 2 year period, we examined the predictive associations between stimulus-elicited and resting-state connectivity in amygdala-cortical circuitry as an exemplar case (given this network's protracted development across these ages). Age-related changes in amygdala functional connectivity converged on the same regions of medial prefrontal cortex (mPFC) and inferior frontal gyrus when elicited by emotional stimuli and when measured at rest. Consistent with the long-term phasic molding hypothesis, prospective analyses for both connections showed that the magnitude of an individual's stimulus-elicited connectivity unidirectionally predicted resting-state functional connectivity 2 years later. For the amygdala-mPFC connection, only stimulus-elicited connectivity during childhood and the transition to adolescence shaped future resting-state connectivity, consistent with a sensitive period ending with adolescence for the amygdala-mPFC circuit. Together, these findings suggest that resting-state functional architecture may arise from phasic patterns of functional connectivity elicited by environmental stimuli over the course of development on the order of years. A fundamental issue in understanding the ontogeny of brain function is how resting-state (intrinsic) functional networks emerge and relate to stimulus-elicited functional connectivity. Here, we posit and test the long

  12. Individual Prediction of Heart Failure Among Childhood Cancer Survivors

    NARCIS (Netherlands)

    Chow, Eric J.; Chen, Yan; Kremer, Leontien C.; Breslow, Norman E.; Hudson, Melissa M.; Armstrong, Gregory T.; Border, William L.; Feijen, Elizabeth A. M.; Green, Daniel M.; Meacham, Lillian R.; Meeske, Kathleen A.; Mulrooney, Daniel A.; Ness, Kirsten K.; Oeffinger, Kevin C.; Sklar, Charles A.; Stovall, Marilyn; van der Pal, Helena J.; Weathers, Rita E.; Robison, Leslie L.; Yasui, Yutaka

    2015-01-01

    Purpose To create clinically useful models that incorporate readily available demographic and cancer treatment characteristics to predict individual risk of heart failure among 5-year survivors of childhood cancer. Patients and Methods Survivors in the Childhood Cancer Survivor Study (CCSS) free of

  13. Precentral gyrus functional connectivity signatures of autism

    Directory of Open Access Journals (Sweden)

    Mary Beth eNebel

    2014-05-01

    Full Text Available Motor impairments are prevalent in children with autism spectrum disorders (ASD and are perhaps the earliest symptoms to develop. In addition, motor skills relate to the communicative/social deficits at the core of ASD diagnosis, and these behavioral deficits may reflect abnormal connectivity within brain networks underlying motor control and learning. Despite the fact that motor abnormalities in ASD are well-characterized, there remains a fundamental disconnect between the complexity of the clinical presentation of ASD and the underlying neurobiological mechanisms. In this study, we examined connectivity within and between functional subregions of a key component of the motor control network, the precentral gyrus, using resting state functional Magnetic Resonance Imaging data collected from a large, heterogeneous sample of individuals with ASD as well as neurotypical controls. We found that the strength of connectivity within and between distinct functional subregions of the precentral gyrus was related to ASD diagnosis and to the severity of ASD traits. In particular, connectivity involving the dorsomedial (lower limb/trunk subregion was abnormal in ASD individuals as predicted by models using a dichotomous variable coding for the presence of ASD, as well as models using symptom severity ratings. These findings provide further support for a link between motor and social/communicative abilities in ASD.

  14. Effective Connectivity from Early Visual Cortex to Posterior Occipitotemporal Face Areas Supports Face Selectivity and Predicts Developmental Prosopagnosia.

    Science.gov (United States)

    Lohse, Michael; Garrido, Lucia; Driver, Jon; Dolan, Raymond J; Duchaine, Bradley C; Furl, Nicholas

    2016-03-30

    Face processing is mediated by interactions between functional areas in the occipital and temporal lobe, and the fusiform face area (FFA) and anterior temporal lobe play key roles in the recognition of facial identity. Individuals with developmental prosopagnosia (DP), a lifelong face recognition impairment, have been shown to have structural and functional neuronal alterations in these areas. The present study investigated how face selectivity is generated in participants with normal face processing, and how functional abnormalities associated with DP, arise as a function of network connectivity. Using functional magnetic resonance imaging and dynamic causal modeling, we examined effective connectivity in normal participants by assessing network models that include early visual cortex (EVC) and face-selective areas and then investigated the integrity of this connectivity in participants with DP. Results showed that a feedforward architecture from EVC to the occipital face area, EVC to FFA, and EVC to posterior superior temporal sulcus (pSTS) best explained how face selectivity arises in both controls and participants with DP. In this architecture, the DP group showed reduced connection strengths on feedforward connections carrying face information from EVC to FFA and EVC to pSTS. These altered network dynamics in DP contribute to the diminished face selectivity in the posterior occipitotemporal areas affected in DP. These findings suggest a novel view on the relevance of feedforward projection from EVC to posterior occipitotemporal face areas in generating cortical face selectivity and differences in face recognition ability. Areas of the human brain showing enhanced activation to faces compared to other objects or places have been extensively studied. However, the factors leading to this face selectively have remained mostly unknown. We show that effective connectivity from early visual cortex to posterior occipitotemporal face areas gives rise to face

  15. Whole-brain functional connectivity predicted by indirect structural connections

    DEFF Research Database (Denmark)

    Røge, Rasmus; Ambrosen, Karen Marie Sandø; Albers, Kristoffer Jon

    2017-01-01

    Modern functional and diffusion magnetic resonance imaging (fMRI and dMRI) provide data from which macro-scale networks of functional and structural whole brain connectivity can be estimated. Although networks derived from these two modalities describe different properties of the human brain, the...

  16. Resting-State Functional Connectivity in Individuals with Down Syndrome and Williams Syndrome Compared with Typically Developing Controls.

    Science.gov (United States)

    Vega, Jennifer N; Hohman, Timothy J; Pryweller, Jennifer R; Dykens, Elisabeth M; Thornton-Wells, Tricia A

    2015-10-01

    The emergence of resting-state functional connectivity (rsFC) analysis, which examines temporal correlations of low-frequency (syndrome (DS) compared with another neurodevelopmental disorder, Williams syndrome (WS), and TD. Ten subjects with DS, 18 subjects with WS, and 40 subjects with TD each participated in a 3-Tesla MRI scan. We tested for group differences (DS vs. TD, DS vs. WS, and WS vs. TD) in between- and within-network rsFC connectivity for seven functional networks. For the DS group, we also examined associations between rsFC and other cognitive and genetic risk factors. In DS compared with TD, we observed higher levels of between-network connectivity in 6 out 21 network pairs but no differences in within-network connectivity. Participants with WS showed lower levels of within-network connectivity and no significant differences in between-network connectivity relative to DS. Finally, our comparison between WS and TD controls revealed lower within-network connectivity in multiple networks and higher between-network connectivity in one network pair relative to TD controls. While preliminary due to modest sample sizes, our findings suggest a global difference in between-network connectivity in individuals with neurodevelopmental disorders compared with controls and that such a difference is exacerbated across many brain regions in DS. However, this alteration in DS does not appear to extend to within-network connections, and therefore, the altered between-network connectivity must be interpreted within the framework of an intact intra-network pattern of activity. In contrast, WS shows markedly lower levels of within-network connectivity in the default mode network and somatomotor network relative to controls. These findings warrant further investigation using a task-based procedure that may help disentangle the relationship between brain function and cognitive performance across the spectrum of neurodevelopmental disorders.

  17. Dynamic Connectivity between Brain Networks Supports Working Memory: Relationships to Dopamine Release and Schizophrenia

    Science.gov (United States)

    Van Snellenberg, Jared X.; Benavides, Caridad; Slifstein, Mark; Wang, Zhishun; Moore, Holly; Abi-Dargham, Anissa

    2016-01-01

    Connectivity between brain networks may adapt flexibly to cognitive demand, a process that could underlie adaptive behaviors and cognitive deficits, such as those observed in neuropsychiatric conditions like schizophrenia. Dopamine signaling is critical for working memory but its influence on internetwork connectivity is relatively unknown. We addressed these questions in healthy humans using functional magnetic resonance imaging (during an n-back working-memory task) and positron emission tomography using the radiotracer [11C]FLB457 before and after amphetamine to measure the capacity for dopamine release in extrastriatal brain regions. Brain networks were defined by spatial independent component analysis (ICA) and working-memory-load-dependent connectivity between task-relevant pairs of networks was determined via a modified psychophysiological interaction analysis. For most pairs of task-relevant networks, connectivity significantly changed as a function of working-memory load. Moreover, load-dependent changes in connectivity between left and right frontoparietal networks (Δ connectivity lFPN-rFPN) predicted interindividual differences in task performance more accurately than other fMRI and PET imaging measures. Δ Connectivity lFPN-rFPN was not related to cortical dopamine release capacity. A second study in unmedicated patients with schizophrenia showed no abnormalities in load-dependent connectivity but showed a weaker relationship between Δ connectivity lFPN-rFPN and working memory performance in patients compared with matched healthy individuals. Poor working memory performance in patients was, in contrast, related to deficient cortical dopamine release. Our findings indicate that interactions between brain networks dynamically adapt to fluctuating environmental demands. These dynamic adaptations underlie successful working memory performance in healthy individuals and are not well predicted by amphetamine-induced dopamine release capacity. SIGNIFICANCE

  18. Dynamic Connectivity between Brain Networks Supports Working Memory: Relationships to Dopamine Release and Schizophrenia.

    Science.gov (United States)

    Cassidy, Clifford M; Van Snellenberg, Jared X; Benavides, Caridad; Slifstein, Mark; Wang, Zhishun; Moore, Holly; Abi-Dargham, Anissa; Horga, Guillermo

    2016-04-13

    Connectivity between brain networks may adapt flexibly to cognitive demand, a process that could underlie adaptive behaviors and cognitive deficits, such as those observed in neuropsychiatric conditions like schizophrenia. Dopamine signaling is critical for working memory but its influence on internetwork connectivity is relatively unknown. We addressed these questions in healthy humans using functional magnetic resonance imaging (during ann-back working-memory task) and positron emission tomography using the radiotracer [(11)C]FLB457 before and after amphetamine to measure the capacity for dopamine release in extrastriatal brain regions. Brain networks were defined by spatial independent component analysis (ICA) and working-memory-load-dependent connectivity between task-relevant pairs of networks was determined via a modified psychophysiological interaction analysis. For most pairs of task-relevant networks, connectivity significantly changed as a function of working-memory load. Moreover, load-dependent changes in connectivity between left and right frontoparietal networks (Δ connectivity lFPN-rFPN) predicted interindividual differences in task performance more accurately than other fMRI and PET imaging measures. Δ Connectivity lFPN-rFPN was not related to cortical dopamine release capacity. A second study in unmedicated patients with schizophrenia showed no abnormalities in load-dependent connectivity but showed a weaker relationship between Δ connectivity lFPN-rFPN and working memory performance in patients compared with matched healthy individuals. Poor working memory performance in patients was, in contrast, related to deficient cortical dopamine release. Our findings indicate that interactions between brain networks dynamically adapt to fluctuating environmental demands. These dynamic adaptations underlie successful working memory performance in healthy individuals and are not well predicted by amphetamine-induced dopamine release capacity. It is unclear

  19. Longitudinal connectome-based predictive modeling for REM sleep behavior disorder from structural brain connectivity

    Science.gov (United States)

    Giancardo, Luca; Ellmore, Timothy M.; Suescun, Jessika; Ocasio, Laura; Kamali, Arash; Riascos-Castaneda, Roy; Schiess, Mya C.

    2018-02-01

    Methods to identify neuroplasticity patterns in human brains are of the utmost importance in understanding and potentially treating neurodegenerative diseases. Parkinson disease (PD) research will greatly benefit and advance from the discovery of biomarkers to quantify brain changes in the early stages of the disease, a prodromal period when subjects show no obvious clinical symptoms. Diffusion tensor imaging (DTI) allows for an in-vivo estimation of the structural connectome inside the brain and may serve to quantify the degenerative process before the appearance of clinical symptoms. In this work, we introduce a novel strategy to compute longitudinal structural connectomes in the context of a whole-brain data-driven pipeline. In these initial tests, we show that our predictive models are able to distinguish controls from asymptomatic subjects at high risk of developing PD (REM sleep behavior disorder, RBD) with an area under the receiving operating characteristic curve of 0.90 (pParkinson's Progression Markers Initiative. By analyzing the brain connections most relevant for the predictive ability of the best performing model, we find connections that are biologically relevant to the disease.

  20. Tracking Neuronal Connectivity from Electric Brain Signals to Predict Performance.

    Science.gov (United States)

    Vecchio, Fabrizio; Miraglia, Francesca; Rossini, Paolo Maria

    2018-05-01

    The human brain is a complex container of interconnected networks. Network neuroscience is a recent venture aiming to explore the connection matrix built from the human brain or human "Connectome." Network-based algorithms provide parameters that define global organization of the brain; when they are applied to electroencephalographic (EEG) signals network, configuration and excitability can be monitored in millisecond time frames, providing remarkable information on their instantaneous efficacy also for a given task's performance via online evaluation of the underlying instantaneous networks before, during, and after the task. Here we provide an updated summary on the connectome analysis for the prediction of performance via the study of task-related dynamics of brain network organization from EEG signals.

  1. New method for probabilistic traffic demand predictions for en route sectors based on uncertain predictions of individual flight events.

    Science.gov (United States)

    2011-06-14

    This paper presents a novel analytical approach to and techniques for translating characteristics of uncertainty in predicting sector entry times and times in sector for individual flights into characteristics of uncertainty in predicting one-minute ...

  2. High Interannual Variability in Connectivity and Genetic Pool of a Temperate Clingfish Matches Oceanographic Transport Predictions

    Science.gov (United States)

    Teixeira, Sara; Assis, Jorge; Serrão, Ester A.; Gonçalves, Emanuel J.; Borges, Rita

    2016-01-01

    Adults of most marine benthic and demersal fish are site-attached, with the dispersal of their larval stages ensuring connectivity among populations. In this study we aimed to infer spatial and temporal variation in population connectivity and dispersal of a marine fish species, using genetic tools and comparing these with oceanographic transport. We focused on an intertidal rocky reef fish species, the shore clingfish Lepadogaster lepadogaster, along the southwest Iberian Peninsula, in 2011 and 2012. We predicted high levels of self-recruitment and distinct populations, due to short pelagic larval duration and because all its developmental stages have previously been found near adult habitats. Genetic analyses based on microsatellites countered our prediction and a biophysical dispersal model showed that oceanographic transport was a good explanation for the patterns observed. Adult sub-populations separated by up to 300 km of coastline displayed no genetic differentiation, revealing a single connected population with larvae potentially dispersing long distances over hundreds of km. Despite this, parentage analysis performed on recruits from one focal site within the Marine Park of Arrábida (Portugal), revealed self-recruitment levels of 2.5% and 7.7% in 2011 and 2012, respectively, suggesting that both long- and short-distance dispersal play an important role in the replenishment of these populations. Population differentiation and patterns of dispersal, which were highly variable between years, could be linked to the variability inherent in local oceanographic processes. Overall, our measures of connectivity based on genetic and oceanographic data highlight the relevance of long-distance dispersal in determining the degree of connectivity, even in species with short pelagic larval durations. PMID:27911952

  3. Ensemble stacking mitigates biases in inference of synaptic connectivity

    Directory of Open Access Journals (Sweden)

    Brendan Chambers

    2018-03-01

    Full Text Available A promising alternative to directly measuring the anatomical connections in a neuronal population is inferring the connections from the activity. We employ simulated spiking neuronal networks to compare and contrast commonly used inference methods that identify likely excitatory synaptic connections using statistical regularities in spike timing. We find that simple adjustments to standard algorithms improve inference accuracy: A signing procedure improves the power of unsigned mutual-information-based approaches and a correction that accounts for differences in mean and variance of background timing relationships, such as those expected to be induced by heterogeneous firing rates, increases the sensitivity of frequency-based methods. We also find that different inference methods reveal distinct subsets of the synaptic network and each method exhibits different biases in the accurate detection of reciprocity and local clustering. To correct for errors and biases specific to single inference algorithms, we combine methods into an ensemble. Ensemble predictions, generated as a linear combination of multiple inference algorithms, are more sensitive than the best individual measures alone, and are more faithful to ground-truth statistics of connectivity, mitigating biases specific to single inference methods. These weightings generalize across simulated datasets, emphasizing the potential for the broad utility of ensemble-based approaches. Mapping the routing of spikes through local circuitry is crucial for understanding neocortical computation. Under appropriate experimental conditions, these maps can be used to infer likely patterns of synaptic recruitment, linking activity to underlying anatomical connections. Such inferences help to reveal the synaptic implementation of population dynamics and computation. We compare a number of standard functional measures to infer underlying connectivity. We find that regularization impacts measures

  4. Specialization does not predict individual efficiency in an ant.

    Directory of Open Access Journals (Sweden)

    Anna Dornhaus

    2008-11-01

    Full Text Available The ecological success of social insects is often attributed to an increase in efficiency achieved through division of labor between workers in a colony. Much research has therefore focused on the mechanism by which a division of labor is implemented, i.e., on how tasks are allocated to workers. However, the important assumption that specialists are indeed more efficient at their work than generalist individuals--the "Jack-of-all-trades is master of none" hypothesis--has rarely been tested. Here, I quantify worker efficiency, measured as work completed per time, in four different tasks in the ant Temnothorax albipennis: honey and protein foraging, collection of nest-building material, and brood transports in a colony emigration. I show that individual efficiency is not predicted by how specialized workers were on the respective task. Worker efficiency is also not consistently predicted by that worker's overall activity or delay to begin the task. Even when only the worker's rank relative to nestmates in the same colony was used, specialization did not predict efficiency in three out of the four tasks, and more specialized workers actually performed worse than others in the fourth task (collection of sand grains. I also show that the above relationships, as well as median individual efficiency, do not change with colony size. My results demonstrate that in an ant species without morphologically differentiated worker castes, workers may nevertheless differ in their ability to perform different tasks. Surprisingly, this variation is not utilized by the colony--worker allocation to tasks is unrelated to their ability to perform them. What, then, are the adaptive benefits of behavioral specialization, and why do workers choose tasks without regard for whether they can perform them well? We are still far from an understanding of the adaptive benefits of division of labor in social insects.

  5. Experimental evidence that simplified forest structure interacts with snow cover to influence functional connectivity for Pacific martens

    Science.gov (United States)

    Katie M. Moriarty; Clinton W. Epps; Matthew G. Betts; Dalton J. Hance; J. D. Bailey; William J. Zielinski

    2015-01-01

    Context Functional connectivity—the facilitation of individual movements among habitat patches—is essential for species’ persistence in fragmented landscapes. Evaluating functional connectivity is critical for predicting range shifts, developing conservation plans, and anticipating effects of disturbance, especially for species affected by climate change. Objectives We...

  6. Convergent Findings of Altered Functional and Structural Brain Connectivity in Individuals with High Functioning Autism: A Multimodal MRI Study.

    Directory of Open Access Journals (Sweden)

    Sophia Mueller

    Full Text Available Brain tissue changes in autism spectrum disorders seem to be rather subtle and widespread than anatomically distinct. Therefore a multimodal, whole brain imaging technique appears to be an appropriate approach to investigate whether alterations in white and gray matter integrity relate to consistent changes in functional resting state connectivity in individuals with high functioning autism (HFA. We applied diffusion tensor imaging (DTI, voxel-based morphometry (VBM and resting state functional connectivity magnetic resonance imaging (fcMRI to assess differences in brain structure and function between 12 individuals with HFA (mean age 35.5, SD 11.4, 9 male and 12 healthy controls (mean age 33.3, SD 9.0, 8 male. Psychological measures of empathy and emotionality were obtained and correlated with the most significant DTI, VBM and fcMRI findings. We found three regions of convergent structural and functional differences between HFA participants and controls. The right temporo-parietal junction area and the left frontal lobe showed decreased fractional anisotropy (FA values along with decreased functional connectivity and a trend towards decreased gray matter volume. The bilateral superior temporal gyrus displayed significantly decreased functional connectivity that was accompanied by the strongest trend of gray matter volume decrease in the temporal lobe of HFA individuals. FA decrease in the right temporo-parietal region was correlated with psychological measurements of decreased emotionality. In conclusion, our results indicate common sites of structural and functional alterations in higher order association cortex areas and may therefore provide multimodal imaging support to the long-standing hypothesis of autism as a disorder of impaired higher-order multisensory integration.

  7. Efficiency at rest: magnetoencephalographic resting-state connectivity and individual differences in verbal working memory.

    Science.gov (United States)

    del Río, David; Cuesta, Pablo; Bajo, Ricardo; García-Pacios, Javier; López-Higes, Ramón; del-Pozo, Francisco; Maestú, Fernando

    2012-11-01

    Inter-individual differences in cognitive performance are based on an efficient use of task-related brain resources. However, little is known yet on how these differences might be reflected on resting-state brain networks. Here we used Magnetoencephalography resting-state recordings to assess the relationship between a behavioral measurement of verbal working memory and functional connectivity as measured through Mutual Information. We studied theta (4-8 Hz), low alpha (8-10 Hz), high alpha (10-13 Hz), low beta (13-18 Hz) and high beta (18-30 Hz) frequency bands. A higher verbal working memory capacity was associated with a lower mutual information in the low alpha band, prominently among right-anterior and left-lateral sensors. The results suggest that an efficient brain organization in the domain of verbal working memory might be related to a lower resting-state functional connectivity across large-scale brain networks possibly involving right prefrontal and left perisylvian areas. Copyright © 2012 Elsevier B.V. All rights reserved.

  8. Link prediction boosted psychiatry disorder classification for functional connectivity network

    Science.gov (United States)

    Li, Weiwei; Mei, Xue; Wang, Hao; Zhou, Yu; Huang, Jiashuang

    2017-02-01

    Functional connectivity network (FCN) is an effective tool in psychiatry disorders classification, and represents cross-correlation of the regional blood oxygenation level dependent signal. However, FCN is often incomplete for suffering from missing and spurious edges. To accurate classify psychiatry disorders and health control with the incomplete FCN, we first `repair' the FCN with link prediction, and then exact the clustering coefficients as features to build a weak classifier for every FCN. Finally, we apply a boosting algorithm to combine these weak classifiers for improving classification accuracy. Our method tested by three datasets of psychiatry disorder, including Alzheimer's Disease, Schizophrenia and Attention Deficit Hyperactivity Disorder. The experimental results show our method not only significantly improves the classification accuracy, but also efficiently reconstructs the incomplete FCN.

  9. Enhanced Voltage Control of VSC-HVDC Connected Offshore Wind Farms Based on Model Predictive Control

    DEFF Research Database (Denmark)

    Guo, Yifei; Gao, Houlei; Wu, Qiuwei

    2018-01-01

    This paper proposes an enhanced voltage control strategy (EVCS) based on model predictive control (MPC) for voltage source converter based high voltage direct current (VSCHVDC) connected offshore wind farms (OWFs). In the proposed MPC based EVCS, all wind turbine generators (WTGs) as well...... as the wind farm side VSC are optimally coordinated to keep voltages within the feasible range and reduce system power losses. Considering the high ratio of the OWF collector system, the effects of active power outputs of WTGs on voltage control are also taken into consideration. The predictive model of VSC...

  10. Delay discounting differences in brain activation, connectivity, and structure in individuals with addiction: a systematic review protocol.

    Science.gov (United States)

    Owens, Max M; Amlung, Michael T; Beach, Steven R H; Sweet, Lawrence H; MacKillop, James

    2017-07-11

    Delayed reward discounting (DRD), the degree to which future rewards are discounted relative to immediate rewards, is used as an index of impulsive decision-making and has been associated with a number of problematic health behaviors. Given the robust behavioral association between DRD and addictive behavior, there is an expanding literature investigating the differences in the functional and structural correlates of DRD in the brain between addicted and healthy individuals. However, there has yet to be a systematic review which characterizes differences in regional brain activation, functional connectivity, and structure and places them in the larger context of the DRD literature. The objective of this systematic review is to summarize and critically appraise the existing literature examining differences between addicted and healthy individuals in the neural correlates of DRD using magnetic resonance imaging (MRI) or functional magnetic resonance imaging (fMRI). A systematic search strategy will be implemented that uses Boolean search terms in PubMed/MEDLINE and PsycINFO, as well as manual search methods, to identify the studies comprehensively. This review will include studies using MRI or fMRI in humans to directly compare brain activation, functional connectivity, or structure in relation to DRD between addicted and healthy individuals or continuously assess addiction severity in the context of DRD. Two independent reviewers will determine studies that meet the inclusion criteria for this review, extract data from included studies, and assess the quality of included studies using the Grading of Recommendations Assessment, Development and Evaluation framework. Then, narrative review will be used to explicate the differences in structural and functional correlates of DRD implicated by the literature and assess the strength of evidence for this conclusion. This review will provide a needed critical exegesis of the MRI studies that have been conducted investigating

  11. Prediction for driving behaviour in connection with socio – demographic characteristics and individual value system

    Directory of Open Access Journals (Sweden)

    Lazdins K.J.

    2018-01-01

    The results can serve as the basis to create new driving behavior interventions and also applicable to psychologist's professional work, when counseling individuals of this group, as well as can be used in the future development of the field, science and research.

  12. Realistic prediction of individual facial emotion expressions for craniofacial surgery simulations

    Science.gov (United States)

    Gladilin, Evgeny; Zachow, Stefan; Deuflhard, Peter; Hege, Hans-Christian

    2003-05-01

    In addition to the static soft tissue prediction, the estimation of individual facial emotion expressions is an important criterion for the evaluation of the carniofacial surgery planning. In this paper, we present an approach for the estimation of individual facial emotion expressions on the basis of geometrical models of human anatomy derived from tomographic data and the finite element modeling of facial tissue biomechanics.

  13. A Method for Individualizing the Prediction of Immunogenicity of Protein Vaccines and Biologic Therapeutics: Individualized T Cell Epitope Measure (iTEM

    Directory of Open Access Journals (Sweden)

    Tobias Cohen

    2010-01-01

    Full Text Available The promise of pharmacogenomics depends on advancing predictive medicine. To address this need in the area of immunology, we developed the individualized T cell epitope measure (iTEM tool to estimate an individual's T cell response to a protein antigen based on HLA binding predictions. In this study, we validated prospective iTEM predictions using data from in vitro and in vivo studies. We used a mathematical formula that converts DRB1∗ allele binding predictions generated by EpiMatrix, an epitope-mapping tool, into an allele-specific scoring system. We then demonstrated that iTEM can be used to define an HLA binding threshold above which immune response is likely and below which immune response is likely to be absent. iTEM's predictive power was strongest when the immune response is focused, such as in subunit vaccination and administration of protein therapeutics. iTEM may be a useful tool for clinical trial design and preclinical evaluation of vaccines and protein therapeutics.

  14. Deficits of entropy modulation in schizophrenia are predicted by functional connectivity strength in the theta band and structural clustering.

    Science.gov (United States)

    Gomez-Pilar, Javier; de Luis-García, Rodrigo; Lubeiro, Alba; de Uribe, Nieves; Poza, Jesús; Núñez, Pablo; Ayuso, Marta; Hornero, Roberto; Molina, Vicente

    2018-01-01

    Spectral entropy (SE) allows comparing task-related modulation of electroencephalogram (EEG) between patients and controls, i.e. spectral changes of the EEG associated to task performance. A SE modulation deficit has been replicated in different schizophrenia samples. To investigate the underpinnings of SE modulation deficits in schizophrenia, we applied graph-theory to EEG recordings during a P300 task and fractional anisotropy (FA) data from diffusion tensor imaging in 48 patients (23 first episodes) and 87 healthy controls. Functional connectivity was assessed from phase-locking values among sensors in the theta band, and structural connectivity was based on FA values for the tracts connecting pairs of regions. From those data, averaged clustering coefficient (CLC), characteristic path-length (PL) and connectivity strength (CS, also known as density) were calculated for both functional and structural networks. The corresponding functional modulation values were calculated as the difference in SE and CLC, PL and CS between the pre-stimulus and response windows during the task. The results revealed a higher functional CS in the pre-stimulus window in patients, predictive of smaller modulation of SE in this group. The amount of increase in theta CS from pre-stimulus to response related to SE modulation in patients and controls. Structural CLC was associated with SE modulation in the patients. SE modulation was predictive of negative symptoms, whereas CLC and PL modulation was associated with cognitive performance in the patients. These results support that a hyperactive functional connectivity and/or structural connective deficits in the patients hamper the dynamical modulation of connectivity underlying cognition.

  15. Resting-state functional connectivity between amygdala and the ventromedial prefrontal cortex following fear reminder predicts fear extinction

    Science.gov (United States)

    Feng, Pan; Zheng, Yong

    2016-01-01

    Investigations of fear conditioning have elucidated the neural mechanisms of fear acquisition, consolidation and extinction, but it is not clear how the neural activation following fear reminder influence the following extinction. To address this question, we measured human brain activity following fear reminder using resting-state functional magnetic resonance imaging, and investigated whether the extinction effect can be predicted by resting-state functional connectivity (RSFC). Behaviorally, we found no significant differences of fear ratings between the reminder group and the no reminder group at the fear acquisition and extinction stages, but spontaneous recovery during re-extinction stage appeared only in the no reminder group. Imaging data showed that functional connectivity between ventromedial prefrontal cortex (vmPFC) and amygdala in the reminder group was greater than that in the no reminder group after fear memory reactivation. More importantly, the functional connectivity between amygdala and vmPFC of the reminder group after fear memory reactivation was positively correlated with extinction effect. These results suggest RSFC between amygdala and the vmPFC following fear reminder can predict fear extinction, which provide important insight into the neural mechanisms of fear memory after fear memory reactivation. PMID:27013104

  16. Brain connectivity during encoding and retrieval of spatial information: individual differences in navigation skills.

    Science.gov (United States)

    Sharma, Greeshma; Gramann, Klaus; Chandra, Sushil; Singh, Vijander; Mittal, Alok Prakash

    2017-09-01

    Emerging evidence suggests that the variations in the ability to navigate through any real or virtual environment are accompanied by distinct underlying cortical activations in multiple regions of the brain. These activations may appear due to the use of different frame of reference (FOR) for representing an environment. The present study investigated the brain dynamics in the good and bad navigators using Graph Theoretical analysis applied to low-density electroencephalography (EEG) data. Individual navigation skills were rated according to the performance in a virtual reality (VR)-based navigation task and the effect of navigator's proclivity towards a particular FOR on the navigation performance was explored. Participants were introduced to a novel virtual environment that they learned from a first-person or an aerial perspective and were subsequently assessed on the basis of efficiency with which they learnt and recalled. The graph theoretical parameters, path length (PL), global efficiency (GE), and clustering coefficient (CC) were computed for the functional connectivity network in the theta and alpha frequency bands. During acquisition of the spatial information, good navigators were distinguished by a lower degree of dispersion in the functional connectivity compared to the bad navigators. Within the groups of good and bad navigators, better performers were characterised by the formation of multiple hubs at various sites and the percentage of connectivity or small world index. The proclivity towards a specific FOR during exploration of a new environment was not found to have any bearing on the spatial learning. These findings may have wider implications for how the functional connectivity in the good and bad navigators differs during spatial information acquisition and retrieval in the domains of rescue operations and defence systems.

  17. Towards personalized therapy for multiple sclerosis: prediction of individual treatment response.

    Science.gov (United States)

    Kalincik, Tomas; Manouchehrinia, Ali; Sobisek, Lukas; Jokubaitis, Vilija; Spelman, Tim; Horakova, Dana; Havrdova, Eva; Trojano, Maria; Izquierdo, Guillermo; Lugaresi, Alessandra; Girard, Marc; Prat, Alexandre; Duquette, Pierre; Grammond, Pierre; Sola, Patrizia; Hupperts, Raymond; Grand'Maison, Francois; Pucci, Eugenio; Boz, Cavit; Alroughani, Raed; Van Pesch, Vincent; Lechner-Scott, Jeannette; Terzi, Murat; Bergamaschi, Roberto; Iuliano, Gerardo; Granella, Franco; Spitaleri, Daniele; Shaygannejad, Vahid; Oreja-Guevara, Celia; Slee, Mark; Ampapa, Radek; Verheul, Freek; McCombe, Pamela; Olascoaga, Javier; Amato, Maria Pia; Vucic, Steve; Hodgkinson, Suzanne; Ramo-Tello, Cristina; Flechter, Shlomo; Cristiano, Edgardo; Rozsa, Csilla; Moore, Fraser; Luis Sanchez-Menoyo, Jose; Laura Saladino, Maria; Barnett, Michael; Hillert, Jan; Butzkueven, Helmut

    2017-09-01

    Timely initiation of effective therapy is crucial for preventing disability in multiple sclerosis; however, treatment response varies greatly among patients. Comprehensive predictive models of individual treatment response are lacking. Our aims were: (i) to develop predictive algorithms for individual treatment response using demographic, clinical and paraclinical predictors in patients with multiple sclerosis; and (ii) to evaluate accuracy, and internal and external validity of these algorithms. This study evaluated 27 demographic, clinical and paraclinical predictors of individual response to seven disease-modifying therapies in MSBase, a large global cohort study. Treatment response was analysed separately for disability progression, disability regression, relapse frequency, conversion to secondary progressive disease, change in the cumulative disease burden, and the probability of treatment discontinuation. Multivariable survival and generalized linear models were used, together with the principal component analysis to reduce model dimensionality and prevent overparameterization. Accuracy of the individual prediction was tested and its internal validity was evaluated in a separate, non-overlapping cohort. External validity was evaluated in a geographically distinct cohort, the Swedish Multiple Sclerosis Registry. In the training cohort (n = 8513), the most prominent modifiers of treatment response comprised age, disease duration, disease course, previous relapse activity, disability, predominant relapse phenotype and previous therapy. Importantly, the magnitude and direction of the associations varied among therapies and disease outcomes. Higher probability of disability progression during treatment with injectable therapies was predominantly associated with a greater disability at treatment start and the previous therapy. For fingolimod, natalizumab or mitoxantrone, it was mainly associated with lower pretreatment relapse activity. The probability of

  18. Brain intrinsic network connectivity in individuals with frequent tanning behavior.

    Science.gov (United States)

    Ketcherside, Ariel; Filbey, Francesca M; Aubert, Pamela M; Seibyl, John P; Price, Julianne L; Adinoff, Bryon

    2018-05-01

    Emergent studies suggest a bidirectional relationship between brain functioning and the skin. This neurocutaneous connection may be responsible for the reward response to tanning and, thus, may contribute to excessive tanning behavior. To date, however, this association has not yet been examined. To explore whether intrinsic brain functional connectivity within the default mode network (DMN) is related to indoor tanning behavior. Resting state functional connectivity (rsFC) was obtained in twenty adults (16 females) with a history of indoor tanning. Using a seed-based [(posterior cingulate cortex (PCC)] approach, the relationship between tanning severity and FC strength was assessed. Tanning severity was measured with symptom count from the Structured Clinical Interview for Tanning Abuse and Dependence (SITAD) and tanning intensity (lifetime indoor tanning episodes/years tanning). rsFC strength between the PCC and other DMN regions (left globus pallidus, left medial frontal gyrus, left superior frontal gyrus) is positively correlated with tanning symptom count. rsFC strength between the PCC and salience network regions (right anterior cingulate cortex, left inferior parietal lobe, left inferior temporal gyrus) is correlated with tanning intensity. Greater connectivity between tanning severity and DMN and salience network connectivity suggests that heightened self-awareness of salient stimuli may be a mechanism that underlies frequent tanning behavior. These findings add to the growing evidence of brain-skin connection and reflect dysregulation in the reward processing networks in those with frequent tanning.

  19. Predictive validity of the Work Ability Index and its individual items in the general population.

    Science.gov (United States)

    Lundin, Andreas; Leijon, Ola; Vaez, Marjan; Hallgren, Mats; Torgén, Margareta

    2017-06-01

    This study assesses the predictive ability of the full Work Ability Index (WAI) as well as its individual items in the general population. The Work, Health and Retirement Study (WHRS) is a stratified random national sample of 25-75-year-olds living in Sweden in 2000 that received a postal questionnaire ( n = 6637, response rate = 53%). Current and subsequent sickness absence was obtained from registers. The ability of the WAI to predict long-term sickness absence (LTSA; ⩾ 90 consecutive days) during a period of four years was analysed by logistic regression, from which the Area Under the Receiver Operating Characteristic curve (AUC) was computed. There were 313 incident LTSA cases among 1786 employed individuals. The full WAI had acceptable ability to predict LTSA during the 4-year follow-up (AUC = 0.79; 95% CI 0.76 to 0.82). Individual items were less stable in their predictive ability. However, three of the individual items: current work ability compared with lifetime best, estimated work impairment due to diseases, and number of diagnosed current diseases, exceeded AUC > 0.70. Excluding the WAI item on number of days on sickness absence did not result in an inferior predictive ability of the WAI. The full WAI has acceptable predictive validity, and is superior to its individual items. For public health surveys, three items may be suitable proxies of the full WAI; current work ability compared with lifetime best, estimated work impairment due to diseases, and number of current diseases diagnosed by a physician.

  20. PREDICTING INDIVIDUAL WELL-BEING THROUGH THE LANGUAGE OF SOCIAL MEDIA.

    Science.gov (United States)

    Schwartz, H Andrew; Sap, Maarten; Kern, Margaret L; Eichstaedt, Johannes C; Kapelner, Adam; Agrawal, Megha; Blanco, Eduardo; Dziurzynski, Lukasz; Park, Gregory; Stillwell, David; Kosinski, Michal; Seligman, Martin E P; Ungar, Lyle H

    2016-01-01

    We present the task of predicting individual well-being, as measured by a life satisfaction scale, through the language people use on social media. Well-being, which encompasses much more than emotion and mood, is linked with good mental and physical health. The ability to quickly and accurately assess it can supplement multi-million dollar national surveys as well as promote whole body health. Through crowd-sourced ratings of tweets and Facebook status updates, we create message-level predictive models for multiple components of well-being. However, well-being is ultimately attributed to people, so we perform an additional evaluation at the user-level, finding that a multi-level cascaded model, using both message-level predictions and userlevel features, performs best and outperforms popular lexicon-based happiness models. Finally, we suggest that analyses of language go beyond prediction by identifying the language that characterizes well-being.

  1. Amygdala functional connectivity, HPA axis genetic variation, and life stress in children and relations to anxiety and emotion regulation

    Science.gov (United States)

    Pagliaccio, David; Luby, Joan L.; Bogdan, Ryan; Agrawal, Arpana; Gaffrey, Michael S.; Belden, Andrew C.; Botteron, Kelly N.; Harms, Michael P.; Barch, Deanna M.

    2015-01-01

    Internalizing pathology is related to alterations in amygdala resting state functional connectivity, potentially implicating altered emotional reactivity and/or emotion regulation in the etiological pathway. Importantly, there is accumulating evidence that stress exposure and genetic vulnerability impact amygdala structure/function and risk for internalizing pathology. The present study examined whether early life stress and genetic profile scores (10 single nucleotide polymorphisms within four hypothalamic-pituitary-adrenal axis genes: CRHR1, NR3C2, NR3C1, and FKBP5) predicted individual differences in amygdala functional connectivity in school-age children (9–14 year olds; N=120). Whole-brain regression analyses indicated that increasing genetic ‘risk’ predicted alterations in amygdala connectivity to the caudate and postcentral gyrus. Experience of more stressful and traumatic life events predicted weakened amygdala-anterior cingulate cortex connectivity. Genetic ‘risk’ and stress exposure interacted to predict weakened connectivity between the amygdala and the inferior and middle frontal gyri, caudate, and parahippocampal gyrus in those children with the greatest genetic and environmental risk load. Furthermore, amygdala connectivity longitudinally predicted anxiety symptoms and emotion regulation skills at a later follow-up. Amygdala connectivity mediated effects of life stress on anxiety and of genetic variants on emotion regulation. The current results suggest that considering the unique and interacting effects of biological vulnerability and environmental risk factors may be key to understanding the development of altered amygdala functional connectivity, a potential factor in the risk trajectory for internalizing pathology. PMID:26595470

  2. Amygdala functional connectivity, HPA axis genetic variation, and life stress in children and relations to anxiety and emotion regulation.

    Science.gov (United States)

    Pagliaccio, David; Luby, Joan L; Bogdan, Ryan; Agrawal, Arpana; Gaffrey, Michael S; Belden, Andrew C; Botteron, Kelly N; Harms, Michael P; Barch, Deanna M

    2015-11-01

    Internalizing pathology is related to alterations in amygdala resting state functional connectivity, potentially implicating altered emotional reactivity and/or emotion regulation in the etiological pathway. Importantly, there is accumulating evidence that stress exposure and genetic vulnerability impact amygdala structure/function and risk for internalizing pathology. The present study examined whether early life stress and genetic profile scores (10 single nucleotide polymorphisms within 4 hypothalamic-pituitary-adrenal axis genes: CRHR1, NR3C2, NR3C1, and FKBP5) predicted individual differences in amygdala functional connectivity in school-age children (9- to 14-year-olds; N = 120). Whole-brain regression analyses indicated that increasing genetic "risk" predicted alterations in amygdala connectivity to the caudate and postcentral gyrus. Experience of more stressful and traumatic life events predicted weakened amygdala-anterior cingulate cortex connectivity. Genetic "risk" and stress exposure interacted to predict weakened connectivity between the amygdala and the inferior and middle frontal gyri, caudate, and parahippocampal gyrus in those children with the greatest genetic and environmental risk load. Furthermore, amygdala connectivity longitudinally predicted anxiety symptoms and emotion regulation skills at a later follow-up. Amygdala connectivity mediated effects of life stress on anxiety and of genetic variants on emotion regulation. The current results suggest that considering the unique and interacting effects of biological vulnerability and environmental risk factors may be key to understanding the development of altered amygdala functional connectivity, a potential factor in the risk trajectory for internalizing pathology. (c) 2015 APA, all rights reserved).

  3. Individual differences in working memory capacity predict visual attention allocation.

    Science.gov (United States)

    Bleckley, M Kathryn; Durso, Francis T; Crutchfield, Jerry M; Engle, Randall W; Khanna, Maya M

    2003-12-01

    To the extent that individual differences in working memory capacity (WMC) reflect differences in attention (Baddeley, 1993; Engle, Kane, & Tuholski, 1999), differences in WMC should predict performance on visual attention tasks. Individuals who scored in the upper and lower quartiles on the OSPAN working memory test performed a modification of Egly and Homa's (1984) selective attention task. In this task, the participants identified a central letter and localized a displaced letter flashed somewhere on one of three concentric rings. When the displaced letter occurred closer to fixation than the cue implied, high-WMC, but not low-WMC, individuals showed a cost in the letter localization task. This suggests that low-WMC participants allocated attention as a spotlight, whereas those with high WMC showed flexible allocation.

  4. High Resolution Forecasts in the Florida Straits: Predicting the Modulations of the Florida Current and Connectivity Around South Florida and Cuba

    Science.gov (United States)

    Kourafalou, V.; Kang, H.; Perlin, N.; Le Henaff, M.; Lamkin, J. T.

    2016-02-01

    Connectivity around the South Florida coastal regions and between South Florida and Cuba are largely influenced by a) local coastal processes and b) circulation in the Florida Straits, which is controlled by the larger scale Florida Current variability. Prediction of the physical connectivity is a necessary component for several activities that require ocean forecasts, such as oil spills, fisheries research, search and rescue. This requires a predictive system that can accommodate the intense coastal to offshore interactions and the linkages to the complex regional circulation. The Florida Straits, South Florida and Florida Keys Hybrid Coordinate Ocean Model is such a regional ocean predictive system, covering a large area over the Florida Straits and the adjacent land areas, representing both coastal and oceanic processes. The real-time ocean forecast system is high resolution ( 900m), embedded in larger scale predictive models. It includes detailed coastal bathymetry, high resolution/high frequency atmospheric forcing and provides 7-day forecasts, updated daily (see: http://coastalmodeling.rsmas.miami.edu/). The unprecedented high resolution and coastal details of this system provide value added on global forecasts through downscaling and allow a variety of applications. Examples will be presented, focusing on the period of a 2015 fisheries cruise around the coastal areas of Cuba, where model predictions helped guide the measurements on biophysical connectivity, under intense variability of the mesoscale eddy field and subsequent Florida Current meandering.

  5. Relationship between individual differences in functional connectivity and facial-emotion recognition abilities in adults with traumatic brain injury.

    Science.gov (United States)

    Rigon, A; Voss, M W; Turkstra, L S; Mutlu, B; Duff, M C

    2017-01-01

    Although several studies have demonstrated that facial-affect recognition impairment is common following moderate-severe traumatic brain injury (TBI), and that there are diffuse alterations in large-scale functional brain networks in TBI populations, little is known about the relationship between the two. Here, in a sample of 26 participants with TBI and 20 healthy comparison participants (HC) we measured facial-affect recognition abilities and resting-state functional connectivity (rs-FC) using fMRI. We then used network-based statistics to examine (A) the presence of rs-FC differences between individuals with TBI and HC within the facial-affect processing network, and (B) the association between inter-individual differences in emotion recognition skills and rs-FC within the facial-affect processing network. We found that participants with TBI showed significantly lower rs-FC in a component comprising homotopic and within-hemisphere, anterior-posterior connections within the facial-affect processing network. In addition, within the TBI group, participants with higher emotion-labeling skills showed stronger rs-FC within a network comprised of intra- and inter-hemispheric bilateral connections. Findings indicate that the ability to successfully recognize facial-affect after TBI is related to rs-FC within components of facial-affective networks, and provide new evidence that further our understanding of the mechanisms underlying emotion recognition impairment in TBI.

  6. Who Punishes? Personality Traits Predict Individual Variation in Punitive Sentiment

    Directory of Open Access Journals (Sweden)

    S. Craig Roberts

    2013-01-01

    Full Text Available Cross-culturally, participants in public goods games reward participants and punish defectors to a degree beyond that warranted by rational, profit-maximizing considerations. Costly punishment, where individuals impose costs on defectors at a cost to themselves, is thought to promote the maintenance of cooperation. However, despite substantial variation in the extent to which people punish, little is known about why some individuals, and not others, choose to pay these costs. Here, we test whether personality traits might contribute to variation in helping and punishment behavior. We first replicate a previous study using public goods scenarios to investigate effects of sex, relatedness and likelihood of future interaction on willingness to help a group member or to punish a transgressor. As in the previous study, we find that individuals are more willing to help related than unrelated needy others and that women are more likely to express desire to help than men. Desire to help was higher if the probability of future interaction is high, at least among women. In contrast, among these variables, only participant sex predicted some measures of punitive sentiment. Extending the replication, we found that punitive sentiment, but not willingness to help, was predicted by personality traits. Most notably, participants scoring lower on Agreeableness expressed more anger towards and greater desire to punish a transgressor, and were more willing to engage in costly punishment, at least in our scenario. Our results suggest that some personality traits may contribute to underpinning individual variation in social enforcement of cooperation.

  7. Altered interhemispheric connectivity in individuals with Tourette's disorder

    DEFF Research Database (Denmark)

    Plessen, Kerstin J; Wentzel-Larsen, Tore; Hugdahl, Kenneth

    2004-01-01

    OBJECTIVE: The corpus callosum is the major commissure connecting the cerebral hemispheres. Prior evidence suggests involvement of the corpus callosum in the pathophysiology of Tourette's disorder. The authors assessed corpus callosum size and anatomical connectivity across the cerebral hemispheres...... in persons with Tourette's disorder. METHOD: The size of the corpus callosum was determined on the true midsagittal slices of reformatted, high-resolution magnetic resonance imaging scans and compared across groups in a cross-sectional case-control study of 158 subjects with Tourette's disorder and 121...... healthy comparison subjects, ages 5-65 years. RESULTS: In the context of increasing midsagittal corpus callosum area from childhood to age 30 years, children with Tourette's disorder had smaller overall corpus callosum size, whereas adults with Tourette's disorder on average had larger corpus callosum...

  8. A Grid Connected Transformerless Inverter and its Model Predictive Control Strategy with Leakage Current Elimination Capability

    Directory of Open Access Journals (Sweden)

    J. Fallah Ardashir

    2017-06-01

    Full Text Available This paper proposes a new single phase transformerless Photovoltaic (PV inverter for grid connected systems. It consists of six power switches, two diodes, one capacitor and filter at the output stage. The neutral of the grid is directly connected to the negative terminal of the source. This results in constant common mode voltage and zero leakage current. Model Predictive Controller (MPC technique is used to modulate the converter to reduce the output current ripple and filter requirements. The main advantages of this inverter are compact size, low cost, flexible grounding configuration. Due to brevity, the operating principle and analysis of the proposed circuit are presented in brief. Simulation and experimental results of 200W prototype are shown at the end to validate the proposed topology and concept. The results obtained clearly verifies the performance of the proposed inverter and its practical application for grid connected PV systems.

  9. [Analysis of 14 individuals who requested predictive genetic testing for hereditary neuromuscular diseases].

    Science.gov (United States)

    Yoshida, Kunihiro; Tamai, Mariko; Kubota, Takeo; Kawame, Hiroshi; Amano, Naoji; Ikeda, Shu-ichi; Fukushima, Yoshimitsu

    2002-02-01

    Predictive genetic testing for hereditary neuromuscular diseases is a delicate issue for individuals at risk and their families, as well as for medical staff because these diseases are often late-onset and intractable. Therefore careful pre- and post-test genetic counseling and psychosocial support should be provided along with such genetic testing. The Division of Clinical and Molecular Genetics was established at our hospital in May 1996 to provide skilled professional genetic counseling. Since its establishment, 14 individuals have visited our clinic to request predictive genetic testing for hereditary neuromuscular diseases (4 for myotonic dystrophy, 6 for spinocerebellar ataxia, 3 for Huntington's disease, and 1 for Alzheimer's disease). The main reasons for considering testing were to remove uncertainty about the genetic status and to plan for the future. Nine of 14 individuals requested testing for making decisions about a forthcoming marriage or pregnancy (family planning). Other reasons raised by the individuals included career or financial planning, planning for their own health care, and knowing the risk for their children. At the first genetic counseling session, all of the individuals expressed hopes of not being a gene carrier and of escaping from fear of disease, and seemed not to be mentally well prepared for an increased-risk result. To date, 7 of the 14 individuals have received genetic testing and only one, who underwent predictive genetic testing for spinocerebellar ataxia, was given an increased-risk result. The seven individuals including the one with an increased-risk result, have coped well with their new knowledge about their genetic status after the testing results were disclosed. None of them has expressed regret. In pre-test genetic counseling sessions, we consider it quite important not only to determine the psychological status of the individual, but also to make the individual try to anticipate the changes in his/her life upon

  10. Cooperation prevails when individuals adjust their social ties.

    Directory of Open Access Journals (Sweden)

    Francisco C Santos

    2006-10-01

    Full Text Available Conventional evolutionary game theory predicts that natural selection favours the selfish and strong even though cooperative interactions thrive at all levels of organization in living systems. Recent investigations demonstrated that a limiting factor for the evolution of cooperative interactions is the way in which they are organized, cooperators becoming evolutionarily competitive whenever individuals are constrained to interact with few others along the edges of networks with low average connectivity. Despite this insight, the conundrum of cooperation remains since recent empirical data shows that real networks exhibit typically high average connectivity and associated single-to-broad-scale heterogeneity. Here, a computational model is constructed in which individuals are able to self-organize both their strategy and their social ties throughout evolution, based exclusively on their self-interest. We show that the entangled evolution of individual strategy and network structure constitutes a key mechanism for the sustainability of cooperation in social networks. For a given average connectivity of the population, there is a critical value for the ratio W between the time scales associated with the evolution of strategy and of structure above which cooperators wipe out defectors. Moreover, the emerging social networks exhibit an overall heterogeneity that accounts very well for the diversity of patterns recently found in acquired data on social networks. Finally, heterogeneity is found to become maximal when W reaches its critical value. These results show that simple topological dynamics reflecting the individual capacity for self-organization of social ties can produce realistic networks of high average connectivity with associated single-to-broad-scale heterogeneity. On the other hand, they show that cooperation cannot evolve as a result of "social viscosity" alone in heterogeneous networks with high average connectivity, requiring the

  11. Big data analytics : predicting traffic flow regimes from simulated connected vehicle messages using data analytics and machine learning.

    Science.gov (United States)

    2016-12-25

    The key objectives of this study were to: 1. Develop advanced analytical techniques that make use of a dynamically configurable connected vehicle message protocol to predict traffic flow regimes in near-real time in a virtual environment and examine ...

  12. Renal endothelial function and blood flow predict the individual susceptibility to adriamycin-induced renal damage

    NARCIS (Netherlands)

    Ochodnicky, Peter; Henning, Robert H.; Buikema, Hendrik; Kluppel, Alex C. A.; van Wattum, Marjolein; de Zeeuw, Dick; van Dokkum, Richard P. E.

    Background. Susceptibility to renal injury varies among individuals. Previously, we found that individual endothelial function of healthy renal arteries in vitro predicted severity of renal damage after 5/6 nephrectomy. Here we hypothesized that individual differences in endothelial function in

  13. Network control principles predict neuron function in the Caenorhabditis elegans connectome

    Science.gov (United States)

    Yan, Gang; Vértes, Petra E.; Towlson, Emma K.; Chew, Yee Lian; Walker, Denise S.; Schafer, William R.; Barabási, Albert-László

    2017-10-01

    Recent studies on the controllability of complex systems offer a powerful mathematical framework to systematically explore the structure-function relationship in biological, social, and technological networks. Despite theoretical advances, we lack direct experimental proof of the validity of these widely used control principles. Here we fill this gap by applying a control framework to the connectome of the nematode Caenorhabditis elegans, allowing us to predict the involvement of each C. elegans neuron in locomotor behaviours. We predict that control of the muscles or motor neurons requires 12 neuronal classes, which include neuronal groups previously implicated in locomotion by laser ablation, as well as one previously uncharacterized neuron, PDB. We validate this prediction experimentally, finding that the ablation of PDB leads to a significant loss of dorsoventral polarity in large body bends. Importantly, control principles also allow us to investigate the involvement of individual neurons within each neuronal class. For example, we predict that, within the class of DD motor neurons, only three (DD04, DD05, or DD06) should affect locomotion when ablated individually. This prediction is also confirmed; single cell ablations of DD04 or DD05 specifically affect posterior body movements, whereas ablations of DD02 or DD03 do not. Our predictions are robust to deletions of weak connections, missing connections, and rewired connections in the current connectome, indicating the potential applicability of this analytical framework to larger and less well-characterized connectomes.

  14. Network control principles predict neuron function in the Caenorhabditis elegans connectome.

    Science.gov (United States)

    Yan, Gang; Vértes, Petra E; Towlson, Emma K; Chew, Yee Lian; Walker, Denise S; Schafer, William R; Barabási, Albert-László

    2017-10-26

    Recent studies on the controllability of complex systems offer a powerful mathematical framework to systematically explore the structure-function relationship in biological, social, and technological networks. Despite theoretical advances, we lack direct experimental proof of the validity of these widely used control principles. Here we fill this gap by applying a control framework to the connectome of the nematode Caenorhabditis elegans, allowing us to predict the involvement of each C. elegans neuron in locomotor behaviours. We predict that control of the muscles or motor neurons requires 12 neuronal classes, which include neuronal groups previously implicated in locomotion by laser ablation, as well as one previously uncharacterized neuron, PDB. We validate this prediction experimentally, finding that the ablation of PDB leads to a significant loss of dorsoventral polarity in large body bends. Importantly, control principles also allow us to investigate the involvement of individual neurons within each neuronal class. For example, we predict that, within the class of DD motor neurons, only three (DD04, DD05, or DD06) should affect locomotion when ablated individually. This prediction is also confirmed; single cell ablations of DD04 or DD05 specifically affect posterior body movements, whereas ablations of DD02 or DD03 do not. Our predictions are robust to deletions of weak connections, missing connections, and rewired connections in the current connectome, indicating the potential applicability of this analytical framework to larger and less well-characterized connectomes.

  15. Personality traits and individual differences predict threat-induced changes in postural control.

    Science.gov (United States)

    Zaback, Martin; Cleworth, Taylor W; Carpenter, Mark G; Adkin, Allan L

    2015-04-01

    This study explored whether specific personality traits and individual differences could predict changes in postural control when presented with a height-induced postural threat. Eighty-two healthy young adults completed questionnaires to assess trait anxiety, trait movement reinvestment (conscious motor processing, movement self-consciousness), physical risk-taking, and previous experience with height-related activities. Tests of static (quiet standing) and anticipatory (rise to toes) postural control were completed under low and high postural threat conditions. Personality traits and individual differences significantly predicted height-induced changes in static, but not anticipatory postural control. Individuals less prone to taking physical risks were more likely to lean further away from the platform edge and sway at higher frequencies and smaller amplitudes. Individuals more prone to conscious motor processing were more likely to lean further away from the platform edge and sway at larger amplitudes. Individuals more self-conscious about their movement appearance were more likely to sway at smaller amplitudes. Evidence is also provided that relationships between physical risk-taking and changes in static postural control are mediated through changes in fear of falling and physiological arousal. Results from this study may have indirect implications for balance assessment and treatment; however, further work exploring these factors in patient populations is necessary. Copyright © 2015 Elsevier B.V. All rights reserved.

  16. A small number of abnormal brain connections predicts adult autism spectrum disorder.

    Science.gov (United States)

    Yahata, Noriaki; Morimoto, Jun; Hashimoto, Ryuichiro; Lisi, Giuseppe; Shibata, Kazuhisa; Kawakubo, Yuki; Kuwabara, Hitoshi; Kuroda, Miho; Yamada, Takashi; Megumi, Fukuda; Imamizu, Hiroshi; Náñez, José E; Takahashi, Hidehiko; Okamoto, Yasumasa; Kasai, Kiyoto; Kato, Nobumasa; Sasaki, Yuka; Watanabe, Takeo; Kawato, Mitsuo

    2016-04-14

    Although autism spectrum disorder (ASD) is a serious lifelong condition, its underlying neural mechanism remains unclear. Recently, neuroimaging-based classifiers for ASD and typically developed (TD) individuals were developed to identify the abnormality of functional connections (FCs). Due to over-fitting and interferential effects of varying measurement conditions and demographic distributions, no classifiers have been strictly validated for independent cohorts. Here we overcome these difficulties by developing a novel machine-learning algorithm that identifies a small number of FCs that separates ASD versus TD. The classifier achieves high accuracy for a Japanese discovery cohort and demonstrates a remarkable degree of generalization for two independent validation cohorts in the USA and Japan. The developed ASD classifier does not distinguish individuals with major depressive disorder and attention-deficit hyperactivity disorder from their controls but moderately distinguishes patients with schizophrenia from their controls. The results leave open the viable possibility of exploring neuroimaging-based dimensions quantifying the multiple-disorder spectrum.

  17. Glutamate concentration in the medial prefrontal cortex predicts resting-state cortical-subcortical functional connectivity in humans.

    Directory of Open Access Journals (Sweden)

    Niall W Duncan

    Full Text Available Communication between cortical and subcortical regions is integral to a wide range of psychological processes and has been implicated in a number of psychiatric conditions. Studies in animals have provided insight into the biochemical and connectivity processes underlying such communication. However, to date no experiments that link these factors in humans in vivo have been carried out. To investigate the role of glutamate in individual differences in communication between the cortex--specifically the medial prefrontal cortex (mPFC--and subcortical regions in humans, a combination of resting-state fMRI, DTI and MRS was performed. The subcortical target regions were the nucleus accumbens (NAc, dorsomedial thalamus (DMT, and periaqueductal grey (PAG. It was found that functional connectivity between the mPFC and each of the NAc and DMT was positively correlated with mPFC glutamate concentrations, whilst functional connectivity between the mPFC and PAG was negatively correlated with glutamate concentration. The correlations involving mPFC glutamate and FC between the mPFC and each of the DMT and PAG were mirrored by correlations with structural connectivity, providing evidence that the glutamatergic relationship may, in part, be due to direct connectivity. These results are in agreement with existing results from animal studies and may have relevance for MDD and schizophrenia.

  18. Renal endothelial function and blood flow predict the individual susceptibility to adriamycin-induced renal damage

    NARCIS (Netherlands)

    Ochodnicky, Peter; Henning, Robert H.; Buikema, Hendrik; Kluppel, Alex C. A.; van Wattum, Marjolein; de Zeeuw, Dick; van Dokkum, Richard P. E.

    2009-01-01

    Susceptibility to renal injury varies among individuals. Previously, we found that individual endothelial function of healthy renal arteries in vitro predicted severity of renal damage after 5/6 nephrectomy. Here we hypothesized that individual differences in endothelial function in vitro and renal

  19. The Role of Affective and Cognitive Individual Differences in Social Perception.

    Science.gov (United States)

    Aquino, Antonio; Haddock, Geoffrey; Maio, Gregory R; Wolf, Lukas J; Alparone, Francesca R

    2016-06-01

    Three studies explored the connection between social perception processes and individual differences in the use of affective and cognitive information in relation to attitudes. Study 1 revealed that individuals high in need for affect (NFA) accentuated differences in evaluations of warm and cold traits, whereas individuals high in need for cognition (NFC) accentuated differences in evaluations of competent and incompetent traits. Study 2 revealed that individual differences in NFA predicted liking of warm or cold targets, whereas individual differences in NFC predicted perceptions of competent or incompetent targets. Furthermore, the effects of NFA and NFC were independent of structural bases and meta-bases of attitudes. Study 3 revealed that differences in the evaluation of warm and cold traits mediated the effects of NFA and NFC on liking of targets. The implications for social perception processes and for individual differences in affect-cognition are discussed. © 2016 by the Society for Personality and Social Psychology, Inc.

  20. Predicting Individual Characteristics from Digital Traces on Social Media: A Meta-Analysis.

    Science.gov (United States)

    Settanni, Michele; Azucar, Danny; Marengo, Davide

    2018-04-01

    The increasing utilization of social media provides a vast and new source of user-generated ecological data (digital traces), which can be automatically collected for research purposes. The availability of these data sets, combined with the convergence between social and computer sciences, has led researchers to develop automated methods to extract digital traces from social media and use them to predict individual psychological characteristics and behaviors. In this article, we reviewed the literature on this topic and conducted a series of meta-analyses to determine the strength of associations between digital traces and specific individual characteristics; personality, psychological well-being, and intelligence. Potential moderator effects were analyzed with respect to type of social media platform, type of digital traces examined, and study quality. Our findings indicate that digital traces from social media can be studied to assess and predict theoretically distant psychosocial characteristics with remarkable accuracy. Analysis of moderators indicated that the collection of specific types of information (i.e., user demographics), and the inclusion of different types of digital traces, could help improve the accuracy of predictions.

  1. Functional corticostriatal connection topographies predict goal directed behaviour in humans

    NARCIS (Netherlands)

    Marquand, A.F.; Haak, K.V.; Beckmann, C.F.

    2017-01-01

    Anatomical tracing studies in non-human primates have suggested that corticostriatal connectivity is topographically organized: nearby locations in striatum are connected with nearby locations in cortex. The topographic organization of corticostriatal connectivity is thought to underpin many

  2. Individual Prediction of Heart Failure Among Childhood Cancer Survivors

    Science.gov (United States)

    Chow, Eric J.; Chen, Yan; Kremer, Leontien C.; Breslow, Norman E.; Hudson, Melissa M.; Armstrong, Gregory T.; Border, William L.; Feijen, Elizabeth A.M.; Green, Daniel M.; Meacham, Lillian R.; Meeske, Kathleen A.; Mulrooney, Daniel A.; Ness, Kirsten K.; Oeffinger, Kevin C.; Sklar, Charles A.; Stovall, Marilyn; van der Pal, Helena J.; Weathers, Rita E.; Robison, Leslie L.; Yasui, Yutaka

    2015-01-01

    Purpose To create clinically useful models that incorporate readily available demographic and cancer treatment characteristics to predict individual risk of heart failure among 5-year survivors of childhood cancer. Patients and Methods Survivors in the Childhood Cancer Survivor Study (CCSS) free of significant cardiovascular disease 5 years after cancer diagnosis (n = 13,060) were observed through age 40 years for the development of heart failure (ie, requiring medications or heart transplantation or leading to death). Siblings (n = 4,023) established the baseline population risk. An additional 3,421 survivors from Emma Children's Hospital (Amsterdam, the Netherlands), the National Wilms Tumor Study, and the St Jude Lifetime Cohort Study were used to validate the CCSS prediction models. Results Heart failure occurred in 285 CCSS participants. Risk scores based on selected exposures (sex, age at cancer diagnosis, and anthracycline and chest radiotherapy doses) achieved an area under the curve of 0.74 and concordance statistic of 0.76 at or through age 40 years. Validation cohort estimates ranged from 0.68 to 0.82. Risk scores were collapsed to form statistically distinct low-, moderate-, and high-risk groups, corresponding to cumulative incidences of heart failure at age 40 years of 0.5% (95% CI, 0.2% to 0.8%), 2.4% (95% CI, 1.8% to 3.0%), and 11.7% (95% CI, 8.8% to 14.5%), respectively. In comparison, siblings had a cumulative incidence of 0.3% (95% CI, 0.1% to 0.5%). Conclusion Using information available to clinicians soon after completion of childhood cancer therapy, individual risk for subsequent heart failure can be predicted with reasonable accuracy and discrimination. These validated models provide a framework on which to base future screening strategies and interventions. PMID:25287823

  3. Predicting sexual infidelity in a population-based sample of married individuals.

    Science.gov (United States)

    Whisman, Mark A; Gordon, Kristina Coop; Chatav, Yael

    2007-06-01

    Predictors of 12-month prevalence of sexual infidelity were examined in a population-based sample of married individuals (N = 2,291). Predictor variables were organized in terms of involved-partner (e.g., personality, religiosity), marital (e.g., marital dissatisfaction, partner affair), and extradyadic (e.g., parenting) variables. Annual prevalence of infidelity was 2.3%. Controlling for marital dissatisfaction and demographic variables, infidelity was predicted by greater neuroticism and lower religiosity; wives' pregnancy also increased the risk of infidelity for husbands. In comparison, self-esteem and partners' suspected affair were predictive of infidelity when controlling for demographic variables but were not uniquely predictive of infidelity when also controlling for marital dissatisfaction. Religiosity and wives' pregnancy moderated the association between marital dissatisfaction and infidelity.

  4. Influences on the Test-Retest Reliability of Functional Connectivity MRI and its Relationship with Behavioral Utility.

    Science.gov (United States)

    Noble, Stephanie; Spann, Marisa N; Tokoglu, Fuyuze; Shen, Xilin; Constable, R Todd; Scheinost, Dustin

    2017-11-01

    Best practices are currently being developed for the acquisition and processing of resting-state magnetic resonance imaging data used to estimate brain functional organization-or "functional connectivity." Standards have been proposed based on test-retest reliability, but open questions remain. These include how amount of data per subject influences whole-brain reliability, the influence of increasing runs versus sessions, the spatial distribution of reliability, the reliability of multivariate methods, and, crucially, how reliability maps onto prediction of behavior. We collected a dataset of 12 extensively sampled individuals (144 min data each across 2 identically configured scanners) to assess test-retest reliability of whole-brain connectivity within the generalizability theory framework. We used Human Connectome Project data to replicate these analyses and relate reliability to behavioral prediction. Overall, the historical 5-min scan produced poor reliability averaged across connections. Increasing the number of sessions was more beneficial than increasing runs. Reliability was lowest for subcortical connections and highest for within-network cortical connections. Multivariate reliability was greater than univariate. Finally, reliability could not be used to improve prediction; these findings are among the first to underscore this distinction for functional connectivity. A comprehensive understanding of test-retest reliability, including its limitations, supports the development of best practices in the field. © The Author 2017. Published by Oxford University Press.

  5. Global brain dynamics during social exclusion predict subsequent behavioral conformity.

    Science.gov (United States)

    Wasylyshyn, Nick; Hemenway Falk, Brett; Garcia, Javier O; Cascio, Christopher N; O'Donnell, Matthew Brook; Bingham, C Raymond; Simons-Morton, Bruce; Vettel, Jean M; Falk, Emily B

    2018-02-01

    Individuals react differently to social experiences; for example, people who are more sensitive to negative social experiences, such as being excluded, may be more likely to adapt their behavior to fit in with others. We examined whether functional brain connectivity during social exclusion in the fMRI scanner can be used to predict subsequent conformity to peer norms. Adolescent males (n = 57) completed a two-part study on teen driving risk: a social exclusion task (Cyberball) during an fMRI session and a subsequent driving simulator session in which they drove alone and in the presence of a peer who expressed risk-averse or risk-accepting driving norms. We computed the difference in functional connectivity between social exclusion and social inclusion from each node in the brain to nodes in two brain networks, one previously associated with mentalizing (medial prefrontal cortex, temporoparietal junction, precuneus, temporal poles) and another with social pain (dorsal anterior cingulate cortex, anterior insula). Using predictive modeling, this measure of global connectivity during exclusion predicted the extent of conformity to peer pressure during driving in the subsequent experimental session. These findings extend our understanding of how global neural dynamics guide social behavior, revealing functional network activity that captures individual differences.

  6. Hormone levels predict individual differences in reproductive success in a passerine bird.

    Science.gov (United States)

    Ouyang, Jenny Q; Sharp, Peter J; Dawson, Alistair; Quetting, Michael; Hau, Michaela

    2011-08-22

    Hormones mediate major physiological and behavioural components of the reproductive phenotype of individuals. To understand basic evolutionary processes in the hormonal regulation of reproductive traits, we need to know whether, and during which reproductive phases, individual variation in hormone concentrations relates to fitness in natural populations. We related circulating concentrations of prolactin and corticosterone to parental behaviour and reproductive success during both the pre-breeding and the chick-rearing stages in both individuals of pairs of free-living house sparrows, Passer domesticus. Prolactin and baseline corticosterone concentrations in pre-breeding females, and prolactin concentrations in pre-breeding males, predicted total number of fledglings. When the strong effect of lay date on total fledgling number was corrected for, only pre-breeding baseline corticosterone, but not prolactin, was negatively correlated with the reproductive success of females. During the breeding season, nestling provisioning rates of both sexes were negatively correlated with stress-induced corticosterone levels. Lastly, individuals of both sexes with low baseline corticosterone before and high baseline corticosterone during breeding raised the most offspring, suggesting that either the plasticity of this trait contributes to reproductive success or that high parental effort leads to increased hormone concentrations. Thus hormone concentrations both before and during breeding, as well as their seasonal dynamics, predict reproductive success, suggesting that individual variation in absolute concentrations and in plasticity is functionally significant, and, if heritable, may be a target of selection.

  7. Within-person Changes in Individual Symptoms of Depression Predict Subsequent Depressive Episodes in Adolescents: A Prospective Study

    Science.gov (United States)

    Kouros, Chrystyna D.; Morris, Matthew C.; Garber, Judy

    2015-01-01

    The current longitudinal study examined which individual symptoms of depression uniquely predicted a subsequent Major Depressive Episode (MDE) in adolescents, and whether these relations differed by sex. Adolescents (N=240) were first interviewed in grade 6 (M=11.86 years old; SD = 0.56; 54% female; 81.5% Caucasian) and then annually through grade 12 regarding their individual symptoms of depression as well as the occurrence of MDEs. Individual symptoms of depression were assessed with the Children’s Depression Rating Scale-Revised (CDRS-R) and depressive episodes were assessed with the Longitudinal Interval Follow-up Evaluation (LIFE). Results showed that within-person changes in sleep problems and low self-esteem/excessive guilt positively predicted an increased likelihood of an MDE for both boys and girls. Significant sex differences also were found. Within-person changes in anhedonia predicted an increased likelihood of a subsequent MDE among boys, whereas irritability predicted a decreased likelihood of a future MDE among boys, and concentration difficulties predicted a decreased likelihood of an MDE in girls. These results identified individual depressive symptoms that predicted subsequent depressive episodes in male and female adolescents, and may be used to guide the early detection, treatment, and prevention of depressive disorders in youth. PMID:26105209

  8. Optimal Parameter Selection for Support Vector Machine Based on Artificial Bee Colony Algorithm: A Case Study of Grid-Connected PV System Power Prediction.

    Science.gov (United States)

    Gao, Xiang-Ming; Yang, Shi-Feng; Pan, San-Bo

    2017-01-01

    Predicting the output power of photovoltaic system with nonstationarity and randomness, an output power prediction model for grid-connected PV systems is proposed based on empirical mode decomposition (EMD) and support vector machine (SVM) optimized with an artificial bee colony (ABC) algorithm. First, according to the weather forecast data sets on the prediction date, the time series data of output power on a similar day with 15-minute intervals are built. Second, the time series data of the output power are decomposed into a series of components, including some intrinsic mode components IMFn and a trend component Res, at different scales using EMD. The corresponding SVM prediction model is established for each IMF component and trend component, and the SVM model parameters are optimized with the artificial bee colony algorithm. Finally, the prediction results of each model are reconstructed, and the predicted values of the output power of the grid-connected PV system can be obtained. The prediction model is tested with actual data, and the results show that the power prediction model based on the EMD and ABC-SVM has a faster calculation speed and higher prediction accuracy than do the single SVM prediction model and the EMD-SVM prediction model without optimization.

  9. Optimal Parameter Selection for Support Vector Machine Based on Artificial Bee Colony Algorithm: A Case Study of Grid-Connected PV System Power Prediction

    Directory of Open Access Journals (Sweden)

    Xiang-ming Gao

    2017-01-01

    Full Text Available Predicting the output power of photovoltaic system with nonstationarity and randomness, an output power prediction model for grid-connected PV systems is proposed based on empirical mode decomposition (EMD and support vector machine (SVM optimized with an artificial bee colony (ABC algorithm. First, according to the weather forecast data sets on the prediction date, the time series data of output power on a similar day with 15-minute intervals are built. Second, the time series data of the output power are decomposed into a series of components, including some intrinsic mode components IMFn and a trend component Res, at different scales using EMD. The corresponding SVM prediction model is established for each IMF component and trend component, and the SVM model parameters are optimized with the artificial bee colony algorithm. Finally, the prediction results of each model are reconstructed, and the predicted values of the output power of the grid-connected PV system can be obtained. The prediction model is tested with actual data, and the results show that the power prediction model based on the EMD and ABC-SVM has a faster calculation speed and higher prediction accuracy than do the single SVM prediction model and the EMD-SVM prediction model without optimization.

  10. Predictive capacity of indicators of adiposity in the metabolic syndrome in elderly individuals

    Directory of Open Access Journals (Sweden)

    Keila Bacelar Duarte de MORAIS

    Full Text Available ABSTRACT Objective To evaluate the predictive ability of adiposity indicators as MetS predictors in elderly individuals. Methods Cross-sectional study enrolled in the Estratégia Saúde da Família (Family Health Strategy. Anthropometric measurements were measured. Body Mass Index, Waist-Hip Ratio, Waist-Height Ratio, Conicity Index and Body Adiposity Index were calculated. Blood was collected and resting blood pressure was measured. MetS was classified according to the harmonizing criteria. The predictive ability of anthropometric variables was evaluated using Receiver Operating Characteristic curves. Results Regarding male individuals, our research indicates that the BMI, Waist-Height Ratio and Waist Hip Ratio are better predictors and they are equivalent to each other. As for female individuals, results show that the Body Mass Index and Waist-Height Ratio are better predictors and equivalent to each other. Conclusion Waist-Height Ratio and Body Mass Index are good MetS predictors for elderly individuals, especially among men. More research in this area is important. Comitê de Ética em Pesquisa com Seres Humanos da Universidade Federal de Viçosa. (Viçosa University Ethics Committee in Research with Human Beings (nº 039/2011.

  11. Limitations of diagnostic precision and predictive utility in the individual case: a challenge for forensic practice.

    Science.gov (United States)

    Cooke, David J; Michie, Christine

    2010-08-01

    Knowledge of group tendencies may not assist accurate predictions in the individual case. This has importance for forensic decision making and for the assessment tools routinely applied in forensic evaluations. In this article, we applied Monte Carlo methods to examine diagnostic agreement with different levels of inter-rater agreement given the distributional characteristics of PCL-R scores. Diagnostic agreement and score agreement were substantially less than expected. In addition, we examined the confidence intervals associated with individual predictions of violent recidivism. On the basis of empirical findings, statistical theory, and logic, we conclude that predictions of future offending cannot be achieved in the individual case with any degree of confidence. We discuss the problems identified in relation to the PCL-R in terms of the broader relevance to all instruments used in forensic decision making.

  12. Food web complexity and stability across habitat connectivity gradients.

    Science.gov (United States)

    LeCraw, Robin M; Kratina, Pavel; Srivastava, Diane S

    2014-12-01

    The effects of habitat connectivity on food webs have been studied both empirically and theoretically, yet the question of whether empirical results support theoretical predictions for any food web metric other than species richness has received little attention. Our synthesis brings together theory and empirical evidence for how habitat connectivity affects both food web stability and complexity. Food web stability is often predicted to be greatest at intermediate levels of connectivity, representing a compromise between the stabilizing effects of dispersal via rescue effects and prey switching, and the destabilizing effects of dispersal via regional synchronization of population dynamics. Empirical studies of food web stability generally support both this pattern and underlying mechanisms. Food chain length has been predicted to have both increasing and unimodal relationships with connectivity as a result of predators being constrained by the patch occupancy of their prey. Although both patterns have been documented empirically, the underlying mechanisms may differ from those predicted by models. In terms of other measures of food web complexity, habitat connectivity has been empirically found to generally increase link density but either reduce or have no effect on connectance, whereas a unimodal relationship is expected. In general, there is growing concordance between empirical patterns and theoretical predictions for some effects of habitat connectivity on food webs, but many predictions remain to be tested over a full connectivity gradient, and empirical metrics of complexity are rarely modeled. Closing these gaps will allow a deeper understanding of how natural and anthropogenic changes in connectivity can affect real food webs.

  13. On the relevance of sophisticated structural annotations for disulfide connectivity pattern prediction.

    Directory of Open Access Journals (Sweden)

    Julien Becker

    Full Text Available Disulfide bridges strongly constrain the native structure of many proteins and predicting their formation is therefore a key sub-problem of protein structure and function inference. Most recently proposed approaches for this prediction problem adopt the following pipeline: first they enrich the primary sequence with structural annotations, second they apply a binary classifier to each candidate pair of cysteines to predict disulfide bonding probabilities and finally, they use a maximum weight graph matching algorithm to derive the predicted disulfide connectivity pattern of a protein. In this paper, we adopt this three step pipeline and propose an extensive study of the relevance of various structural annotations and feature encodings. In particular, we consider five kinds of structural annotations, among which three are novel in the context of disulfide bridge prediction. So as to be usable by machine learning algorithms, these annotations must be encoded into features. For this purpose, we propose four different feature encodings based on local windows and on different kinds of histograms. The combination of structural annotations with these possible encodings leads to a large number of possible feature functions. In order to identify a minimal subset of relevant feature functions among those, we propose an efficient and interpretable feature function selection scheme, designed so as to avoid any form of overfitting. We apply this scheme on top of three supervised learning algorithms: k-nearest neighbors, support vector machines and extremely randomized trees. Our results indicate that the use of only the PSSM (position-specific scoring matrix together with the CSP (cysteine separation profile are sufficient to construct a high performance disulfide pattern predictor and that extremely randomized trees reach a disulfide pattern prediction accuracy of [Formula: see text] on the benchmark dataset SPX[Formula: see text], which corresponds to

  14. Study on model current predictive control method of PV grid- connected inverters systems with voltage sag

    Science.gov (United States)

    Jin, N.; Yang, F.; Shang, S. Y.; Tao, T.; Liu, J. S.

    2016-08-01

    According to the limitations of the LVRT technology of traditional photovoltaic inverter existed, this paper proposes a low voltage ride through (LVRT) control method based on model current predictive control (MCPC). This method can effectively improve the photovoltaic inverter output characteristics and response speed. The MCPC method of photovoltaic grid-connected inverter designed, the sum of the absolute value of the predictive current and the given current error is adopted as the cost function with the model predictive control method. According to the MCPC, the optimal space voltage vector is selected. Photovoltaic inverter has achieved automatically switches of priority active or reactive power control of two control modes according to the different operating states, which effectively improve the inverter capability of LVRT. The simulation and experimental results proves that the proposed method is correct and effective.

  15. Can a mathematical model predict an individual's trait-like response to both total and partial sleep loss?

    Science.gov (United States)

    Ramakrishnan, Sridhar; Lu, Wei; Laxminarayan, Srinivas; Wesensten, Nancy J; Rupp, Tracy L; Balkin, Thomas J; Reifman, Jaques

    2015-06-01

    Humans display a trait-like response to sleep loss. However, it is not known whether this trait-like response can be captured by a mathematical model from only one sleep-loss condition to facilitate neurobehavioural performance prediction of the same individual during a different sleep-loss condition. In this paper, we investigated the extent to which the recently developed unified mathematical model of performance (UMP) captured such trait-like features for different sleep-loss conditions. We used the UMP to develop two sets of individual-specific models for 15 healthy adults who underwent two different sleep-loss challenges (order counterbalanced; separated by 2-4 weeks): (i) 64 h of total sleep deprivation (TSD) and (ii) chronic sleep restriction (CSR) of 7 days of 3 h nightly time in bed. We then quantified the extent to which models developed using psychomotor vigilance task data under TSD predicted performance data under CSR, and vice versa. The results showed that the models customized to an individual under one sleep-loss condition accurately predicted performance of the same individual under the other condition, yielding, on average, up to 50% improvement over non-individualized, group-average model predictions. This finding supports the notion that the UMP captures an individual's trait-like response to different sleep-loss conditions. © 2014 European Sleep Research Society.

  16. Structural habitat predicts functional dispersal habitat of a large carnivore: how leopards change spots.

    Science.gov (United States)

    Fattebert, Julien; Robinson, Hugh S; Balme, Guy; Slotow, Rob; Hunter, Luke

    2015-10-01

    Natal dispersal promotes inter-population linkage, and is key to spatial distribution of populations. Degradation of suitable landscape structures beyond the specific threshold of an individual's ability to disperse can therefore lead to disruption of functional landscape connectivity and impact metapopulation function. Because it ignores behavioral responses of individuals, structural connectivity is easier to assess than functional connectivity and is often used as a surrogate for landscape connectivity modeling. However using structural resource selection models as surrogate for modeling functional connectivity through dispersal could be erroneous. We tested how well a second-order resource selection function (RSF) models (structural connectivity), based on GPS telemetry data from resident adult leopard (Panthera pardus L.), could predict subadult habitat use during dispersal (functional connectivity). We created eight non-exclusive subsets of the subadult data based on differing definitions of dispersal to assess the predictive ability of our adult-based RSF model extrapolated over a broader landscape. Dispersing leopards used habitats in accordance with adult selection patterns, regardless of the definition of dispersal considered. We demonstrate that, for a wide-ranging apex carnivore, functional connectivity through natal dispersal corresponds to structural connectivity as modeled by a second-order RSF. Mapping of the adult-based habitat classes provides direct visualization of the potential linkages between populations, without the need to model paths between a priori starting and destination points. The use of such landscape scale RSFs may provide insight into predicting suitable dispersal habitat peninsulas in human-dominated landscapes where mitigation of human-wildlife conflict should be focused. We recommend the use of second-order RSFs for landscape conservation planning and propose a similar approach to the conservation of other wide-ranging large

  17. Real-time prediction models for output power and efficiency of grid-connected solar photovoltaic systems

    International Nuclear Information System (INIS)

    Su, Yan; Chan, Lai-Cheong; Shu, Lianjie; Tsui, Kwok-Leung

    2012-01-01

    Highlights: ► We develop online prediction models for solar photovoltaic system performance. ► The proposed prediction models are simple but with reasonable accuracy. ► The maximum monthly average minutely efficiency varies 10.81–12.63%. ► The average efficiency tends to be slightly higher in winter months. - Abstract: This paper develops new real time prediction models for output power and energy efficiency of solar photovoltaic (PV) systems. These models were validated using measured data of a grid-connected solar PV system in Macau. Both time frames based on yearly average and monthly average are considered. It is shown that the prediction model for the yearly/monthly average of the minutely output power fits the measured data very well with high value of R 2 . The online prediction model for system efficiency is based on the ratio of the predicted output power to the predicted solar irradiance. This ratio model is shown to be able to fit the intermediate phase (9 am to 4 pm) very well but not accurate for the growth and decay phases where the system efficiency is near zero. However, it can still serve as a useful purpose for practitioners as most PV systems work in the most efficient manner over this period. It is shown that the maximum monthly average minutely efficiency varies over a small range of 10.81% to 12.63% in different months with slightly higher efficiency in winter months.

  18. Oxytocin biases men but not women to restore social connections with individuals who socially exclude them.

    Science.gov (United States)

    Xu, Xiaolei; Yao, Shuxia; Xu, Lei; Geng, Yayuan; Zhao, Weihua; Ma, Xiaole; Kou, Juan; Luo, Ruixue; Kendrick, Keith M

    2017-01-12

    We normally react to individuals who exclude us socially by either avoiding them or increasing our attempts to interact with them. The neuropeptide oxytocin can promote social bonds and reduce social conflict and we therefore investigated whether it facilitates more positive social responses towards individuals who exclude or include us. In a double-blind, placebo-controlled, between-subject design 77 healthy Chinese male and female participants received intranasal oxytocin (40 IU) or placebo before playing a modified virtual ball-tossing game with three fictitious partners who either showed exclusion, inclusion or neutral behavioral interactions with them. Results showed that both male and female subjects threw the ball more often to individuals who excluded rather than included them, although oxytocin did not alter this or awareness/feelings of exclusion or inclusion. However, when subjects returned a week later males, but not females, in the oxytocin group exhibited an increased liking for, and preference for playing again with, players who had previously excluded them. This oxytocin effect was positively associated with independent traits. Our findings suggest that in a collectivist culture oxytocin may promote the desire of males, but not females, with a stronger independent orientation to rebuild social connections with individuals who have previously excluded them.

  19. Using circuit theory to model connectivity in ecology, evolution, and conservation.

    Science.gov (United States)

    McRae, Brad H; Dickson, Brett G; Keitt, Timothy H; Shah, Viral B

    2008-10-01

    Connectivity among populations and habitats is important for a wide range of ecological processes. Understanding, preserving, and restoring connectivity in complex landscapes requires connectivity models and metrics that are reliable, efficient, and process based. We introduce a new class of ecological connectivity models based in electrical circuit theory. Although they have been applied in other disciplines, circuit-theoretic connectivity models are new to ecology. They offer distinct advantages over common analytic connectivity models, including a theoretical basis in random walk theory and an ability to evaluate contributions of multiple dispersal pathways. Resistance, current, and voltage calculated across graphs or raster grids can be related to ecological processes (such as individual movement and gene flow) that occur across large population networks or landscapes. Efficient algorithms can quickly solve networks with millions of nodes, or landscapes with millions of raster cells. Here we review basic circuit theory, discuss relationships between circuit and random walk theories, and describe applications in ecology, evolution, and conservation. We provide examples of how circuit models can be used to predict movement patterns and fates of random walkers in complex landscapes and to identify important habitat patches and movement corridors for conservation planning.

  20. Using individual-condition measures to predict the long-term importance of habitat extent for population persistence.

    Science.gov (United States)

    Cosgrove, Anita J; McWhorter, Todd J; Maron, Martine

    2017-10-01

    Habitat loss and fragmentation are causing widespread population declines, but identifying how and when to intervene remains challenging. Predicting where extirpations are likely to occur and implementing management actions before losses result may be more cost-effective than trying to reestablish lost populations. Early indicators of pressure on populations could be used to make such predictions. Previous work conducted in 2009 and 2010 identified that the presence of Eastern Yellow Robins (Eopsaltria australis) in 42 sites in a fragmented region of eastern Australia was unrelated to woodland extent within 500 m of a site, but the robins' heterophil:lymphocyte (H:L) ratios (an indicator of chronic stress) were elevated at sites with low levels of surrounding woodland. We resurveyed these 42 sites in 2013 and 2014 for robin presence to determine whether the H:L ratios obtained in 2009 and 2010 predicted the locations of extirpations and whether the previous pattern in H:L ratios was an early sign that woodland extent would become an important predictor of occupancy. We also surveyed for robins at 43 additional sites to determine whether current occupancy could be better predicted by landscape context at a larger scale, relevant to dispersal movements. At the original 42 sites, H:L ratios and extirpations were not related, although only 4 extirpations were observed. Woodland extent within 500 m had become a strong predictor of occupancy. Taken together, these results provide mixed evidence as to whether patterns of individual condition can reveal habitat relationships that become evident as local shifts in occupancy occur but that are not revealed by a single snapshot of species distribution. Across all 85 sites, woodland extent at scales relevant to dispersal (5 km) was not related to occurrence. We recommend that conservation actions focus on regenerating areas of habitat large enough to support robin territories rather than increasing connectivity within the

  1. Predictive value of dorso-lateral prefrontal connectivity for rTMS response in treatment-resistant depression: A brain perfusion SPECT study.

    Science.gov (United States)

    Richieri, Raphaëlle; Verger, Antoine; Boyer, Laurent; Boucekine, Mohamed; David, Anthony; Lançon, Christophe; Cermolacce, Michel; Guedj, Eric

    2018-05-18

    Previous clinical trials have suggested that repetitive transcranial magnetic stimulation (rTMS) has a significant antidepressant effect in patients with treatment resistant depression (TRD). However, results remain heterogeneous with many patients without effective response. The aim of this SPECT study was to determine before treatment the predictive value of the connectivity of the stimulated area on further rTMS response in patients with TRD. Fifty-eight TRD patients performed a brain perfusion SPECT before high frequency rTMS of the left dorsolateral prefrontal cortex (DLPFC). A voxel based-analysis was achieved to compare connectivity of the left DLPFC in responders and non-responders using inter-regional correlations (p left DLPFC and the right cerebellum in comparison to non-responders, independently of age, gender, severity of depression, and severity of treatment resistance. The area under the curve for the combination of these two SPECT clusters to predict rTMS response was 0.756 (p left DLPFC predicts rTMS response before treatment. Crown Copyright © 2018. Published by Elsevier Inc. All rights reserved.

  2. Structural and functional properties of a probabilistic model of neuronal connectivity in a simple locomotor network

    Science.gov (United States)

    Merrison-Hort, Robert; Soffe, Stephen R; Borisyuk, Roman

    2018-01-01

    Although, in most animals, brain connectivity varies between individuals, behaviour is often similar across a species. What fundamental structural properties are shared across individual networks that define this behaviour? We describe a probabilistic model of connectivity in the hatchling Xenopus tadpole spinal cord which, when combined with a spiking model, reliably produces rhythmic activity corresponding to swimming. The probabilistic model allows calculation of structural characteristics that reflect common network properties, independent of individual network realisations. We use the structural characteristics to study examples of neuronal dynamics, in the complete network and various sub-networks, and this allows us to explain the basis for key experimental findings, and make predictions for experiments. We also study how structural and functional features differ between detailed anatomical connectomes and those generated by our new, simpler, model (meta-model). PMID:29589828

  3. Comparison of measured and predicted long term performance of grid a connected photovoltaic system

    International Nuclear Information System (INIS)

    Mondol, Jayanta Deb; Yohanis, Yigzaw G.; Norton, Brian

    2007-01-01

    Predicted performance of a grid connected photovoltaic (PV) system using TRNSYS was compared with measured data. A site specific global-diffuse correlation model was developed and used to calculate the beam and diffuse components of global horizontal insolation. A PV module temperature equation and a correlation relating input and output power of an inverter were developed using measured data and used in TRNSYS to perform PV array and inverter outputs simulation. Different combinations of the tilted surface radiation model, global-diffuse correlation model and PV module temperature equation were used in the simulations. Statistical error analysis was performed to compare the results for each combination. The simulation accuracy was improved by using the new global-diffuse correlation and module temperature equation in the TRNSYS simulation. For an isotropic sky tilted surface radiation model, the average monthly difference between measured and predicted PV output before and after modification of the TRNSYS component were 10.2% and 3.3%, respectively, and, for an anisotropic sky model, 15.4% and 10.7%, respectively. For inverter output, the corresponding errors were 10.4% and 3.3% and 15.8% and 8.6%, respectively. Measured PV efficiency, overall system efficiency, inverter efficiency and performance ratio of the system were compared with the predicted results. The predicted PV performance parameters agreed more closely with the measured parameters in summer than in winter. The difference between predicted performances using an isotropic and an anisotropic sky tilted surface models is between 1% and 2%

  4. Individual differences in episodic memory abilities predict successful prospective memory output monitoring.

    Science.gov (United States)

    Hunter Ball, B; Pitães, Margarida; Brewer, Gene A

    2018-02-07

    Output monitoring refers to memory for one's previously completed actions. In the context of prospective memory (PM) (e.g., remembering to take medication), failures of output monitoring can result in repetitions and omissions of planned actions (e.g., over- or under-medication). To be successful in output monitoring paradigms, participants must flexibly control attention to detect PM cues as well as engage controlled retrieval of previous actions whenever a particular cue is encountered. The current study examined individual differences in output monitoring abilities in a group of younger adults differing in attention control (AC) and episodic memory (EM) abilities. The results showed that AC ability uniquely predicted successful cue detection on the first presentation, whereas EM ability uniquely predicted successful output monitoring on the second presentation. The current study highlights the importance of examining external correlates of PM abilities and contributes to the growing body of research on individual differences in PM.

  5. Assessing habitat connectivity for ground-dwelling animals in an urban environment.

    Science.gov (United States)

    Braaker, S; Moretti, M; Boesch, R; Ghazoul, J; Obrist, M K; Bontadina, F

    To ensure viable species populations in fragmented landscapes, individuals must be able to move between suitable habitat patches. Despite the increased interest in biodiversity assessment in urban environments, the ecological relevance of habitat connectivity in highly fragmented landscapes remains largely unknown. The first step to understanding the role of habitat connectivity in urban ecology is the challenging task of assessing connectivity in the complex patchwork of contrasting habitats that is found in cities. We developed a data-based framework, minimizing the use of subjective assumptions, to assess habitat connectivity that consists of the following sequential steps: (1) identification of habitat preference based on empirical habitat-use data; (2) derivation of habitat resistance surfaces evaluating various transformation functions; (3) modeling of different connectivity maps with electrical circuit theory (Circuitscape), a method considering all possible pathways across the landscape simultaneously; and (4) identification of the best connectivity map with information-theoretic model selection. We applied this analytical framework to assess habitat connectivity for the European hedgehog Erinaceus europaeus, a model species for ground-dwelling animals, in the city of Zurich, Switzerland, using GPS track points from 40 individuals. The best model revealed spatially explicit connectivity “pinch points,” as well as multiple habitat connections. Cross-validation indicated the general validity of the selected connectivity model. The results show that both habitat connectivity and habitat quality affect the movement of urban hedgehogs (relative importance of the two variables was 19.2% and 80.8%, respectively), and are thus both relevant for predicting urban animal movements. Our study demonstrates that even in the complex habitat patchwork of cities, habitat connectivity plays a major role for ground-dwelling animal movement. Data-based habitat connectivity

  6. Strength of Structural and Functional Frontostriatal Connectivity Predicts Self-Control in the Healthy Elderly

    Science.gov (United States)

    Hänggi, Jürgen; Lohrey, Corinna; Drobetz, Reinhard; Baetschmann, Hansruedi; Forstmeier, Simon; Maercker, Andreas; Jäncke, Lutz

    2016-01-01

    Self-regulation refers to the successful use of executive functions and initiation of top-down processes to control one's thoughts, behavior, and emotions, and it is crucial to perform self-control. Self-control is needed to overcome impulses and can be assessed by delay of gratification (DoG) and delay discounting (DD) paradigms. In children/adolescents, good DoG/DD ability depends on the maturity of frontostriatal connectivity, and its decline in strength with advancing age might adversely affect self-control because prefrontal brain regions are more prone to normal age-related atrophy than other regions. Here, we aimed at highlighting the relationship between frontostriatal connectivity strength and DoG performance in advanced age. We recruited 40 healthy elderly individuals (mean age 74.0 ± 7.7 years) and assessed the DoG ability using the German version of the DoG test for adults in addition to the delay discounting (DD) paradigm. Based on diffusion-weighted and resting-state functional magnetic resonance imaging data, respectively, the structural and functional whole-brain connectome were reconstructed based on 90 different brain regions of interest in addition to a 12-node frontostriatal DoG-specific network and the resulting connectivity matrices were subjected to network-based statistics. The 90-nodes whole-brain connectome analyses revealed subnetworks significantly associated with DoG and DD with a preponderance of frontostriatal nodes involved suggesting a high specificity of the findings. Structural and functional connectivity strengths between the putamen, caudate nucleus, and nucleus accumbens on the one hand and orbitofrontal, dorsal, and ventral lateral prefrontal cortices on the other hand showed strong positive correlations with DoG and negative correlations with DD corrected for age, sex, intracranial volume, and head motion parameters. These associations cannot be explained by differences in impulsivity and executive functioning. This pattern

  7. Predicting Smartphone Operating System from Personality and Individual Differences.

    Science.gov (United States)

    Shaw, Heather; Ellis, David A; Kendrick, Libby-Rae; Ziegler, Fenja; Wiseman, Richard

    2016-12-01

    Android and iPhone devices account for over 90 percent of all smartphones sold worldwide. Despite being very similar in functionality, current discourse and marketing campaigns suggest that key individual differences exist between users of these two devices; however, this has never been investigated empirically. This is surprising, as smartphones continue to gain momentum across a variety of research disciplines. In this article, we consider if individual differences exist between these two distinct groups. In comparison to Android users, we found that iPhone owners are more likely to be female, younger, and increasingly concerned about their smartphone being viewed as a status object. Key differences in personality were also observed with iPhone users displaying lower levels of Honesty-Humility and higher levels of emotionality. Following this analysis, we were also able to build and test a model that predicted smartphone ownership at above chance level based on these individual differences. In line with extended self-theory, the type of smartphone owned provides some valuable information about its owner. These findings have implications for the increasing use of smartphones within research particularly for those working within Computational Social Science and PsychoInformatics, where data are typically collected from devices and applications running a single smartphone operating system.

  8. Methylphenidate Modulates Functional Network Connectivity to Enhance Attention

    OpenAIRE

    Rosenberg, Monica D.; Zhang, Sheng; Hsu, Wei-Ting; Scheinost, Dustin; Finn, Emily S.; Shen, Xilin; Constable, R. Todd; Li, Chiang-Shan R.; Chun, Marvin M.

    2016-01-01

    Recent work has demonstrated that human whole-brain functional connectivity patterns measured with fMRI contain information about cognitive abilities, including sustained attention. To derive behavioral predictions from connectivity patterns, our group developed a connectome-based predictive modeling (CPM) approach (Finn et al., 2015; Rosenberg et al., 2016). Previously using CPM, we defined a high-attention network, comprising connections positively correlated with performance on a sustained...

  9. All roads lead to Iran: Predicting landscape connectivity of the last stronghold for the critically endangered Asiatic cheetah

    Science.gov (United States)

    E. M. Moqanaki; Samuel Cushman

    2016-01-01

    Effective conservation solutions for small and isolated wildlife populations depend on identifying and preserving critical biological corridors and dispersal routes. With a worldwide population of ≤70 individuals, the critically endangered Asiatic cheetah Acinonyx jubatus venaticus persists in several fragmented nuclei in Iran. Connectivity between nuclei is...

  10. Predicting the peak growth velocity in the individual child: validation of a new growth model.

    NARCIS (Netherlands)

    Busscher, I.; Kingma, I.; de Bruin, R.; Wapstra, F.H.; Verkerke, G.J.; Veldhuizen, A.G.

    2012-01-01

    Predicting the peak growth velocity in an individual patient with adolescent idiopathic scoliosis is essential or determining the prognosis of the disorder and timing of the (surgical) treatment. Until the present time, no accurate method has been found to predict the timing and magnitude of the

  11. Predicting the peak growth velocity in the individual child : validation of a new growth model

    NARCIS (Netherlands)

    Busscher, Iris; Kingma, Idsart; de Bruin, Rob; Wapstra, Frits Hein; Verkerke, Gijsvertus J.; Veldhuizen, Albert G.

    Predicting the peak growth velocity in an individual patient with adolescent idiopathic scoliosis is essential or determining the prognosis of the disorder and timing of the (surgical) treatment. Until the present time, no accurate method has been found to predict the timing and magnitude of the

  12. Predicting the peak growth velocity in the individual child: validation of a new growth model

    NARCIS (Netherlands)

    Busscher, I.; Kingma, I.; Bruin, R.; Wapstra, F.H.; Verkerke, Gijsbertus Jacob; Veldhuizen, A.G.

    2012-01-01

    Predicting the peak growth velocity in an individual patient with adolescent idiopathic scoliosis is essential or determining the prognosis of the disorder and timing of the (surgical) treatment. Until the present time, no accurate method has been found to predict the timing and magnitude of the

  13. Life Prediction Model for Grid-Connected Li-ion Battery Energy Storage System: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Smith, Kandler A [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Saxon, Aron R [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Keyser, Matthew A [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Lundstrom, Blake R [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Cao, Ziwei [SunPower Corporation; Roc, Albert [SunPower Corp.

    2017-08-25

    Life Prediction Model for Grid-Connected Li-ion Battery Energy Storage System: Preprint Lithium-ion (Li-ion) batteries are being deployed on the electrical grid for a variety of purposes, such as to smooth fluctuations in solar renewable power generation. The lifetime of these batteries will vary depending on their thermal environment and how they are charged and discharged. To optimal utilization of a battery over its lifetime requires characterization of its performance degradation under different storage and cycling conditions. Aging tests were conducted on commercial graphite/nickel-manganese-cobalt (NMC) Li-ion cells. A general lifetime prognostic model framework is applied to model changes in capacity and resistance as the battery degrades. Across 9 aging test conditions from 0oC to 55oC, the model predicts capacity fade with 1.4 percent RMS error and resistance growth with 15 percent RMS error. The model, recast in state variable form with 8 states representing separate fade mechanisms, is used to extrapolate lifetime for example applications of the energy storage system integrated with renewable photovoltaic (PV) power generation.

  14. A multi-scale modeling framework for individualized, spatiotemporal prediction of drug effects and toxicological risk

    Directory of Open Access Journals (Sweden)

    Juan Guillermo eDiaz Ochoa

    2013-01-01

    Full Text Available In this study, we focus on a novel multi-scale modeling approach for spatiotemporal prediction of the distribution of substances and resulting hepatotoxicity by combining cellular models, a 2D liver model, and whole-body model. As a case study, we focused on predicting human hepatotoxicity upon treatment with acetaminophen based on in vitro toxicity data and potential inter-individual variability in gene expression and enzyme activities. By aggregating mechanistic, genome-based in silico cells to a novel 2D liver model and eventually to a whole body model, we predicted pharmacokinetic properties, metabolism, and the onset of hepatotoxicity in an in silico patient. Depending on the concentration of acetaminophen in the liver and the accumulation of toxic metabolites, cell integrity in the liver as a function of space and time as well as changes in the elimination rate of substances were estimated. We show that the variations in elimination rates also influence the distribution of acetaminophen and its metabolites in the whole body. Our results are in agreement with experimental results. What is more, the integrated model also predicted variations in drug toxicity depending on alterations of metabolic enzyme activities. Variations in enzyme activity, in turn, reflect genetic characteristics or diseases of individuals. In conclusion, this framework presents an important basis for efficiently integrating inter-individual variability data into models, paving the way for personalized or stratified predictions of drug toxicity and efficacy.

  15. Potential use and challenges of functional connectivity mapping in intractable epilepsy

    Directory of Open Access Journals (Sweden)

    Robert Todd Constable

    2013-05-01

    Full Text Available This review focuses on the use of resting-state functional magnetic resonance imaging data to assess functional connectivity in the human brain for surgical planning in intractable epilepsy. This approach has the potential to predict outcomes for a given surgical procedure based on the pre-surgical functional organization of the brain. Functional connectivity can also identify cortical regions that are organized differently in epilepsy patients either as a direct function of the disease or through indirect compensatory responses. Functional connectivity mapping can also potentially help identify epileptogenic tissue, whether this is a single focal location or a network of seizure-generating tissues and this information can assist in guiding the implantation of electrodes for invasive monitoring. This review covers the basics of connectivity analysis and discusses particular issues associated with analyzing such data. These issues include how to define nodes, as well as differences between connectivity analyses of individual nodes, groups of nodes, and whole-brain assessment at the voxel level. The need for arbitrary thresholds in some connectivity analyses is discussed and a solution to this problem is reviewed. Overall, functional connectivity analysis is becoming an important tool for assessing functional brain organization in surgical planning in epilepsy.

  16. Predicting the behavior of a grid-connected photovoltaic system from measurements of solar radiation and ambient temperature

    International Nuclear Information System (INIS)

    Hernandez, J.; Gordillo, G.; Vallejo, W.

    2013-01-01

    Highlights: ► A model to predict in a reliable way the behavior of a GCPV system is presented. ► Radiation and temperature behavior were shaped with probability density functions. ► This probability density functions were made from real measurements. ► This model was verified for comparing their behavior with real measurements. ► It can be used in any electrical systems language which have programming routines. - Abstract: This paper presents a methodology to predict in a statistically reliable way the behavior of a grid-connected photovoltaic system. The methodology developed can be implemented either in common programming software or through an off-the-shelf simulation of electrical systems. Initially, the atmospheric parameters that influence the behavior of PV generators (radiation and temperature) are characterized in a probabilistic manner. In parallel, a model compound by various PV generator components is defined: the modules (and their electrical and physical characteristics), their connection to form the generator, and the inverter type. This model was verified for comparing their behavior with output measured on a real installed system of 3.6 kWp. The solar resource characterized and the photovoltaic system model are integrated in a non-deterministic approach using the stochastic Monte Carlo method, developed in the programming language DPL of the electrical-systems simulation software DIGSILENT®. It is done to estimate the steady-state electrical parameters describing the influence of the grid-connected photovoltaic system. Specifically, we estimated the nominal peak power of the PV generator to minimize network losses, subject to constraints on nodes voltages and conductor currents

  17. Supervised hub-detection for brain connectivity

    DEFF Research Database (Denmark)

    Kasenburg, Niklas; Liptrot, Matthew George; Reislev, Nina Linde

    2016-01-01

    , but can smooth discriminative signals in the population, degrading predictive performance. We present a novel hub-detection optimized for supervised learning that both clusters network nodes based on population level variation in connectivity and also takes the learning problem into account. The found......A structural brain network consists of physical connections between brain regions. Brain network analysis aims to find features associated with a parameter of interest through supervised prediction models such as regression. Unsupervised preprocessing steps like clustering are often applied...... hubs are a low-dimensional representation of the network and are chosen based on predictive performance as features for a linear regression. We apply our method to the problem of finding age-related changes in structural connectivity. We compare our supervised hub-detection (SHD) to an unsupervised hub...

  18. Orbitofrontal cortex activity and connectivity predict future depression symptoms in adolescence.

    Science.gov (United States)

    Jin, Jingwen; Narayanan, Ananth; Perlman, Greg; Luking, Katherine; DeLorenzo, Christine; Hajcak, Greg; Klein, Daniel N; Kotov, Roman; Mohanty, Aprajita

    2017-10-01

    Major depressive disorder is a leading cause of disability worldwide; however, little is known about pathological mechanisms involved in its development. Research in adolescent depression has focused on reward sensitivity and striatal mechanisms implementing it. The contribution of loss sensitivity to future depression, as well as the orbitofrontal cortex (OFC) mechanisms critical for processing losses and rewards, remain unexplored. Furthermore, it is unclear whether OFC functioning interacts with familial history in predicting future depression. In this longitudinal study we recorded functional magnetic resonance imaging (fMRI) data while 229 adolescent females with or without parental history of depression completed a monetary gambling task. We examined if OFC blood-oxygen-level-dependent (BOLD) response and functional connectivity during loss and win feedback was associated with depression symptoms concurrently and prospectively (9 months later), and whether this relationship was moderated by parental history of depression. Reduced OFC response during loss was associated with higher depression symptoms concurrently and prospectively, even after controlling for concurrent depression, specifically in adolescents with parental history of depression. Similarly, increased OFC-posterior insula connectivity during loss was associated with future depression symptoms but this relationship was not moderated by parental history of depression. This study provides the first evidence for loss-related alterations in OFC functioning and its interaction with familial history of depression as possible mechanisms in the development of depression. While the current fMRI literature has mainly focused on reward, the present findings underscore the need to include prefrontal loss processing in existing developmental models of depression.

  19. The growth benefits of aggressive behavior vary with individual metabolism and resource predictability

    NARCIS (Netherlands)

    Hoogenboom, Mia O.; Armstrong, John D.; Groothuis, Ton G. G.; Metcalfe, Neil B.

    2013-01-01

    Differences in behavioral responses to environmental conditions and biological interactions are a key determinant of individual performance. This study investigated how the availability and predictability of food resources modulates the growth of animals that adopt different behavioral strategies.

  20. Cortical Response Similarities Predict which Audiovisual Clips Individuals Viewed, but Are Unrelated to Clip Preference.

    Directory of Open Access Journals (Sweden)

    David A Bridwell

    Full Text Available Cortical responses to complex natural stimuli can be isolated by examining the relationship between neural measures obtained while multiple individuals view the same stimuli. These inter-subject correlation's (ISC's emerge from similarities in individual's cortical response to the shared audiovisual inputs, which may be related to their emergent cognitive and perceptual experience. Within the present study, our goal is to examine the utility of using ISC's for predicting which audiovisual clips individuals viewed, and to examine the relationship between neural responses to natural stimuli and subjective reports. The ability to predict which clips individuals viewed depends on the relationship of the EEG response across subjects and the nature in which this information is aggregated. We conceived of three approaches for aggregating responses, i.e. three assignment algorithms, which we evaluated in Experiment 1A. The aggregate correlations algorithm generated the highest assignment accuracy (70.83% chance = 33.33% and was selected as the assignment algorithm for the larger sample of individuals and clips within Experiment 1B. The overall assignment accuracy was 33.46% within Experiment 1B (chance = 06.25%, with accuracies ranging from 52.9% (Silver Linings Playbook to 11.75% (Seinfeld within individual clips. ISC's were significantly greater than zero for 15 out of 16 clips, and fluctuations within the delta frequency band (i.e. 0-4 Hz primarily contributed to response similarities across subjects. Interestingly, there was insufficient evidence to indicate that individuals with greater similarities in clip preference demonstrate greater similarities in cortical responses, suggesting a lack of association between ISC and clip preference. Overall these results demonstrate the utility of using ISC's for prediction, and further characterize the relationship between ISC magnitudes and subjective reports.

  1. Cortical Response Similarities Predict which Audiovisual Clips Individuals Viewed, but Are Unrelated to Clip Preference.

    Science.gov (United States)

    Bridwell, David A; Roth, Cullen; Gupta, Cota Navin; Calhoun, Vince D

    2015-01-01

    Cortical responses to complex natural stimuli can be isolated by examining the relationship between neural measures obtained while multiple individuals view the same stimuli. These inter-subject correlation's (ISC's) emerge from similarities in individual's cortical response to the shared audiovisual inputs, which may be related to their emergent cognitive and perceptual experience. Within the present study, our goal is to examine the utility of using ISC's for predicting which audiovisual clips individuals viewed, and to examine the relationship between neural responses to natural stimuli and subjective reports. The ability to predict which clips individuals viewed depends on the relationship of the EEG response across subjects and the nature in which this information is aggregated. We conceived of three approaches for aggregating responses, i.e. three assignment algorithms, which we evaluated in Experiment 1A. The aggregate correlations algorithm generated the highest assignment accuracy (70.83% chance = 33.33%) and was selected as the assignment algorithm for the larger sample of individuals and clips within Experiment 1B. The overall assignment accuracy was 33.46% within Experiment 1B (chance = 06.25%), with accuracies ranging from 52.9% (Silver Linings Playbook) to 11.75% (Seinfeld) within individual clips. ISC's were significantly greater than zero for 15 out of 16 clips, and fluctuations within the delta frequency band (i.e. 0-4 Hz) primarily contributed to response similarities across subjects. Interestingly, there was insufficient evidence to indicate that individuals with greater similarities in clip preference demonstrate greater similarities in cortical responses, suggesting a lack of association between ISC and clip preference. Overall these results demonstrate the utility of using ISC's for prediction, and further characterize the relationship between ISC magnitudes and subjective reports.

  2. Development and Validation of a Prediction Model to Estimate Individual Risk of Pancreatic Cancer.

    Science.gov (United States)

    Yu, Ami; Woo, Sang Myung; Joo, Jungnam; Yang, Hye-Ryung; Lee, Woo Jin; Park, Sang-Jae; Nam, Byung-Ho

    2016-01-01

    There is no reliable screening tool to identify people with high risk of developing pancreatic cancer even though pancreatic cancer represents the fifth-leading cause of cancer-related death in Korea. The goal of this study was to develop an individualized risk prediction model that can be used to screen for asymptomatic pancreatic cancer in Korean men and women. Gender-specific risk prediction models for pancreatic cancer were developed using the Cox proportional hazards model based on an 8-year follow-up of a cohort study of 1,289,933 men and 557,701 women in Korea who had biennial examinations in 1996-1997. The performance of the models was evaluated with respect to their discrimination and calibration ability based on the C-statistic and Hosmer-Lemeshow type χ2 statistic. A total of 1,634 (0.13%) men and 561 (0.10%) women were newly diagnosed with pancreatic cancer. Age, height, BMI, fasting glucose, urine glucose, smoking, and age at smoking initiation were included in the risk prediction model for men. Height, BMI, fasting glucose, urine glucose, smoking, and drinking habit were included in the risk prediction model for women. Smoking was the most significant risk factor for developing pancreatic cancer in both men and women. The risk prediction model exhibited good discrimination and calibration ability, and in external validation it had excellent prediction ability. Gender-specific risk prediction models for pancreatic cancer were developed and validated for the first time. The prediction models will be a useful tool for detecting high-risk individuals who may benefit from increased surveillance for pancreatic cancer.

  3. Predictive information speeds up visual awareness in an individuation task by modulating threshold setting, not processing efficiency.

    Science.gov (United States)

    De Loof, Esther; Van Opstal, Filip; Verguts, Tom

    2016-04-01

    Theories on visual awareness claim that predicted stimuli reach awareness faster than unpredicted ones. In the current study, we disentangle whether prior information about the upcoming stimulus affects visual awareness of stimulus location (i.e., individuation) by modulating processing efficiency or threshold setting. Analogous research on stimulus identification revealed that prior information modulates threshold setting. However, as identification and individuation are two functionally and neurally distinct processes, the mechanisms underlying identification cannot simply be extrapolated directly to individuation. The goal of this study was therefore to investigate how individuation is influenced by prior information about the upcoming stimulus. To do so, a drift diffusion model was fitted to estimate the processing efficiency and threshold setting for predicted versus unpredicted stimuli in a cued individuation paradigm. Participants were asked to locate a picture, following a cue that was congruent, incongruent or neutral with respect to the picture's identity. Pictures were individuated faster in the congruent and neutral condition compared to the incongruent condition. In the diffusion model analysis, the processing efficiency was not significantly different across conditions. However, the threshold setting was significantly higher following an incongruent cue compared to both congruent and neutral cues. Our results indicate that predictive information about the upcoming stimulus influences visual awareness by shifting the threshold for individuation rather than by enhancing processing efficiency. Copyright © 2016 Elsevier Ltd. All rights reserved.

  4. Model Predictive Control of Grid Connected Modular Multilevel Converter for Integration of Photovoltaic Power Systems

    DEFF Research Database (Denmark)

    Hajizadeh, Amin; Shahirinia, Amir

    2017-01-01

    Investigation of an advanced control structure for integration of Photovoltaic Power Systems through Grid Connected-Modular Multilevel Converter (GC-MMC) is proposed in this paper. To achieve this goal, a non-linear model of MMC regarding considering of negative and positive sequence components has...... been presented. Then, due to existence of unbalance voltage faults in distribution grid, non-linarites and uncertainties in model, model predictive controller which is developed for GC-MMC. They are implemented based upon positive and negative components of voltage and current to mitigate the power...

  5. Individual response to ionising radiation: What predictive assay(s) to choose?

    International Nuclear Information System (INIS)

    Granzotto, A.; Viau, M.; Devic, C.; Maalouf, M.; Thomas, Ch.; Vogin, G.; Foray, N.; Granzotto, A.; Vogin, G.; Balosso, J.; Joubert, A.; Maalouf, M.; Vogin, G.; Colin, C.; Malek, K.; Balosso, J.; Colin, C.

    2011-01-01

    Individual response to ionizing radiation is an important information required to apply an efficient radiotherapy treatment against tumour and to avoid any adverse effects in normal tissues. In 1981, Fertil and Malaise have demonstrated that the post-irradiation local tumor control determined in vivo is correlated with clonogenic cell survival assessed in vitro. Furthermore, these authors have reminded the relevance of the concept of intrinsic radiosensitivity that is specific to each individual organ (Fertil and Malaise, 1981) [1]. To date, since clonogenicity assays are too time-consuming and do not provide any other molecular information, a plethora of research groups have attempted to determine the molecular bases of intrinsic radiosensitivity in order to propose reliable and faster predictive assays. To this aim, several approaches have been developed. Notably, the recent revolution in genomic and proteomics technologies is providing a considerable number of data but their link with radiosensitivity still remains to be elucidated. On another hand, the systematic screening of some candidate genes potentially involved in the radiation response is highlighting the complexity of the molecular and cellular mechanisms of DNA damage sensing and signalling and shows that an abnormal radiation response is not necessarily due to the impairment of one single protein. Finally, more modest approaches consisting in focusing some specific functions of DNA repair seem to provide more reliable clues to predict over-acute reactions caused by radiotherapy. In this review, we endeavored to analyse the contributions of these major approaches to predict human radiosensitivity. (authors)

  6. A posteriori model validation for the temporal order of directed functional connectivity maps.

    Science.gov (United States)

    Beltz, Adriene M; Molenaar, Peter C M

    2015-01-01

    A posteriori model validation for the temporal order of neural directed functional connectivity maps is rare. This is striking because models that require sequential independence among residuals are regularly implemented. The aim of the current study was (a) to apply to directed functional connectivity maps of functional magnetic resonance imaging data an a posteriori model validation procedure (i.e., white noise tests of one-step-ahead prediction errors combined with decision criteria for revising the maps based upon Lagrange Multiplier tests), and (b) to demonstrate how the procedure applies to single-subject simulated, single-subject task-related, and multi-subject resting state data. Directed functional connectivity was determined by the unified structural equation model family of approaches in order to map contemporaneous and first order lagged connections among brain regions at the group- and individual-levels while incorporating external input, then white noise tests were run. Findings revealed that the validation procedure successfully detected unmodeled sequential dependencies among residuals and recovered higher order (greater than one) simulated connections, and that the procedure can accommodate task-related input. Findings also revealed that lags greater than one were present in resting state data: With a group-level network that contained only contemporaneous and first order connections, 44% of subjects required second order, individual-level connections in order to obtain maps with white noise residuals. Results have broad methodological relevance (e.g., temporal validation is necessary after directed functional connectivity analyses because the presence of unmodeled higher order sequential dependencies may bias parameter estimates) and substantive implications (e.g., higher order lags may be common in resting state data).

  7. A posteriori model validation for the temporal order of directed functional connectivity maps

    Directory of Open Access Journals (Sweden)

    Adriene M. Beltz

    2015-08-01

    Full Text Available A posteriori model validation for the temporal order of neural directed functional connectivity maps is rare. This is striking because models that require sequential independence among residuals are regularly implemented. The aim of the current study was (a to apply to directed functional connectivity maps of functional magnetic resonance imaging data an a posteriori model validation procedure (i.e., white noise tests of one-step-ahead prediction errors combined with decision criteria for revising the maps based upon Lagrange Multiplier tests, and (b to demonstrate how the procedure applies to single-subject simulated, single-subject task-related, and multi-subject resting state data. Directed functional connectivity was determined by the unified structural equation model family of approaches in order to map contemporaneous and first order lagged connections among brain regions at the group- and individual-levels while incorporating external input, then white noise tests were run. Findings revealed that the validation procedure successfully detected unmodeled sequential dependencies among residuals and recovered higher order (greater than one simulated connections, and that the procedure can accommodate task-related input. Findings also revealed that lags greater than one were present in resting state data: With a group-level network that contained only contemporaneous and first order connections, 44% of subjects required second order, individual-level connections in order to obtain maps with white noise residuals. Results have broad methodological relevance (e.g., temporal validation is necessary after directed functional connectivity analyses because the presence of unmodeled higher order sequential dependencies may bias parameter estimates and substantive implications (e.g., higher order lags may be common in resting state data.

  8. Predicting patriarchy: using individual and contextual factors to examine patriarchal endorsement in communities.

    Science.gov (United States)

    Crittenden, Courtney A; Wright, Emily M

    2013-04-01

    In much feminist literature, patriarchy has often been studied as a predictive variable for attitudes toward or acts of violence against women. However, rarely has patriarchy been examined as an outcome across studies. The current study works toward filling this gap by examining several individual-and neighborhood-level factors that might influence patriarchy. Specifically, this research seeks to determine if neighborhood-level attributes related to socioeconomic status, family composition, and demographic information affect patriarchal views after individual-level correlates of patriarchy were controlled. Findings suggest that factors at both the individual- and neighborhood levels, particularly familial characteristics and dynamics, do influence the endorsement of patriarchal views.

  9. Effect of genetic architecture on the prediction accuracy of quantitative traits in samples of unrelated individuals.

    Science.gov (United States)

    Morgante, Fabio; Huang, Wen; Maltecca, Christian; Mackay, Trudy F C

    2018-06-01

    Predicting complex phenotypes from genomic data is a fundamental aim of animal and plant breeding, where we wish to predict genetic merits of selection candidates; and of human genetics, where we wish to predict disease risk. While genomic prediction models work well with populations of related individuals and high linkage disequilibrium (LD) (e.g., livestock), comparable models perform poorly for populations of unrelated individuals and low LD (e.g., humans). We hypothesized that low prediction accuracies in the latter situation may occur when the genetics architecture of the trait departs from the infinitesimal and additive architecture assumed by most prediction models. We used simulated data for 10,000 lines based on sequence data from a population of unrelated, inbred Drosophila melanogaster lines to evaluate this hypothesis. We show that, even in very simplified scenarios meant as a stress test of the commonly used Genomic Best Linear Unbiased Predictor (G-BLUP) method, using all common variants yields low prediction accuracy regardless of the trait genetic architecture. However, prediction accuracy increases when predictions are informed by the genetic architecture inferred from mapping the top variants affecting main effects and interactions in the training data, provided there is sufficient power for mapping. When the true genetic architecture is largely or partially due to epistatic interactions, the additive model may not perform well, while models that account explicitly for interactions generally increase prediction accuracy. Our results indicate that accounting for genetic architecture can improve prediction accuracy for quantitative traits.

  10. Engineering connectivity by multiscale micropatterning of individual populations of neurons.

    Science.gov (United States)

    Albers, Jonas; Toma, Koji; Offenhäusser, Andreas

    2015-02-01

    Functional networks are the basis of information processing in the central nervous system. Essential for their formation are guided neuronal growth as well as controlled connectivity and information flow. The basis of neuronal development is generated by guiding cues and geometric constraints. To investigate the neuronal growth and connectivity of adjacent neuronal networks, two-dimensional protein patterns were created. A mixture of poly-L-lysine and laminin was transferred onto a silanized glass surface by microcontact printing. The structures were populated with dissociated primary cortical embryonic rat neurons. Triangular structures with diverse opening angles, height, and design were chosen as two-dimensional structures to allow network formation with constricted gateways. Neuronal development was observed by immunohistochemistry to pursue the influence of the chosen structures on the neuronal outgrowth. Neurons were stained for MAP2, while poly-L-lysine was FITC labeled. With this study we present an easy-to-use technique to engineer two-dimensional networks in vitro with defined gateways. The presented micropatterning method is used to generate daisy-chained neuronal networks with predefined connectivity. Signal propagation among geometrically constrained networks can easily be monitored by calcium-sensitive dyes, providing insights into network communication in vitro. Copyright © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  11. Predictive model for the heat capacity of ionic liquids using the mass connectivity index

    International Nuclear Information System (INIS)

    Valderrama, Jose O.; Martinez, Gwendolyn; Rojas, Roberto E.

    2011-01-01

    A simple and accurate model to predict the heat capacity of ionic liquids is presented. The proposed model considers variables readily available for ionic liquids and that have important effect on heat capacity, according to the literature information. Additionally a recently defined structural parameter known as mass connectivity index is incorporated into the model. A set of 602 heat capacity data for 146 ionic liquids have been used in the study. The results were compared with experimental data and with values reported by other available estimation methods. Results show that the new simple correlation gives low deviations and can be used with confidence in thermodynamic and engineering calculations.

  12. Always Connected at Work? : The Role of Information Novelty and Individual Needs

    NARCIS (Netherlands)

    De Jonge, Kiki M.M.; Rietzschel, Eric F.; Van Yperen, Nico W.

    2015-01-01

    Purpose: As a result of new ICT developments, many workers are almost constantly connected to job-relevant information and co-workers, regardless of when or where they are working. Depending on workers’ psychological needs, constantly being connected may be perceived as favorable (e.g., when it

  13. [Predicting individual risk of high healthcare cost to identify complex chronic patients].

    Science.gov (United States)

    Coderch, Jordi; Sánchez-Pérez, Inma; Ibern, Pere; Carreras, Marc; Pérez-Berruezo, Xavier; Inoriza, José M

    2014-01-01

    To develop a predictive model for the risk of high consumption of healthcare resources, and assess the ability of the model to identify complex chronic patients. A cross-sectional study was performed within a healthcare management organization by using individual data from 2 consecutive years (88,795 people). The dependent variable consisted of healthcare costs above the 95th percentile (P95), including all services provided by the organization and pharmaceutical consumption outside of the institution. The predictive variables were age, sex, morbidity-based on clinical risk groups (CRG)-and selected data from previous utilization (use of hospitalization, use of high-cost drugs in ambulatory care, pharmaceutical expenditure). A univariate descriptive analysis was performed. We constructed a logistic regression model with a 95% confidence level and analyzed sensitivity, specificity, positive predictive values (PPV), and the area under the ROC curve (AUC). Individuals incurring costs >P95 accumulated 44% of total healthcare costs and were concentrated in ACRG3 (aggregated CRG level 3) categories related to multiple chronic diseases. All variables were statistically significant except for sex. The model had a sensitivity of 48.4% (CI: 46.9%-49.8%), specificity of 97.2% (CI: 97.0%-97.3%), PPV of 46.5% (CI: 45.0%-47.9%), and an AUC of 0.897 (CI: 0.892 to 0.902). High consumption of healthcare resources is associated with complex chronic morbidity. A model based on age, morbidity, and prior utilization is able to predict high-cost risk and identify a target population requiring proactive care. Copyright © 2013 SESPAS. Published by Elsevier Espana. All rights reserved.

  14. Individual reactions to stress predict performance during a critical aviation incident.

    Science.gov (United States)

    Vine, Samuel J; Uiga, Liis; Lavric, Aureliu; Moore, Lee J; Tsaneva-Atanasova, Krasimira; Wilson, Mark R

    2015-01-01

    Understanding the influence of stress on human performance is of theoretical and practical importance. An individual's reaction to stress predicts their subsequent performance; with a "challenge" response to stress leading to better performance than a "threat" response. However, this contention has not been tested in truly stressful environments with highly skilled individuals. Furthermore, the effect of challenge and threat responses on attentional control during visuomotor tasks is poorly understood. Thus, this study aimed to examine individual reactions to stress and their influence on attentional control, among a cohort of commercial pilots performing a stressful flight assessment. Sixteen pilots performed an "engine failure on take-off" scenario, in a high-fidelity flight simulator. Reactions to stress were indexed via self-report; performance was assessed subjectively (flight instructor assessment) and objectively (simulator metrics); gaze behavior data were captured using a mobile eye tracker, and measures of attentional control were subsequently calculated (search rate, stimulus driven attention, and entropy). Hierarchical regression analyses revealed that a threat response was associated with poorer performance and disrupted attentional control. The findings add to previous research showing that individual reactions to stress influence performance and shed light on the processes through which stress influences performance.

  15. Effects of uncertainty in model predictions of individual tree volume on large area volume estimates

    Science.gov (United States)

    Ronald E. McRoberts; James A. Westfall

    2014-01-01

    Forest inventory estimates of tree volume for large areas are typically calculated by adding model predictions of volumes for individual trees. However, the uncertainty in the model predictions is generally ignored with the result that the precision of the large area volume estimates is overestimated. The primary study objective was to estimate the effects of model...

  16. How restructuring river connectivity changes freshwater fish biodiversity and biogeography

    Science.gov (United States)

    Lynch, Heather L.; Grant, Evan H. Campbell; Muneepeerakul, Rachata; Arunachalam, Muthukumarasamy; Rodriguez-Iturbe, Ignacio; Fagan, William F.

    2011-01-01

    Interbasin water transfer projects, in which river connectivity is restructured via man-made canals, are an increasingly popular solution to address the spatial mismatch between supply and demand of fresh water. However, the ecological consequences of such restructuring remain largely unexplored, and there are no general theoretical guidelines from which to derive these expectations. River systems provide excellent opportunities to explore how network connectivity shapes habitat occupancy, community dynamics, and biogeographic patterns. We apply a neutral model (which assumes competitive equivalence among species within a stochastic framework) to an empirically derived river network to explore how proposed changes in network connectivity may impact patterns of freshwater fish biodiversity. Without predicting the responses of individual extant species, we find the addition of canals connecting hydrologically isolated river basins facilitates the spread of common species and increases average local species richness without changing the total species richness of the system. These impacts are sensitive to the parameters controlling the spatial scale of fish dispersal, with increased dispersal affording more opportunities for biotic restructuring at the community and landscape scales. Connections between isolated basins have a much larger effect on local species richness than those connecting reaches within a river basin, even when those within-basin reaches are far apart. As a result, interbasin canal projects have the potential for long-term impacts to continental-scale riverine communities.

  17. Resting-State Connectivity Predicts Levodopa-Induced Dyskinesias in Parkinson's Disease

    DEFF Research Database (Denmark)

    Herz, Damian M.; Haagensen, Brian N.; Nielsen, Silas H.

    2016-01-01

    Background: Levodopa-induced dyskinesias are a common side effect of dopaminergic therapy in PD, but their neural correlates remain poorly understood. Objectives: This study examines whether dyskinesias are associated with abnormal dopaminergic modulation of resting-state cortico-striatal connect......Background: Levodopa-induced dyskinesias are a common side effect of dopaminergic therapy in PD, but their neural correlates remain poorly understood. Objectives: This study examines whether dyskinesias are associated with abnormal dopaminergic modulation of resting-state cortico......-striatal connectivity. Methods: Twelve PD patients with peak-of-dose dyskinesias and 12 patients without dyskinesias were withdrawn from dopaminergic medication. All patients received a single dose of fast-acting soluble levodopa and then underwent resting-state functional magnetic resonance imaging before any...... dyskinesias emerged. Levodopa-induced modulation of cortico-striatal resting-state connectivity was assessed between the putamen and the following 3 cortical regions of interest: supplementary motor area, primary sensorimotor cortex, and right inferior frontal gyrus. These functional connectivity measures...

  18. Prediction Equations Overestimate the Energy Requirements More for Obesity-Susceptible Individuals.

    Science.gov (United States)

    McLay-Cooke, Rebecca T; Gray, Andrew R; Jones, Lynnette M; Taylor, Rachael W; Skidmore, Paula M L; Brown, Rachel C

    2017-09-13

    Predictive equations to estimate resting metabolic rate (RMR) are often used in dietary counseling and by online apps to set energy intake goals for weight loss. It is critical to know whether such equations are appropriate for those susceptible to obesity. We measured RMR by indirect calorimetry after an overnight fast in 26 obesity susceptible (OSI) and 30 obesity resistant (ORI) individuals, identified using a simple 6-item screening tool. Predicted RMR was calculated using the FAO/WHO/UNU (Food and Agricultural Organisation/World Health Organisation/United Nations University), Oxford and Miflin-St Jeor equations. Absolute measured RMR did not differ significantly between OSI versus ORI (6339 vs. 5893 kJ·d -1 , p = 0.313). All three prediction equations over-estimated RMR for both OSI and ORI when measured RMR was ≤5000 kJ·d -1 . For measured RMR ≤7000 kJ·d -1 there was statistically significant evidence that the equations overestimate RMR to a greater extent for those classified as obesity susceptible with biases ranging between around 10% to nearly 30% depending on the equation. The use of prediction equations may overestimate RMR and energy requirements particularly in those who self-identify as being susceptible to obesity, which has implications for effective weight management.

  19. Ensemble stacking mitigates biases in inference of synaptic connectivity.

    Science.gov (United States)

    Chambers, Brendan; Levy, Maayan; Dechery, Joseph B; MacLean, Jason N

    2018-01-01

    A promising alternative to directly measuring the anatomical connections in a neuronal population is inferring the connections from the activity. We employ simulated spiking neuronal networks to compare and contrast commonly used inference methods that identify likely excitatory synaptic connections using statistical regularities in spike timing. We find that simple adjustments to standard algorithms improve inference accuracy: A signing procedure improves the power of unsigned mutual-information-based approaches and a correction that accounts for differences in mean and variance of background timing relationships, such as those expected to be induced by heterogeneous firing rates, increases the sensitivity of frequency-based methods. We also find that different inference methods reveal distinct subsets of the synaptic network and each method exhibits different biases in the accurate detection of reciprocity and local clustering. To correct for errors and biases specific to single inference algorithms, we combine methods into an ensemble. Ensemble predictions, generated as a linear combination of multiple inference algorithms, are more sensitive than the best individual measures alone, and are more faithful to ground-truth statistics of connectivity, mitigating biases specific to single inference methods. These weightings generalize across simulated datasets, emphasizing the potential for the broad utility of ensemble-based approaches.

  20. Medial prefrontal-hippocampal connectivity during emotional memory encoding predicts individual differences in the loss of associative memory specificity

    NARCIS (Netherlands)

    Berkers, R.M.W.J.; Klumpers, F.; Fernandez, G.S.E.

    2016-01-01

    Emotionally charged items are often remembered better, whereas a paradoxical loss of specificity is found for associative emotional information (specific memory). The balance between specific and generalized emotional memories appears to show large individual differences, potentially related to

  1. Medial prefrontal–hippocampal connectivity during emotional memory encoding predicts individual differences in the loss of associative memory specificity

    NARCIS (Netherlands)

    Berkers, R.M.W.J.; Klumpers, F.; Fernandez, G.S.E.

    2016-01-01

    Emotionally charged items are often remembered better, whereas a paradoxical loss of specificity is found for associative emotional information (specific memory). The balance between specific and generalized emotional memories appears to show large individual differences, potentially related to

  2. Neural predictors of individual differences in response to math tutoring in primary-grade school children.

    Science.gov (United States)

    Supekar, Kaustubh; Swigart, Anna G; Tenison, Caitlin; Jolles, Dietsje D; Rosenberg-Lee, Miriam; Fuchs, Lynn; Menon, Vinod

    2013-05-14

    Now, more than ever, the ability to acquire mathematical skills efficiently is critical for academic and professional success, yet little is known about the behavioral and neural mechanisms that drive some children to acquire these skills faster than others. Here we investigate the behavioral and neural predictors of individual differences in arithmetic skill acquisition in response to 8-wk of one-to-one math tutoring. Twenty-four children in grade 3 (ages 8-9 y), a critical period for acquisition of basic mathematical skills, underwent structural and resting-state functional MRI scans pretutoring. A significant shift in arithmetic problem-solving strategies from counting to fact retrieval was observed with tutoring. Notably, the speed and accuracy of arithmetic problem solving increased with tutoring, with some children improving significantly more than others. Next, we examined whether pretutoring behavioral and brain measures could predict individual differences in arithmetic performance improvements with tutoring. No behavioral measures, including intelligence quotient, working memory, or mathematical abilities, predicted performance improvements. In contrast, pretutoring hippocampal volume predicted performance improvements. Furthermore, pretutoring intrinsic functional connectivity of the hippocampus with dorsolateral and ventrolateral prefrontal cortices and the basal ganglia also predicted performance improvements. Our findings provide evidence that individual differences in morphometry and connectivity of brain regions associated with learning and memory, and not regions typically involved in arithmetic processing, are strong predictors of responsiveness to math tutoring in children. More generally, our study suggests that quantitative measures of brain structure and intrinsic brain organization can provide a more sensitive marker of skill acquisition than behavioral measures.

  3. A Validated Prediction Model for Overall Survival From Stage III Non-Small Cell Lung Cancer: Toward Survival Prediction for Individual Patients

    Energy Technology Data Exchange (ETDEWEB)

    Oberije, Cary, E-mail: cary.oberije@maastro.nl [Radiation Oncology, Research Institute GROW of Oncology, Maastricht University Medical Center, Maastricht (Netherlands); De Ruysscher, Dirk [Radiation Oncology, Research Institute GROW of Oncology, Maastricht University Medical Center, Maastricht (Netherlands); Universitaire Ziekenhuizen Leuven, KU Leuven (Belgium); Houben, Ruud [Radiation Oncology, Research Institute GROW of Oncology, Maastricht University Medical Center, Maastricht (Netherlands); Heuvel, Michel van de; Uyterlinde, Wilma [Department of Thoracic Oncology, Netherlands Cancer Institute, Amsterdam (Netherlands); Deasy, Joseph O. [Memorial Sloan Kettering Cancer Center, New York (United States); Belderbos, Jose [Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam (Netherlands); Dingemans, Anne-Marie C. [Department of Pulmonology, University Hospital Maastricht, Research Institute GROW of Oncology, Maastricht (Netherlands); Rimner, Andreas; Din, Shaun [Memorial Sloan Kettering Cancer Center, New York (United States); Lambin, Philippe [Radiation Oncology, Research Institute GROW of Oncology, Maastricht University Medical Center, Maastricht (Netherlands)

    2015-07-15

    Purpose: Although patients with stage III non-small cell lung cancer (NSCLC) are homogeneous according to the TNM staging system, they form a heterogeneous group, which is reflected in the survival outcome. The increasing amount of information for an individual patient and the growing number of treatment options facilitate personalized treatment, but they also complicate treatment decision making. Decision support systems (DSS), which provide individualized prognostic information, can overcome this but are currently lacking. A DSS for stage III NSCLC requires the development and integration of multiple models. The current study takes the first step in this process by developing and validating a model that can provide physicians with a survival probability for an individual NSCLC patient. Methods and Materials: Data from 548 patients with stage III NSCLC were available to enable the development of a prediction model, using stratified Cox regression. Variables were selected by using a bootstrap procedure. Performance of the model was expressed as the c statistic, assessed internally and on 2 external data sets (n=174 and n=130). Results: The final multivariate model, stratified for treatment, consisted of age, gender, World Health Organization performance status, overall treatment time, equivalent radiation dose, number of positive lymph node stations, and gross tumor volume. The bootstrapped c statistic was 0.62. The model could identify risk groups in external data sets. Nomograms were constructed to predict an individual patient's survival probability ( (www.predictcancer.org)). The data set can be downloaded at (https://www.cancerdata.org/10.1016/j.ijrobp.2015.02.048). Conclusions: The prediction model for overall survival of patients with stage III NSCLC highlights the importance of combining patient, clinical, and treatment variables. Nomograms were developed and validated. This tool could be used as a first building block for a decision support system.

  4. Individualized prediction of seizure relapse and outcomes following antiepileptic drug withdrawal after pediatric epilepsy surgery.

    Science.gov (United States)

    Lamberink, Herm J; Boshuisen, Kim; Otte, Willem M; Geleijns, Karin; Braun, Kees P J

    2018-03-01

    The objective of this study was to create a clinically useful tool for individualized prediction of seizure outcomes following antiepileptic drug withdrawal after pediatric epilepsy surgery. We used data from the European retrospective TimeToStop study, which included 766 children from 15 centers, to perform a proportional hazard regression analysis. The 2 outcome measures were seizure recurrence and seizure freedom in the last year of follow-up. Prognostic factors were identified through systematic review of the literature. The strongest predictors for each outcome were selected through backward selection, after which nomograms were created. The final models included 3 to 5 factors per model. Discrimination in terms of adjusted concordance statistic was 0.68 (95% confidence interval [CI] 0.67-0.69) for predicting seizure recurrence and 0.73 (95% CI 0.72-0.75) for predicting eventual seizure freedom. An online prediction tool is provided on www.epilepsypredictiontools.info/ttswithdrawal. The presented models can improve counseling of patients and parents regarding postoperative antiepileptic drug policies, by estimating individualized risks of seizure recurrence and eventual outcome. Wiley Periodicals, Inc. © 2018 International League Against Epilepsy.

  5. Transnational Connections and Multiple Belongings

    DEFF Research Database (Denmark)

    Galal, Lise Paulsen; Sparre, Sara Cathrine Lei

    With the purpose of presenting DIMECCE key findings, we in this paper present different aspects, potentials and challenges related to the Middle Eastern Christians transnational connections and multiple belonging. We distinguish between individual transnational connections and practices, such as ......, such as family relations, churches as transnational – or global – institutions, and other organisations and associations established to support politically, socially or culturally connections and development in the country or region of origin....

  6. A reciprocal model of face recognition and autistic traits: evidence from an individual differences perspective.

    Science.gov (United States)

    Halliday, Drew W R; MacDonald, Stuart W S; Scherf, K Suzanne; Sherf, Suzanne K; Tanaka, James W

    2014-01-01

    Although not a core symptom of the disorder, individuals with autism often exhibit selective impairments in their face processing abilities. Importantly, the reciprocal connection between autistic traits and face perception has rarely been examined within the typically developing population. In this study, university participants from the social sciences, physical sciences, and humanities completed a battery of measures that assessed face, object and emotion recognition abilities, general perceptual-cognitive style, and sub-clinical autistic traits (the Autism Quotient (AQ)). We employed separate hierarchical multiple regression analyses to evaluate which factors could predict face recognition scores and AQ scores. Gender, object recognition performance, and AQ scores predicted face recognition behaviour. Specifically, males, individuals with more autistic traits, and those with lower object recognition scores performed more poorly on the face recognition test. Conversely, university major, gender and face recognition performance reliably predicted AQ scores. Science majors, males, and individuals with poor face recognition skills showed more autistic-like traits. These results suggest that the broader autism phenotype is associated with lower face recognition abilities, even among typically developing individuals.

  7. Personalized Prediction of Lifetime Benefits with Statin Therapy for Asymptomatic Individuals: A Modeling Study

    NARCIS (Netherlands)

    B.S. Ferket (Bart); B.J.H. van Kempen (Bob); J. Heeringa (Jan); S. Spronk (Sandra); K.E. Fleischmann (Kirsten); R.L. Nijhuis (Rogier); A. Hofman (Albert); E.W. Steyerberg (Ewout); M.G.M. Hunink (Myriam)

    2012-01-01

    textabstractBackground: Physicians need to inform asymptomatic individuals about personalized outcomes of statin therapy for primary prevention of cardiovascular disease (CVD). However, current prediction models focus on short-term outcomes and ignore the competing risk of death due to other causes.

  8. Altered Gray Matter Volume and Resting-State Connectivity in Individuals With Internet Gaming Disorder: A Voxel-Based Morphometry and Resting-State Functional Magnetic Resonance Imaging Study

    Science.gov (United States)

    Seok, Ji-Woo; Sohn, Jin-Hun

    2018-01-01

    Neuroimaging studies on the characteristics of individuals with Internet gaming disorder (IGD) have been accumulating due to growing concerns regarding the psychological and social problems associated with Internet use. However, relatively little is known about the brain characteristics underlying IGD, such as the associated functional connectivity and structure. The aim of this study was to investigate alterations in gray matter (GM) volume and functional connectivity during resting state in individuals with IGD using voxel-based morphometry and a resting-state connectivity analysis. The participants included 20 individuals with IGD and 20 age- and sex-matched healthy controls. Resting-state functional and structural images were acquired for all participants using 3 T magnetic resonance imaging. We also measured the severity of IGD and impulsivity using psychological scales. The results show that IGD severity was positively correlated with GM volume in the left caudate (p < 0.05, corrected for multiple comparisons), and negatively associated with functional connectivity between the left caudate and the right middle frontal gyrus (p < 0.05, corrected for multiple comparisons). This study demonstrates that IGD is associated with neuroanatomical changes in the right middle frontal cortex and the left caudate. These are important brain regions for reward and cognitive control processes, and structural and functional abnormalities in these regions have been reported for other addictions, such as substance abuse and pathological gambling. The findings suggest that structural deficits and resting-state functional impairments in the frontostriatal network may be associated with IGD and provide new insights into the underlying neural mechanisms of IGD. PMID:29636704

  9. Altered Gray Matter Volume and Resting-State Connectivity in Individuals With Internet Gaming Disorder: A Voxel-Based Morphometry and Resting-State Functional Magnetic Resonance Imaging Study

    Directory of Open Access Journals (Sweden)

    Ji-Woo Seok

    2018-03-01

    Full Text Available Neuroimaging studies on the characteristics of individuals with Internet gaming disorder (IGD have been accumulating due to growing concerns regarding the psychological and social problems associated with Internet use. However, relatively little is known about the brain characteristics underlying IGD, such as the associated functional connectivity and structure. The aim of this study was to investigate alterations in gray matter (GM volume and functional connectivity during resting state in individuals with IGD using voxel-based morphometry and a resting-state connectivity analysis. The participants included 20 individuals with IGD and 20 age- and sex-matched healthy controls. Resting-state functional and structural images were acquired for all participants using 3 T magnetic resonance imaging. We also measured the severity of IGD and impulsivity using psychological scales. The results show that IGD severity was positively correlated with GM volume in the left caudate (p < 0.05, corrected for multiple comparisons, and negatively associated with functional connectivity between the left caudate and the right middle frontal gyrus (p < 0.05, corrected for multiple comparisons. This study demonstrates that IGD is associated with neuroanatomical changes in the right middle frontal cortex and the left caudate. These are important brain regions for reward and cognitive control processes, and structural and functional abnormalities in these regions have been reported for other addictions, such as substance abuse and pathological gambling. The findings suggest that structural deficits and resting-state functional impairments in the frontostriatal network may be associated with IGD and provide new insights into the underlying neural mechanisms of IGD.

  10. Days on radiosensitivity: individual variability and predictive tests; Radiosensibilite: variabilite individuelle et tests predictifs

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2008-07-01

    The radiosensitivity is a part of usual clinical observations. It is already included in the therapy protocols. however, some questions stay on its individual variability and on the difficulty to evaluate it. The point will be stocked on its origin and its usefulness in predictive medicine. Through examples on the use of predictive tests and ethical and legal questions that they raise, concrete cases will be presented by specialists such radio biologists, geneticists, immunologists, jurists and occupational physicians. (N.C.)

  11. Individualized prediction of illness course at the first psychotic episode: a support vector machine MRI study.

    LENUS (Irish Health Repository)

    Mourao-Miranda, J

    2012-05-01

    To date, magnetic resonance imaging (MRI) has made little impact on the diagnosis and monitoring of psychoses in individual patients. In this study, we used a support vector machine (SVM) whole-brain classification approach to predict future illness course at the individual level from MRI data obtained at the first psychotic episode.

  12. Predicting the efficacy of radiotherapy in individual glioblastoma patients in vivo: a mathematical modeling approach

    International Nuclear Information System (INIS)

    Rockne, R; Alvord, E C Jr; Swanson, K R; Rockhill, J K; Kalet, I; Hendrickson, K; Mrugala, M; Spence, A M; Lai, A; Cloughesy, T

    2010-01-01

    Glioblastoma multiforme (GBM) is the most malignant form of primary brain tumors known as gliomas. They proliferate and invade extensively and yield short life expectancies despite aggressive treatment. Response to treatment is usually measured in terms of the survival of groups of patients treated similarly, but this statistical approach misses the subgroups that may have responded to or may have been injured by treatment. Such statistics offer scant reassurance to individual patients who have suffered through these treatments. Furthermore, current imaging-based treatment response metrics in individual patients ignore patient-specific differences in tumor growth kinetics, which have been shown to vary widely across patients even within the same histological diagnosis and, unfortunately, these metrics have shown only minimal success in predicting patient outcome. We consider nine newly diagnosed GBM patients receiving diagnostic biopsy followed by standard-of-care external beam radiation therapy (XRT). We present and apply a patient-specific, biologically based mathematical model for glioma growth that quantifies response to XRT in individual patients in vivo. The mathematical model uses net rates of proliferation and migration of malignant tumor cells to characterize the tumor's growth and invasion along with the linear-quadratic model for the response to radiation therapy. Using only routinely available pre-treatment MRIs to inform the patient-specific bio-mathematical model simulations, we find that radiation response in these patients, quantified by both clinical and model-generated measures, could have been predicted prior to treatment with high accuracy. Specifically, we find that the net proliferation rate is correlated with the radiation response parameter (r = 0.89, p = 0.0007), resulting in a predictive relationship that is tested with a leave-one-out cross-validation technique. This relationship predicts the tumor size post-therapy to within inter

  13. Using the knowledge-and-appraisal personality architecture to predict self-efficacy within individual persons.

    Science.gov (United States)

    Wise, James B

    2008-10-01

    The knowledge-and-appraisal personality architecture has potential as a theoretical framework for understanding the formation of self-efficacy in individuals. Two patterns were observed within 14 of 17 individual persons: a pattern of strong self-efficacy was displayed across outdoor recreation activities for which a self-descriptive attribute was viewed as an asset to successful performances, and a pattern of relatively weak self-efficacy was observed across outdoor recreation activities for which the same attribute was considered a hindrance to performances. Although the theory predicts self-efficacy within individuals, more research is needed to assess why the theory is not accurate in all cases.

  14. Predicting epidemic outbreak from individual features of the spreaders

    International Nuclear Information System (INIS)

    Da Silva, Renato Aparecido Pimentel; Viana, Matheus Palhares; Da Fontoura Costa, Luciano

    2012-01-01

    Knowing which individuals can be more efficient in spreading a pathogen throughout a determinate environment is a fundamental question in disease control. Indeed, over recent years the spread of epidemic diseases and its relationship with the topology of the involved system have been a recurrent topic in complex network theory, taking into account both network models and real-world data. In this paper we explore possible correlations between the heterogeneous spread of an epidemic disease governed by the susceptible–infected–recovered (SIR) model, and several attributes of the originating vertices, considering Erdös–Rényi (ER), Barabási–Albert (BA) and random geometric graphs (RGG), as well as a real case study, the US air transportation network, which comprises the 500 busiest airports in the US along with inter-connections. Initially, the heterogeneity of the spreading is achieved by considering the RGG networks, in which we analytically derive an expression for the distribution of the spreading rates among the established contacts, by assuming that such rates decay exponentially with the distance that separates the individuals. Such a distribution is also considered for the ER and BA models, where we observe topological effects on the correlations. In the case of the airport network, the spreading rates are empirically defined, assumed to be directly proportional to the seat availability. Among both the theoretical and real networks considered, we observe a high correlation between the total epidemic prevalence and the degree, as well as the strength and the accessibility of the epidemic sources. For attributes such as the betweenness centrality and the k-shell index, however, the correlation depends on the topology considered. (paper)

  15. Verbal working memory predicts co-speech gesture: evidence from individual differences.

    Science.gov (United States)

    Gillespie, Maureen; James, Ariel N; Federmeier, Kara D; Watson, Duane G

    2014-08-01

    Gesture facilitates language production, but there is debate surrounding its exact role. It has been argued that gestures lighten the load on verbal working memory (VWM; Goldin-Meadow, Nusbaum, Kelly, & Wagner, 2001), but gestures have also been argued to aid in lexical retrieval (Krauss, 1998). In the current study, 50 speakers completed an individual differences battery that included measures of VWM and lexical retrieval. To elicit gesture, each speaker described short cartoon clips immediately after viewing. Measures of lexical retrieval did not predict spontaneous gesture rates, but lower VWM was associated with higher gesture rates, suggesting that gestures can facilitate language production by supporting VWM when resources are taxed. These data also suggest that individual variability in the propensity to gesture is partly linked to cognitive capacities. Copyright © 2014 Elsevier B.V. All rights reserved.

  16. Gut Microbiota Signatures Predict Host and Microbiota Responses to Dietary Interventions in Obese Individuals

    Science.gov (United States)

    Korpela, Katri; Flint, Harry J.; Johnstone, Alexandra M.; Lappi, Jenni; Poutanen, Kaisa; Dewulf, Evelyne; Delzenne, Nathalie; de Vos, Willem M.; Salonen, Anne

    2014-01-01

    Background Interactions between the diet and intestinal microbiota play a role in health and disease, including obesity and related metabolic complications. There is great interest to use dietary means to manipulate the microbiota to promote health. Currently, the impact of dietary change on the microbiota and the host metabolism is poorly predictable and highly individual. We propose that the responsiveness of the gut microbiota may depend on its composition, and associate with metabolic changes in the host. Methodology Our study involved three independent cohorts of obese adults (n = 78) from Belgium, Finland, and Britain, participating in different dietary interventions aiming to improve metabolic health. We used a phylogenetic microarray for comprehensive fecal microbiota analysis at baseline and after the intervention. Blood cholesterol, insulin and inflammation markers were analyzed as indicators of host response. The data were divided into four training set – test set pairs; each intervention acted both as a part of a training set and as an independent test set. We used linear models to predict the responsiveness of the microbiota and the host, and logistic regression to predict responder vs. non-responder status, or increase vs. decrease of the health parameters. Principal Findings Our models, based on the abundance of several, mainly Firmicute species at baseline, predicted the responsiveness of the microbiota (AUC  =  0.77–1; predicted vs. observed correlation  =  0.67–0.88). Many of the predictive taxa showed a non-linear relationship with the responsiveness. The microbiota response associated with the change in serum cholesterol levels with an AUC of 0.96, highlighting the involvement of the intestinal microbiota in metabolic health. Conclusion This proof-of-principle study introduces the first potential microbial biomarkers for dietary responsiveness in obese individuals with impaired metabolic health, and reveals the potential of

  17. Muscle connective tissue content of endurance-trained and inactive individuals

    DEFF Research Database (Denmark)

    Mackey, Abigail; Donnelly, A E; Roper, H P

    2005-01-01

    Although it is known that exercise exerts a positive regulatory effect on collagen synthesis, the effects of endurance training on muscle endomysial connective tissue in man are not so well documented. To investigate this, a single muscle biopsy was collected from two groups of volunteers...

  18. Patterns and persistence of larval retention and connectivity in a marine fish metapopulation

    KAUST Repository

    Saenz Agudelo, Pablo

    2012-08-14

    Connectivity, the demographic linking of local populations through the dispersal of individuals, is one of the most poorly understood processes in population dynamics, yet has profound implications for conservation and harvest strategies. For marine species with pelagic larvae, direct estimation of connectivity remains logistically challenging and has mostly been limited to single snapshots in time. Here, we document seasonal and interannual patterns of larval dispersal in a metapopulation of the coral reef fish Amphiprion polymnus. A 3-year record of larval trajectories within and among nine discrete local populations from an area of approximately 35 km was established by determining the natal origin of settled juveniles through DNA parentage analysis. We found that spatial patterns of both self-recruitment and connectivity were remarkably consistent over time, with a low level of self-recruitment at the scale of individual sites. Connectivity among sites was common and multidirectional in all years and was not significantly influenced by seasonal variability of predominant surface current directions. However, approximately 75% of the sampled juveniles could not be assigned to parents within the study area, indicating high levels of immigrations from sources outside the study area. The data support predictions that the magnitude and temporal stability of larval connectivity decreases significantly with increasing distance between subpopulations, but increases with the size of subpopulations. Given the considerable effort needed to directly measure larval exchange, the consistent patterns suggest snapshot parentage analyses can provide useful dispersal estimates to inform spatial management decisions. © 2012 Blackwell Publishing Ltd.

  19. Prediction of Individual Serum Infliximab Concentrations in Inflammatory Bowel Disease by a Bayesian Dashboard System.

    Science.gov (United States)

    Eser, Alexander; Primas, Christian; Reinisch, Sieglinde; Vogelsang, Harald; Novacek, Gottfried; Mould, Diane R; Reinisch, Walter

    2018-01-30

    Despite a robust exposure-response relationship of infliximab in inflammatory bowel disease (IBD), attempts to adjust dosing to individually predicted serum concentrations of infliximab (SICs) are lacking. Compared with labor-intensive conventional software for pharmacokinetic (PK) modeling (eg, NONMEM) dashboards are easy-to-use programs incorporating complex Bayesian statistics to determine individual pharmacokinetics. We evaluated various infliximab detection assays and the number of samples needed to precisely forecast individual SICs using a Bayesian dashboard. We assessed long-term infliximab retention in patients being dosed concordantly versus discordantly with Bayesian dashboard recommendations. Three hundred eighty-two serum samples from 117 adult IBD patients on infliximab maintenance therapy were analyzed by 3 commercially available assays. Data from each assay was modeled using NONMEM and a Bayesian dashboard. PK parameter precision and residual variability were assessed. Forecast concentrations from both systems were compared with observed concentrations. Infliximab retention was assessed by prediction for dose intensification via Bayesian dashboard versus real-life practice. Forecast precision of SICs varied between detection assays. At least 3 SICs from a reliable assay are needed for an accurate forecast. The Bayesian dashboard performed similarly to NONMEM to predict SICs. Patients dosed concordantly with Bayesian dashboard recommendations had a significantly longer median drug survival than those dosed discordantly (51.5 versus 4.6 months, P dashboard helps to assess the diagnostic performance of infliximab detection assays. Three, not single, SICs provide sufficient information for individualized dose adjustment when incorporated into the Bayesian dashboard. Treatment adjusted to forecasted SICs is associated with longer drug retention of infliximab. © 2018, The American College of Clinical Pharmacology.

  20. Individual differences in decision making competence revealed by multivariate fMRI.

    Science.gov (United States)

    Talukdar, Tanveer; Román, Francisco J; Operskalski, Joachim T; Zwilling, Christopher E; Barbey, Aron K

    2018-06-01

    While an extensive literature in decision neuroscience has elucidated the neurobiological foundations of decision making, prior research has focused primarily on group-level effects in a sample population. Due to the presence of inherent differences between individuals' cognitive abilities, it is also important to examine the neural correlates of decision making that explain interindividual variability in cognitive performance. This study therefore investigated how individual differences in decision making competence, as measured by the Adult Decision Making Competence (A-DMC) battery, are related to functional brain connectivity patterns derived from resting-state fMRI data in a sample of 304 healthy participants. We examined connectome-wide associations, identifying regions within frontal, parietal, temporal, and occipital cortex that demonstrated significant associations with decision making competence. We then assessed whether the functional interactions between brain regions sensitive to decision making competence and seven intrinsic connectivity networks (ICNs) were predictive of specific facets of decision making assessed by subtests of the A-DMC battery. Our findings suggest that individual differences in specific facets of decision making competence are mediated by ICNs that support executive, social, and perceptual processes, and motivate an integrative framework for understanding the neural basis of individual differences in decision making competence. © 2018 Wiley Periodicals, Inc.

  1. Inter-individual variability in cortical excitability and motor network connectivity following multiple blocks of rTMS.

    Science.gov (United States)

    Nettekoven, Charlotte; Volz, Lukas J; Leimbach, Martha; Pool, Eva-Maria; Rehme, Anne K; Eickhoff, Simon B; Fink, Gereon R; Grefkes, Christian

    2015-09-01

    The responsiveness to non-invasive neuromodulation protocols shows high inter-individual variability, the reasons of which remain poorly understood. We here tested whether the response to intermittent theta-burst stimulation (iTBS) - an effective repetitive transcranial magnetic stimulation (rTMS) protocol for increasing cortical excitability - depends on network properties of the cortical motor system. We furthermore investigated whether the responsiveness to iTBS is dose-dependent. To this end, we used a sham-stimulation controlled, single-blinded within-subject design testing for the relationship between iTBS aftereffects and (i) motor-evoked potentials (MEPs) as well as (ii) resting-state functional connectivity (rsFC) in 16 healthy subjects. In each session, three blocks of iTBS were applied, separated by 15min. We found that non-responders (subjects not showing an MEP increase of ≥10% after one iTBS block) featured stronger rsFC between the stimulated primary motor cortex (M1) and premotor areas before stimulation compared to responders. However, only the group of responders showed increases in rsFC and MEPs, while most non-responders remained close to baseline levels after all three blocks of iTBS. Importantly, there was still a large amount of variability in both groups. Our data suggest that responsiveness to iTBS at the local level (i.e., M1 excitability) depends upon the pre-interventional network connectivity of the stimulated region. Of note, increasing iTBS dose did not turn non-responders into responders. The finding that higher levels of pre-interventional connectivity precluded a response to iTBS could reflect a ceiling effect underlying non-responsiveness to iTBS at the systems level. Copyright © 2015 Elsevier Inc. All rights reserved.

  2. Predicting Optimal Outcomes in Cognitive Therapy or Interpersonal Psychotherapy for Depressed Individuals Using the Personalized Advantage Index Approach.

    Directory of Open Access Journals (Sweden)

    Marcus J H Huibers

    Full Text Available Although psychotherapies for depression produce equivalent outcomes, individual patients respond differently to different therapies. Predictors of outcome have been identified in the context of randomized trials, but this information has not been used to predict which treatment works best for the depressed individual. In this paper, we aim to replicate a recently developed treatment selection method, using data from an RCT comparing the effects of cognitive therapy (CT and interpersonal psychotherapy (IPT.134 depressed patients completed the pre- and post-treatment BDI-II assessment. First, we identified baseline predictors and moderators. Second, individual treatment recommendations were generated by combining the identified predictors and moderators in an algorithm that produces the Personalized Advantage Index (PAI, a measure of the predicted advantage in one therapy compared to the other, using standard regression analyses and the leave-one-out cross-validation approach.We found five predictors (gender, employment status, anxiety, personality disorder and quality of life and six moderators (somatic complaints, cognitive problems, paranoid symptoms, interpersonal self-sacrificing, attributional style and number of life events of treatment outcome. The mean average PAI value was 8.9 BDI points, and 63% of the sample was predicted to have a clinically meaningful advantage in one of the therapies. Those who were randomized to their predicted optimal treatment (either CT or IPT had an observed mean end-BDI of 11.8, while those who received their predicted non-optimal treatment had an end-BDI of 17.8 (effect size for the difference = 0.51.Depressed patients who were randomized to their predicted optimal treatment fared much better than those randomized to their predicted non-optimal treatment. The PAI provides a great opportunity for formal decision-making to improve individual patient outcomes in depression. Although the utility of the PAI

  3. Family Connections versus optimised treatment-as-usual for family members of individuals with borderline personality disorder: non-randomised controlled study.

    LENUS (Irish Health Repository)

    Flynn, Daniel

    2017-01-01

    Borderline personality disorder (BPD) is challenging for family members who are often required to fulfil multiple roles such as those of advocate, caregiver, coach and guardian. To date, two uncontrolled studies by the treatment developers suggest that Family Connections (FC) is an effective programme to support, educate and teach skills to family members of individuals with BPD. However, such studies have been limited by lack of comparison to other treatment approaches. This study aimed to compare the effectiveness of FC with an optimised treatment-as-usual (OTAU) programme for family members of individuals with BPD. A secondary aim was to introduce a long term follow-up to investigate if positive gains from the intervention would be maintained following programme completion.

  4. Personalized prediction of lifetime benefits with statin therapy for asymptomatic individuals: a modeling study.

    Directory of Open Access Journals (Sweden)

    Bart S Ferket

    Full Text Available BACKGROUND: Physicians need to inform asymptomatic individuals about personalized outcomes of statin therapy for primary prevention of cardiovascular disease (CVD. However, current prediction models focus on short-term outcomes and ignore the competing risk of death due to other causes. We aimed to predict the potential lifetime benefits with statin therapy, taking into account competing risks. METHODS AND FINDINGS: A microsimulation model based on 5-y follow-up data from the Rotterdam Study, a population-based cohort of individuals aged 55 y and older living in the Ommoord district of Rotterdam, the Netherlands, was used to estimate lifetime outcomes with and without statin therapy. The model was validated in-sample using 10-y follow-up data. We used baseline variables and model output to construct (1 a web-based calculator for gains in total and CVD-free life expectancy and (2 color charts for comparing these gains to the Systematic Coronary Risk Evaluation (SCORE charts. In 2,428 participants (mean age 67.7 y, 35.5% men, statin therapy increased total life expectancy by 0.3 y (SD 0.2 and CVD-free life expectancy by 0.7 y (SD 0.4. Age, sex, smoking, blood pressure, hypertension, lipids, diabetes, glucose, body mass index, waist-to-hip ratio, and creatinine were included in the calculator. Gains in total and CVD-free life expectancy increased with blood pressure, unfavorable lipid levels, and body mass index after multivariable adjustment. Gains decreased considerably with advancing age, while SCORE 10-y CVD mortality risk increased with age. Twenty-five percent of participants with a low SCORE risk achieved equal or larger gains in CVD-free life expectancy than the median gain in participants with a high SCORE risk. CONCLUSIONS: We developed tools to predict personalized increases in total and CVD-free life expectancy with statin therapy. The predicted gains we found are small. If the underlying model is validated in an independent cohort, the

  5. Similar circuits but different connectivity patterns between the cerebellum, basal ganglia, and supplementary motor area in early Parkinson's disease patients and controls during predictive motor timing.

    Science.gov (United States)

    Husárová, Ivica; Mikl, Michal; Lungu, Ovidiu V; Mareček, Radek; Vaníček, Jiří; Bareš, Martin

    2013-10-01

    The cerebellum, basal ganglia (BG), and other cortical regions, such as supplementary motor area (SMA) have emerged as important structures dealing with various aspects of timing, yet the modulation of functional connectivity between them during motor timing tasks remains unexplored. We used dynamic causal modeling to investigate the differences in effective connectivity (EC) between these regions and its modulation by behavioral outcome during a motor timing prediction task in a group of 16 patients with early Parkinson's disease (PD) and 17 healthy controls. Behavioral events (hits and errors) constituted the driving input connected to the cerebellum, and the modulation in connectivity was assessed relative to the hit condition (successful interception of target). The driving input elicited response in the target area, while modulatory input changed the specific connection strength. The neuroimaging data revealed similar structure of intrinsic connectivity in both groups with unidirectional connections from cerebellum to both sides of the BG, from BG to the SMA, and then from SMA to the cerebellum. However, the type of intrinsic connection was different between two groups. In the PD group, the connection between the SMA and cerebellum was inhibitory in comparison to the HC group, where the connection was activated. Furthermore, the modulation of connectivity by the performance in the task was different between the two groups, with decreased connectivity between the cerebellum and left BG and SMA and a more pronounced symmetry of these connections in controls. In the same time, there was an increased EC between the cerebellum and both sides of BG with more pronounced asymmetry (stronger connection with left BG) in patients. In addition, in the PD group the modulatory input strengthened inhibitory connectivity between the SMA and the cerebellum, while in the HC group the excitatory connection was slightly strengthened. Our findings indicate that although early PD

  6. Intrinsic brain connectivity predicts impulse control disorders in patients with Parkinson's disease.

    Science.gov (United States)

    Tessitore, Alessandro; De Micco, Rosa; Giordano, Alfonso; di Nardo, Federica; Caiazzo, Giuseppina; Siciliano, Mattia; De Stefano, Manuela; Russo, Antonio; Esposito, Fabrizio; Tedeschi, Gioacchino

    2017-12-01

    Impulse control disorders can be triggered by dopamine replacement therapies in patients with PD. Using resting-state functional MRI, we investigated the intrinsic brain network connectivity at baseline in a cohort of drug-naive PD patients who successively developed impulse control disorders over a 36-month follow-up period compared with patients who did not. Baseline 3-Tesla MRI images of 30 drug-naive PD patients and 20 matched healthy controls were analyzed. The impulse control disorders' presence and severity at follow-up were assessed by the Questionnaire for Impulsive-Compulsive Disorders in Parkinson's Disease Rating Scale. Single-subject and group-level independent component analysis was used to investigate functional connectivity differences within the major resting-state networks. We also compared internetwork connectivity between patients. Finally, a multivariate Cox regression model was used to investigate baseline predictors of impulse control disorder development. At baseline, decreased connectivity in the default-mode and right central executive networks and increased connectivity in the salience network were detected in PD patients with impulse control disorders at follow-up compared with those without. Increased default-mode/central executive internetwork connectivity was significantly associated with impulse control disorders development (P impulse control disorders while on dopaminergic treatment. We hypothesize that these divergent cognitive and limbic network connectivity changes could represent a potential biomarker and an additional risk factor for the emergence of impulse control disorders. © 2017 International Parkinson and Movement Disorder Society. © 2017 International Parkinson and Movement Disorder Society.

  7. Multimodal movement prediction - towards an individual assistance of patients.

    Directory of Open Access Journals (Sweden)

    Elsa Andrea Kirchner

    Full Text Available Assistive devices, like exoskeletons or orthoses, often make use of physiological data that allow the detection or prediction of movement onset. Movement onset can be detected at the executing site, the skeletal muscles, as by means of electromyography. Movement intention can be detected by the analysis of brain activity, recorded by, e.g., electroencephalography, or in the behavior of the subject by, e.g., eye movement analysis. These different approaches can be used depending on the kind of neuromuscular disorder, state of therapy or assistive device. In this work we conducted experiments with healthy subjects while performing self-initiated and self-paced arm movements. While other studies showed that multimodal signal analysis can improve the performance of predictions, we show that a sensible combination of electroencephalographic and electromyographic data can potentially improve the adaptability of assistive technical devices with respect to the individual demands of, e.g., early and late stages in rehabilitation therapy. In earlier stages for patients with weak muscle or motor related brain activity it is important to achieve high positive detection rates to support self-initiated movements. To detect most movement intentions from electroencephalographic or electromyographic data motivates a patient and can enhance her/his progress in rehabilitation. In a later stage for patients with stronger muscle or brain activity, reliable movement prediction is more important to encourage patients to behave more accurately and to invest more effort in the task. Further, the false detection rate needs to be reduced. We propose that both types of physiological data can be used in an and combination, where both signals must be detected to drive a movement. By this approach the behavior of the patient during later therapy can be controlled better and false positive detections, which can be very annoying for patients who are further advanced in

  8. Individual differences in fluid intelligence predicts inattentional blindness in a sample of older adults: a preliminary study.

    Science.gov (United States)

    O'Shea, Deirdre M; Fieo, Robert A

    2015-07-01

    Previous research has shown that aging increases susceptibility to inattentional blindness (Graham and Burke, Psychol Aging 26:162, 2011) as well as individual differences in cognitive ability related to working memory and executive functions in separate studies. Therefore, the present study was conducted in an attempt to bridge a gap that involved investigating 'age-sensitive' cognitive abilities that may predict inattentional blindness in a sample of older adults. We investigated whether individual differences in general fluid intelligence and speed of processing would predict inattentional blindness in our sample of older adults. Thirty-six healthy older adults took part in the study. Using the inattentional blindness paradigm developed by Most et al. (Psychol Rev 112:217, 2005), we investigated whether rates of inattentional blindness could be predicted by participant's performance on the Raven's Advanced Progressive Matrices and a choice-reaction time task. A Mann-Whitney U test revealed that a higher score on the Raven's Advanced Progressive Matrices was significantly associated with lower incidences of inattentional blindness. However, a t test revealed that choice-reaction times were not significantly associated with inattentional blindness. Preliminary results from the present study suggest that individual differences in general fluid intelligence are predictive of inattentional blindness in older adults but not speed of processing. Moreover, our findings are consistent with previous studies that have suggested executive attention control may be the source of these individual differences. These findings also highlight the association between attention and general fluid intelligence and how it may impact environmental awareness. Future research would benefit from repeating these analyses in a larger sample and also including a younger comparison group.

  9. Utilizing individual fish biomass and relative abundance models to map environmental niche associations of adult and juvenile targeted fishes.

    Science.gov (United States)

    Galaiduk, Ronen; Radford, Ben T; Harvey, Euan S

    2018-06-21

    Many fishes undergo ontogenetic habitat shifts to meet their energy and resource needs as they grow. Habitat resource partitioning and patterns of habitat connectivity between conspecific fishes at different life-history stages is a significant knowledge gap. Species distribution models were used to examine patterns in the relative abundance, individual biomass estimates and environmental niche associations of different life stages of three iconic West Australian fishes. Continuous predictive maps describing the spatial distribution of abundance and individual biomass of the study species were created as well predictive hotspot maps that identify possible areas for aggregation of individuals of similar life stages of multiple species (i.e. spawning grounds, fisheries refugia or nursery areas). The models and maps indicate that processes driving the abundance patterns could be different from the body size associated demographic processes throughout an individual's life cycle. Incorporating life-history in the spatially explicit management plans can ensure that critical habitat of the vulnerable stages (e.g. juvenile fish, spawning stock) is included within proposed protected areas and can enhance connectivity between various functional areas (e.g. nursery areas and adult populations) which, in turn, can improve the abundance of targeted species as well as other fish species relying on healthy ecosystem functioning.

  10. Predictability of the individual clinical outcome of extracorporeal shock wave therapy for cellulite.

    Science.gov (United States)

    Schlaudraff, Kai-Uwe; Kiessling, Maren C; Császár, Nikolaus Bm; Schmitz, Christoph

    2014-01-01

    Extracorporeal shock wave therapy has been successfully introduced for the treatment of cellulite in recent years. However, it is still unknown whether the individual clinical outcome of cellulite treatment with extracorporeal shock wave therapy can be predicted by the patient's individual cellulite grade at baseline, individual patient age, body mass index (BMI), weight, and/or height. Fourteen Caucasian females with cellulite were enrolled in a prospective, single-center, randomized, open-label Phase II study. The mean (± standard error of the mean) cellulite grade at baseline was 2.5±0.09 and mean BMI was 22.8±1.17. All patients were treated with radial extracorporeal shock waves using the Swiss DolorClast(®) device (Electro Medical Systems, S.A., Nyon, Switzerland). Patients were treated unilaterally with 2 weekly treatments for 4 weeks on a randomly selected side (left or right), totaling eight treatments on the selected side. Treatment was performed at 3.5-4.0 bar, with 15,000 impulses per session applied at 15 Hz. Impulses were homogeneously distributed over the posterior thigh and buttock area (resulting in 7,500 impulses per area). Treatment success was evaluated after the last treatment and 4 weeks later by clinical examination, photographic documentation, contact thermography, and patient satisfaction questionnaires. The mean cellulite grade improved from 2.5±0.09 at baseline to 1.57±0.18 after the last treatment (ie, mean δ-1 was 0.93 cellulite grades) and 1.68±0.16 at follow-up (ie, mean δ-2 was 0.82 cellulite grades). Compared with baseline, no patient's condition worsened, the treatment was well tolerated, and no unwanted side effects were observed. No statistically significant (ie, Pcellulite grade at baseline, BMI, weight, height, or age. Radial shock wave therapy is a safe and effective treatment option for cellulite. The individual clinical outcome cannot be predicted by the patient's individual cellulite grade at baseline, BMI, weight

  11. Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD): The TRIPOD Statement.

    Science.gov (United States)

    Collins, Gary S; Reitsma, Johannes B; Altman, Douglas G; Moons, Karel G M

    2015-06-01

    Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org). The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations

  12. Enhanced Cortical Connectivity in Absolute Pitch Musicians: A Model for Local Hyperconnectivity

    Science.gov (United States)

    Loui, Psyche; Li, H. Charles; Hohmann, Anja; Schlaug, Gottfried

    2011-01-01

    Connectivity in the human brain has received increased scientific interest in recent years. Although connection disorders can affect perception, production, learning, and memory, few studies have associated brain connectivity with graded variations in human behavior, especially among normal individuals. One group of normal individuals who possess…

  13. Rare copy number deletions predict individual variation in intelligence.

    Directory of Open Access Journals (Sweden)

    Ronald A Yeo

    2011-01-01

    Full Text Available Phenotypic variation in human intellectual functioning shows substantial heritability, as demonstrated by a long history of behavior genetic studies. Many recent molecular genetic studies have attempted to uncover specific genetic variations responsible for this heritability, but identified effects capture little variance and have proven difficult to replicate. The present study, motivated an interest in "mutation load" emerging from evolutionary perspectives, examined the importance of the number of rare (or infrequent copy number variations (CNVs, and the total number of base pairs included in such deletions, for psychometric intelligence. Genetic data was collected using the Illumina 1MDuoBeadChip Array from a sample of 202 adult individuals with alcohol dependence, and a subset of these (N = 77 had been administered the Wechsler Abbreviated Scale of Intelligence (WASI. After removing CNV outliers, the impact of rare genetic deletions on psychometric intelligence was investigated in 74 individuals. The total length of the rare deletions significantly and negatively predicted intelligence (r = -.30, p = .01. As prior studies have indicated greater heritability in individuals with relatively higher parental socioeconomic status (SES, we also examined the impact of ethnicity (Anglo/White vs. Other, as a proxy measure of SES; these groups did not differ on any genetic variable. This categorical variable significantly moderated the effect of length of deletions on intelligence, with larger effects being noted in the Anglo/White group. Overall, these results suggest that rare deletions (between 5% and 1% population frequency or less adversely affect intellectual functioning, and that pleotropic effects might partly account for the association of intelligence with health and mental health status. Significant limitations of this research, including issues of generalizability and CNV measurement, are discussed.

  14. Rare Copy Number Deletions Predict Individual Variation in Intelligence

    Science.gov (United States)

    Yeo, Ronald A.; Gangestad, Steven W.; Liu, Jingyu; Calhoun, Vince D.; Hutchison, Kent E.

    2011-01-01

    Phenotypic variation in human intellectual functioning shows substantial heritability, as demonstrated by a long history of behavior genetic studies. Many recent molecular genetic studies have attempted to uncover specific genetic variations responsible for this heritability, but identified effects capture little variance and have proven difficult to replicate. The present study, motivated an interest in “mutation load” emerging from evolutionary perspectives, examined the importance of the number of rare (or infrequent) copy number variations (CNVs), and the total number of base pairs included in such deletions, for psychometric intelligence. Genetic data was collected using the Illumina 1MDuoBeadChip Array from a sample of 202 adult individuals with alcohol dependence, and a subset of these (N = 77) had been administered the Wechsler Abbreviated Scale of Intelligence (WASI). After removing CNV outliers, the impact of rare genetic deletions on psychometric intelligence was investigated in 74 individuals. The total length of the rare deletions significantly and negatively predicted intelligence (r = −.30, p = .01). As prior studies have indicated greater heritability in individuals with relatively higher parental socioeconomic status (SES), we also examined the impact of ethnicity (Anglo/White vs. Other), as a proxy measure of SES; these groups did not differ on any genetic variable. This categorical variable significantly moderated the effect of length of deletions on intelligence, with larger effects being noted in the Anglo/White group. Overall, these results suggest that rare deletions (between 5% and 1% population frequency or less) adversely affect intellectual functioning, and that pleotropic effects might partly account for the association of intelligence with health and mental health status. Significant limitations of this research, including issues of generalizability and CNV measurement, are discussed. PMID:21298096

  15. Using individual differences to predict job performance: correcting for direct and indirect restriction of range.

    Science.gov (United States)

    Sjöberg, Sofia; Sjöberg, Anders; Näswall, Katharina; Sverke, Magnus

    2012-08-01

    The present study investigates the relationship between individual differences, indicated by personality (FFM) and general mental ability (GMA), and job performance applying two different methods of correction for range restriction. The results, derived by analyzing meta-analytic correlations, show that the more accurate method of correcting for indirect range restriction increased the operational validity of individual differences in predicting job performance and that this increase primarily was due to general mental ability being a stronger predictor than any of the personality traits. The estimates for single traits can be applied in practice to maximize prediction of job performance. Further, differences in the relative importance of general mental ability in relation to overall personality assessment methods was substantive and the estimates provided enables practitioners to perform a correct utility analysis of their overall selection procedure. © 2012 The Authors. Scandinavian Journal of Psychology © 2012 The Scandinavian Psychological Associations.

  16. The predictive value of arterial stiffness on major adverse cardiovascular events in individuals with mildly impaired renal function

    Directory of Open Access Journals (Sweden)

    Han J

    2016-08-01

    Full Text Available Jie Han,* Xiaona Wang,* Ping Ye, Ruihua Cao, Xu Yang, Wenkai Xiao, Yun Zhang, Yongyi Bai, Hongmei Wu Department of Geriatric Cardiology, Chinese PLA General Hospital, Beijing, People’s Republic of China *These authors contributed equally to this work Objectives: Despite growing evidence that arterial stiffness has important predictive value for cardiovascular disease in patients with advanced stages of chronic kidney disease, the predictive significance of arterial stiffness in individuals with mildly impaired renal function has not been established. The aim of this study was to evaluate the predictive value of arterial stiffness on cardiovascular disease in this specific population. Materials and methods: We analyzed measurements of arterial stiffness (carotid–femoral pulse-wave velocity [cf-PWV] and the incidence of major adverse cardiovascular events (MACEs in 1,499 subjects from a 4.8-year longitudinal study. Results: A multivariate Cox proportional-hazard regression analysis showed that in individuals with normal renal function (estimated glomerular filtration rate [eGFR] ≥90 mL/min/1.73 m2, the baseline cf-PWV was not associated with occurrence of MACEs (hazard ratio 1.398, 95% confidence interval 0.748–2.613; P=0.293. In individuals with mildly impaired renal function (eGFR <90 mL/min/1.73 m2, a higher baseline cf-PWV level was associated with a higher risk of MACEs (hazard ratio 2.334, 95% confidence interval 1.082–5.036; P=0.031. Conclusion: Arterial stiffness is a moderate and independent predictive factor for MACEs in individuals with mildly impaired renal function (eGFR <90 mL/min/1.73 m2. Keywords: epidemiology, arterial stiffness, impaired renal function, predictive value, MACEs

  17. Predicting financial trouble using call data—On social capital, phone logs, and financial trouble

    Science.gov (United States)

    Lin, Chia-Ching; Chen, Kuan-Ta; Singh, Vivek Kumar

    2018-01-01

    An ability to understand and predict financial wellbeing for individuals is of interest to economists, policy designers, financial institutions, and the individuals themselves. According to the Nilson reports, there were more than 3 billion credit cards in use in 2013, accounting for purchases exceeding US$ 2.2 trillion, and according to the Federal Reserve report, 39% of American households were carrying credit card debt from month to month. Prior literature has connected individual financial wellbeing with social capital. However, as yet, there is limited empirical evidence connecting social interaction behavior with financial outcomes. This work reports results from one of the largest known studies connecting financial outcomes and phone-based social behavior (180,000 individuals; 2 years’ time frame; 82.2 million monthly bills, and 350 million call logs). Our methodology tackles highly imbalanced dataset, which is a pertinent problem with modelling credit risk behavior, and offers a novel hybrid method that yields improvements over, both, a traditional transaction data only approach, and an approach that uses only call data. The results pave way for better financial modelling of billions of unbanked and underbanked customers using non-traditional metrics like phone-based credit scoring. PMID:29474411

  18. Predicting financial trouble using call data-On social capital, phone logs, and financial trouble.

    Science.gov (United States)

    Agarwal, Rishav Raj; Lin, Chia-Ching; Chen, Kuan-Ta; Singh, Vivek Kumar

    2018-01-01

    An ability to understand and predict financial wellbeing for individuals is of interest to economists, policy designers, financial institutions, and the individuals themselves. According to the Nilson reports, there were more than 3 billion credit cards in use in 2013, accounting for purchases exceeding US$ 2.2 trillion, and according to the Federal Reserve report, 39% of American households were carrying credit card debt from month to month. Prior literature has connected individual financial wellbeing with social capital. However, as yet, there is limited empirical evidence connecting social interaction behavior with financial outcomes. This work reports results from one of the largest known studies connecting financial outcomes and phone-based social behavior (180,000 individuals; 2 years' time frame; 82.2 million monthly bills, and 350 million call logs). Our methodology tackles highly imbalanced dataset, which is a pertinent problem with modelling credit risk behavior, and offers a novel hybrid method that yields improvements over, both, a traditional transaction data only approach, and an approach that uses only call data. The results pave way for better financial modelling of billions of unbanked and underbanked customers using non-traditional metrics like phone-based credit scoring.

  19. Predicting highly-connected hubs in protein interaction networks by QSAR and biological data descriptors

    Science.gov (United States)

    Hsing, Michael; Byler, Kendall; Cherkasov, Artem

    2009-01-01

    Hub proteins (those engaged in most physical interactions in a protein interaction network (PIN) have recently gained much research interest due to their essential role in mediating cellular processes and their potential therapeutic value. It is straightforward to identify hubs if the underlying PIN is experimentally determined; however, theoretical hub prediction remains a very challenging task, as physicochemical properties that differentiate hubs from less connected proteins remain mostly uncharacterized. To adequately distinguish hubs from non-hub proteins we have utilized over 1300 protein descriptors, some of which represent QSAR (quantitative structure-activity relationship) parameters, and some reflect sequence-derived characteristics of proteins including domain composition and functional annotations. Those protein descriptors, together with available protein interaction data have been processed by a machine learning method (boosting trees) and resulted in the development of hub classifiers that are capable of predicting highly interacting proteins for four model organisms: Escherichia coli, Saccharomyces cerevisiae, Drosophila melanogaster and Homo sapiens. More importantly, through the analyses of the most relevant protein descriptors, we are able to demonstrate that hub proteins not only share certain common physicochemical and structural characteristics that make them different from non-hub counterparts, but they also exhibit species-specific characteristics that should be taken into account when analyzing different PINs. The developed prediction models can be used for determining highly interacting proteins in the four studied species to assist future proteomics experiments and PIN analyses. Availability The source code and executable program of the hub classifier are available for download at: http://www.cnbi2.ca/hub-analysis/ PMID:20198194

  20. Tailoring the implementation of new biomarkers based on their added predictive value in subgroups of individuals.

    Directory of Open Access Journals (Sweden)

    A van Giessen

    Full Text Available The value of new biomarkers or imaging tests, when added to a prediction model, is currently evaluated using reclassification measures, such as the net reclassification improvement (NRI. However, these measures only provide an estimate of improved reclassification at population level. We present a straightforward approach to characterize subgroups of reclassified individuals in order to tailor implementation of a new prediction model to individuals expected to benefit from it.In a large Dutch population cohort (n = 21,992 we classified individuals to low (< 5% and high (≥ 5% fatal cardiovascular disease risk by the Framingham risk score (FRS and reclassified them based on the systematic coronary risk evaluation (SCORE. Subsequently, we characterized the reclassified individuals and, in case of heterogeneity, applied cluster analysis to identify and characterize subgroups. These characterizations were used to select individuals expected to benefit from implementation of SCORE.Reclassification after applying SCORE in all individuals resulted in an NRI of 5.00% (95% CI [-0.53%; 11.50%] within the events, 0.06% (95% CI [-0.08%; 0.22%] within the nonevents, and a total NRI of 0.051 (95% CI [-0.004; 0.116]. Among the correctly downward reclassified individuals cluster analysis identified three subgroups. Using the characterizations of the typically correctly reclassified individuals, implementing SCORE only in individuals expected to benefit (n = 2,707,12.3% improved the NRI to 5.32% (95% CI [-0.13%; 12.06%] within the events, 0.24% (95% CI [0.10%; 0.36%] within the nonevents, and a total NRI of 0.055 (95% CI [0.001; 0.123]. Overall, the risk levels for individuals reclassified by tailored implementation of SCORE were more accurate.In our empirical example the presented approach successfully characterized subgroups of reclassified individuals that could be used to improve reclassification and reduce implementation burden. In particular when newly

  1. Deafferentation-Induced Plasticity of Visual Callosal Connections: Predicting Critical Periods and Analyzing Cortical Abnormalities Using Diffusion Tensor Imaging

    Directory of Open Access Journals (Sweden)

    Jaime F. Olavarria

    2012-01-01

    Full Text Available Callosal connections form elaborate patterns that bear close association with striate and extrastriate visual areas. Although it is known that retinal input is required for normal callosal development, there is little information regarding the period during which the retina is critically needed and whether this period correlates with the same developmental stage across species. Here we review the timing of this critical period, identified in rodents and ferrets by the effects that timed enucleations have on mature callosal connections, and compare it to other developmental milestones in these species. Subsequently, we compare these events to diffusion tensor imaging (DTI measurements of water diffusion anisotropy within developing cerebral cortex. We observed that the relationship between the timing of the critical period and the DTI-characterized developmental trajectory is strikingly similar in rodents and ferrets, which opens the possibility of using cortical DTI trajectories for predicting the critical period in species, such as humans, in which this period likely occurs prenatally. Last, we discuss the potential of utilizing DTI to distinguish normal from abnormal cerebral cortical development, both within the context of aberrant connectivity induced by early retinal deafferentation, and more generally as a potential tool for detecting abnormalities associated with neurodevelopmental disorders.

  2. Quantifying the connectivity of scale-free and biological networks

    Energy Technology Data Exchange (ETDEWEB)

    Shiner, J.S. E-mail: shiner@alumni.duke.edu; Davison, Matt E-mail: mdavison@uwo.ca

    2004-07-01

    Scale-free and biological networks follow a power law distribution p{sub k}{proportional_to}k{sup -{alpha}} for the probability that a node is connected to k other nodes; the corresponding ranges for {alpha} (biological: 1<{alpha}<2; scale-free: 2<{alpha}{<=}3) yield a diverging variance for the connectivity k and lack of predictability for the average connectivity. Predictability can be achieved with the Renyi, Tsallis and Landsberg-Vedral extended entropies and corresponding 'disorders' for correctly chosen values of the entropy index q. Escort distributions p{sub k}{proportional_to}k{sup -{alpha}}{sup q} with q>3/{alpha} also yield a nondiverging variance and predictability. It is argued that the Tsallis entropies may be the appropriate quantities for the study of scale-free and biological networks.

  3. Fuzzy-predictive direct power control implementation of a grid connected photovoltaic system, associated with an active power filter

    International Nuclear Information System (INIS)

    Ouchen, Sabir; Betka, Achour; Abdeddaim, Sabrina; Menadi, Abdelkrim

    2016-01-01

    Highlights: • An implementation on dSPACE 1104 of a double stage grid connected photovoltaic system, associated with an active power filter. • A fuzzy logic controller for maximum power point tracking of photovoltaic generator using a boost converter. • Predictive direct power control almost eliminates the effect of harmonics under a unite power factor. • The robustness of control strategies was examined in different irradiance level conditions. - Abstract: The present paper proposes a real time implementation of an optimal operation of a double stage grid connected photovoltaic system, associated with a shunt active power filter. On the photovoltaic side, a fuzzy logic based maximum power point taking control is proposed to track permanently the optimum point through an adequate tuning of a boost converter regardless the solar irradiance variations; whereas, on the grid side, a model predictive direct power control is applied, to ensure both supplying a part of the load demand with the extracted photovoltaic power, and a compensation of undesirable harmonic contents of the grid current, under a unity power factor operation. The implementation of the control strategies is conducted on a small scale photovoltaic system, controlled via a dSPACE 1104 single card. The obtained experimental results show on one hand, that the proposed Fuzzy logic based maximum power taking point technique provides fast and high performances under different irradiance levels while compared with a sliding mode control, and ensures 1.57% more in efficiency. On the other hand, the predictive power control ensures a flexible settlement of active power amounts exchanges with the grid, under a unity power functioning. Furthermore, the grid current presents a sinusoidal shape with a tolerable total harmonic distortion coefficient 4.71%.

  4. Resting state functional connectivity of the anterior striatum and prefrontal cortex predicts reading performance in school-age children.

    Science.gov (United States)

    Alcauter, Sarael; García-Mondragón, Liliana; Gracia-Tabuenca, Zeus; Moreno, Martha B; Ortiz, Juan J; Barrios, Fernando A

    2017-11-01

    The current study investigated the neural basis of reading performance in 60 school-age Spanish-speaking children, aged 6 to 9years. By using a data-driven approach and an automated matching procedure, we identified a left-lateralized resting state network that included typical language regions (Wernicke's and Broca's regions), prefrontal cortex, pre- and post-central gyri, superior and middle temporal gyri, cerebellum, and subcortical regions, and explored its relevance for reading performance (accuracy, comprehension and speed). Functional connectivity of the left frontal and temporal cortices and subcortical regions predicted reading speed. These results extend previous findings on the relationship between functional connectivity and reading competence in children, providing new evidence about such relationships in previously unexplored regions in the resting brain, including the left caudate, putamen and thalamus. This work highlights the relevance of a broad network, functionally synchronized in the resting state, for the acquisition and perfecting of reading abilities in young children. Copyright © 2017 Elsevier Inc. All rights reserved.

  5. Task-dependent activity and connectivity predict episodic memory network-based responses to brain stimulation in healthy aging.

    Science.gov (United States)

    Vidal-Piñeiro, Dídac; Martin-Trias, Pablo; Arenaza-Urquijo, Eider M; Sala-Llonch, Roser; Clemente, Imma C; Mena-Sánchez, Isaias; Bargalló, Núria; Falcón, Carles; Pascual-Leone, Álvaro; Bartrés-Faz, David

    2014-01-01

    Transcranial magnetic stimulation (TMS) can affect episodic memory, one of the main cognitive hallmarks of aging, but the mechanisms of action remain unclear. To evaluate the behavioral and functional impact of excitatory TMS in a group of healthy elders. We applied a paradigm of repetitive TMS - intermittent theta-burst stimulation - over left inferior frontal gyrus in healthy elders (n = 24) and evaluated its impact on the performance of an episodic memory task with two levels of processing and the associated brain activity as captured by a pre and post fMRI scans. In the post-TMS fMRI we found TMS-related activity increases in left prefrontal and cerebellum-occipital areas specifically during deep encoding but not during shallow encoding or at rest. Furthermore, we found a task-dependent change in connectivity during the encoding task between cerebellum-occipital areas and the TMS-targeted left inferior frontal region. This connectivity change correlated with the TMS effects over brain networks. The results suggest that the aged brain responds to brain stimulation in a state-dependent manner as engaged by different tasks components and that TMS effect is related to inter-individual connectivity changes measures. These findings reveal fundamental insights into brain network dynamics in aging and the capacity to probe them with combined behavioral and stimulation approaches. Copyright © 2014 Elsevier Inc. All rights reserved.

  6. Allometric scaling of population variance with mean body size is predicted from Taylor's law and density-mass allometry.

    Science.gov (United States)

    Cohen, Joel E; Xu, Meng; Schuster, William S F

    2012-09-25

    Two widely tested empirical patterns in ecology are combined here to predict how the variation of population density relates to the average body size of organisms. Taylor's law (TL) asserts that the variance of the population density of a set of populations is a power-law function of the mean population density. Density-mass allometry (DMA) asserts that the mean population density of a set of populations is a power-law function of the mean individual body mass. Combined, DMA and TL predict that the variance of the population density is a power-law function of mean individual body mass. We call this relationship "variance-mass allometry" (VMA). We confirmed the theoretically predicted power-law form and the theoretically predicted parameters of VMA, using detailed data on individual oak trees (Quercus spp.) of Black Rock Forest, Cornwall, New York. These results connect the variability of population density to the mean body mass of individuals.

  7. Individual Differences in Diurnal Preference and Time-of-Exercise Interact to Predict Exercise Frequency.

    Science.gov (United States)

    Hisler, Garrett C; Phillips, Alison L; Krizan, Zlatan

    2017-06-01

    Diurnal preference (and chronotype more generally) has been implicated in exercise behavior, but this relation has not been examined using objective exercise measurements nor have potential psychosocial mediators been examined. Furthermore, time-of-day often moderates diurnal preference's influence on outcomes, and it is unknown whether time-of-exercise may influence the relation between chronotype and exercise frequency. The current study examined whether individual differences in diurnal preference ("morningness-eveningness") predict unique variance in exercise frequency and if commonly studied psychosocial variables mediate this relation (i.e., behavioral intentions, internal exercise control, external exercise control, and conscientiousness). Moreover, the study sought to test whether individuals' typical time-of-exercise moderated the impact of diurnal preference on exercise frequency. One hundred twelve healthy adults (mean age = 25.4; SD = 11.6 years) completed baseline demographics and then wore Fitbit Zips® for 4 weeks to objectively measure exercise frequency and typical time-of-exercise. At the end of the study, participants also self-reported recent exercise. Diurnal preference predicted both self-reported exercise and Fitbit-recorded exercise frequency. When evaluating mediators, only conscientiousness emerged as a partial mediator of the relation between diurnal preference and self-reported exercise. In addition, time-of-exercise moderated diurnal preference's relation to both self-reported exercise and Fitbit-recorded exercise frequency such that diurnal preference predicted higher exercise frequency when exercise occurred at a time that was congruent with one's diurnal preference. Based on these findings, diurnal preference is valuable, above and beyond other psychological constructs, in predicting exercise frequency and represents an important variable to incorporate into interventions seeking to increase exercise.

  8. Privacy and the Connected Society

    DEFF Research Database (Denmark)

    Sørensen, Lene Tolstrup; Khajuria, Samant; Skouby, Knud Erik

    The Vision of the 5G enabled connected society is highly based on the evolution and implementation of Internet of Things. This involves, amongst others, a significant raise in devices, sensors and communication in pervasive interconnections as well as cooperation amongst devices and entities across...... the society. Enabling the vision of the connected society, researchers point in the direction of security and privacy as areas to challenge the vision. By use of the Internet of Things reference model as well as the vision of the connected society, this paper identifies privacy of the individual with respect...... to three selected areas: Shopping, connected cars and online gaming. The paper concludes that privacy is a complexity within the connected society vision and that thee is a need for more privacy use cases to shed light on the challenge....

  9. Socio-economic development and emotion-health connection revisited: a multilevel modeling analysis using data from 162 counties in China.

    Science.gov (United States)

    Yu, Zonghuo; Wang, Fei

    2016-03-12

    Substantial research has shown that emotions play a critical role in physical health. However, most of these studies were conducted in industrialized countries, and it is still an open question whether the emotion-health connection is a "first-world problem". In the current study, we examined socio-economic development's influence on emotion-health connection by performing multilevel-modeling analysis in a dataset of 33,600 individuals from 162 counties in China. Results showed that both positive emotions and negative emotions predicted level of physical health and regional Gross Domestic Product Per Capita (GDPPC) had some impact on the association between emotion and health through accessibility of medical resources and educational status. But these impacts were suppressed, and the total effects of GDPPC on emotion-health connections were not significant. These results support the universality of emotion-health connection across levels of GDPPC and provide new insight into how socio-economic development might affect these connections.

  10. Robust motion estimation using connected operators

    OpenAIRE

    Salembier Clairon, Philippe Jean; Sanson, H

    1997-01-01

    This paper discusses the use of connected operators for robust motion estimation The proposed strategy involves a motion estimation step extracting the dominant motion and a ltering step relying on connected operators that remove objects that do not fol low the dominant motion. These two steps are iterated in order to obtain an accurate motion estimation and a precise de nition of the objects fol lowing this motion This strategy can be applied on the entire frame or on individual connected c...

  11. Nonlinear joint models for individual dynamic prediction of risk of death using Hamiltonian Monte Carlo: application to metastatic prostate cancer

    Directory of Open Access Journals (Sweden)

    Solène Desmée

    2017-07-01

    Full Text Available Abstract Background Joint models of longitudinal and time-to-event data are increasingly used to perform individual dynamic prediction of a risk of event. However the difficulty to perform inference in nonlinear models and to calculate the distribution of individual parameters has long limited this approach to linear mixed-effect models for the longitudinal part. Here we use a Bayesian algorithm and a nonlinear joint model to calculate individual dynamic predictions. We apply this approach to predict the risk of death in metastatic castration-resistant prostate cancer (mCRPC patients with frequent Prostate-Specific Antigen (PSA measurements. Methods A joint model is built using a large population of 400 mCRPC patients where PSA kinetics is described by a biexponential function and the hazard function is a PSA-dependent function. Using Hamiltonian Monte Carlo algorithm implemented in Stan software and the estimated population parameters in this population as priors, the a posteriori distribution of the hazard function is computed for a new patient knowing his PSA measurements until a given landmark time. Time-dependent area under the ROC curve (AUC and Brier score are derived to assess discrimination and calibration of the model predictions, first on 200 simulated patients and then on 196 real patients that are not included to build the model. Results Satisfying coverage probabilities of Monte Carlo prediction intervals are obtained for longitudinal and hazard functions. Individual dynamic predictions provide good predictive performances for landmark times larger than 12 months and horizon time of up to 18 months for both simulated and real data. Conclusions As nonlinear joint models can characterize the kinetics of biomarkers and their link with a time-to-event, this approach could be useful to improve patient’s follow-up and the early detection of most at risk patients.

  12. Intrinsic functional connectivity between amygdala and hippocampus during rest predicts enhanced memory under stress.

    Science.gov (United States)

    de Voogd, Lycia D; Klumpers, Floris; Fernández, Guillén; Hermans, Erno J

    2017-01-01

    Declarative memories of stressful events are less prone to forgetting than mundane events. Animal research has demonstrated that such stress effects on consolidation of hippocampal-dependent memories require the amygdala. In humans, it has been shown that during learning, increased amygdala-hippocampal interactions are related to more efficient memory encoding. Animal models predict that following learning, amygdala-hippocampal interactions are instrumental to strengthening the consolidation of such declarative memories. Whether this is the case in humans is unknown and remains to be empirically verified. To test this, we analyzed data from a sample of 120 healthy male participants who performed an incidental encoding task and subsequently underwent resting-state functional MRI in a stressful and a neutral context. Stress was assessed by measures of salivary cortisol, blood pressure, heart rate, and subjective ratings. Memory was tested afterwards outside of the scanner. Our data show that memory was stronger in the stress context compared to the neutral context and that stress-induced cortisol responses were associated with this memory enhancement. Interestingly, amygdala-hippocampal connectivity during post-encoding awake rest regardless of context (stress or neutral) was associated with the enhanced memory performance under stress. Thus, our findings are in line with a role for intrinsic functional connectivity during rest between the amygdala and the hippocampus in the state effects of stress on strengthening memory. Copyright © 2016 Elsevier Ltd. All rights reserved.

  13. Predicting individual differences in decision-making process from signature movement styles: an illustrative study of leaders

    OpenAIRE

    Connors, Brenda L.; Rende, Richard; Colton, Timothy J.

    2013-01-01

    There has been a surge of interest in examining the utility of methods for capturing individual differences in decision-making style. We illustrate the potential offered by Movement Pattern Analysis (MPA), an observational methodology that has been used in business and by the US Department of Defense to record body movements that provide predictive insight into individual differences in decision-making motivations and actions. Twelve military officers participated in an intensive 2-h intervie...

  14. Searching for an Accurate Marker-Based Prediction of an Individual Quantitative Trait in Molecular Plant Breeding.

    Science.gov (United States)

    Fu, Yong-Bi; Yang, Mo-Hua; Zeng, Fangqin; Biligetu, Bill

    2017-01-01

    Molecular plant breeding with the aid of molecular markers has played an important role in modern plant breeding over the last two decades. Many marker-based predictions for quantitative traits have been made to enhance parental selection, but the trait prediction accuracy remains generally low, even with the aid of dense, genome-wide SNP markers. To search for more accurate trait-specific prediction with informative SNP markers, we conducted a literature review on the prediction issues in molecular plant breeding and on the applicability of an RNA-Seq technique for developing function-associated specific trait (FAST) SNP markers. To understand whether and how FAST SNP markers could enhance trait prediction, we also performed a theoretical reasoning on the effectiveness of these markers in a trait-specific prediction, and verified the reasoning through computer simulation. To the end, the search yielded an alternative to regular genomic selection with FAST SNP markers that could be explored to achieve more accurate trait-specific prediction. Continuous search for better alternatives is encouraged to enhance marker-based predictions for an individual quantitative trait in molecular plant breeding.

  15. Attentional and motor impulsivity interactively predict 'food addiction' in obese individuals.

    Science.gov (United States)

    Meule, Adrian; de Zwaan, Martina; Müller, Astrid

    2017-01-01

    Impulsivity is a multifaceted construct and constitutes a common risk factor for a range of behaviors associated with poor self-control (e.g., substance use or binge eating). The short form of the Barratt Impulsiveness Scale (BIS-15) measures impulsive behaviors related to attentional (inability to focus attention or concentrate), motor (acting without thinking), and non-planning (lack of future orientation or forethought) impulsivity. Eating-related measures appear to be particularly related to attentional and motor impulsivity and recent findings suggest that interactive effects between these two facets may play a role in eating- and weight-regulation. One-hundred thirty-three obese individuals presenting for bariatric surgery (77.4% female) completed the BIS-15 and the Yale Food Addiction Scale (YFAS) 2.0, which measures addiction-like eating based on the eleven symptoms of substance use disorder outlined in the fifth version of the Diagnostic and Statistical Manual of Mental Disorders. Sixty-three participants (47.4%) were classified as being 'food addicted'. Scores on attentional and motor impulsivity interactively predicted 'food addiction' status: higher attentional impulsivity was associated with a higher likelihood of receiving a YFAS 2.0 diagnosis only at high (+1 SD), but not at low (-1 SD) levels of motor impulsivity. Results support previous findings showing that non-planning impulsivity does not appear to play a role in eating-related self-regulation. Furthermore, this is the first study that shows interactive effects between different impulsivity facets when predicting 'food addiction' in obese individuals. Self-regulatory failure in eating-regulation (e.g., addiction-like overeating) may particularly emerge when both attentional and motor impulsivity levels are elevated. Copyright © 2016 Elsevier Inc. All rights reserved.

  16. Development of newborn screening connect (NBS connect): a self-reported patient registry and its role in improvement of care for patients with inherited metabolic disorders.

    Science.gov (United States)

    Osara, Yetsa; Coakley, Kathryn; Devarajan, Aishwarya; Singh, Rani H

    2017-07-19

    Newborn Screening Connect (NBS Connect) is a web-based self-reported patient registry and resource for individuals and families affected by disorders included in the newborn screening panel. NBS Connect was launched in 2012 by Emory University after years of planning and grassroots work by professionals, consumers, and industry. Individuals with phenylketonuria (PKU), maple syrup urine disease (MSUD) or tyrosinemia (TYR) have been recruited through distribution of outreach materials, presentations at parent organization meetings and direct recruitment at clinic appointments. Participants complete online profiles generating data on diagnosis, treatment, symptoms, outcomes, barriers to care, and quality of life. Resources such as education materials, information on the latest research and clinical trials, recipes, interactive health tracking systems, and professional support tools are described. In addition, to examine the ability of NBS Connect to generate data that guides hypothesis-driven research, data pertaining to age at diagnosis, bone health, and skin conditions in individuals with PKU were assessed. The objective of this paper is to describe the development of NBS Connect and highlight its data, resources and research contributions. In September 2016, NBS Connect had 442 registered participants: 314 (71%) individuals with PKU, 68 (15%) with MSUD, 20 (5%) with TYR, and 40 (9%) with other disorders on the NBS panel. Age at diagnosis was less than 4 weeks in 285 (89%) of 319 respondents to this question and between 1 month and 14 years in 29 (9%) individuals. Of 216 respondents with PKU, 33 (15%) had a DXA scan in the past year. Of 217 respondents with PKU, 99 (46%) reported at least one skin condition. NBS Connect was built and refined with feedback from all stakeholders, including individuals with inherited metabolic disorders. Based on patient-reported data, future studies can be initiated to test hypotheses such as the relationship between PKU and skin

  17. Trait aspects of auditory mismatch negativity predict response to auditory training in individuals with early illness schizophrenia.

    Science.gov (United States)

    Biagianti, Bruno; Roach, Brian J; Fisher, Melissa; Loewy, Rachel; Ford, Judith M; Vinogradov, Sophia; Mathalon, Daniel H

    2017-01-01

    Individuals with schizophrenia have heterogeneous impairments of the auditory processing system that likely mediate differences in the cognitive gains induced by auditory training (AT). Mismatch negativity (MMN) is an event-related potential component reflecting auditory echoic memory, and its amplitude reduction in schizophrenia has been linked to cognitive deficits. Therefore, MMN may predict response to AT and identify individuals with schizophrenia who have the most to gain from AT. Furthermore, to the extent that AT strengthens auditory deviance processing, MMN may also serve as a readout of the underlying changes in the auditory system induced by AT. Fifty-six individuals early in the course of a schizophrenia-spectrum illness (ESZ) were randomly assigned to 40 h of AT or Computer Games (CG). Cognitive assessments and EEG recordings during a multi-deviant MMN paradigm were obtained before and after AT and CG. Changes in these measures were compared between the treatment groups. Baseline and trait-like MMN data were evaluated as predictors of treatment response. MMN data collected with the same paradigm from a sample of Healthy Controls (HC; n = 105) were compared to baseline MMN data from the ESZ group. Compared to HC, ESZ individuals showed significant MMN reductions at baseline ( p = .003). Reduced Double-Deviant MMN was associated with greater general cognitive impairment in ESZ individuals ( p = .020). Neither ESZ intervention group showed significant change in MMN. We found high correlations in all MMN deviant types (rs = .59-.68, all ps < .001) between baseline and post-intervention amplitudes irrespective of treatment group, suggesting trait-like stability of the MMN signal. Greater deficits in trait-like Double-Deviant MMN predicted greater cognitive improvements in the AT group ( p = .02), but not in the CG group. In this sample of ESZ individuals, AT had no effect on auditory deviance processing as assessed by MMN. In ESZ individuals, baseline MMN

  18. A Pilot Study of Individual Muscle Force Prediction during Elbow Flexion and Extension in the Neurorehabilitation Field

    Directory of Open Access Journals (Sweden)

    Jiateng Hou

    2016-11-01

    Full Text Available This paper proposes a neuromusculoskeletal (NMS model to predict individual muscle force during elbow flexion and extension. Four male subjects were asked to do voluntary elbow flexion and extension. An inertial sensor and surface electromyography (sEMG sensors were attached to subject's forearm. Joint angle calculated by fusion of acceleration and angular rate using an extended Kalman filter (EKF and muscle activations obtained from the sEMG signals were taken as the inputs of the proposed NMS model to determine individual muscle force. The result shows that our NMS model can predict individual muscle force accurately, with the ability to reflect subject-specific joint dynamics and neural control solutions. Our method incorporates sEMG and motion data, making it possible to get a deeper understanding of neurological, physiological, and anatomical characteristics of human dynamic movement. We demonstrate the potential of the proposed NMS model for evaluating the function of upper limb movements in the field of neurorehabilitation.

  19. Religious and spiritual importance moderate relation between default mode network connectivity and familial risk for depression.

    Science.gov (United States)

    Svob, Connie; Wang, Zhishun; Weissman, Myrna M; Wickramaratne, Priya; Posner, Jonathan

    2016-11-10

    Individuals at high risk for depression have increased default mode network (DMN) connectivity, as well as reduced inverse connectivity between the DMN and the central executive network (CEN) [8]. Other studies have indicated that the belief in the importance of religion/spirituality (R/S) is protective against depression in high risk individuals [5]. Given these findings, we hypothesized that R/S importance would moderate DMN connectivity, potentially reducing DMN connectivity or increasing DMN-CEN inverse connectivity in individuals at high risk for depression. Using resting-state functional connectivity MRI (rs-fcMRI) in a sample of 104 individuals (aged 11-60) at high and low risk for familial depression, we previously reported increased DMN connectivity and reduced DMN-CEN inverse connectivity in high risk individuals. Here, we found that this effect was moderated by self-report measures of R/S importance. Greater R/S importance in the high risk group was associated with decreased DMN connectivity. These results may represent a protective neural adaptation in the DMN of individuals at high risk for depression, and may have implications for other meditation-based therapies for depression. Published by Elsevier Ireland Ltd.

  20. Predictability of the individual clinical outcome of extracorporeal shock wave therapy for cellulite

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    Schlaudraff KU

    2014-05-01

    Full Text Available Kai-Uwe Schlaudraff,1 Maren C Kiessling,2 Nikolaus BM Császár,2 Christoph Schmitz21Concept Clinic, Geneva, Switzerland; 2Department of Anatomy II, Ludwig-Maximilians-University of Munich, Munich, GermanyBackground: Extracorporeal shock wave therapy has been successfully introduced for the treatment of cellulite in recent years. However, it is still unknown whether the individual clinical outcome of cellulite treatment with extracorporeal shock wave therapy can be predicted by the patient's individual cellulite grade at baseline, individual patient age, body mass index (BMI, weight, and/or height.Methods: Fourteen Caucasian females with cellulite were enrolled in a prospective, single-center, randomized, open-label Phase II study. The mean (± standard error of the mean cellulite grade at baseline was 2.5±0.09 and mean BMI was 22.8±1.17. All patients were treated with radial extracorporeal shock waves using the Swiss DolorClast® device (Electro Medical Systems, S.A., Nyon, Switzerland. Patients were treated unilaterally with 2 weekly treatments for 4 weeks on a randomly selected side (left or right, totaling eight treatments on the selected side. Treatment was performed at 3.5–4.0 bar, with 15,000 impulses per session applied at 15 Hz. Impulses were homogeneously distributed over the posterior thigh and buttock area (resulting in 7,500 impulses per area. Treatment success was evaluated after the last treatment and 4 weeks later by clinical examination, photographic documentation, contact thermography, and patient satisfaction questionnaires.Results: The mean cellulite grade improved from 2.5±0.09 at baseline to 1.57±0.18 after the last treatment (ie, mean δ-1 was 0.93 cellulite grades and 1.68±0.16 at follow-up (ie, mean δ-2 was 0.82 cellulite grades. Compared with baseline, no patient's condition worsened, the treatment was well tolerated, and no unwanted side effects were observed. No statistically significant (ie, P<0

  1. Predictability of the individual clinical outcome of extracorporeal shock wave therapy for cellulite

    OpenAIRE

    Schlaudraff, Kai-Uwe; Kiessling, Maren C; Császár, Nikolaus BM; Schmitz, Christoph

    2014-01-01

    Kai-Uwe Schlaudraff,1 Maren C Kiessling,2 Nikolaus BM Császár,2 Christoph Schmitz21Concept Clinic, Geneva, Switzerland; 2Department of Anatomy II, Ludwig-Maximilians-University of Munich, Munich, GermanyBackground: Extracorporeal shock wave therapy has been successfully introduced for the treatment of cellulite in recent years. However, it is still unknown whether the individual clinical outcome of cellulite treatment with extracorporeal shock wave therapy can be predict...

  2. Perceived helplessness is associated with individual differences in the central motor output system.

    Science.gov (United States)

    Salomons, Tim V; Moayedi, Massieh; Weissman-Fogel, Irit; Goldberg, Michael B; Freeman, Bruce V; Tenenbaum, Howard C; Davis, Karen D

    2012-05-01

    Learned helplessness is a maladaptive response to uncontrollable stress characterized by impaired motor escape responses, reduced motivation and learning deficits. There are important individual differences in the likelihood of becoming helpless following exposure to uncontrollable stress but little is known about the neural mechanisms underlying these individual differences. Here we used structural MRI to measure gray and white matter in individuals with chronic pain, a population at high risk for helplessness due to prolonged exposure to a poorly controlled stressor (pain). Given that self-reported helplessness is predictive of treatment outcomes in chronic pain, understanding such differences might provide valuable clinical insight. We found that the magnitude of self-reported helplessness correlated with cortical thickness in the supplementary motor area (SMA) and midcingulate cortex, regions implicated in cognitive aspects of motor behavior. We then examined the white matter connectivity of these regions and found that fractional anisotropy of connected white matter tracts along the corticospinal tract was associated with helplessness and mediated the relationship between SMA cortical thickness and helplessness. These data provide novel evidence that links individual differences in the motor output pathway with perceived helplessness over a chronic and poorly controlled stressor. © 2012 The Authors. European Journal of Neuroscience © 2012 Federation of European Neuroscience Societies and Blackwell Publishing Ltd.

  3. Identification of the Predictive Power of Five Factor Personality Traits for Individual Instrument Performance Anxiety

    Science.gov (United States)

    Özdemir, Gökhan; Dalkiran, Esra

    2017-01-01

    This study, with the aim of identifying the predictive power of the five-factor personality traits of music teacher candidates on individual instrument performance anxiety, was designed according to the relational screening model. The study population was students attending the Music Education branch of Fine Arts Education Departments in…

  4. Identification of neural connectivity signatures of autism using machine learning

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    Gopikrishna eDeshpande

    2013-10-01

    Full Text Available Alterations in neural connectivity have been suggested as a signature of the pathobiology of autism. Although disrupted correlation between cortical regions observed from functional MRI is considered to be an explanatory model for autism, the directional causal influence between brain regions is a vital link missing in these studies. The current study focuses on addressing this in an fMRI study of Theory-of-Mind in 15 high-functioning adolescents and adults with autism (ASD and 15 typically developing (TD controls. Participants viewed a series of comic strip vignettes in the MRI scanner and were asked to choose the most logical end to the story from three alternatives, separately for trials involving physical and intentional causality. Causal brain connectivity obtained from a multivariate autoregressive model, along with assessment scores, functional connectivity values, and fractional anisotropy obtained from DTI data for each participant, were submitted to a recursive cluster elimination based support vector machine classifier to determine the accuracy with which the classifier can predict a novel participant’s group membership (ASD or TD. We found a maximum classification accuracy of 95.9 % with 19 features which had the highest discriminative ability between the groups. All of the 19 features were effective connectivity paths, indicating that causal information may be critical in discriminating between ASD and TD groups. These effective connectivity paths were also found to be significantly greater in controls as compared to ASD participants and consisted predominantly of outputs from the fusiform face area and middle temporal gyrus indicating impaired connectivity in ASD participants, particularly in the social brain areas. These findings collectively point towards the fact that alterations in causal brain connectivity in individuals with ASD could serve as a potential non-invasive neuroimaging signature for autism

  5. Prediction of coronary artery calcium progression by FDG uptake of large arteries in asymptomatic individuals

    Energy Technology Data Exchange (ETDEWEB)

    Cho, Sang-Geon; Park, Ki Seong; Kim, Jahae; Song, Ho-Chun [Chonnam National University Hospital, Department of Nuclear Medicine, Gwang-ju (Korea, Republic of); Kang, Sae-Ryung; Kwon, Seong Young; Jabin, Zeenat; Kim, Young Jae; Jeong, Geum-Cheol; Song, Minchul; Min, Jung-Joon; Bom, Hee-Seung [Chonnam National University Hwasun Hospital, Department of Nuclear Medicine, Hwasun-gun, Jeollanam-do (Korea, Republic of); Seon, Hyun Ju [Chonnam National University Hwasun Hospital, Department of Radiology, Hwasun-gun, Jeollanam-do (Korea, Republic of)

    2017-01-15

    The purpose of this study is to evaluate whether fluorodeoxyglucose (FDG) uptake of the large arteries can predict coronary artery calcium (CAC) progression in asymptomatic individuals. Ninety-six asymptomatic individuals who underwent FDG positron emission tomography (PET) and CAC scoring on the same day for health screening and follow-up CAC scoring ≥1 year after baseline studies (mean 4.3 years) were included. Vascular FDG uptake was measured and corrected for blood pool activity to obtain peak and average target-to-blood pool ratios (TBRpeak and TBRavg, respectively) for the carotid arteries, and ascending and abdominal aorta. CAC scores at baseline and follow-up of each individual were measured and absolute CAC change (ΔCAC), annual CAC change (ΔCAC/year), and annual CAC change rate (ΔCAC%/year) were calculated. CAC progression was defined as ΔCAC >0 for individuals with negative baseline CAC; ΔCAC/year ≥10 for those with baseline CAC of 0predict CAC progression. Thirty-one subjects showed CAC progression. CAC-progressors showed significantly higher TBRpeak and TBRavg as compared to non-CAC-progressors for all three arteries. TBRpeak of the abdominal aorta was significantly associated with CAC progression in multivariate analysis, with age and baseline CAC. A higher TBRpeak of the abdominal aorta (≥2.11) was associated with CAC progression among subjects with negative baseline CAC only. In subjects with positive baseline CAC, only the amount of baseline CAC was significantly associated with CAC progression. However, the positive predictive value of the TBRpeak of the abdominal aorta was <40 % when age was <58 or baseline CAC was negative. Higher FDG uptake of the large arteries is

  6. Why harmless sensations might hurt in individuals with chronic pain: About heightened prediction and perception of pain in the mind

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    Tanja Hechler

    2016-10-01

    Full Text Available In individuals with chronic pain harmless bodily sensations can elicit anticipatory fear of pain resulting in maladaptive responses such as taking pain medication. Here, we aim to broaden the perspective taking into account recent evidence that suggests that interoceptive perception is largely a construction of beliefs, which are based on past experience and that are kept in check by the actual state of the body. Taking a Bayesian perspective, we propose that individuals with chronic pain display a heightened prediction of pain (prior probability p(pain, which results in heightened pain perception (posterior probability p(pain|sensation due to an assumed link between pain and a harmless bodily sensation (p(sensation│pain. This pain perception emerges because their mind infers pain as the most likely cause for the sensation. When confronted with a mismatch between predicted pain and a (harmless bodily sensation, individuals with chronic pain try to minimize the mismatch most likely by active inference of pain or by an attentional shift. The active inference results in activities that produce a stronger sensation that will match with the prediction, allowing subsequent perceptual inference of pain. Here, we depict heightened pain perception in individuals with chronic pain by reformulating and extending the assumptions of the interoceptive predictive coding model from a Bayesian perspective. The review concludes with a research agenda and clinical considerations.

  7. Individual differences in the rubber-hand illusion: predicting self-reports of people's personal experiences.

    Science.gov (United States)

    Haans, Antal; Kaiser, Florian G; Bouwhuis, Don G; Ijsselsteijn, Wijnand A

    2012-10-01

    Can we assess individual differences in the extent to which a person perceives the rubber-hand illusion on the basis of self-reported experiences? In this research, we develop such an instrument using Rasch-type models. In our conception, incorporating an object (e.g., a rubber hand) into one's body image requires various sensorimotor and cognitive processes. The extent to which people can meet these requirements thus determines how intensely people experience and, simultaneously, describe the illusion. As a consequence, individual differences in people's susceptibility to the rubber-hand illusion can be determined by inspecting reports of their personal experiences. The proposed model turned out to be functional in its capability to predict self-reports of people's experiences and to reliably assess individual differences in susceptibility to the illusion. Regarding validity, we found a small, but significant, correlation between individual susceptibility and proprioceptive drift. Additionally, we found that asynchrony, and tapping rather than stroking the fingers constrain the experience of the illusion. Copyright © 2012 Elsevier B.V. All rights reserved.

  8. Heterozygosity in an isolated population of a large mammal founded by four individuals is predicted by an individual-based genetic model.

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    Jaana Kekkonen

    Full Text Available Within-population genetic diversity is expected to be dramatically reduced if a population is founded by a low number of individuals. Three females and one male white-tailed deer Odocoileus virginianus, a North American species, were successfully introduced in Finland in 1934 and the population has since been growing rapidly, but remained in complete isolation from other populations.Based on 14 microsatellite loci, the expected heterozygosity H was 0.692 with a mean allelic richness (AR of 5.36, which was significantly lower than what was found in Oklahoma, U.S.A. (H = 0.742; AR = 9.07, demonstrating that a bottleneck occurred. Observed H was in line with predictions from an individual-based model where the genealogy of the males and females in the population were tracked and the population's demography was included.Our findings provide a rare within-population empirical test of the founder effect and suggest that founding a population by a small number of individuals need not have a dramatic impact on heterozygosity in an iteroparous species.

  9. Searching for an Accurate Marker-Based Prediction of an Individual Quantitative Trait in Molecular Plant Breeding

    Directory of Open Access Journals (Sweden)

    Yong-Bi Fu

    2017-07-01

    Full Text Available Molecular plant breeding with the aid of molecular markers has played an important role in modern plant breeding over the last two decades. Many marker-based predictions for quantitative traits have been made to enhance parental selection, but the trait prediction accuracy remains generally low, even with the aid of dense, genome-wide SNP markers. To search for more accurate trait-specific prediction with informative SNP markers, we conducted a literature review on the prediction issues in molecular plant breeding and on the applicability of an RNA-Seq technique for developing function-associated specific trait (FAST SNP markers. To understand whether and how FAST SNP markers could enhance trait prediction, we also performed a theoretical reasoning on the effectiveness of these markers in a trait-specific prediction, and verified the reasoning through computer simulation. To the end, the search yielded an alternative to regular genomic selection with FAST SNP markers that could be explored to achieve more accurate trait-specific prediction. Continuous search for better alternatives is encouraged to enhance marker-based predictions for an individual quantitative trait in molecular plant breeding.

  10. Searching for an Accurate Marker-Based Prediction of an Individual Quantitative Trait in Molecular Plant Breeding

    Science.gov (United States)

    Fu, Yong-Bi; Yang, Mo-Hua; Zeng, Fangqin; Biligetu, Bill

    2017-01-01

    Molecular plant breeding with the aid of molecular markers has played an important role in modern plant breeding over the last two decades. Many marker-based predictions for quantitative traits have been made to enhance parental selection, but the trait prediction accuracy remains generally low, even with the aid of dense, genome-wide SNP markers. To search for more accurate trait-specific prediction with informative SNP markers, we conducted a literature review on the prediction issues in molecular plant breeding and on the applicability of an RNA-Seq technique for developing function-associated specific trait (FAST) SNP markers. To understand whether and how FAST SNP markers could enhance trait prediction, we also performed a theoretical reasoning on the effectiveness of these markers in a trait-specific prediction, and verified the reasoning through computer simulation. To the end, the search yielded an alternative to regular genomic selection with FAST SNP markers that could be explored to achieve more accurate trait-specific prediction. Continuous search for better alternatives is encouraged to enhance marker-based predictions for an individual quantitative trait in molecular plant breeding. PMID:28729875

  11. Prediction of Driving Safety in Individuals with Homonymous Hemianopia and Quadrantanopia from Clinical Neuroimaging

    Directory of Open Access Journals (Sweden)

    Michael S. Vaphiades

    2014-01-01

    Full Text Available Background. This study aimed to determine whether it is possible to predict driving safety of individuals with homonymous hemianopia or quadrantanopia based upon a clinical review of neuroimages that are routinely available in clinical practice. Methods. Two experienced neuroophthalmologists viewed a summary report of the CT/MRI scans of 16 participants with homonymous hemianopic or quadrantanopic field defects which indicated the site and extent of the lesion and they made predictions regarding whether participants would be safe/unsafe to drive. Driving safety was independently defined at the time of the study using state-recorded motor vehicle crashes (all crashes and at-fault for the previous 5 years and ratings of driving safety determined through a standardized on-road driving assessment by a certified driving rehabilitation specialist. Results. The ability to predict driving safety was highly variable regardless of the driving safety measure, ranging from 31% to 63% (kappa levels ranged from −0.29 to 0.04. The level of agreement between the neuroophthalmologists was only fair (kappa = 0.28. Conclusions. Clinical evaluation of summary reports of currently available neuroimages by neuroophthalmologists is not predictive of driving safety. Future research should be directed at identifying and/or developing alternative tests or strategies to better enable clinicians to make these predictions.

  12. Prediction of driving safety in individuals with homonymous hemianopia and quadrantanopia from clinical neuroimaging.

    Science.gov (United States)

    Vaphiades, Michael S; Kline, Lanning B; McGwin, Gerald; Owsley, Cynthia; Shah, Ritu; Wood, Joanne M

    2014-01-01

    Background. This study aimed to determine whether it is possible to predict driving safety of individuals with homonymous hemianopia or quadrantanopia based upon a clinical review of neuroimages that are routinely available in clinical practice. Methods. Two experienced neuroophthalmologists viewed a summary report of the CT/MRI scans of 16 participants with homonymous hemianopic or quadrantanopic field defects which indicated the site and extent of the lesion and they made predictions regarding whether participants would be safe/unsafe to drive. Driving safety was independently defined at the time of the study using state-recorded motor vehicle crashes (all crashes and at-fault) for the previous 5 years and ratings of driving safety determined through a standardized on-road driving assessment by a certified driving rehabilitation specialist. Results. The ability to predict driving safety was highly variable regardless of the driving safety measure, ranging from 31% to 63% (kappa levels ranged from -0.29 to 0.04). The level of agreement between the neuroophthalmologists was only fair (kappa = 0.28). Conclusions. Clinical evaluation of summary reports of currently available neuroimages by neuroophthalmologists is not predictive of driving safety. Future research should be directed at identifying and/or developing alternative tests or strategies to better enable clinicians to make these predictions.

  13. Forecasting craniofacial growth in individuals with class III malocclusion by computational modelling.

    Science.gov (United States)

    Auconi, Pietro; Scazzocchio, Marco; Defraia, Efisio; McNamara, James A; Franchi, Lorenzo

    2014-04-01

    To develop a mathematical model that adequately represented the pattern of craniofacial growth in class III subject consistently, with the goal of using this information to make growth predictions that could be amenable to longitudinal verification and clinical use. A combination of computational techniques (i.e. Fuzzy clustering and Network analysis) was applied to cephalometric data derived from 429 untreated growing female patients with class III malocclusion to visualize craniofacial growth dynamics and correlations. Four age groups of subjects were examined individually: from 7 to 9 years of age, from 10 to 12 years, from 13 to 14 years, and from 15 to 17 years. The connections between pathway components of class III craniofacial growth can be visualized from Network profiles. Fuzzy clustering analysis was able to define further growth patterns and coherences of the traditionally reported dentoskeletal characteristics of this structural imbalance. Craniofacial growth can be visualized as a biological, space-constraint-based optimization process; the prediction of individual growth trajectories depends on the rate of membership to a specific 'winner' cluster, i.e. on a specific individual growth strategy. The reliability of the information thus gained was tested to forecast craniofacial growth of 28 untreated female class III subjects followed longitudinally. The combination of Fuzzy clustering and Network algorithms allowed the development of principles for combining multiple auxological cephalometric features into a joint global model and to predict the individual risk of the facial pattern imbalance during growth.

  14. Personality traits predict brain activation and connectivity when witnessing a violent conflict.

    Science.gov (United States)

    Van den Stock, Jan; Hortensius, Ruud; Sinke, Charlotte; Goebel, Rainer; de Gelder, Beatrice

    2015-09-04

    As observers we excel in decoding the emotional signals telling us that a social interaction is turning violent. The neural substrate and its modulation by personality traits remain ill understood. We performed an fMRI experiment in which participants watched videos displaying a violent conflict between two people. Observers' attention was directed to either the aggressor or the victim. Focusing on the aggressor (vs. focusing on the victim) activated the superior temporal sulcus (STS), extra-striate body area (EBA), occipital poles and centro-medial amygdala (CMA). Stronger instantaneous connectivity occurred between these and the EBA, insula, and the red nucleus. When focusing on the victim, basolateral amygdala (BLA) activation was related to trait empathy and showed increased connectivity with the insula and red nucleus. STS activation was associated with trait aggression and increased connectivity with the hypothalamus. The findings reveal that focusing on the aggressor of a violent conflict triggers more activation in categorical (EBA) and emotion (CMA, STS) areas. This is associated with increased instantaneous connectivity among emotion areas (CMA-insula) and between categorical and emotion (EBA-STS) areas. When the focus is on the victim, personality traits (aggression/empathy) modulate activity in emotion areas (respectively STS and postcentral gyrus/ BLA), along with connectivity in the emotional diencephalon (hypothalamus) and early visual areas (occipital pole).

  15. Who will become dominant? Investigating the roles of individual behaviour, body size, and environmental predictability in brown trout fry hierarchies

    Directory of Open Access Journals (Sweden)

    Näslund Joacim

    2018-02-01

    Full Text Available This paper presents a study investigating performance of brown trout fry, with different behavioural characteristics, in environments differing in food predictability. Based on previous experimental findings, we hypothesised that more active individuals would be favoured by a predictable environment, as compared to an unpredictable environment, as a consequence of being more aggressive and likely to dominate the best feeding stations. This hypothesis was not supported, as more active individuals instead tended to perform better, in terms of growth and survival, in unpredictable environments. However, this effect may stem from initial size differences, as more active fish also tended to be larger. In predictable environments, no trends between activity (or size and performance were detected. Dominant individuals could be identified based on lighter body colouration in 9 out of 10 rearing tanks, but dominance appeared not to be related to activity score. The results highlight a potential advantage of more active and/or larger fry in unpredictable environments, while performance in predictable environments is likely depending on other phenotypic characteristics. Our general experimental approach can be useful for further developments in the investigation of performance of different ethotypes of brown trout fry.

  16. Does resting-state connectivity reflect depressive rumination? A tale of two analyses.

    Science.gov (United States)

    Berman, Marc G; Misic, Bratislav; Buschkuehl, Martin; Kross, Ethan; Deldin, Patricia J; Peltier, Scott; Churchill, Nathan W; Jaeggi, Susanne M; Vakorin, Vasily; McIntosh, Anthony R; Jonides, John

    2014-12-01

    Major Depressive Disorder (MDD) is characterized by rumination. Prior research suggests that resting-state brain activation reflects rumination when depressed individuals are not task engaged. However, no study has directly tested this. Here we investigated whether resting-state epochs differ from induced ruminative states for healthy and depressed individuals. Most previous research on resting-state networks comes from seed-based analyses with the posterior cingulate cortex (PCC). By contrast, we examined resting state connectivity by using the complete multivariate connectivity profile (i.e., connections across all brain nodes) and by comparing these results to seeded analyses. We find that unconstrained resting-state intervals differ from active rumination states in strength of connectivity and that overall connectivity was higher for healthy vs. depressed individuals. Relationships between connectivity and subjective mood (i.e., behavior) were strongly observed during induced rumination epochs. Furthermore, connectivity patterns that related to subjective mood were strikingly different for MDD and healthy control (HC) groups suggesting different mood regulation mechanisms. Copyright © 2014 Elsevier Inc. All rights reserved.

  17. Me After You: Partner Influence and Individual Effort Predict Rejection of Self-Aspects and Self-Concept Clarity After Relationship Dissolution.

    Science.gov (United States)

    Slotter, Erica B; Emery, Lydia F; Luchies, Laura B

    2014-07-01

    Individuals in ongoing romantic relationships incorporate attributes from their partner into their own self-concepts. However, little research has investigated what happens to these attributes should the relationship end. Across three studies, the present research sought to examine factors that predicted whether individuals retain or reject attributes from their self-concept that they initially gained during a relationship. We predicted that individuals would be more likely to reject attributes from their self post-dissolution if their ex-partner was influential in them adding those attributes to the self in the first place. However, we expected this effect to be moderated such that individuals who exerted greater, versus lesser, effort in maintaining relevant attributes would retain them as part of the self, regardless of whether the attribute originated from the partner. In addition, in two of our three studies, we explored the roles of partner influence, effort, and attribute rejection on individuals' post-dissolution self-concept clarity. © 2014 by the Society for Personality and Social Psychology, Inc.

  18. Replicated landscape genetic and network analyses reveal wide variation in functional connectivity for American pikas.

    Science.gov (United States)

    Castillo, Jessica A; Epps, Clinton W; Jeffress, Mackenzie R; Ray, Chris; Rodhouse, Thomas J; Schwalm, Donelle

    2016-09-01

    Landscape connectivity is essential for maintaining viable populations, particularly for species restricted to fragmented habitats or naturally arrayed in metapopulations and facing rapid climate change. The importance of assessing both structural connectivity (physical distribution of favorable habitat patches) and functional connectivity (how species move among habitat patches) for managing such species is well understood. However, the degree to which functional connectivity for a species varies among landscapes, and the resulting implications for conservation, have rarely been assessed. We used a landscape genetics approach to evaluate resistance to gene flow and, thus, to determine how landscape and climate-related variables influence gene flow for American pikas (Ochotona princeps) in eight federally managed sites in the western United States. We used empirically derived, individual-based landscape resistance models in conjunction with predictive occupancy models to generate patch-based network models describing functional landscape connectivity. Metareplication across landscapes enabled identification of limiting factors for dispersal that would not otherwise have been apparent. Despite the cool microclimates characteristic of pika habitat, south-facing aspects consistently represented higher resistance to movement, supporting the previous hypothesis that exposure to relatively high temperatures may limit dispersal in American pikas. We found that other barriers to dispersal included areas with a high degree of topographic relief, such as cliffs and ravines, as well as streams and distances greater than 1-4 km depending on the site. Using the empirically derived network models of habitat patch connectivity, we identified habitat patches that were likely disproportionately important for maintaining functional connectivity, areas in which habitat appeared fragmented, and locations that could be targeted for management actions to improve functional connectivity

  19. Factors Motivating Individuals to Consider Genetic Testing for Type 2 Diabetes Risk Prediction.

    Directory of Open Access Journals (Sweden)

    Jennifer Wessel

    Full Text Available The purpose of this study was to identify attitudes and perceptions of willingness to participate in genetic testing for type 2 diabetes (T2D risk prediction in the general population. Adults (n = 598 were surveyed on attitudes about utilizing genetic testing to predict future risk of T2D. Participants were recruited from public libraries (53%, online registry (37% and a safety net hospital emergency department (10%. Respondents were 37 ± 11 years old, primarily White (54%, female (69%, college educated (46%, with an annual income ≥$25,000 (56%. Half of participants were interested in genetic testing for T2D (52% and 81% agreed/strongly agreed genetic testing should be available to the public. Only 57% of individuals knew T2D is preventable. A multivariate model to predict interest in genetic testing was adjusted for age, gender, recruitment location and BMI; significant predictors were motivation (high perceived personal risk of T2D [OR = 4.38 (1.76, 10.9]; family history [OR = 2.56 (1.46, 4.48]; desire to know risk prior to disease onset [OR = 3.25 (1.94, 5.42]; and knowing T2D is preventable [OR = 2.11 (1.24, 3.60], intention (if the cost is free [OR = 10.2 (4.27, 24.6]; and learning T2D is preventable [OR = 5.18 (1.95, 13.7] and trust of genetic testing results [OR = 0.03 (0.003, 0.30]. Individuals are interested in genetic testing for T2D risk which offers unique information that is personalized. Financial accessibility, validity of the test and availability of diabetes prevention programs were identified as predictors of interest in T2D testing.

  20. Electrophysiological signatures of atypical intrinsic brain connectivity networks in autism

    Science.gov (United States)

    Shou, Guofa; Mosconi, Matthew W.; Wang, Jun; Ethridge, Lauren E.; Sweeney, John A.; Ding, Lei

    2017-08-01

    Objective. Abnormal local and long-range brain connectivity have been widely reported in autism spectrum disorder (ASD), yet the nature of these abnormalities and their functional relevance at distinct cortical rhythms remains unknown. Investigations of intrinsic connectivity networks (ICNs) and their coherence across whole brain networks hold promise for determining whether patterns of functional connectivity abnormalities vary across frequencies and networks in ASD. In the present study, we aimed to probe atypical intrinsic brain connectivity networks in ASD from resting-state electroencephalography (EEG) data via characterizing the whole brain network. Approach. Connectivity within individual ICNs (measured by spectral power) and between ICNs (measured by coherence) were examined at four canonical frequency bands via a time-frequency independent component analysis on high-density EEG, which were recorded from 20 ASD and 20 typical developing (TD) subjects during an eyes-closed resting state. Main results. Among twelve identified electrophysiological ICNs, individuals with ASD showed hyper-connectivity in individual ICNs and hypo-connectivity between ICNs. Functional connectivity alterations in ASD were more severe in the frontal lobe and the default mode network (DMN) and at low frequency bands. These functional connectivity measures also showed abnormal age-related associations in ICNs related to frontal, temporal and motor regions in ASD. Significance. Our findings suggest that ASD is characterized by the opposite directions of abnormalities (i.e. hypo- and hyper-connectivity) in the hierarchical structure of the whole brain network, with more impairments in the frontal lobe and the DMN at low frequency bands, which are critical for top-down control of sensory systems, as well as for both cognition and social skills.

  1. Individual differences in in-person and social media television coviewing: the role of emotional contagion, need to belong, and coviewing orientation.

    Science.gov (United States)

    Cohen, Elizabeth L; Lancaster, Alexander L

    2014-08-01

    The popularity of social media television coviewing is growing, but little is known about why people engage in these connected viewing experiences or how they differ from in-person coviewing. This study investigated how engaging in in-person and social media coviewing is predicted by individual differences: emotional contagion, need to belong, and three dimensions of a coviewing orientation scale created for this research (need for company, need for solitude, and audience monitoring). On Amazon Mechanical Turk, 451 people were recruited for an online survey. The mean age was 34.64 years (SD=13.16 years), and 52% of the sample was female. Emotional contagion predicted in-person coviewing only. Need to belong predicted several mediated co-viewing activities. Need for solitude negatively predicted in-person coviewing, but need for company positively predicted in-person coviewing. Results indicate that viewers have different motivations for engaging in various coviewing activities. Findings also suggest that social media coviewing can provide valuable opportunities for social connection among viewers who watch television in physical solitude.

  2. Predicting hemispheric dominance for language production in healthy individuals using support vector machine.

    Science.gov (United States)

    Zago, Laure; Hervé, Pierre-Yves; Genuer, Robin; Laurent, Alexandre; Mazoyer, Bernard; Tzourio-Mazoyer, Nathalie; Joliot, Marc

    2017-12-01

    We used a Support Vector Machine (SVM) classifier to assess hemispheric pattern of language dominance of 47 individuals categorized as non-typical for language from their hemispheric functional laterality index (HFLI) measured on a sentence minus word-list production fMRI-BOLD contrast map. The SVM classifier was trained at discriminating between Dominant and Non-Dominant hemispheric language production activation pattern on a group of 250 participants previously identified as Typicals (HFLI strongly leftward). Then, SVM was applied to each hemispheric language activation pattern of 47 non-typical individuals. The results showed that at least one hemisphere (left or right) was found to be Dominant in every, except 3 individuals, indicating that the "dominant" type of functional organization is the most frequent in non-typicals. Specifically, left hemisphere dominance was predicted in all non-typical right-handers (RH) and in 57.4% of non-typical left-handers (LH). When both hemisphere classifications were jointly considered, four types of brain patterns were observed. The most often predicted pattern (51%) was left-dominant (Dominant left-hemisphere and Non-Dominant right-hemisphere), followed by right-dominant (23%, Dominant right-hemisphere and Non-Dominant left-hemisphere) and co-dominant (19%, 2 Dominant hemispheres) patterns. Co-non-dominant was rare (6%, 2 Non-Dominant hemispheres), but was normal variants of hemispheric specialization. In RH, only left-dominant (72%) and co-dominant patterns were detected, while for LH, all types were found, although with different occurrences. Among the 10 LH with a strong rightward HFLI, 8 had a right-dominant brain pattern. Whole-brain analysis of the right-dominant pattern group confirmed that it exhibited a functional organization strictly mirroring that of left-dominant pattern group. Hum Brain Mapp 38:5871-5889, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  3. Dynamic brain connectivity is a better predictor of PTSD than static connectivity.

    Science.gov (United States)

    Jin, Changfeng; Jia, Hao; Lanka, Pradyumna; Rangaprakash, D; Li, Lingjiang; Liu, Tianming; Hu, Xiaoping; Deshpande, Gopikrishna

    2017-09-01

    Using resting-state functional magnetic resonance imaging, we test the hypothesis that subjects with post-traumatic stress disorder (PTSD) are characterized by reduced temporal variability of brain connectivity compared to matched healthy controls. Specifically, we test whether PTSD is characterized by elevated static connectivity, coupled with decreased temporal variability of those connections, with the latter providing greater sensitivity toward the pathology than the former. Static functional connectivity (FC; nondirectional zero-lag correlation) and static effective connectivity (EC; directional time-lagged relationships) were obtained over the entire brain using conventional models. Dynamic FC and dynamic EC were estimated by letting the conventional models to vary as a function of time. Statistical separation and discriminability of these metrics between the groups and their ability to accurately predict the diagnostic label of a novel subject were ascertained using separate support vector machine classifiers. Our findings support our hypothesis that PTSD subjects have stronger static connectivity, but reduced temporal variability of connectivity. Further, machine learning classification accuracy obtained with dynamic FC and dynamic EC was significantly higher than that obtained with static FC and static EC, respectively. Furthermore, results also indicate that the ease with which brain regions engage or disengage with other regions may be more sensitive to underlying pathology than the strength with which they are engaged. Future studies must examine whether this is true only in the case of PTSD or is a general organizing principle in the human brain. Hum Brain Mapp 38:4479-4496, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  4. Individualization as driving force of clustering phenomena in humans.

    Directory of Open Access Journals (Sweden)

    Michael Mäs

    Full Text Available One of the most intriguing dynamics in biological systems is the emergence of clustering, in the sense that individuals self-organize into separate agglomerations in physical or behavioral space. Several theories have been developed to explain clustering in, for instance, multi-cellular organisms, ant colonies, bee hives, flocks of birds, schools of fish, and animal herds. A persistent puzzle, however, is the clustering of opinions in human populations, particularly when opinions vary continuously, such as the degree to which citizens are in favor of or against a vaccination program. Existing continuous opinion formation models predict "monoculture" in the long run, unless subsets of the population are perfectly separated from each other. Yet, social diversity is a robust empirical phenomenon, although perfect separation is hardly possible in an increasingly connected world. Considering randomness has not overcome the theoretical shortcomings so far. Small perturbations of individual opinions trigger social influence cascades that inevitably lead to monoculture, while larger noise disrupts opinion clusters and results in rampant individualism without any social structure. Our solution to the puzzle builds on recent empirical research, combining the integrative tendencies of social influence with the disintegrative effects of individualization. A key element of the new computational model is an adaptive kind of noise. We conduct computer simulation experiments demonstrating that with this kind of noise a third phase besides individualism and monoculture becomes possible, characterized by the formation of metastable clusters with diversity between and consensus within clusters. When clusters are small, individualization tendencies are too weak to prohibit a fusion of clusters. When clusters grow too large, however, individualization increases in strength, which promotes their splitting. In summary, the new model can explain cultural clustering in

  5. Future migratory behaviour predicted from premigratory levels of gill Na+/K(+-)ATPase activity in individual wild brown trout ( Salmo trutta )

    DEFF Research Database (Denmark)

    Nielsen, C.; Aarestrup, Kim; Norum, U.

    2004-01-01

    The relationship between premigratory gill Na+/K(+-)ATPase activity, determined at two dates during spring, and future migratory behaviour was investigated using non-lethal gill biopsies and PIT-tagging in wild brown trout (Salmo trutta) from two tributaries. No significant relationship between......(-1)), with an average of 91 % of the predictions being correct. The present study shows that a non-lethal premigratory biochemical measurement can successfully select individual brown trout with high probability of migration...... was obtained. The ability of this regression model from the tributaries to predict future migratory behaviour in an independent group of trout caught in early April in the mainstream was evaluated. A threshold probability of migration was used to predict the behaviour of the mainstream individuals as either...

  6. Functional connectivity with ventromedial prefrontal cortex reflects subjective value for social rewards.

    Science.gov (United States)

    Smith, David V; Clithero, John A; Boltuck, Sarah E; Huettel, Scott A

    2014-12-01

    According to many studies, the ventromedial prefrontal cortex (VMPFC) encodes the subjective value of disparate rewards on a common scale. Yet, a host of other reward factors-likely represented outside of VMPFC-must be integrated to construct such signals for valuation. Using functional magnetic resonance imaging (fMRI), we tested whether the interactions between posterior VMPFC and functionally connected brain regions predict subjective value. During fMRI scanning, participants rated the attractiveness of unfamiliar faces. We found that activation in dorsal anterior cingulate cortex, anterior VMPFC and caudate increased with higher attractiveness ratings. Using data from a post-scan task in which participants spent money to view attractive faces, we quantified each individual's subjective value for attractiveness. We found that connectivity between posterior VMPFC and regions frequently modulated by social information-including the temporal-parietal junction (TPJ) and middle temporal gyrus-was correlated with individual differences in subjective value. Crucially, these additional regions explained unique variation in subjective value beyond that extracted from value regions alone. These findings indicate not only that posterior VMPFC interacts with additional brain regions during valuation, but also that these additional regions carry information employed to construct the subjective value for social reward. © The Author (2014). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  7. Identification and prediction of diabetic sensorimotor polyneuropathy using individual and simple combinations of nerve conduction study parameters.

    Directory of Open Access Journals (Sweden)

    Alanna Weisman

    Full Text Available OBJECTIVE: Evaluation of diabetic sensorimotor polyneuropathy (DSP is hindered by the need for complex nerve conduction study (NCS protocols and lack of predictive biomarkers. We aimed to determine the performance of single and simple combinations of NCS parameters for identification and future prediction of DSP. MATERIALS AND METHODS: 406 participants (61 with type 1 diabetes and 345 with type 2 diabetes with a broad spectrum of neuropathy, from none to severe, underwent NCS to determine presence or absence of DSP for cross-sectional (concurrent validity analysis. The 109 participants without baseline DSP were re-evaluated for its future onset (predictive validity. Performance of NCS parameters was compared by area under the receiver operating characteristic curve (AROC. RESULTS: At baseline there were 246 (60% Prevalent Cases. After 3.9 years mean follow-up, 25 (23% of the 109 Prevalent Controls that were followed became Incident DSP Cases. Threshold values for peroneal conduction velocity and sural amplitude potential best identified Prevalent Cases (AROC 0.90 and 0.83, sensitivity 80 and 83%, specificity 89 and 72%, respectively. Baseline tibial F-wave latency, peroneal conduction velocity and the sum of three lower limb nerve conduction velocities (sural, peroneal, and tibial best predicted 4-year incidence (AROC 0.79, 0.79, and 0.85; sensitivity 79, 70, and 81%; specificity 63, 74 and 77%, respectively. DISCUSSION: Individual NCS parameters or their simple combinations are valid measures for identification and future prediction of DSP. Further research into the predictive roles of tibial F-wave latencies, peroneal conduction velocity, and sum of conduction velocities as markers of incipient nerve injury is needed to risk-stratify individuals for clinical and research protocols.

  8. The concept of individual approach in sport

    Directory of Open Access Journals (Sweden)

    Zh.L. Kozina

    2015-03-01

    Full Text Available Purpose : prove the concept of individual approach to sports training. Develop a common scheme ways individualization process of training athletes. Material: the study involved 149 athletes: 38 volleyball players and 111 players. Was carried out comprehensive testing athletes for 33 pedagogical, psycho-physiological, biochemical parameters. Also conducted an analysis of indicators of competitive activity. Results : we propose the following areas of the individualization process of preparation of athletes: 1 - systematization of mathematical indicators of preparedness and condition of the athlete in a single point in time; 2 - regression analysis of the dynamics of individual game performance athletes; 3 - the use of universal methods of individualization of various aspects of the training process. It is established that the individual characteristics of players in basketball and volleyball are connected not only with the anthropometric data, but also depend on the physiological and psychophysiological indicators. In this aspect there is provided use of cluster and factor analysis for the construction of individual training programs for players. It was found that the dynamics of the gaming performance is described by quadratic, cubic and sinusoidal functions. In the case of sinusoidal oscillation period of regression models in girls is 25-30 days, 31-38 days in boys. This allows to determine the most preferred times of increasing and reducing the efficiency of competitive. Conclusions : the concept of individual approach in sport involves the separation of a wide range of indicators leading factors in the individual structure of athletes, in the analysis and prediction of individual dynamics of competitive performance, to develop universal methods of individualization with the activation of awareness of various aspects of the training process.

  9. Customizing Countermeasure Prescriptions using Predictive Measures of Sensorimotor Adaptability

    Science.gov (United States)

    Bloomberg, J. J.; Peters, B. T.; Mulavara, A. P.; Miller, C. A.; Batson, C. D.; Wood, S. J.; Guined, J. R.; Cohen, H. S.; Buccello-Stout, R.; DeDios, Y. E.; hide

    2014-01-01

    a test of locomotor function designed specifically to delineate both mechanisms. Aim 3: Develop predictors of sensorimotor adaptability using brain structural and functional metrics. We will measure individual differences in regional brain volumes (structural MRI), white matter integrity (diffusion tensor imaging, or DTI), functional network integrity (resting state functional connectivity MRI), and sensorimotor adaptation task-related functional brain activation (functional MRI). We decided to complete the data collection for Specific Aims 1, 2 and 3 simultaneously on the same subjects to increase data capture. By having the same subjects perform all three specific aims we can enhance our ability to detect how a wider range of factors can predict adaptability in a specific individual. This provides a much richer database and potentially a better understanding of the predictive power of the selected factors. In this presentation I will discuss preliminary data obtained to date.

  10. Emotional experiences predict the conversion of individuals with Attenuated Psychosis Syndrome to psychosis: A six-month follow up study

    Directory of Open Access Journals (Sweden)

    Fa Zhan Chen

    2016-06-01

    Full Text Available The present study explored the conversion rate in individuals with Attenuated Psychosis Syndrome (APS and potential predictor for transition in China. Sixty-three participants were identified as APS were followed up six months later. The results showed that 17% of individuals with APS converted to psychosis. The converters exhibited poorer emotional experience and expression than the non-converters at baseline. A further binary logistic regression analysis showed that emotional experience could predict the transition (Wald = 4.18, p = 0.041, 95% CI = 1.04~6.82. The current study suggested an important role of emotional processing in the prediction of the development of full-blown psychosis.

  11. MEG connectivity analysis in patients with Alzheimer's disease using cross mutual information and spectral coherence.

    Science.gov (United States)

    Alonso, Joan Francesc; Poza, Jesús; Mañanas, Miguel Angel; Romero, Sergio; Fernández, Alberto; Hornero, Roberto

    2011-01-01

    Alzheimer's disease (AD) is an irreversible brain disorder which represents the most common form of dementia in western countries. An early and accurate diagnosis of AD would enable to develop new strategies for managing the disease; however, nowadays there is no single test that can accurately predict the development of AD. In this sense, only a few studies have focused on the magnetoencephalographic (MEG) AD connectivity patterns. This study compares brain connectivity in terms of linear and nonlinear couplings by means of spectral coherence and cross mutual information function (CMIF), respectively. The variables defined from these functions provide statistically significant differences (p CMIF. The results suggest that AD is characterized by both decreases and increases of functional couplings in different frequency bands as well as by an increase in regularity, that is, more evident statistical deterministic relationships in AD patients' MEG connectivity. The significant differences obtained indicate that AD could disturb brain interactions causing abnormal brain connectivity and operation. Furthermore, the combination of coherence and CMIF features to perform a diagnostic test based on logistic regression improved the tests based on individual variables for its robustness.

  12. Neural Elements for Predictive Coding

    Directory of Open Access Journals (Sweden)

    Stewart SHIPP

    2016-11-01

    Full Text Available Predictive coding theories of sensory brain function interpret the hierarchical construction of the cerebral cortex as a Bayesian, generative model capable of predicting the sensory data consistent with any given percept. Predictions are fed backwards in the hierarchy and reciprocated by prediction error in the forward direction, acting to modify the representation of the outside world at increasing levels of abstraction, and so to optimize the nature of perception over a series of iterations. This accounts for many ‘illusory’ instances of perception where what is seen (heard, etc is unduly influenced by what is expected, based on past experience. This simple conception, the hierarchical exchange of prediction and prediction error, confronts a rich cortical microcircuitry that is yet to be fully documented. This article presents the view that, in the current state of theory and practice, it is profitable to begin a two-way exchange: that predictive coding theory can support an understanding of cortical microcircuit function, and prompt particular aspects of future investigation, whilst existing knowledge of microcircuitry can, in return, influence theoretical development. As an example, a neural inference arising from the earliest formulations of predictive coding is that the source populations of forwards and backwards pathways should be completely separate, given their functional distinction; this aspect of circuitry – that neurons with extrinsically bifurcating axons do not project in both directions – has only recently been confirmed. Here, the computational architecture prescribed by a generalized (free-energy formulation of predictive coding is combined with the classic ‘canonical microcircuit’ and the laminar architecture of hierarchical extrinsic connectivity to produce a template schematic, that is further examined in the light of (a updates in the microcircuitry of primate visual cortex, and (b rapid technical advances made

  13. Neural Elements for Predictive Coding.

    Science.gov (United States)

    Shipp, Stewart

    2016-01-01

    Predictive coding theories of sensory brain function interpret the hierarchical construction of the cerebral cortex as a Bayesian, generative model capable of predicting the sensory data consistent with any given percept. Predictions are fed backward in the hierarchy and reciprocated by prediction error in the forward direction, acting to modify the representation of the outside world at increasing levels of abstraction, and so to optimize the nature of perception over a series of iterations. This accounts for many 'illusory' instances of perception where what is seen (heard, etc.) is unduly influenced by what is expected, based on past experience. This simple conception, the hierarchical exchange of prediction and prediction error, confronts a rich cortical microcircuitry that is yet to be fully documented. This article presents the view that, in the current state of theory and practice, it is profitable to begin a two-way exchange: that predictive coding theory can support an understanding of cortical microcircuit function, and prompt particular aspects of future investigation, whilst existing knowledge of microcircuitry can, in return, influence theoretical development. As an example, a neural inference arising from the earliest formulations of predictive coding is that the source populations of forward and backward pathways should be completely separate, given their functional distinction; this aspect of circuitry - that neurons with extrinsically bifurcating axons do not project in both directions - has only recently been confirmed. Here, the computational architecture prescribed by a generalized (free-energy) formulation of predictive coding is combined with the classic 'canonical microcircuit' and the laminar architecture of hierarchical extrinsic connectivity to produce a template schematic, that is further examined in the light of (a) updates in the microcircuitry of primate visual cortex, and (b) rapid technical advances made possible by transgenic neural

  14. Evaluation of different biomarkers to predict individual radiosensitivity in an inter-laboratory comparison--lessons for future studies.

    Directory of Open Access Journals (Sweden)

    Burkhard Greve

    Full Text Available Radiotherapy is a powerful cure for several types of solid tumours, but its application is often limited because of severe side effects in individual patients. With the aim to find biomarkers capable of predicting normal tissue side reactions we analysed the radiation responses of cells from individual head and neck tumour and breast cancer patients of different clinical radiosensitivity in a multicentric study. Multiple parameters of cellular radiosensitivity were analysed in coded samples of peripheral blood lymphocytes (PBLs and derived lymphoblastoid cell lines (LCLs from 15 clinical radio-hypersensitive tumour patients and compared to age- and sex-matched non-radiosensitive patient controls and 15 lymphoblastoid cell lines from age- and sex- matched healthy controls of the KORA study. Experimental parameters included ionizing radiation (IR-induced cell death (AnnexinV, induction and repair of DNA strand breaks (Comet assay, induction of yH2AX foci (as a result of DNA double strand breaks, and whole genome expression analyses. Considerable inter-individual differences in IR-induced DNA strand breaks and their repair and/or cell death could be detected in primary and immortalised cells with the applied assays. The group of clinically radiosensitive patients was not unequivocally distinguishable from normal responding patients nor were individual overreacting patients in the test system unambiguously identified by two different laboratories. Thus, the in vitro test systems investigated here seem not to be appropriate for a general prediction of clinical reactions during or after radiotherapy due to the experimental variability compared to the small effect of radiation sensitivity. Genome-wide expression analysis however revealed a set of 67 marker genes which were differentially induced 6 h after in vitro-irradiation in lymphocytes from radio-hypersensitive and non-radiosensitive patients. These results warrant future validation in larger

  15. How we choose one over another: predicting trial-by-trial preference decision.

    Directory of Open Access Journals (Sweden)

    Vidya Bhushan

    Full Text Available Preference formation is a complex problem as it is subjective, involves emotion, is led by implicit processes, and changes depending on the context even within the same individual. Thus, scientific attempts to predict preference are challenging, yet quite important for basic understanding of human decision making mechanisms, but prediction in a group-average sense has only a limited significance. In this study, we predicted preferential decisions on a trial by trial basis based on brain responses occurring before the individuals made their decisions explicit. Participants made a binary preference decision of approachability based on faces while their electrophysiological responses were recorded. An artificial neural network based pattern-classifier was used with time-frequency resolved patterns of a functional connectivity measure as features for the classifier. We were able to predict preference decisions with a mean accuracy of 74.3 ± 2.79% at participant-independent level and of 91.4 ± 3.8% at participant-dependent level. Further, we revealed a causal role of the first impression on final decision and demonstrated the temporal trajectory of preference decision formation.

  16. Complexity in Individual Trajectories toward Online Extremism

    Directory of Open Access Journals (Sweden)

    Z. Cao

    2018-01-01

    Full Text Available Society faces a fundamental global problem of understanding which individuals are currently developing strong support for some extremist entity such as ISIS (Islamic State, even if they never end up doing anything in the real world. The importance of online connectivity in developing intent has been confirmed by recent case studies of already convicted terrorists. Here we use ideas from Complexity to identify dynamical patterns in the online trajectories that individuals take toward developing a high level of extremist support, specifically, for ISIS. Strong memory effects emerge among individuals whose transition is fastest and hence may become “out of the blue” threats in the real world. A generalization of diagrammatic expansion theory helps quantify these characteristics, including the impact of changes in geographical location, and can facilitate prediction of future risks. By quantifying the trajectories that individuals follow on their journey toward expressing high levels of pro-ISIS support—irrespective of whether they then carry out a real-world attack or not—our findings can help move safety debates beyond reliance on static watch-list identifiers such as ethnic background or immigration status and/or postfact interviews with already convicted individuals. Given the broad commonality of social media platforms, our results likely apply quite generally; for example, even on Telegram where (like Twitter there is no built-in group feature as in our study, individuals tend to collectively build and pass through the so-called super-group accounts.

  17. Cannabis use in children with individualized risk profiles: Predicting the effect of universal prevention intervention.

    Science.gov (United States)

    Miovský, Michal; Vonkova, Hana; Čablová, Lenka; Gabrhelík, Roman

    2015-11-01

    To study the effect of a universal prevention intervention targeting cannabis use in individual children with different risk profiles. A school-based randomized controlled prevention trial was conducted over a period of 33 months (n=1874 sixth-graders, baseline mean age 11.82). We used a two-level random intercept logistic model for panel data to predict the probabilities of cannabis use for each child. Specifically, we used eight risk/protective factors to characterize each child and then predicted two probabilities of cannabis use for each child if the child had the intervention or not. Using the two probabilities, we calculated the absolute and relative effect of the intervention for each child. According to the two probabilities, we also divided the sample into a low-risk group (the quarter of the children with the lowest probabilities), a moderate-risk group, and a high-risk group (the quarter of the children with the highest probabilities) and showed the average effect of the intervention on these groups. The differences between the intervention group and the control group were statistically significant in each risk group. The average predicted probabilities of cannabis use for a child from the low-risk group were 4.3% if the child had the intervention and 6.53% if no intervention was provided. The corresponding probabilities for a child from the moderate-risk group were 10.91% and 15.34% and for a child from the high-risk group 25.51% and 32.61%. School grades, thoughts of hurting oneself, and breaking the rules were the three most important factors distinguishing high-risk and low-risk children. We predicted the effect of the intervention on individual children, characterized by their risk/protective factors. The predicted absolute effect and relative effect of any intervention for any selected risk/protective profile of a given child may be utilized in both prevention practice and research. Copyright © 2015 Elsevier Ltd. All rights reserved.

  18. Plausibility of Individual Decisions from Random Forests in Clinical Predictive Modelling Applications.

    Science.gov (United States)

    Hayn, Dieter; Walch, Harald; Stieg, Jörg; Kreiner, Karl; Ebner, Hubert; Schreier, Günter

    2017-01-01

    Machine learning algorithms are a promising approach to help physicians to deal with the ever increasing amount of data collected in healthcare each day. However, interpretation of suggestions derived from predictive models can be difficult. The aim of this work was to quantify the influence of a specific feature on an individual decision proposed by a random forest (RF). For each decision tree within the RF, the influence of each feature on a specific decision (FID) was quantified. For each feature, changes in outcome value due to the feature were summarized along the path. Results from all the trees in the RF were statistically merged. The ratio of FID to the respective feature's global importance was calculated (FIDrel). Global feature importance, FID and FIDrel significantly differed, depending on the individual input data. Therefore, we suggest to present the most important features as determined for FID and for FIDrel, whenever results of a RF are visualized. Feature influence on a specific decision can be quantified in RFs. Further studies will be necessary to evaluate our approach in a real world scenario.

  19. Individual stress vulnerability is predicted by short-term memory and AMPA receptor subunit ratio in the hippocampus.

    Science.gov (United States)

    Schmidt, Mathias V; Trümbach, Dietrich; Weber, Peter; Wagner, Klaus; Scharf, Sebastian H; Liebl, Claudia; Datson, Nicole; Namendorf, Christian; Gerlach, Tamara; Kühne, Claudia; Uhr, Manfred; Deussing, Jan M; Wurst, Wolfgang; Binder, Elisabeth B; Holsboer, Florian; Müller, Marianne B

    2010-12-15

    Increased vulnerability to aversive experiences is one of the main risk factors for stress-related psychiatric disorders as major depression. However, the molecular bases of vulnerability, on the one hand, and stress resilience, on the other hand, are still not understood. Increasing clinical and preclinical evidence suggests a central involvement of the glutamatergic system in the pathogenesis of major depression. Using a mouse paradigm, modeling increased stress vulnerability and depression-like symptoms in a genetically diverse outbred strain, and we tested the hypothesis that differences in AMPA receptor function may be linked to individual variations in stress vulnerability. Vulnerable and resilient animals differed significantly in their dorsal hippocampal AMPA receptor expression and AMPA receptor binding. Treatment with an AMPA receptor potentiator during the stress exposure prevented the lasting effects of chronic social stress exposure on physiological, neuroendocrine, and behavioral parameters. In addition, spatial short-term memory, an AMPA receptor-dependent behavior, was found to be predictive of individual stress vulnerability and response to AMPA potentiator treatment. Finally, we provide evidence that genetic variations in the AMPA receptor subunit GluR1 are linked to the vulnerable phenotype. Therefore, we propose genetic variations in the AMPA receptor system to shape individual stress vulnerability. Those individual differences can be predicted by the assessment of short-term memory, thereby opening up the possibility for a specific treatment by enhancing AMPA receptor function.

  20. Moral competence and brain connectivity: a resting-state fMRI study

    Science.gov (United States)

    Jung, Wi Hoon; Prehn, Kristin; Fang, Zhuo; Korczykowski, Marc; Kable, Joseph W.; Rao, Hengyi; Robertson, Diana C.

    2016-01-01

    Moral competence (MC) refers to the ability to apply certain moral orientations in a consistent and differentiated manner when judging moral issues. People greatly differ in terms of MC, however, little is known about how these differences are implemented in the brain. To investigate this question, we used functional magnetic resonance imaging and examined resting-state functional connectivity (RSFC) in n=31 individuals with MC scores in the highest 15% of the population and n=33 individuals with MC scores in the lowest 15%, selected from a large sample of 730 Master of Business Administration (MBA) students. Compared to individuals with lower MC, individuals with higher MC showed greater amygdala-ventromedial prefrontal connectivity, which may reflect better ability to cope with emotional conflicts elicited by moral dilemmas. Moreover, individuals with higher MC showed less inter-network connectivity between the amygdalar and fronto-parietal networks, suggesting a more independent operation of these networks. Our findings provide novel insights into how individual differences in moral judgment are associated with RSFC in brain circuits related to emotion processing and cognitive control. PMID:27456537

  1. Connectivity features for identifying cognitive impairment in presymptomatic carotid stenosis.

    Directory of Open Access Journals (Sweden)

    Chun-Jen Lin

    Full Text Available Severe asymptomatic stenosis of the internal carotid artery (ICA leads to increased incidence of mild cognitive impairment (MCI likely through silent embolic infarcts and/or chronic hypoperfusion, but the brain dysfunction is poorly understood and difficult to diagnose. Thirty cognitively intact subjects with asymptomatic, severe (≥ 70%, unilateral stenosis of the ICA were compared with 30 healthy controls, matched for age, sex, cardiovascular risk factors and education level, on a battery of neuropsychiatric tests, voxel-based morphometry of magnetic resonance imaging (MRI, diffusion tensor imaging and brain-wise, seed-based analysis of resting-state functional MRI. Multivariate regression models and multivariate pattern classification (support vector machines were computed to assess the relationship between connectivity measures and neurocognitive performance. The patients had worse dizziness scores and poorer verbal memory, executive function and complex visuo-spatial performance than controls. Twelve out of the 30 patients (40% were considered to have MCI. Nonetheless, the leukoaraiosis Sheltens scores, hippocampal and brain volumes were not different between groups. Their whole-brain mean fractional anisotropy (FA was significantly reduced and regional functional connectivity (Fc was significantly impaired in the dorsal attention network (DAN, frontoparietal network, sensorimotor network and default mode network. In particular, the Fc strength at the insula of the DAN and the mean FA were linearly related with attention performance and dizziness severity, respectively. The multivariate pattern classification gave over 90% predictive accuracy of individuals with MCI or severe dizziness. Cognitive decline in stroke-free individuals with severe carotid stenosis may arise from nonselective widespread disconnections of long-range, predominantly interhemispheric non-hippocampal pathways. Connectivity measures may serve as both predictors for

  2. Urine and plasma metabolites predict the development of diabetic nephropathy in individuals with Type 2 diabetes mellitus

    NARCIS (Netherlands)

    Pena, M. J.; Lambers Heerspink, H. J.; Hellemons, M. E.; Friedrich, T.; Dallmann, G.; Lajer, M.; Bakker, S. J. L.; Gansevoort, R. T.; Rossing, P.; de Zeeuw, D.; Roscioni, S. S.

    Aims Early detection of individuals with Type 2 diabetes mellitus or hypertension at risk for micro- or macroalbuminuria may facilitate prevention and treatment of renal disease. We aimed to discover plasma and urine metabolites that predict the development of micro-or macroalbuminuria. Methods

  3. Optimization of biomathematical model predictions for cognitive performance impairment in individuals: accounting for unknown traits and uncertain states in homeostatic and circadian processes.

    Science.gov (United States)

    Van Dongen, Hans P A; Mott, Christopher G; Huang, Jen-Kuang; Mollicone, Daniel J; McKenzie, Frederic D; Dinges, David F

    2007-09-01

    Current biomathematical models of fatigue and performance do not accurately predict cognitive performance for individuals with a priori unknown degrees of trait vulnerability to sleep loss, do not predict performance reliably when initial conditions are uncertain, and do not yield statistically valid estimates of prediction accuracy. These limitations diminish their usefulness for predicting the performance of individuals in operational environments. To overcome these 3 limitations, a novel modeling approach was developed, based on the expansion of a statistical technique called Bayesian forecasting. The expanded Bayesian forecasting procedure was implemented in the two-process model of sleep regulation, which has been used to predict performance on the basis of the combination of a sleep homeostatic process and a circadian process. Employing the two-process model with the Bayesian forecasting procedure to predict performance for individual subjects in the face of unknown traits and uncertain states entailed subject-specific optimization of 3 trait parameters (homeostatic build-up rate, circadian amplitude, and basal performance level) and 2 initial state parameters (initial homeostatic state and circadian phase angle). Prior information about the distribution of the trait parameters in the population at large was extracted from psychomotor vigilance test (PVT) performance measurements in 10 subjects who had participated in a laboratory experiment with 88 h of total sleep deprivation. The PVT performance data of 3 additional subjects in this experiment were set aside beforehand for use in prospective computer simulations. The simulations involved updating the subject-specific model parameters every time the next performance measurement became available, and then predicting performance 24 h ahead. Comparison of the predictions to the subjects' actual data revealed that as more data became available for the individuals at hand, the performance predictions became

  4. Predicting Vulnerability of the Integrity and Connectivity Associated with Culverts in Low Order Streams of Northern Michigan

    Science.gov (United States)

    King, C. H.; Wagenbrenner, J.; Fedora, M.; Watkins, D.; Watkins, M. K.; Huckins, C.

    2017-12-01

    The Great Lakes Region of North America has experienced more frequent extreme precipitation events in recent decades, resulting in a large number of stream crossing failures. While there are accepted methods for designing stream crossings to accommodate peak storm discharges, less attention has been paid to assessing the risk of failure. To evaluate failure risk and potential impacts, coarse-resolution stream crossing surveys were completed on 51 stream crossings and dams in the North Branch Paint River watershed in Michigan's Upper Peninsula. These inventories determined stream crossing dimensions along with stream and watershed characteristics. Eleven culverts were selected from the coarse surveys for high resolution hydraulic analysis to estimate discharge conditions expected at crossing failure. Watershed attributes upstream of the crossing, including area, slope, and storage, were acquired. Sediment discharge and the economic impact associated with a failure event were also estimated for each stream crossing. Impacts to stream connectivity and fish passability were assessed from the coarse-level surveys. Using information from both the coarse and high-resolution surveys, we also developed indicators to predict failure risk without the need for complex hydraulic modeling. These passability scores and failure risk indicators will help to prioritize infrastructure replacement and improve the overall connectivity of river systems throughout the upper Great Lakes Region.

  5. Using the Theory of Planned Behavior to Predict College Students' Intention to Intervene With a Suicidal Individual.

    Science.gov (United States)

    Aldrich, Rosalie S

    2015-01-01

    Suicide among college students is an issue of serious concern. College peers may effectively intervene with at-risk persons due to their regular contact and close personal relationships with others in this population of significantly enhanced risk. The current study was designed to investigate whether the theory of planned behavior constructs predicted intention to intervene when a college peer is suicidal. Undergraduate students (n = 367) completed an on-line questionnaire; they answered questions about their attitudes, subjective norms, perceived behavioral control regarding suicide and suicide intervention, as well as their intention to intervene when someone is suicidal. The data were analyzed using multiple regression. The statistical significance of this cross-sectional study indicates that the theory of planned behavior constructs predicts self-reported intention to intervene with a suicidal individual. Theory of planned behavior is an effective framework for understanding peers' intention to intervene with a suicidal individual.

  6. The devil is in the dispersers: Predictions of landscape connectivity change with demography

    Science.gov (United States)

    Nicholas B. Elliot; Samuel A. Cushman; David W. Macdonald; Andrew J. Loveridge

    2014-01-01

    Concern about the effects of habitat fragmentation has led to increasing interest in dispersal and connectivity modelling. Most modern techniques for connectivity modelling have resistance surfaces as their foundation. However, resistance surfaces for animal movement are frequently estimated without considering dispersal, despite being the principal natural mechanism...

  7. FDG-PET Response Prediction in Pediatric Hodgkin’s Lymphoma: Impact of Metabolically Defined Tumor Volumes and Individualized SUV Measurements on the Positive Predictive Value

    Energy Technology Data Exchange (ETDEWEB)

    Hussien, Amr Elsayed M. [Department of Nuclear Medicine (KME), Forschungszentrum Jülich, Medical Faculty, Heinrich-Heine-University Düsseldorf, Jülich, 52426 (Germany); Department of Nuclear Medicine, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, 40225 (Germany); Furth, Christian [Department of Radiology and Nuclear Medicine, Medical School, Otto-von-Guericke University Magdeburg, Magdeburg, 39120 (Germany); Schönberger, Stefan [Department of Pediatric Oncology, Hematology and Clinical Immunology, University Children’s Hospital, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, 40225 (Germany); Hundsdoerfer, Patrick [Department of Pediatric Oncology and Hematology, Charité Campus Virchow, Humboldt-University Berlin, Berlin, 13353 (Germany); Steffen, Ingo G.; Amthauer, Holger [Department of Radiology and Nuclear Medicine, Medical School, Otto-von-Guericke University Magdeburg, Magdeburg, 39120 (Germany); Müller, Hans-Wilhelm; Hautzel, Hubertus, E-mail: h.hautzel@fz-juelich.de [Department of Nuclear Medicine (KME), Forschungszentrum Jülich, Medical Faculty, Heinrich-Heine-University Düsseldorf, Jülich, 52426 (Germany); Department of Nuclear Medicine, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, 40225 (Germany)

    2015-01-28

    Background: In pediatric Hodgkin’s lymphoma (pHL) early response-to-therapy prediction is metabolically assessed by (18)F-FDG PET carrying an excellent negative predictive value (NPV) but an impaired positive predictive value (PPV). Aim of this study was to improve the PPV while keeping the optimal NPV. A comparison of different PET data analyses was performed applying individualized standardized uptake values (SUV), PET-derived metabolic tumor volume (MTV) and the product of both parameters, termed total lesion glycolysis (TLG); Methods: One-hundred-eight PET datasets (PET1, n = 54; PET2, n = 54) of 54 children were analysed by visual and semi-quantitative means. SUVmax, SUVmean, MTV and TLG were obtained the results of both PETs and the relative change from PET1 to PET2 (Δ in %) were compared for their capability of identifying responders and non-responders using receiver operating characteristics (ROC)-curves. In consideration of individual variations in noise and contrasts levels all parameters were additionally obtained after threshold correction to lean body mass and background; Results: All semi-quantitative SUV estimates obtained at PET2 were significantly superior to the visual PET2 analysis. However, ΔSUVmax revealed the best results (area under the curve, 0.92; p < 0.001; sensitivity 100%; specificity 85.4%; PPV 46.2%; NPV 100%; accuracy, 87.0%) but was not significantly superior to SUVmax-estimation at PET2 and ΔTLGmax. Likewise, the lean body mass and background individualization of the datasets did not impove the results of the ROC analyses; Conclusions: Sophisticated semi-quantitative PET measures in early response assessment of pHL patients do not perform significantly better than the previously proposed ΔSUVmax. All analytical strategies failed to improve the impaired PPV to a clinically acceptable level while preserving the excellent NPV.

  8. FDG-PET Response Prediction in Pediatric Hodgkin’s Lymphoma: Impact of Metabolically Defined Tumor Volumes and Individualized SUV Measurements on the Positive Predictive Value

    International Nuclear Information System (INIS)

    Hussien, Amr Elsayed M.; Furth, Christian; Schönberger, Stefan; Hundsdoerfer, Patrick; Steffen, Ingo G.; Amthauer, Holger; Müller, Hans-Wilhelm; Hautzel, Hubertus

    2015-01-01

    Background: In pediatric Hodgkin’s lymphoma (pHL) early response-to-therapy prediction is metabolically assessed by (18)F-FDG PET carrying an excellent negative predictive value (NPV) but an impaired positive predictive value (PPV). Aim of this study was to improve the PPV while keeping the optimal NPV. A comparison of different PET data analyses was performed applying individualized standardized uptake values (SUV), PET-derived metabolic tumor volume (MTV) and the product of both parameters, termed total lesion glycolysis (TLG); Methods: One-hundred-eight PET datasets (PET1, n = 54; PET2, n = 54) of 54 children were analysed by visual and semi-quantitative means. SUVmax, SUVmean, MTV and TLG were obtained the results of both PETs and the relative change from PET1 to PET2 (Δ in %) were compared for their capability of identifying responders and non-responders using receiver operating characteristics (ROC)-curves. In consideration of individual variations in noise and contrasts levels all parameters were additionally obtained after threshold correction to lean body mass and background; Results: All semi-quantitative SUV estimates obtained at PET2 were significantly superior to the visual PET2 analysis. However, ΔSUVmax revealed the best results (area under the curve, 0.92; p < 0.001; sensitivity 100%; specificity 85.4%; PPV 46.2%; NPV 100%; accuracy, 87.0%) but was not significantly superior to SUVmax-estimation at PET2 and ΔTLGmax. Likewise, the lean body mass and background individualization of the datasets did not impove the results of the ROC analyses; Conclusions: Sophisticated semi-quantitative PET measures in early response assessment of pHL patients do not perform significantly better than the previously proposed ΔSUVmax. All analytical strategies failed to improve the impaired PPV to a clinically acceptable level while preserving the excellent NPV

  9. Thalamo-Sensorimotor Functional Connectivity Correlates with World Ranking of Olympic, Elite, and High Performance Athletes

    Directory of Open Access Journals (Sweden)

    Zirui Huang

    2017-01-01

    Full Text Available Brain plasticity studies have shown functional reorganization in participants with outstanding motor expertise. Little is known about neural plasticity associated with exceptionally long motor training or of its predictive value for motor performance excellence. The present study utilised resting-state functional magnetic resonance imaging (rs-fMRI in a unique sample of world-class athletes: Olympic, elite, and internationally ranked swimmers (n=30. Their world ranking ranged from 1st to 250th: each had prepared for participation in the Olympic Games. Combining rs-fMRI graph-theoretical and seed-based functional connectivity analyses, it was discovered that the thalamus has its strongest connections with the sensorimotor network in elite swimmers with the highest world rankings (career best rank: 1–35. Strikingly, thalamo-sensorimotor functional connections were highly correlated with the swimmers’ motor performance excellence, that is, accounting for 41% of the individual variance in best world ranking. Our findings shed light on neural correlates of long-term athletic performance involving thalamo-sensorimotor functional circuits.

  10. Connecting clinical and actuarial prediction with rule-based methods

    NARCIS (Netherlands)

    Fokkema, M.; Smits, N.; Kelderman, H.; Penninx, B.W.J.H.

    2015-01-01

    Meta-analyses comparing the accuracy of clinical versus actuarial prediction have shown actuarial methods to outperform clinical methods, on average. However, actuarial methods are still not widely used in clinical practice, and there has been a call for the development of actuarial prediction

  11. Mutual Connectivity Analysis (MCA) Using Generalized Radial Basis Function Neural Networks for Nonlinear Functional Connectivity Network Recovery in Resting-State Functional MRI.

    Science.gov (United States)

    DSouza, Adora M; Abidin, Anas Zainul; Nagarajan, Mahesh B; Wismüller, Axel

    2016-03-29

    We investigate the applicability of a computational framework, called mutual connectivity analysis (MCA), for directed functional connectivity analysis in both synthetic and resting-state functional MRI data. This framework comprises of first evaluating non-linear cross-predictability between every pair of time series prior to recovering the underlying network structure using community detection algorithms. We obtain the non-linear cross-prediction score between time series using Generalized Radial Basis Functions (GRBF) neural networks. These cross-prediction scores characterize the underlying functionally connected networks within the resting brain, which can be extracted using non-metric clustering approaches, such as the Louvain method. We first test our approach on synthetic models with known directional influence and network structure. Our method is able to capture the directional relationships between time series (with an area under the ROC curve = 0.92 ± 0.037) as well as the underlying network structure (Rand index = 0.87 ± 0.063) with high accuracy. Furthermore, we test this method for network recovery on resting-state fMRI data, where results are compared to the motor cortex network recovered from a motor stimulation sequence, resulting in a strong agreement between the two (Dice coefficient = 0.45). We conclude that our MCA approach is effective in analyzing non-linear directed functional connectivity and in revealing underlying functional network structure in complex systems.

  12. Monitoring Effective Connectivity in the Preterm Brain: A Graph Approach to Study Maturation

    Directory of Open Access Journals (Sweden)

    M. Lavanga

    2017-01-01

    Full Text Available In recent years, functional connectivity in the developmental science received increasing attention. Although it has been reported that the anatomical connectivity in the preterm brain develops dramatically during the last months of pregnancy, little is known about how functional and effective connectivity change with maturation. The present study investigated how effective connectivity in premature infants evolves. To assess it, we use EEG measurements and graph-theory methodologies. We recorded data from 25 preterm babies, who underwent long-EEG monitoring at least twice during their stay in the NICU. The recordings took place from 27 weeks postmenstrual age (PMA until 42 weeks PMA. Results showed that the EEG-connectivity, assessed using graph-theory indices, moved from a small-world network to a random one, since the clustering coefficient increases and the path length decreases. This shift can be due to the development of the thalamocortical connections and long-range cortical connections. Based on the network indices, we developed different age-prediction models. The best result showed that it is possible to predict the age of the infant with a root mean-squared error (MSE equal to 2.11 weeks. These results are similar to the ones reported in the literature for age prediction in preterm babies.

  13. Individual Differences in Dynamic Functional Brain Connectivity across the Human Lifespan.

    Directory of Open Access Journals (Sweden)

    Elizabeth N Davison

    2016-11-01

    Full Text Available Individual differences in brain functional networks may be related to complex personal identifiers, including health, age, and ability. Dynamic network theory has been used to identify properties of dynamic brain function from fMRI data, but the majority of analyses and findings remain at the level of the group. Here, we apply hypergraph analysis, a method from dynamic network theory, to quantify individual differences in brain functional dynamics. Using a summary metric derived from the hypergraph formalism-hypergraph cardinality-we investigate individual variations in two separate, complementary data sets. The first data set ("multi-task" consists of 77 individuals engaging in four consecutive cognitive tasks. We observe that hypergraph cardinality exhibits variation across individuals while remaining consistent within individuals between tasks; moreover, the analysis of one of the memory tasks revealed a marginally significant correspondence between hypergraph cardinality and age. This finding motivated a similar analysis of the second data set ("age-memory", in which 95 individuals, aged 18-75, performed a memory task with a similar structure to the multi-task memory task. With the increased age range in the age-memory data set, the correlation between hypergraph cardinality and age correspondence becomes significant. We discuss these results in the context of the well-known finding linking age with network structure, and suggest that hypergraph analysis should serve as a useful tool in furthering our understanding of the dynamic network structure of the brain.

  14. Prediction of the period of psychotic episode in individual schizophrenics by simulation-data construction approach.

    Science.gov (United States)

    Huang, Chun-Jung; Wang, Hsiao-Fan; Chiu, Hsien-Jane; Lan, Tsuo-Hung; Hu, Tsung-Ming; Loh, El-Wui

    2010-10-01

    Although schizophrenia can be treated, most patients still experience inevitable psychotic episodes from time to time. Precautious actions can be taken if the next onset can be predicted. However, sufficient information is always lacking in the clinical scenario. A possible solution is to use the virtual data generated from limited of original data. Data construction method (DCM) has been shown to generate the virtual felt earthquake data effectively and used in the prediction of further events. Here we investigated the performance of DCM in deriving the membership functions and discrete-event simulations (DES) in predicting the period embracing the initiation and termination time-points of the next psychotic episode of 35 individual schizophrenic patients. The results showed that 21 subjects had a success of simulations (RSS) ≥70%. Further analysis demonstrated that the co-morbidity of coronary heart diseases (CHD), risks of CHD, and the frequency of previous psychotic episodes increased the RSS.

  15. Predicting individual differences in autonomy-connectedness: the role of body awareness, alexithymia, and assertiveness.

    Science.gov (United States)

    Bekker, Marrie H J; Croon, Marcel A; van Balkom, Esther G A; Vermee, Jennifer B G

    2008-06-01

    Autonomy-connectedness is the capacity for being on one's own as well as for satisfactorily engaging in interpersonal relationships. Associations have been shown between autonomy-connectedness components (self-awareness, sensitivity to others, and the capacity for managing new situations) and various indices of psychopathology. Both in a theoretical sense as well as for enhancing treatment and prevention, it is relevant to identify which factors most powerfully predict individual differences in autonomy-connectedness: body awareness, alexithymia, or assertiveness. The present study examined this question in a clinical sample of women who were diagnosed as having autonomy problems (N=52) and in a female nonclinical community sample (N=59). In line with expectations, assertiveness was a strong predictor of (all three components of) autonomy-connectedness, as was emotionalizing, one of the alexithymia-components, but the latter in an opposite direction than we had expected: the higher an individual's ability to emotionalize was, the less self-aware and capable to manage new situations that person was, and the more sensitive to others. Cognitive alexithymia contributed to self-awareness as well as to the capacity for managing new situations, and one of the components of body awareness appeared to predict capacity for managing new situations. Our results indicate that assertiveness training and the enhancement of emotion regulation are important elements of autonomy-connectedness targeted interventions. (c) 2008 Wiley Periodicals, Inc.

  16. Distractor inhibition predicts individual differences in recovery from the attentional blink.

    Directory of Open Access Journals (Sweden)

    Heleen A Slagter

    Full Text Available BACKGROUND: The attentional blink (AB refers to an impairment in detecting the second of two target stimuli presented in close succession in a rapid stream of distractors. Recent studies indicate that the AB results, in part, from distractor suppression mechanisms, that may be mediated by striatal dopamine. Yet, it is currently unclear how distractor suppression ability may contribute to the AB. Here, we examined whether distractor suppression ability is predictive of an individual's AB depth and/or recovery. In addition, we investigated the relationship between individual spontaneous eye blink rate (sEBR, a marker of striatal dopaminergic functioning, and AB performance. METHODOLOGY/PRINCIPAL FINDINGS: Subjects were presented with rapid streams of letters containing white distractors, a red T1 and a green T2. T2 was presented either at Lag2, Lag4 or Lag10, and preceded by a distractor that shared the same identity as T2 (T2 primed or not (T2 not primed. Replicating previous work [1], we found that slow AB recovery (poor T2 performance in Lag4 vs. Lag10 was associated with a failure to inhibit distractors, as indexed by greater positive priming. However, no relationship was observed between a subject's ability to suppress distractors and AB depth (Lag10 vs. Lag2. Moreover, no relationship between sEBR and AB performance was observed. RESULTS/SIGNIFICANCE: These results indicate that a failure to inhibit distracting information impairs AB recovery, possibly by interfering with target encoding in working memory - but does not affect AB magnitude. The absence of a relationship between individual sEBR and AB performance may be explained by task specifics.

  17. Cerebello-thalamo-cortical networks predict positive symptom progression in individuals at ultra-high risk for psychosis

    Directory of Open Access Journals (Sweden)

    Jessica A. Bernard

    2017-01-01

    Full Text Available Prospective longitudinal evaluation of adolescents at ultra-high-risk (UHR for the development of psychosis enables an enriched neurodevelopmental perspective of disease progression in the absence of many of the factors that typically confound research with formally psychotic patients (antipsychotic medications, drug/alcohol dependence. The cerebellum has been linked to cognitive dysfunction and symptom severity in schizophrenia and recent work from our team suggests that it is a promising target for investigation in UHR individuals as well. However, the cerebellum and cerebello-thalamo-cortical networks have not been investigated developmentally or with respect to disease progression in this critical population. Further, to date, the types of longitudinal multimodal connectivity studies that would substantially inform our understanding of this area have not yet been conducted. In the present investigation 26 UHR and 24 healthy control adolescents were administered structured clinical interviews and scanned at baseline and then again at 12-month time points to investigate both functional and structural connectivity development of cerebello-thalamo-cortical networks in conjunction with symptom progression. Our results provide evidence of abnormal functional and structural cerebellar network development in the UHR group. Crucially, we also found that cerebello-thalamo-cortical network development and connectivity at baseline are associated with positive symptom course, suggesting that cerebellar networks may be a biomarker of disease progression. Together, these findings provide support for neurodevelopmental models of psychotic disorders and suggest that the cerebellum and respective networks with the cortex may be especially important for elucidating the pathophysiology of psychosis and highlighting novel treatment targets.

  18. Global Brain Dynamics During Social Exclusion Predict Subsequent Behavioral Conformity

    OpenAIRE

    Wasylyshyn, Nick; Hemenway, Brett; Garcia, Javier O.; Cascio, Christopher N.; O'Donnell, Matthew Brook; Bingham, C. Raymond; Simons-Morton, Bruce; Vettel, Jean M.; Falk, Emily B.

    2017-01-01

    Individuals react differently to social experiences; for example, people who are more sensitive to negative social experiences, such as being excluded, may be more likely to adapt their behavior to fit in with others. We examined whether functional brain connectivity during social exclusion in the fMRI scanner can be used to predict subsequent conformity to peer norms. Adolescent males (N = 57) completed a two-part study on teen driving risk: a social exclusion task (Cyberball) during an fMRI...

  19. Brain structures and functional connectivity associated with individual differences in Internet tendency in healthy young adults.

    Science.gov (United States)

    Li, Weiwei; Li, Yadan; Yang, Wenjing; Zhang, Qinglin; Wei, Dongtao; Li, Wenfu; Hitchman, Glenn; Qiu, Jiang

    2015-04-01

    Internet addiction (IA) incurs significant social and financial costs in the form of physical side-effects, academic and occupational impairment, and serious relationship problems. The majority of previous studies on Internet addiction disorders (IAD) have focused on structural and functional abnormalities, while few studies have simultaneously investigated the structural and functional brain alterations underlying individual differences in IA tendencies measured by questionnaires in a healthy sample. Here we combined structural (regional gray matter volume, rGMV) and functional (resting-state functional connectivity, rsFC) information to explore the neural mechanisms underlying IAT in a large sample of 260 healthy young adults. The results showed that IAT scores were significantly and positively correlated with rGMV in the right dorsolateral prefrontal cortex (DLPFC, one key node of the cognitive control network, CCN), which might reflect reduced functioning of inhibitory control. More interestingly, decreased anticorrelations between the right DLPFC and the medial prefrontal cortex/rostral anterior cingulate cortex (mPFC/rACC, one key node of the default mode network, DMN) were associated with higher IAT scores, which might be associated with reduced efficiency of the CCN and DMN (e.g., diminished cognitive control and self-monitoring). Furthermore, the Stroop interference effect was positively associated with the volume of the DLPFC and with the IA scores, as well as with the connectivity between DLPFC and mPFC, which further indicated that rGMV variations in the DLPFC and decreased anticonnections between the DLPFC and mPFC may reflect addiction-related reduced inhibitory control and cognitive efficiency. These findings suggest the combination of structural and functional information can provide a valuable basis for further understanding of the mechanisms and pathogenesis of IA. Copyright © 2015 Elsevier Ltd. All rights reserved.

  20. Culture and self in South Africa: individualism-collectivism predictions.

    Science.gov (United States)

    Eaton, L; Louw, J

    2000-04-01

    People from collectivist cultures may have more concrete and interdependent self-concepts than do people from individualist cultures (G. Hofstede, 1980). African cultures are considered collectivist (H. C. Triandis, 1989), but research on self-concept and culture has neglected this continent. The authors attempted a partial replication in an African context of cross-cultural findings on the abstract-concrete and independent-interdependent dimensions of self-construal (referred to as the abstract-specific and the autonomous-social dimensions, respectively, by E. Rhee, J. S. Uleman, H. K. Lee, & R. J. Roman, 1995). University students in South Africa took the 20 Statements Test (M. Kuhn & T. S. McPartland, 1954; Rhee et al.); home languages were rough indicators of cultural identity. The authors used 3 coding schemes to analyze the content of 78 protocols from African-language speakers and 77 protocols from English speakers. In accord with predictions from individualism-collectivism theory, the African-language speakers produced more interdependent and concrete self-descriptions than did the English speakers. Additional findings concerned the orthogonality of the 2 dimensions and the nature and assessment of the social self-concept.

  1. Prediction of diabetes based on baseline metabolic characteristics in individuals at high risk.

    Science.gov (United States)

    Defronzo, Ralph A; Tripathy, Devjit; Schwenke, Dawn C; Banerji, Maryann; Bray, George A; Buchanan, Thomas A; Clement, Stephen C; Henry, Robert R; Kitabchi, Abbas E; Mudaliar, Sunder; Ratner, Robert E; Stentz, Frankie B; Musi, Nicolas; Reaven, Peter D; Gastaldelli, Amalia

    2013-11-01

    Individuals with impaired glucose tolerance (IGT) are at high risk for developing type 2 diabetes mellitus (T2DM). We examined which characteristics at baseline predicted the development of T2DM versus maintenance of IGT or conversion to normal glucose tolerance. We studied 228 subjects at high risk with IGT who received treatment with placebo in ACT NOW and who underwent baseline anthropometric measures and oral glucose tolerance test (OGTT) at baseline and after a mean follow-up of 2.4 years. In a univariate analysis, 45 of 228 (19.7%) IGT individuals developed diabetes. After adjusting for age, sex, and center, increased fasting plasma glucose, 2-h plasma glucose, G0-120 during OGTT, HbA1c, adipocyte insulin resistance index, ln fasting plasma insulin, and ln I0-120, as well as family history of diabetes and presence of metabolic syndrome, were associated with increased risk of diabetes. At baseline, higher insulin secretion (ln [I0-120/G0-120]) during the OGTT was associated with decreased risk of diabetes. Higher β-cell function (insulin secretion/insulin resistance or disposition index; ln [I0-120/G0-120 × Matsuda index of insulin sensitivity]; odds ratio 0.11; P < 0.0001) was the variable most closely associated with reduced risk of diabetes. In a stepwise multiple-variable analysis, only HbA1c and β-cell function (ln insulin secretion/insulin resistance index) predicted the development of diabetes (r = 0.49; P < 0.0001).

  2. Reduced hippocampal volume is associated with overgeneralization of negative context in individuals with PTSD.

    Science.gov (United States)

    Levy-Gigi, Einat; Szabo, Csilla; Richter-Levin, Gal; Kéri, Szabolcs

    2015-01-01

    Previous studies demonstrated reduced hippocampal volume in individuals with posttraumatic stress disorder (PTSD). However, the functional role the hippocampus plays in PTSD symptomatology is still unclear. The aim of the present study was to explore generalization learning and its connection to hippocampal volume in individuals with and without PTSD. Animal and human models argue that hippocampal deficit may result in failure to process contextual information. Therefore we predicted associations between reduced hippocampal volume and overgeneralization of context in individuals with PTSD. We conducted MRI scans of bilateral hippocampal and amygdala formations as well as intracranial and total brain volumes. Generalization was measured using a novel-learning paradigm, which separately evaluates generalization of cue and context in conditions of negative and positive outcomes. As expected, MRI scans indicated reduced hippocampal volume in PTSD compared to non-PTSD participants. Behavioral results revealed a selective deficit in context generalization learning in individuals with PTSD, F(1, 43) = 8.27, p < .01, η(p)² = .16. Specifically, as predicted, while generalization of cue was spared in both groups, individuals with PTSD showed overgeneralization of negative context. Hence, they could not learn that a previously negative context is later associated with a positive outcome, F(1, 43) = 7.33, p = .01, η(p)² = .15. Most importantly, overgeneralization of negative context significantly correlated with right and left hippocampal volume (r = .61, p = .000; r = .5, p = .000). Finally, bilateral hippocampal volume provided the strongest prediction of overgeneralization of negative context. Reduced hippocampal volume may account for the difficulty of individuals with PTSD to differentiate negative and novel conditions and hence may facilitate reexperiencing symptoms. PsycINFO Database Record (c) 2015 APA, all rights reserved.

  3. Examining speed versus selection in connectivity models using elk migration as an example

    Science.gov (United States)

    Brennan, Angela; Hanks, Ephraim M.; Merkle, Jerod A.; Cole, Eric K.; Dewey, Sarah R.; Courtemanch, Alyson B.; Cross, Paul C.

    2018-01-01

    ContextLandscape resistance is vital to connectivity modeling and frequently derived from resource selection functions (RSFs). RSFs estimate relative probability of use and tend to focus on understanding habitat preferences during slow, routine animal movements (e.g., foraging). Dispersal and migration, however, can produce rarer, faster movements, in which case models of movement speed rather than resource selection may be more realistic for identifying habitats that facilitate connectivity.ObjectiveTo compare two connectivity modeling approaches applied to resistance estimated from models of movement rate and resource selection.MethodsUsing movement data from migrating elk, we evaluated continuous time Markov chain (CTMC) and movement-based RSF models (i.e., step selection functions [SSFs]). We applied circuit theory and shortest random path (SRP) algorithms to CTMC, SSF and null (i.e., flat) resistance surfaces to predict corridors between elk seasonal ranges. We evaluated prediction accuracy by comparing model predictions to empirical elk movements.ResultsAll connectivity models predicted elk movements well, but models applied to CTMC resistance were more accurate than models applied to SSF and null resistance. Circuit theory models were more accurate on average than SRP models.ConclusionsCTMC can be more realistic than SSFs for estimating resistance for fast movements, though SSFs may demonstrate some predictive ability when animals also move slowly through corridors (e.g., stopover use during migration). High null model accuracy suggests seasonal range data may also be critical for predicting direct migration routes. For animals that migrate or disperse across large landscapes, we recommend incorporating CTMC into the connectivity modeling toolkit.

  4. The impact of bilingualism on brain reserve and metabolic connectivity in Alzheimer's dementia.

    Science.gov (United States)

    Perani, Daniela; Farsad, Mohsen; Ballarini, Tommaso; Lubian, Francesca; Malpetti, Maura; Fracchetti, Alessandro; Magnani, Giuseppe; March, Albert; Abutalebi, Jubin

    2017-02-14

    Cognitive reserve (CR) prevents cognitive decline and delays neurodegeneration. Recent epidemiological evidence suggests that lifelong bilingualism may act as CR delaying the onset of dementia by ∼4.5 y. Much controversy surrounds the issue of bilingualism and its putative neuroprotective effects. We studied brain metabolism, a direct index of synaptic function and density, and neural connectivity to shed light on the effects of bilingualism in vivo in Alzheimer's dementia (AD). Eighty-five patients with probable AD and matched for disease duration (45 German-Italian bilingual speakers and 40 monolingual speakers) were included. Notably, bilingual individuals were on average 5 y older than their monolingual peers. In agreement with our predictions and with models of CR, cerebral hypometabolism was more severe in the group of bilingual individuals with AD. The metabolic connectivity analyses crucially supported the neuroprotective effect of bilingualism by showing an increased connectivity in the executive control and the default mode networks in the bilingual, compared with the monolingual, AD patients. Furthermore, the degree of lifelong bilingualism (i.e., high, moderate, or low use) was significantly correlated to functional modulations in crucial neural networks, suggesting both neural reserve and compensatory mechanisms. These findings indicate that lifelong bilingualism acts as a powerful CR proxy in dementia and exerts neuroprotective effects against neurodegeneration. Delaying the onset of dementia is a top priority of modern societies, and the present in vivo neurobiological evidence should stimulate social programs and interventions to support bilingual or multilingual education and the maintenance of the second language among senior citizens.

  5. Multi-taxa population connectivity in the northern Rocky Mountains

    Science.gov (United States)

    Samuel A. Cushman; Erin L. Landguth

    2012-01-01

    Effective broad-spectrum biodiversity conservation requires that conservation strategies simultaneously meet the needs of multiple species. However, little is known about how maintaining habitat connectivity for one species or species group may also act as an umbrella for other species. We evaluated the degree to which predicted connected habitat for each of 144...

  6. Study protocol for a prospective cohort study examining the predictive potential of dynamic symptom networks for the onset and progression of psychosis: the Mapping Individual Routes of Risk and Resilience (Mirorr) study.

    Science.gov (United States)

    Booij, Sanne H; Wichers, Marieke; de Jonge, Peter; Sytema, Sjoerd; van Os, Jim; Wunderink, Lex; Wigman, Johanna T W

    2018-01-21

    Our current ability to predict the course and outcome of early psychotic symptoms is limited, hampering timely treatment. To improve our understanding of the development of psychosis, a different approach to psychopathology may be productive. We propose to reconceptualise psychopathology from a network perspective, according to which symptoms act as a dynamic, interconnected system, impacting on each other over time and across diagnostic boundaries to form symptom networks. Adopting this network approach, the Mapping Individual Routes of Risk and Resilience study aims to determine whether characteristics of symptom networks can predict illness course and outcome of early psychotic symptoms. The sample consists of n=100 participants aged 18-35 years, divided into four subgroups (n=4×25) with increasing levels of severity of psychopathology, representing successive stages of clinical progression. Individuals representing the initial stage have a relatively low expression of psychotic experiences (general population), whereas individuals representing the end stage are help seeking and display a psychometric expression of psychosis, putting them at ultra-high risk for transition to psychotic disorder. At baseline and 1-year follow-up, participants report their symptoms, affective states and experiences for three consecutive months in short, daily questionnaires on their smartphone, which will be used to map individual networks. Network parameters, including the strength and directionality of symptom connections and centrality indices, will be estimated and associated to individual differences in and within-individual progression through stages of clinical severity and functioning over the next 3 years. The study has been approved by the local medical ethical committee (ABR no. NL52974.042.15). The results of the study will be published in (inter)national peer-reviewed journals, presented at research, clinical and general public conferences. The results will assist

  7. Added value of ovarian reserve testing on patient characteristics in the prediction of ovarian response and ongoing pregnancy: an individual patient data approach

    NARCIS (Netherlands)

    Broer, S.L.; Disseldorp, J. van; Broeze, K.A.; Dolleman, M.; Opmeer, B.C.; Bossuyt, P.; Eijkemans, M.J.; Mol, B.W.; Broekmans, F.J.; Anderson, R.A.; Ashrafi, M.; Bancsi, L.F.; Caroppo, E.; Copperman, A.; Ebner, T.; Eldar Geva, M.; Erdem, M.; Greenblatt, E.M.; Jayaprakasan, K.; Fenning, R.; Klinkert, E.R.; Kwee, J.; Lambalk, C.B.; La Marca, A.; McIlveen, M.; Merce, L.T.; Muttukrishna, S.; Nelson, S.M.; Ng, H.Y.; Popovic-Todorovic, B.; Smeenk, J.M.J.; Tomas, C.; Linden, P.J. van der; Rooij, I.A. van; et al.,

    2013-01-01

    BACKGROUND Although ovarian reserve tests (ORTs) are frequently used prior to IVF treatment for outcome prediction, their added predictive value is unclear. We assessed the added value of ORTs to patient characteristics in the prediction of IVF outcome. METHODS An individual patient data (IPD)

  8. Gene flow connects coastal populations of a habitat specialist, the Clapper Rail Rallus crepitans

    Science.gov (United States)

    Coster, Stephanie S.; Welsh, Amy B.; Costanzo, Gary R.; Harding, Sergio R.; Anderson, James T.; Katzner, Todd

    2018-01-01

    Examining population genetic structure can reveal patterns of reproductive isolation or population mixing and inform conservation management. Some avian species are predicted to exhibit minimal genetic differentiation among populations as a result of the species high mobility, with habitat specialists tending to show greater fine‐scale genetic structure. To explore the relationship between habitat specialization and gene flow, we investigated the genetic structure of a saltmarsh specialist with high potential mobility across a wide geographic range of fragmented habitat. Little variation among mitochondrial sequences (620 bp from ND2) was observed among 149 individual Clapper Rails Rallus crepitans sampled along the Atlantic coast of North America, with the majority of individuals at all sampling sites sharing a single haplotype. Genotyping of nine microsatellite loci across 136 individuals revealed moderate genetic diversity, no evidence of bottlenecks, and a weak pattern of genetic differentiation that increased with geographic distance. Multivariate analyses, Bayesian clustering and an AMOVA all suggested a lack of genetic structuring across the North American Atlantic coast, with all individuals grouped into a single interbreeding population. Spatial autocorrelation analyses showed evidence of weak female philopatry and a lack of male philopatry. We conclude that high gene flow connecting populations of this habitat specialist may result from the interaction of ecological and behavioral factors that promote dispersal and limit natal philopatry and breeding‐site fidelity. As climate change threatens saltmarshes, the genetic diversity and population connectivity of Clapper Rails may promote resilience of their populations. This finding helps inform about potential fates of other similarly behaving saltmarsh specialists on the Atlantic coast.

  9. Connecting clinical and actuarial prediction with rule-based methods.

    Science.gov (United States)

    Fokkema, Marjolein; Smits, Niels; Kelderman, Henk; Penninx, Brenda W J H

    2015-06-01

    Meta-analyses comparing the accuracy of clinical versus actuarial prediction have shown actuarial methods to outperform clinical methods, on average. However, actuarial methods are still not widely used in clinical practice, and there has been a call for the development of actuarial prediction methods for clinical practice. We argue that rule-based methods may be more useful than the linear main effect models usually employed in prediction studies, from a data and decision analytic as well as a practical perspective. In addition, decision rules derived with rule-based methods can be represented as fast and frugal trees, which, unlike main effects models, can be used in a sequential fashion, reducing the number of cues that have to be evaluated before making a prediction. We illustrate the usability of rule-based methods by applying RuleFit, an algorithm for deriving decision rules for classification and regression problems, to a dataset on prediction of the course of depressive and anxiety disorders from Penninx et al. (2011). The RuleFit algorithm provided a model consisting of 2 simple decision rules, requiring evaluation of only 2 to 4 cues. Predictive accuracy of the 2-rule model was very similar to that of a logistic regression model incorporating 20 predictor variables, originally applied to the dataset. In addition, the 2-rule model required, on average, evaluation of only 3 cues. Therefore, the RuleFit algorithm appears to be a promising method for creating decision tools that are less time consuming and easier to apply in psychological practice, and with accuracy comparable to traditional actuarial methods. (c) 2015 APA, all rights reserved).

  10. The interaction between individualism and wellbeing in predicting mortality: Survey of Health Ageing and Retirement in Europe.

    Science.gov (United States)

    Okely, Judith A; Weiss, Alexander; Gale, Catharine R

    2018-02-01

    The link between greater wellbeing and longevity is well documented. The aim of the current study was to test whether this association is consistent across individualistic and collectivistic cultures. The sample consisted of 13,596 participants from 11 European countries, each of which was assigned an individualism score according to Hofstede et al.'s (Cultures and organizations: software of the mind, McGraw Hill, New York, 2010) cultural dimension of individualism. We tested whether individualism moderated the cross-sectional association between wellbeing and self-rated health or the longitudinal association between wellbeing and mortality risk. Our analysis revealed a significant interaction between individualism and wellbeing such that the association between wellbeing and self-rated health or risk of mortality from cardiovascular disease was stronger in more individualistic countries. However, the interaction between wellbeing and individualism was not significant in analysis predicting all-cause mortality. Further prospective studies are needed to confirm our finding and to explore the factors responsible for this culturally dependent effect.

  11. Boldness predicts an individual's position along an exploration-exploitation foraging trade-off.

    Science.gov (United States)

    Patrick, Samantha C; Pinaud, David; Weimerskirch, Henri

    2017-09-01

    Individuals do not have complete information about the environment and therefore they face a trade-off between gathering information (exploration) and gathering resources (exploitation). Studies have shown individual differences in components of this trade-off but how stable these strategies are in a population and the intrinsic drivers of these differences is not well understood. Top marine predators are expected to experience a particularly strong trade-off as many species have large foraging ranges and their prey often have a patchy distribution. This environment leads these species to exhibit pronounced exploration and exploitation phases but differences between individuals are poorly resolved. Personality differences are known to be important in foraging behaviour but also in the trade-off between exploration and exploitation. Here we test whether personality predicts an individual exploration-exploitation strategy using wide ranging wandering albatrosses (Diomedea exulans) as a model system. Using GPS tracking data from 276 wandering albatrosses, we extract foraging parameters indicative of exploration (searching) and exploitation (foraging) and show that foraging effort, time in patch and size of patch are strongly correlated, demonstrating these are indicative of an exploration-exploitation (EE) strategy. Furthermore, we show these are consistent within individuals and appear stable in the population, with no reproductive advantage. The searching and foraging behaviour of bolder birds placed them towards the exploration end of the trade-off, whereas shy birds showed greater exploitation. This result provides a mechanism through which individual foraging strategies may emerge. Age and sex affected components of the trade-off, but not the trade-off itself, suggesting these factors may drive behavioural compensation to maintain resource acquisition and this was supported by the evidence that there were no fitness consequence of any EE trait nor the trade

  12. Moral competence and brain connectivity: A resting-state fMRI study.

    Science.gov (United States)

    Jung, Wi Hoon; Prehn, Kristin; Fang, Zhuo; Korczykowski, Marc; Kable, Joseph W; Rao, Hengyi; Robertson, Diana C

    2016-11-01

    Moral competence (MC) refers to the ability to apply certain moral orientations in a consistent and differentiated manner when judging moral issues. People greatly differ in terms of MC, however, little is known about how these differences are implemented in the brain. To investigate this question, we used functional magnetic resonance imaging and examined resting-state functional connectivity (RSFC) in n=31 individuals with MC scores in the highest 15% of the population and n=33 individuals with MC scores in the lowest 15%, selected from a large sample of 730 Master of Business Administration (MBA) students. Compared to individuals with lower MC, individuals with higher MC showed greater amygdala-ventromedial prefrontal connectivity, which may reflect better ability to cope with emotional conflicts elicited by moral dilemmas. Moreover, individuals with higher MC showed less inter-network connectivity between the amygdalar and fronto-parietal networks, suggesting a more independent operation of these networks. Our findings provide novel insights into how individual differences in moral judgment are associated with RSFC in brain circuits related to emotion processing and cognitive control. Copyright © 2016 Elsevier Inc. All rights reserved.

  13. Species- and sex-specific connectivity effects of habitat fragmentation in a suite of woodland birds.

    Science.gov (United States)

    Amos, Nevil; Harrisson, Katherine A; Radford, James Q; White, Matt; Newell, Graeme; Mac Nally, Ralph; Sunnucks, Paul; Pavlova, Alexandra

    2014-06-01

    Loss of functional connectivity following habitat loss and fragmentation could drive species declines. A comprehensive understanding of fragmentation effects on functional connectivity of an ecological assemblage requires investigation of multiple species with different mobilities, at different spatial scales, for each sex, and in different landscapes. Based on published data on mobility and ecological responses to fragmentation of 10 woodland-dependent birds, and using simulation studies, we predicted that (1) fragmentation would impede dispersal and gene flow of eight "decliners" (species that disappear from suitable patches when landscape-level tree cover falls below species-specific thresholds), but not of two "tolerant" species (whose occurrence in suitable habitat patches is independent of landscape tree cover); and that fragmentation effects would be stronger (2) in the least mobile species, (3) in the more philopatric sex, and (4) in the more fragmented region. We tested these predictions by evaluating spatially explicit isolation-by-landscape-resistance models of gene flow in fragmented landscapes across a 50 x 170 km study area in central Victoria, Australia, using individual and population genetic distances. To account for sex-biased dispersal and potential scale- and configuration-specific effects, we fitted models specific to sex and geographic zones. As predicted, four of the least mobile decliners showed evidence of reduced genetic connectivity. The responses were strongly sex specific, but in opposite directions in the two most sedentary species. Both tolerant species and (unexpectedly) four of the more mobile decliners showed no reduction in gene flow. This is unlikely to be due to time lags because more mobile species develop genetic signatures of fragmentation faster than do less mobile ones. Weaker genetic effects were observed in the geographic zone with more aggregated vegetation, consistent with gene flow being unimpeded by landscape

  14. Cyclophosphamide for connective tissue disease-associated interstitial lung disease.

    Science.gov (United States)

    Barnes, Hayley; Holland, Anne E; Westall, Glen P; Goh, Nicole Sl; Glaspole, Ian N

    2018-01-03

    Approximately one-third of individuals with interstitial lung disease (ILD) have associated connective tissue disease (CTD). The connective tissue disorders most commonly associated with ILD include scleroderma/systemic sclerosis (SSc), rheumatoid arthritis, polymyositis/dermatomyositis, and Sjögren's syndrome. Although many people with CTD-ILD do not develop progressive lung disease, a significant proportion do progress, leading to reduced physical function, decreased quality of life, and death. ILD is now the major cause of death amongst individuals with systemic sclerosis.Cyclophosphamide is a highly potent immunosuppressant that has demonstrated efficacy in inducing and maintaining remission in autoimmune and inflammatory illnesses. However this comes with potential toxicities, including nausea, haemorrhagic cystitis, bladder cancer, bone marrow suppression, increased risk of opportunistic infections, and haematological and solid organ malignancies.Decision-making in the treatment of individuals with CTD-ILD is difficult; the clinician needs to identify those who will develop progressive disease, and to weigh up the balance between a high level of need for therapy in a severely unwell patient population against the potential for adverse effects from highly toxic therapy, for which only relatively limited data on efficacy can be found. Similarly, it is not clear whether histological subtype, disease duration, or disease extent can be used to predict treatment responsiveness. To assess the efficacy and adverse effects of cyclophosphamide in the treatment of individuals with CTD-ILD. We performed searches on CENTRAL, MEDLINE, Embase, CINAHL, and Web of Science up to May 2017. We handsearched review articles, clinical trial registries, and reference lists of retrieved articles. We included randomised controlled parallel-group trials that compared cyclophosphamide in any form, used individually or concomitantly with other immunomodulating therapies, versus non

  15. Did the Decline in Social Connections Depress Americans' Happiness?

    Science.gov (United States)

    Bartolini, Stefano; Bilancini, Ennio; Pugno, Maurizio

    2013-01-01

    During the last 30 years US citizens experienced, on average, a decline in reported happiness, social connections, and confidence in institutions. We show that a remarkable portion of the decrease in happiness is predicted by the decline in social connections and confidence in institutions. We carry out our investigation in three steps. First, we…

  16. Predicting optimal outcomes in cognitive therapy or interpersonal psychotherapy for depressed individuals using the personalized advantage index approach

    NARCIS (Netherlands)

    Huibers, M.J.H.; Cohen, Z.D.; Lemmens, L.H.J.M.; Arntz, A.; Peeters, F.P.M.L.; Cuijpers, P.; DeRubeis, R.J.

    2015-01-01

    Introduction: Although psychotherapies for depression produce equivalent outcomes, individual patients respond differently to different therapies. Predictors of outcome have been identified in the context of randomized trials, but this information has not been used to predict which treatment works

  17. Predicting optimal outcomes in cognitive therapy or interpersonal psychotherapy for depressed individuals using the personalized advantage index approach

    NARCIS (Netherlands)

    Huibers, M.J.H.; Cohen, Z.D.; Lemmens, L.H.J.M.; Arntz, A.; Peeters, F.P.M.L.; Cuijpers, P.; DeRubeis, R.J.

    2015-01-01

    Introduction Although psychotherapies for depression produce equivalent outcomes, individual patients respond differently to different therapies. Predictors of outcome have been identified in the context of randomized trials, but this information has not been used to predict which treatment works

  18. A population-based validation study of the DCIS Score predicting recurrence risk in individuals treated by breast-conserving surgery alone

    OpenAIRE

    Rakovitch, Eileen; Nofech-Mozes, Sharon; Hanna, Wedad; Baehner, Frederick L.; Saskin, Refik; Butler, Steven M.; Tuck, Alan; Sengupta, Sandip; Elavathil, Leela; Jani, Prashant A.; Bonin, Michel; Chang, Martin C.; Robertson, Susan J.; Slodkowska, Elzbieta; Fong, Cindy

    2015-01-01

    Validated biomarkers are needed to improve risk assessment and treatment decision-making for women with ductal carcinoma in situ (DCIS) of the breast. The Oncotype DX? DCIS Score (DS) was shown to predict the risk of local recurrence (LR) in individuals with low-risk DCIS treated by breast-conserving surgery (BCS) alone. Our objective was to confirm these results in a larger population-based cohort of individuals. We used an established population-based cohort of individuals diagnosed with DC...

  19. Prediction of Individual Response to Electroconvulsive Therapy via Machine Learning on Structural Magnetic Resonance Imaging Data.

    Science.gov (United States)

    Redlich, Ronny; Opel, Nils; Grotegerd, Dominik; Dohm, Katharina; Zaremba, Dario; Bürger, Christian; Münker, Sandra; Mühlmann, Lisa; Wahl, Patricia; Heindel, Walter; Arolt, Volker; Alferink, Judith; Zwanzger, Peter; Zavorotnyy, Maxim; Kugel, Harald; Dannlowski, Udo

    2016-06-01

    Electroconvulsive therapy (ECT) is one of the most effective treatments for severe depression. However, biomarkers that accurately predict a response to ECT remain unidentified. To investigate whether certain factors identified by structural magnetic resonance imaging (MRI) techniques are able to predict ECT response. In this nonrandomized prospective study, gray matter structure was assessed twice at approximately 6 weeks apart using 3-T MRI and voxel-based morphometry. Patients were recruited through the inpatient service of the Department of Psychiatry, University of Muenster, from March 11, 2010, to March 27, 2015. Two patient groups with acute major depressive disorder were included. One group received an ECT series in addition to antidepressants (n = 24); a comparison sample was treated solely with antidepressants (n = 23). Both groups were compared with a sample of healthy control participants (n = 21). Binary pattern classification was used to predict ECT response by structural MRI that was performed before treatment. In addition, univariate analysis was conducted to predict reduction of the Hamilton Depression Rating Scale score by pretreatment gray matter volumes and to investigate ECT-related structural changes. One participant in the ECT sample was excluded from the analysis, leaving 67 participants (27 men and 40 women; mean [SD] age, 43.7 [10.6] years). The binary pattern classification yielded a successful prediction of ECT response, with accuracy rates of 78.3% (18 of 23 patients in the ECT sample) and sensitivity rates of 100% (13 of 13 who responded to ECT). Furthermore, a support vector regression yielded a significant prediction of relative reduction in the Hamilton Depression Rating Scale score. The principal findings of the univariate model indicated a positive association between pretreatment subgenual cingulate volume and individual ECT response (Montreal Neurological Institute [MNI] coordinates x = 8, y = 21, z = -18

  20. Watershed erosion modeling using the probability of sediment connectivity in a gently rolling system

    Science.gov (United States)

    Mahoney, David Tyler; Fox, James Forrest; Al Aamery, Nabil

    2018-06-01

    Sediment connectivity has been shown in recent years to explain how the watershed configuration controls sediment transport. However, we find no studies develop a watershed erosion modeling framework based on sediment connectivity, and few, if any, studies have quantified sediment connectivity for gently rolling systems. We develop a new predictive sediment connectivity model that relies on the intersecting probabilities for sediment supply, detachment, transport, and buffers to sediment transport, which is integrated in a watershed erosion model framework. The model predicts sediment flux temporally and spatially across a watershed using field reconnaissance results, a high-resolution digital elevation models, a hydrologic model, and shear-based erosion formulae. Model results validate the capability of the model to predict erosion pathways causing sediment connectivity. More notably, disconnectivity dominates the gently rolling watershed across all morphologic levels of the uplands, including, microtopography from low energy undulating surfaces across the landscape, swales and gullies only active in the highest events, karst sinkholes that disconnect drainage areas, and floodplains that de-couple the hillslopes from the stream corridor. Results show that sediment connectivity is predicted for about 2% or more the watershed's area 37 days of the year, with the remaining days showing very little or no connectivity. Only 12.8 ± 0.7% of the gently rolling watershed shows sediment connectivity on the wettest day of the study year. Results also highlight the importance of urban/suburban sediment pathways in gently rolling watersheds, and dynamic and longitudinal distributions of sediment connectivity might be further investigated in future work. We suggest the method herein provides the modeler with an added tool to account for sediment transport criteria and has the potential to reduce computational costs in watershed erosion modeling.

  1. Decreased Cerebellar-Orbitofrontal Connectivity Correlates with Stuttering Severity: Whole-Brain Functional and Structural Connectivity Associations with Persistent Developmental Stuttering.

    Science.gov (United States)

    Sitek, Kevin R; Cai, Shanqing; Beal, Deryk S; Perkell, Joseph S; Guenther, Frank H; Ghosh, Satrajit S

    2016-01-01

    Persistent developmental stuttering is characterized by speech production disfluency and affects 1% of adults. The degree of impairment varies widely across individuals and the neural mechanisms underlying the disorder and this variability remain poorly understood. Here we elucidate compensatory mechanisms related to this variability in impairment using whole-brain functional and white matter connectivity analyses in persistent developmental stuttering. We found that people who stutter had stronger functional connectivity between cerebellum and thalamus than people with fluent speech, while stutterers with the least severe symptoms had greater functional connectivity between left cerebellum and left orbitofrontal cortex (OFC). Additionally, people who stutter had decreased functional and white matter connectivity among the perisylvian auditory, motor, and speech planning regions compared to typical speakers, but greater functional connectivity between the right basal ganglia and bilateral temporal auditory regions. Structurally, disfluency ratings were negatively correlated with white matter connections to left perisylvian regions and to the brain stem. Overall, we found increased connectivity among subcortical and reward network structures in people who stutter compared to controls. These connections were negatively correlated with stuttering severity, suggesting the involvement of cerebellum and OFC may underlie successful compensatory mechanisms by more fluent stutterers.

  2. Decreased Cerebellar-Orbitofrontal Connectivity Correlates with Stuttering Severity: Whole-Brain Functional and Structural Connectivity Associations with Persistent Developmental Stuttering

    Science.gov (United States)

    Sitek, Kevin R.; Cai, Shanqing; Beal, Deryk S.; Perkell, Joseph S.; Guenther, Frank H.; Ghosh, Satrajit S.

    2016-01-01

    Persistent developmental stuttering is characterized by speech production disfluency and affects 1% of adults. The degree of impairment varies widely across individuals and the neural mechanisms underlying the disorder and this variability remain poorly understood. Here we elucidate compensatory mechanisms related to this variability in impairment using whole-brain functional and white matter connectivity analyses in persistent developmental stuttering. We found that people who stutter had stronger functional connectivity between cerebellum and thalamus than people with fluent speech, while stutterers with the least severe symptoms had greater functional connectivity between left cerebellum and left orbitofrontal cortex (OFC). Additionally, people who stutter had decreased functional and white matter connectivity among the perisylvian auditory, motor, and speech planning regions compared to typical speakers, but greater functional connectivity between the right basal ganglia and bilateral temporal auditory regions. Structurally, disfluency ratings were negatively correlated with white matter connections to left perisylvian regions and to the brain stem. Overall, we found increased connectivity among subcortical and reward network structures in people who stutter compared to controls. These connections were negatively correlated with stuttering severity, suggesting the involvement of cerebellum and OFC may underlie successful compensatory mechanisms by more fluent stutterers. PMID:27199712

  3. Decreased cerebellar-orbitofrontal connectivity correlates with stuttering severity: Whole-brain functional and structural connectivity associations with persistent developmental stuttering

    Directory of Open Access Journals (Sweden)

    Kevin Richard Sitek

    2016-05-01

    Full Text Available Persistent developmental stuttering is characterized by speech production disfluency and affects 1% of adults. The degree of impairment varies widely across individuals and the neural mechanisms underlying the disorder and this variability remain poorly understood. Here, we elucidate compensatory mechanisms related to this variability in impairment using whole-brain functional and white matter connectivity analyses in persistent developmental stuttering. We found that people who stutter had stronger functional connectivity between cerebellum and thalamus than people with fluent speech, while stutterers with the least severe symptoms had greater functional connectivity between left cerebellum and left orbitofrontal cortex. Additionally, people who stutter had decreased functional and white matter connectivity among the perisylvian auditory, motor, and speech planning regions compared to typical speakers, but greater functional connectivity between the right basal ganglia and bilateral temporal auditory regions. Structurally, disfluency ratings were negatively correlated with white matter connections to left perisylvian regions and to the brain stem. Overall, we found increased connectivity among subcortical and reward network structures in people who stutter compared to controls. These connections were negatively correlated with stuttering severity, suggesting the involvement of cerebellum and orbitofrontal cortex may underlie successful compensatory mechanisms by more fluent stutterers.

  4. A Tensor Statistical Model for Quantifying Dynamic Functional Connectivity.

    Science.gov (United States)

    Zhu, Yingying; Zhu, Xiaofeng; Kim, Minjeong; Yan, Jin; Wu, Guorong

    2017-06-01

    Functional connectivity (FC) has been widely investigated in many imaging-based neuroscience and clinical studies. Since functional Magnetic Resonance Image (MRI) signal is just an indirect reflection of brain activity, it is difficult to accurately quantify the FC strength only based on signal correlation. To address this limitation, we propose a learning-based tensor model to derive high sensitivity and specificity connectome biomarkers at the individual level from resting-state fMRI images. First, we propose a learning-based approach to estimate the intrinsic functional connectivity. In addition to the low level region-to-region signal correlation, latent module-to-module connection is also estimated and used to provide high level heuristics for measuring connectivity strength. Furthermore, sparsity constraint is employed to automatically remove the spurious connections, thus alleviating the issue of searching for optimal threshold. Second, we integrate our learning-based approach with the sliding-window technique to further reveal the dynamics of functional connectivity. Specifically, we stack the functional connectivity matrix within each sliding window and form a 3D tensor where the third dimension denotes for time. Then we obtain dynamic functional connectivity (dFC) for each individual subject by simultaneously estimating the within-sliding-window functional connectivity and characterizing the across-sliding-window temporal dynamics. Third, in order to enhance the robustness of the connectome patterns extracted from dFC, we extend the individual-based 3D tensors to a population-based 4D tensor (with the fourth dimension stands for the training subjects) and learn the statistics of connectome patterns via 4D tensor analysis. Since our 4D tensor model jointly (1) optimizes dFC for each training subject and (2) captures the principle connectome patterns, our statistical model gains more statistical power of representing new subject than current state

  5. ELUCIDATING BRAIN CONNECTIVITY NETWORKS IN MAJOR DEPRESSIVE DISORDER USING CLASSIFICATION-BASED SCORING.

    Science.gov (United States)

    Sacchet, Matthew D; Prasad, Gautam; Foland-Ross, Lara C; Thompson, Paul M; Gotlib, Ian H

    2014-04-01

    Graph theory is increasingly used in the field of neuroscience to understand the large-scale network structure of the human brain. There is also considerable interest in applying machine learning techniques in clinical settings, for example, to make diagnoses or predict treatment outcomes. Here we used support-vector machines (SVMs), in conjunction with whole-brain tractography, to identify graph metrics that best differentiate individuals with Major Depressive Disorder (MDD) from nondepressed controls. To do this, we applied a novel feature-scoring procedure that incorporates iterative classifier performance to assess feature robustness. We found that small-worldness , a measure of the balance between global integration and local specialization, most reliably differentiated MDD from nondepressed individuals. Post-hoc regional analyses suggested that heightened connectivity of the subcallosal cingulate gyrus (SCG) in MDDs contributes to these differences. The current study provides a novel way to assess the robustness of classification features and reveals anomalies in large-scale neural networks in MDD.

  6. Pore Space Connectivity and the Transport Properties of Rocks

    Directory of Open Access Journals (Sweden)

    Bernabé Yves

    2016-07-01

    Full Text Available Pore connectivity is likely one of the most important factors affecting the permeability of reservoir rocks. Furthermore, connectivity effects are not restricted to materials approaching a percolation transition but can continuously and gradually occur in rocks undergoing geological processes such as mechanical and chemical diagenesis. In this study, we compiled sets of published measurements of porosity, permeability and formation factor, performed in samples of unconsolidated granular aggregates, in which connectivity does not change, and in two other materials, sintered glass beads and Fontainebleau sandstone, in which connectivity does change. We compared these data to the predictions of a Kozeny-Carman model of permeability, which does not account for variations in connectivity, and to those of Bernabé et al. (2010, 2011 model, which does [Bernabé Y., Li M., Maineult A. (2010 Permeability and pore connectivity: a new model based on network simulations, J. Geophys. Res. 115, B10203; Bernabé Y., Zamora M., Li M., Maineult A., Tang Y.B. (2011 Pore connectivity, permeability and electrical formation factor: a new model and comparison to experimental data, J. Geophys. Res. 116, B11204]. Both models agreed equally well with experimental data obtained in unconsolidated granular media. But, in the other materials, especially in the low porosity samples that had undergone the greatest amount of sintering or diagenesis, only Bernabé et al. model matched the experimental data satisfactorily. In comparison, predictions of the Kozeny-Carman model differed by orders of magnitude. The advantage of the Bernabé et al. model was its ability to account for a continuous, gradual reduction in pore connectivity during sintering or diagenesis. Although we can only speculate at this juncture about the mechanisms responsible for the connectivity reduction, we propose two possible mechanisms, likely to be active at different stages of sintering and diagenesis

  7. FKBP5 and emotional neglect interact to predict individual differences in amygdala reactivity.

    Science.gov (United States)

    White, M G; Bogdan, R; Fisher, P M; Muñoz, K E; Williamson, D E; Hariri, A R

    2012-10-01

    Individual variation in physiological responsiveness to stress mediates risk for mental illness and is influenced by both experiential and genetic factors. Common polymorphisms in the human gene for FK506 binding protein 5 (FKBP5), which is involved in transcriptional regulation of the hypothalamic-pituitary-adrenal (HPA) axis, have been shown to interact with childhood abuse and trauma to predict stress-related psychopathology. In the current study, we examined if such gene-environment interaction effects may be related to variability in the threat-related reactivity of the amygdala, which plays a critical role in mediating physiological and behavioral adaptations to stress including modulation of the HPA axis. To this end, 139 healthy Caucasian youth completed a blood oxygen level-dependent functional magnetic resonance imaging probe of amygdala reactivity and self-report assessments of emotional neglect (EN) and other forms of maltreatment. These individuals were genotyped for 6 FKBP5 polymorphisms (rs7748266, rs1360780, rs9296158, rs3800373, rs9470080 and rs9394309) previously associated with psychopathology and/or HPA axis function. Interactions between each SNP and EN emerged such that risk alleles predicted relatively increased dorsal amygdala reactivity in the context of higher EN, even after correcting for multiple testing. Two different haplotype analyses confirmed this relationship as haplotypes with risk alleles also exhibited increased amygdala reactivity in the context of higher EN. Our results suggest that increased threat-related amygdala reactivity may represent a mechanism linking psychopathology to interactions between common genetic variants affecting HPA axis function and childhood trauma. © 2012 The Authors. Genes, Brain and Behavior © 2012 Blackwell Publishing Ltd and International Behavioural and Neural Genetics Society.

  8. Anticipation-related brain connectivity in bipolar and unipolar depression: a graph theory approach.

    Science.gov (United States)

    Manelis, Anna; Almeida, Jorge R C; Stiffler, Richelle; Lockovich, Jeanette C; Aslam, Haris A; Phillips, Mary L

    2016-09-01

    Bipolar disorder is often misdiagnosed as major depressive disorder, which leads to inadequate treatment. Depressed individuals versus healthy control subjects, show increased expectation of negative outcomes. Due to increased impulsivity and risk for mania, however, depressed individuals with bipolar disorder may differ from those with major depressive disorder in neural mechanisms underlying anticipation processes. Graph theory methods for neuroimaging data analysis allow the identification of connectivity between multiple brain regions without prior model specification, and may help to identify neurobiological markers differentiating these disorders, thereby facilitating development of better therapeutic interventions. This study aimed to compare brain connectivity among regions involved in win/loss anticipation in depressed individuals with bipolar disorder (BDD) versus depressed individuals with major depressive disorder (MDD) versus healthy control subjects using graph theory methods. The study was conducted at the University of Pittsburgh Medical Center and included 31 BDD, 39 MDD, and 36 healthy control subjects. Participants were scanned while performing a number guessing reward task that included the periods of win and loss anticipation. We first identified the anticipatory network across all 106 participants by contrasting brain activation during all anticipation periods (win anticipation + loss anticipation) versus baseline, and win anticipation versus loss anticipation. Brain connectivity within the identified network was determined using the Independent Multiple sample Greedy Equivalence Search (IMaGES) and Linear non-Gaussian Orientation, Fixed Structure (LOFS) algorithms. Density of connections (the number of connections in the network), path length, and the global connectivity direction ('top-down' versus 'bottom-up') were compared across groups (BDD/MDD/healthy control subjects) and conditions (win/loss anticipation). These analyses showed that

  9. Transit signal priority with connected vehicle technology.

    Science.gov (United States)

    2014-01-01

    A new TSP logic was proposed, taking advantage of the resources provided by Connected Vehicle (CV) : technology, including two-way communication between the bus and the traffic signal controller, accurate bus : location detection and prediction, and ...

  10. Micromechanical modeling of rate-dependent behavior of Connective tissues.

    Science.gov (United States)

    Fallah, A; Ahmadian, M T; Firozbakhsh, K; Aghdam, M M

    2017-03-07

    In this paper, a constitutive and micromechanical model for prediction of rate-dependent behavior of connective tissues (CTs) is presented. Connective tissues are considered as nonlinear viscoelastic material. The rate-dependent behavior of CTs is incorporated into model using the well-known quasi-linear viscoelasticity (QLV) theory. A planar wavy representative volume element (RVE) is considered based on the tissue microstructure histological evidences. The presented model parameters are identified based on the available experiments in the literature. The presented constitutive model introduced to ABAQUS by means of UMAT subroutine. Results show that, monotonic uniaxial test predictions of the presented model at different strain rates for rat tail tendon (RTT) and human patellar tendon (HPT) are in good agreement with experimental data. Results of incremental stress-relaxation test are also presented to investigate both instantaneous and viscoelastic behavior of connective tissues. Copyright © 2017 Elsevier Ltd. All rights reserved.

  11. An IoT Based Predictive Connected Car Maintenance Approach

    OpenAIRE

    Rohit Dhall; Vijender Kumar Solanki

    2017-01-01

    Internet of Things (IoT) is fast emerging and becoming an almost basic necessity in general life. The concepts of using technology in our daily life is not new, but with the advancements in technology, the impact of technology in daily activities of a person can be seen in almost all the aspects of life. Today, all aspects of our daily life, be it health of a person, his location, movement, etc. can be monitored and analyzed using information captured from various connected devices. This pape...

  12. Connectivity-based parcellation reveals distinct cortico-striatal connectivity fingerprints in Autism Spectrum Disorder.

    Science.gov (United States)

    Balsters, Joshua H; Mantini, Dante; Wenderoth, Nicole

    2018-04-15

    Autism Spectrum Disorder (ASD) has been associated with abnormal synaptic development causing a breakdown in functional connectivity. However, when measured at the macro scale using resting state fMRI, these alterations are subtle and often difficult to detect due to the large heterogeneity of the pathology. Recently, we outlined a novel approach for generating robust biomarkers of resting state functional magnetic resonance imaging (RS-fMRI) using connectivity based parcellation of gross morphological structures to improve single-subject reproducibility and generate more robust connectivity fingerprints. Here we apply this novel approach to investigating the organization and connectivity strength of the cortico-striatal system in a large sample of ASD individuals and typically developed (TD) controls (N=130 per group). Our results showed differences in the parcellation of the striatum in ASD. Specifically, the putamen was found to be one single structure in ASD, whereas this was split into anterior and posterior segments in an age, IQ, and head movement matched TD group. An analysis of the connectivity fingerprints revealed that the group differences in clustering were driven by differential connectivity between striatum and the supplementary motor area, posterior cingulate cortex, and posterior insula. Our approach for analysing RS-fMRI in clinical populations has provided clear evidence that cortico-striatal circuits are organized differently in ASD. Based on previous task-based segmentations of the striatum, we believe that the anterior putamen cluster present in TD, but not in ASD, likely contributes to social and language processes. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

  13. Can We Predict Individual Combined Benefit and Harm of Therapy? Warfarin Therapy for Atrial Fibrillation as a Test Case.

    Directory of Open Access Journals (Sweden)

    Guowei Li

    Full Text Available To construct and validate a prediction model for individual combined benefit and harm outcomes (stroke with no major bleeding, major bleeding with no stroke, neither event, or both in patients with atrial fibrillation (AF with and without warfarin therapy.Using the Kaiser Permanente Colorado databases, we included patients newly diagnosed with AF between January 1, 2005 and December 31, 2012 for model construction and validation. The primary outcome was a prediction model of composite of stroke or major bleeding using polytomous logistic regression (PLR modelling. The secondary outcome was a prediction model of all-cause mortality using the Cox regression modelling.We included 9074 patients with 4537 and 4537 warfarin users and non-users, respectively. In the derivation cohort (n = 4632, there were 136 strokes (2.94%, 280 major bleedings (6.04% and 1194 deaths (25.78% occurred. In the prediction models, warfarin use was not significantly associated with risk of stroke, but increased the risk of major bleeding and decreased the risk of death. Both the PLR and Cox models were robust, internally and externally validated, and with acceptable model performances.In this study, we introduce a new methodology for predicting individual combined benefit and harm outcomes associated with warfarin therapy for patients with AF. Should this approach be validated in other patient populations, it has potential advantages over existing risk stratification approaches as a patient-physician aid for shared decision-making.

  14. The UCLA Multimodal Connectivity Database: A web-based platform for brain connectivity matrix sharing and analysis

    Directory of Open Access Journals (Sweden)

    Jesse A. Brown

    2012-11-01

    Full Text Available Brain connectomics research has rapidly expanded using functional MRI (fMRI and diffusion-weighted MRI (dwMRI. A common product of these varied analyses is a connectivity matrix (CM. A CM stores the connection strength between any two regions (nodes in a brain network. This format is useful for several reasons: 1 it is highly distilled, with minimal data size and complexity, 2 graph theory can be applied to characterize the network’s topology, and 3 it retains sufficient information to capture individual differences such as age, gender, intelligence quotient, or disease state. Here we introduce the UCLA Multimodal Connectivity Database (http://umcd.humanconnectomeproject.org, an openly available website for brain network analysis and data sharing. The site is a repository for researchers to publicly share CMs derived from their data. The site also allows users to select any CM shared by another user, compute graph theoretical metrics on the site, visualize a report of results, or download the raw CM. To date, users have contributed over 2000 individual CMs, spanning different imaging modalities (fMRI, dwMRI and disorders (Alzheimer’s, autism, Attention Deficit Hyperactive Disorder. To demonstrate the site’s functionality, whole brain functional and structural connectivity matrices are derived from 60 subjects’ (ages 26-45 resting state fMRI (rs-fMRI and dwMRI data and uploaded to the site. The site is utilized to derive graph theory global and regional measures for the rs-fMRI and dwMRI networks. Global and nodal graph theoretical measures between functional and structural networks exhibit low correspondence. This example demonstrates how this tool can enhance the comparability of brain networks from different imaging modalities and studies. The existence of this connectivity-based repository should foster broader data sharing and enable larger-scale meta analyses comparing networks across imaging modality, age group, and disease state.

  15. Individual differences in working memory capacity predict learned control over attentional capture.

    Science.gov (United States)

    Robison, Matthew K; Unsworth, Nash

    2017-11-01

    Although individual differences in working memory capacity (WMC) typically predict susceptibility to attentional capture in various paradigms (e.g., Stroop, antisaccade, flankers), it sometimes fails to correlate with the magnitude of attentional capture effects in visual search (e.g., Stokes, 2016), which is 1 of the most frequently studied tasks to study capture (Theeuwes, 2010). But some studies have shown that search modes can mitigate the effects of attentional capture (Leber & Egeth, 2006). Therefore, the present study examined whether or not the relationship between WMC and attentional capture changes as a function of the search modes available. In Experiment 1, WMC was unrelated to attentional capture, but only 1 search mode (singleton-detection) could be employed. In Experiment 2, greater WMC predicted smaller attentional capture effects, but only when multiple search modes (feature-search and singleton-detection) could be employed. Importantly this relationship was entirely independent of variation in attention control, which suggests that this effect is driven by WMC-related long-term memory differences (Cosman & Vecera, 2013a, 2013b). The present set of findings help to further our understanding of the nuanced ways in which memory and attention interact. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  16. The Art of Athlete Leadership: Identifying High-Quality Athlete Leadership at the Individual and Team Level Through Social Network Analysis.

    Science.gov (United States)

    Fransen, Katrien; Van Puyenbroeck, Stef; Loughead, Todd M; Vanbeselaere, Norbert; De Cuyper, Bert; Vande Broek, Gert; Boen, Filip

    2015-06-01

    This research aimed to introduce social network analysis as a novel technique in sports teams to identify the attributes of high-quality athlete leadership, both at the individual and at the team level. Study 1 included 25 sports teams (N = 308 athletes) and focused on athletes' general leadership quality. Study 2 comprised 21 sports teams (N = 267 athletes) and focused on athletes' specific leadership quality as a task, motivational, social, and external leader. The extent to which athletes felt connected with their leader proved to be most predictive for athletes' perceptions of that leader's quality on each leadership role. Also at the team level, teams with higher athlete leadership quality were more strongly connected. We conclude that social network analysis constitutes a valuable tool to provide more insight in the attributes of high-quality leadership both at the individual and at the team level.

  17. The Predictive Role of Difficulties in Emotion Regulation and Self-Control with Susceptibility to Addiction in Drug-Dependent Individuals

    OpenAIRE

    Mahmoud Shirazi; Monavar Janfaza

    2015-01-01

    Objective: The present study aimed to examine the predictive role of difficulties in emotion regulation and self-control in potential for addiction among drug-dependent individuals. Method: This was a correlational study which falls within the category of descriptive studies. The statistical population of the current study included all patients under treatment in outpatient health centers in Bam, among whom 315 individuals were selected through cluster sampling method as the participants of t...

  18. Predictive patient-specific dosimetry and individualized dosing of pretargeted radioimmunotherapy in patients with advanced colorectal cancer

    Energy Technology Data Exchange (ETDEWEB)

    Schoffelen, Rafke; Woliner-van der Weg, Wietske; Visser, Eric P.; Oyen, Wim J.G.; Boerman, Otto C. [Radboud University Medical Center, Department of Radiology and Nuclear Medicine, PO Box 9101, Nijmegen (Netherlands); Goldenberg, David M. [Garden State Cancer Center, Morris Plains, NJ (United States); Immunomedics, Inc., Morris Plains, NJ (United States); IBC Pharmaceuticals, Inc., Morris Plains, NJ (United States); Sharkey, Robert M.; McBride, William J.; Chang, Chien-Hsing [Immunomedics, Inc., Morris Plains, NJ (United States); Rossi, Edmund A. [IBC Pharmaceuticals, Inc., Morris Plains, NJ (United States); Graaf, Winette T.A. van der [Radboud University Medical Center, Department of Medical Oncology, Nijmegen (Netherlands)

    2014-08-15

    Pretargeted radioimmunotherapy (PRIT) with bispecific antibodies (bsMAb) and a radiolabeled peptide reduces the radiation dose to normal tissues. Here we report the accuracy of an {sup 111}In-labeled pretherapy test dose for personalized dosing of {sup 177}Lu-labeled IMP288 following pretargeting with the anti-CEA x anti-hapten bsMAb, TF2, in patients with metastatic colorectal cancer (CRC). In 20 patients bone marrow absorbed doses (BMD) and doses to the kidneys were predicted based on blood samples and scintigrams acquired after {sup 111}In-IMP288 injection for individualized dosing of PRIT with {sup 177}Lu-IMP288. Different dose schedules were studied, varying the interval between the bsMAb and peptide administration (5 days vs. 1 day), increasing the bsMAb dose (75 mg vs. 150 mg), and lowering the peptide dose (100 μg vs. 25 μg). TF2 and {sup 111}In/{sup 177}Lu-IMP288 clearance was highly variable. A strong correlation was observed between peptide residence times and individual TF2 blood concentrations at the time of peptide injection (Spearman's ρ = 0.94, P < 0.0001). PRIT with 7.4 GBq {sup 177}Lu-IMP288 resulted in low radiation doses to normal tissues (BMD <0.5 Gy, kidney dose <3 Gy). Predicted {sup 177}Lu-IMP288 BMD were in good agreement with the actual measured doses (mean ± SD difference -0.0026 ± 0.028 mGy/MBq). Hematological toxicity was mild in most patients, with only two (10 %) having grade 3-4 thrombocytopenia. A correlation was found between platelet toxicity and BMD (Spearman's ρ = 0.58, P = 0.008). No nonhematological toxicity was observed. These results show that individual high activity doses in PRIT in patients with CEA-expressing CRC could be safely administered by predicting the radiation dose to red marrow and kidneys, based on dosimetric analysis of a test dose of TF2 and {sup 111}In-IMP288. (orig.)

  19. Functional hierarchy underlies preferential connectivity disturbances in schizophrenia.

    Science.gov (United States)

    Yang, Genevieve J; Murray, John D; Wang, Xiao-Jing; Glahn, David C; Pearlson, Godfrey D; Repovs, Grega; Krystal, John H; Anticevic, Alan

    2016-01-12

    Schizophrenia may involve an elevated excitation/inhibition (E/I) ratio in cortical microcircuits. It remains unknown how this regulatory disturbance maps onto neuroimaging findings. To address this issue, we implemented E/I perturbations within a neural model of large-scale functional connectivity, which predicted hyperconnectivity following E/I elevation. To test predictions, we examined resting-state functional MRI in 161 schizophrenia patients and 164 healthy subjects. As predicted, patients exhibited elevated functional connectivity that correlated with symptom levels, and was most prominent in association cortices, such as the fronto-parietal control network. This pattern was absent in patients with bipolar disorder (n = 73). To account for the pattern observed in schizophrenia, we integrated neurobiologically plausible, hierarchical differences in association vs. sensory recurrent neuronal dynamics into our model. This in silico architecture revealed preferential vulnerability of association networks to E/I imbalance, which we verified empirically. Reported effects implicate widespread microcircuit E/I imbalance as a parsimonious mechanism for emergent inhomogeneous dysconnectivity in schizophrenia.

  20. Random glucose is useful for individual prediction of type 2 diabetes: results of the Study of Health in Pomerania (SHIP).

    Science.gov (United States)

    Kowall, Bernd; Rathmann, Wolfgang; Giani, Guido; Schipf, Sabine; Baumeister, Sebastian; Wallaschofski, Henri; Nauck, Matthias; Völzke, Henry

    2013-04-01

    Random glucose is widely used in routine clinical practice. We investigated whether this non-standardized glycemic measure is useful for individual diabetes prediction. The Study of Health in Pomerania (SHIP), a population-based cohort study in north-east Germany, included 3107 diabetes-free persons aged 31-81 years at baseline in 1997-2001. 2475 persons participated at 5-year follow-up and gave self-reports of incident diabetes. For the total sample and for subjects aged ≥50 years, statistical properties of prediction models with and without random glucose were compared. A basic model (including age, sex, diabetes of parents, hypertension and waist circumference) and a comprehensive model (additionally including various lifestyle variables and blood parameters, but not HbA1c) performed statistically significantly better after adding random glucose (e.g., the area under the receiver-operating curve (AROC) increased from 0.824 to 0.856 after adding random glucose to the comprehensive model in the total sample). Likewise, adding random glucose to prediction models which included HbA1c led to significant improvements of predictive ability (e.g., for subjects ≥50 years, AROC increased from 0.824 to 0.849 after adding random glucose to the comprehensive model+HbA1c). Random glucose is useful for individual diabetes prediction, and improves prediction models including HbA1c. Copyright © 2012 Primary Care Diabetes Europe. Published by Elsevier Ltd. All rights reserved.

  1. Do the Big Five personality traits predict individual differences in the left cheek bias for emotion perception?

    Science.gov (United States)

    Galea, Samantha; Lindell, Annukka K

    2016-01-01

    Like language, emotion is a lateralized function. Because the right hemisphere typically dominates emotion processing, people express stronger emotion on the left side of their face. This prompts a left cheek bias: we offer the left cheek to express emotion and rate left cheek portraits more emotionally expressive than right cheek portraits. Though the majority of the population show this left cheek bias (60-70%), individual differences exist but remain largely unexplained. Given that people with higher self-rated emotional expressivity show a stronger left cheek bias, personality variables associated with increased emotional expressivity and emotional intelligence, such as extraversion and openness, may help account for individual differences. The present study thus examined whether the Big Five traits predict left cheek preferences. Participants (M = 58, F = 116) completed the NEO-Five Factor Personality Inventory (NEO-FFI) [Costa, P. T. J., & McCrae, R. R. (1992). NEO PI-R professional manual. Odessa, FL: Psychological Assessment Resources] and viewed pairs of left and right cheek images (half mirror-reversed); participants made forced-choice decisions, indicating which image in each pair looked happier. Hierarchical regression indicated that neither trait extraversion nor openness predicted left cheek selections, with NEO-FFI personality subscales accounting for negligible variance in preferences. As the Big Five traits have been discounted, exploration of other potential contributors to individual differences in the left cheek bias is clearly needed.

  2. Individual differences in maternal response to immune challenge predict offspring behavior: Contribution of environmental factors

    Science.gov (United States)

    Bronson, Stefanie L.; Ahlbrand, Rebecca; Horn, Paul S.; Kern, Joseph R.; Richtand, Neil M.

    2011-01-01

    Maternal infection during pregnancy elevates risk for schizophrenia and related disorders in offspring. Converging evidence suggests the maternal inflammatory response mediates the interaction between maternal infection, altered brain development, and behavioral outcome. The extent to which individual differences in the maternal response to immune challenge influence the development of these abnormalities is unknown. The present study investigated the impact of individual differences in maternal response to the viral mimic polyinosinic:polycytidylic acid (poly I:C) on offspring behavior. We observed significant variability in body weight alterations of pregnant rats induced by administration of poly I:C on gestational day 14. Furthermore, the presence or absence of maternal weight loss predicted MK-801 and amphetamine stimulated locomotor abnormalities in offspring. MK-801 stimulated locomotion was altered in offspring of all poly I:C treated dams; however, the presence or absence of maternal weight loss resulted in decreased and modestly increased locomotion, respectively. Adult offspring of poly I:C treated dams that lost weight exhibited significantly decreased amphetamine stimulated locomotion, while offspring of poly I:C treated dams without weight loss performed similarly to vehicle controls. Social isolation and increased maternal age predicted weight loss in response to poly I:C but not vehicle injection. In combination, these data identify environmental factors associated with the maternal response to immune challenge and functional outcome of offspring exposed to maternal immune activation. PMID:21255612

  3. Alveolar ridge augmentation by connective tissue grafting using a pouch method and modified connective tissue technique: A prospective study.

    Science.gov (United States)

    Agarwal, Ashish; Gupta, Narinder Dev

    2015-01-01

    Localized alveolar ridge defect may create physiological and pathological problems. Developments in surgical techniques have made it simpler to change the configuration of a ridge to create a more aesthetic and more easily cleansable shape. The purpose of this study was to compare the efficacy of alveolar ridge augmentation using a subepithelial connective tissue graft in pouch and modified connective tissue graft technique. In this randomized, double blind, parallel and prospective study, 40 non-smoker individuals with 40 class III alveolar ridge defects in maxillary anterior were randomly divided in two groups. Group I received modified connective tissue graft, while group II were treated with subepithelial connective tissue graft in pouch technique. The defect size was measured in its horizontal and vertical dimension by utilizing a periodontal probe in a stone cast at base line, after 3 months, and 6 months post surgically. Analysis of variance and Bonferroni post-hoc test were used for statistical analysis. A two-tailed P connective tissue graft proposed significantly more improvement as compare to connective tissue graft in pouch.

  4. Review international standards for grid connected photovoltaic systems in Malaysia

    International Nuclear Information System (INIS)

    Mekhilef, S.; Rahim, N.A.

    2006-01-01

    Grid connected PV is being applied on variety application including large centralised stations, commercial building and individual houses. There is a need for specific standard to address distinctive new issue created by grid connected PV power system. Internationally many countries are attempting to develop standards for building integration, Dc side issues and grid connection issues. This paper surveys the current development state of the major countries standards in this area, comparing and contrasting, standards and guideline under development, also addressing the need of standards for grid connected in Malaysia

  5. Connection behaviour and the robustness of steel-framed structures in fire

    Directory of Open Access Journals (Sweden)

    Burgess Ian

    2018-01-01

    Full Text Available The full-scale fire tests at Cardington in the 1990s, and the collapse of at least one of the WTC buildings in 2001, illustrated that connections are potentially the most vulnerable parts of a structure in fire. Fracture of connections causes structural discontinuities and reduces the robustness provided by alternative load paths. An understanding of connection performance is essential to the assessment of structural robustness, and so to structural design against progressive collapse. The forces and deformations to which connectionscan be subjected during a fire differ significantly from those assumed in general design. The internal forces i generally start with moment and shear at ambient temperature, then superposing compression in the initial stages of a fire, which finally changes to catenary tension at high temperatures. If a connection does not have sufficient resistance or ductility to accommodate simultaneous large rotations and normal forces, then connections may fracture, leading to extensive damage or progressive collapse of the structure. Practical assessment of the robustness of steel connections in fire will inevitably rely largely on numerical modelling, but this is unlikely to include general-purpose finite element modelling, because of the complexity of such models. The most promising alternative is the component method, a practical approach which can be included within global three-dimensional frame analysis. The connection is represented by an assembly of individual components with known mechanical properties. Component characterization must include high-deflection elevated-temperature behaviour, and represent it up to fracture.In reality a connection may either be able to regain its stability after the initial fracture of one (or a few components, or the first failure may trigger a cascade of failures of other components, leading to complete detachment of the supported member. Numerical modelling must be capable of

  6. Predicting and preventing ovarian hyperstimulation syndrome (OHSS: the need for individualized not standardized treatment

    Directory of Open Access Journals (Sweden)

    Fiedler Klaus

    2012-04-01

    Full Text Available Abstract Ovarian hyperstimulation syndrome (OHSS is the most serious complication of controlled ovarian stimulation (COS as part of assisted reproductive technologies (ART. While the safety and efficacy of ART is well established, physicians should always be aware of the risk of OHSS in patients undergoing COS, as it can be fatal. This article will briefly present the pathophysiology of OHSS, including the key role of vascular endothelial growth factor (VEGF, to provide the foundation for an overview of current techniques for the prevention of OHSS. Risk factors and predictive factors for OHSS will be presented, as recognizing these risk factors and individualizing the COS protocol appropriately is the key to the primary prevention of OHSS, as the benefits and risks of each COS strategy vary among individuals. Individualized COS (iCOS could effectively eradicate OHSS, and the identification of hormonal, functional and genetic markers of ovarian response will facilitate iCOS. However, if iCOS is not properly applied, various preventive measures can be instituted once COS has begun, including cancelling the cycle, coasting, individualizing the human chorionic gonadotropin trigger dose or using a gonadotropin-releasing hormone (GnRH agonist (for those using a GnRH antagonist protocol, the use of intravenous fluids at the time of oocyte retrieval, and cryopreserving/vitrifying all embryos for subsequent transfer in an unstimulated cycle. Some of these techniques have been widely adopted, despite the scarcity of data from randomized clinical trials to support their use.

  7. Falls and fear of falling predict future falls and related injuries in ambulatory individuals with spinal cord injury: a longitudinal observational study

    Directory of Open Access Journals (Sweden)

    Vivien Jørgensen

    2017-04-01

    Conclusion: Ambulatory individuals have a high risk of falling and of fall-related injuries. Fall history, fear of falling and walking speed could predict recurrent falls and injurious falls. Further studies with larger samples are needed to validate these findings. [Jørgensen V, Butler Forslund E, Opheim A, Franzén E, Wahman K, Hultling C, Seiger Å, Ståhle A, Stanghelle JK, Roaldsen KS (2017 Falls and fear of falling predict future falls and related injuries in ambulatory individuals with spinal cord injury: a longitudinal observational study. Journal of Physiotherapy 63: 108–113

  8. A probabilistic approach to quantifying spatial patterns of flow regimes and network-scale connectivity

    Science.gov (United States)

    Garbin, Silvia; Alessi Celegon, Elisa; Fanton, Pietro; Botter, Gianluca

    2017-04-01

    The temporal variability of river flow regime is a key feature structuring and controlling fluvial ecological communities and ecosystem processes. In particular, streamflow variability induced by climate/landscape heterogeneities or other anthropogenic factors significantly affects the connectivity between streams with notable implication for river fragmentation. Hydrologic connectivity is a fundamental property that guarantees species persistence and ecosystem integrity in riverine systems. In riverine landscapes, most ecological transitions are flow-dependent and the structure of flow regimes may affect ecological functions of endemic biota (i.e., fish spawning or grazing of invertebrate species). Therefore, minimum flow thresholds must be guaranteed to support specific ecosystem services, like fish migration, aquatic biodiversity and habitat suitability. In this contribution, we present a probabilistic approach aiming at a spatially-explicit, quantitative assessment of hydrologic connectivity at the network-scale as derived from river flow variability. Dynamics of daily streamflows are estimated based on catchment-scale climatic and morphological features, integrating a stochastic, physically based approach that accounts for the stochasticity of rainfall with a water balance model and a geomorphic recession flow model. The non-exceedance probability of ecologically meaningful flow thresholds is used to evaluate the fragmentation of individual stream reaches, and the ensuing network-scale connectivity metrics. A multi-dimensional Poisson Process for the stochastic generation of rainfall is used to evaluate the impact of climate signature on reach-scale and catchment-scale connectivity. The analysis shows that streamflow patterns and network-scale connectivity are influenced by the topology of the river network and the spatial variability of climatic properties (rainfall, evapotranspiration). The framework offers a robust basis for the prediction of the impact of

  9. Healthy brain connectivity predicts atrophy progression in non-fluent variant of primary progressive aphasia.

    Science.gov (United States)

    Mandelli, Maria Luisa; Vilaplana, Eduard; Brown, Jesse A; Hubbard, H Isabel; Binney, Richard J; Attygalle, Suneth; Santos-Santos, Miguel A; Miller, Zachary A; Pakvasa, Mikhail; Henry, Maya L; Rosen, Howard J; Henry, Roland G; Rabinovici, Gil D; Miller, Bruce L; Seeley, William W; Gorno-Tempini, Maria Luisa

    2016-10-01

    Neurodegeneration has been hypothesized to follow predetermined large-scale networks through the trans-synaptic spread of toxic proteins from a syndrome-specific epicentre. To date, no longitudinal neuroimaging study has tested this hypothesis in vivo in frontotemporal dementia spectrum disorders. The aim of this study was to demonstrate that longitudinal progression of atrophy in non-fluent/agrammatic variant primary progressive aphasia spreads over time from a syndrome-specific epicentre to additional regions, based on their connectivity to the epicentre in healthy control subjects. The syndrome-specific epicentre of the non-fluent/agrammatic variant of primary progressive aphasia was derived in a group of 10 mildly affected patients (clinical dementia rating equal to 0) using voxel-based morphometry. From this region, the inferior frontal gyrus (pars opercularis), we derived functional and structural connectivity maps in healthy controls (n = 30) using functional magnetic resonance imaging at rest and diffusion-weighted imaging tractography. Graph theory analysis was applied to derive functional network features. Atrophy progression was calculated using voxel-based morphometry longitudinal analysis on 34 non-fluent/agrammatic patients. Correlation analyses were performed to compare volume changes in patients with connectivity measures of the healthy functional and structural speech/language network. The default mode network was used as a control network. From the epicentre, the healthy functional connectivity network included the left supplementary motor area and the prefrontal, inferior parietal and temporal regions, which were connected through the aslant, superior longitudinal and arcuate fasciculi. Longitudinal grey and white matter changes were found in the left language-related regions and in the right inferior frontal gyrus. Functional connectivity strength in the healthy speech/language network, but not in the default network, correlated with

  10. Individual-based ecology of coastal birds.

    Science.gov (United States)

    Stillman, Richard A; Goss-Custard, John D

    2010-08-01

    Conservation objectives for non-breeding coastal birds (shorebirds and wildfowl) are determined from their population size at coastal sites. To advise coastal managers, models must predict quantitatively the effects of environmental change on population size or the demographic rates (mortality and reproduction) that determine it. As habitat association models and depletion models are not able to do this, we developed an approach that has produced such predictions thereby enabling policy makers to make evidence-based decisions. Our conceptual framework is individual-based ecology, in which populations are viewed as having properties (e.g. size) that arise from the traits (e.g. behaviour, physiology) and interactions of their constituent individuals. The link between individuals and populations is made through individual-based models (IBMs) that follow the fitness-maximising decisions of individuals and predict population-level consequences (e.g. mortality rate) from the fates of these individuals. Our first IBM was for oystercatchers Haematopus ostralegus and accurately predicted their density-dependent mortality. Subsequently, IBMs were developed for several shorebird and wildfowl species at several European sites, and were shown to predict accurately overwinter mortality, and the foraging behaviour from which predictions are derived. They have been used to predict the effect on survival in coastal birds of sea level rise, habitat loss, wind farm development, shellfishing and human disturbance. This review emphasises the wider applicability of the approach, and identifies other systems to which it could be applied. We view the IBM approach as a very useful contribution to the general problem of how to advance ecology to the point where we can routinely make meaningful predictions of how populations respond to environmental change.

  11. Connecting phenological predictions with population growth rates for mountain pine beetle, an outbreak insect

    Science.gov (United States)

    James A. Powell; Barbara J. Bentz

    2009-01-01

    It is expected that a significant impact of global warming will be disruption of phenology as environmental cues become disassociated from their selective impacts. However there are few, if any, models directly connecting phenology with population growth rates. In this paper we discuss connecting a distributional model describing mountain pine beetle phenology with a...

  12. Harmonic Stability Analysis of Offshore Wind Farm with Component Connection Method

    DEFF Research Database (Denmark)

    Hou, Peng; Ebrahimzadeh, Esmaeil; Wang, Xiongfei

    2017-01-01

    In this paper, an eigenvalue-based harmonic stability analysis method for offshore wind farm is proposed. Considering the internal cable connection layout, a component connection method (CCM) is adopted to divide the system into individual blocks as current controller of converters, LCL filters...

  13. Inhibition and Updating, but Not Switching, Predict Developmental Dyslexia and Individual Variation in Reading Ability

    Directory of Open Access Journals (Sweden)

    Caoilainn Doyle

    2018-05-01

    Full Text Available To elucidate the core executive function profile (strengths and weaknesses in inhibition, updating, and switching associated with dyslexia, this study explored executive function in 27 children with dyslexia and 29 age matched controls using sensitive z-mean measures of each ability and controlled for individual differences in processing speed. This study found that developmental dyslexia is associated with inhibition and updating, but not switching impairments, at the error z-mean composite level, whilst controlling for processing speed. Inhibition and updating (but not switching error composites predicted both dyslexia likelihood and reading ability across the full range of variation from typical to atypical. The predictive relationships were such that those with poorer performance on inhibition and updating measures were significantly more likely to have a diagnosis of developmental dyslexia and also demonstrate poorer reading ability. These findings suggest that inhibition and updating abilities are associated with developmental dyslexia and predict reading ability. Future studies should explore executive function training as an intervention for children with dyslexia as core executive functions appear to be modifiable with training and may transfer to improved reading ability.

  14. Prediction of outcome in individuals with diabetic foot ulcers: focus on the differences between individuals with and without peripheral arterial disease. The EURODIALE Study

    DEFF Research Database (Denmark)

    Prompers, L.; Schaper, N.; Apelqvist, J.

    2008-01-01

    ulcer size, peripheral neuropathy and PAD. When analyses were performed according to PAD status, infection emerged as a specific predictor of non-healing in PAD patients only. Conclusions/Interpretation Predictors of healing differ between patients with and without PAD, suggesting that diabetic foot......Aims/hypothesis Outcome data on individuals with diabetic foot ulcers are scarce, especially in those with peripheral arterial disease (PAD). We therefore examined the clinical characteristics that best predict poor outcome in a large population of diabetic foot ulcer patients and examined whether...

  15. Functional brain connectivity is predictable from anatomic network's Laplacian eigen-structure.

    Science.gov (United States)

    Abdelnour, Farras; Dayan, Michael; Devinsky, Orrin; Thesen, Thomas; Raj, Ashish

    2018-05-15

    How structural connectivity (SC) gives rise to functional connectivity (FC) is not fully understood. Here we mathematically derive a simple relationship between SC measured from diffusion tensor imaging, and FC from resting state fMRI. We establish that SC and FC are related via (structural) Laplacian spectra, whereby FC and SC share eigenvectors and their eigenvalues are exponentially related. This gives, for the first time, a simple and analytical relationship between the graph spectra of structural and functional networks. Laplacian eigenvectors are shown to be good predictors of functional eigenvectors and networks based on independent component analysis of functional time series. A small number of Laplacian eigenmodes are shown to be sufficient to reconstruct FC matrices, serving as basis functions. This approach is fast, and requires no time-consuming simulations. It was tested on two empirical SC/FC datasets, and was found to significantly outperform generative model simulations of coupled neural masses. Copyright © 2018. Published by Elsevier Inc.

  16. Cooperation of axisymmetric connection elements under dynamic load

    Directory of Open Access Journals (Sweden)

    Kołodziej Andrzej

    2018-01-01

    Full Text Available The article presents a method for determining the parameters that define the cooperation of the elements in the axisymmetic connection. The connection, which constitutes a shaft cooperating with a sleeve, has been tested for reaction forces in the connection during shaft rotation in the static sleeve. The shaft was characterized by deliberately modelled roundness deviations in the form of ovality, triangularity and quadrangularity. In addition, the research programme has taken into account the determination of the impact of tolerance of the outside diameter of the shaft. Determination of reaction forces has been carried out using the FEM software. The shaft has been modelled as a rigid element that rotates with a given rotational speed in the deformable sleeve. The conclusions present the impact of roundness deviation types and the tolerance value on reaction forces in the connection restraint. The method presented in the article can be used to predict the behaviour of the elements of axisymmetic connections under dynamic load, which can contribute to forecasting the durability of the connection.

  17. Thalamocortical Connectivity and Microstructural Changes in Congenital and Late Blindness

    DEFF Research Database (Denmark)

    Reislev, N H; Dyrby, Tim Bjørn; Siebner, H. R.

    2017-01-01

    There is ample evidence that the occipital cortex of congenitally blind individuals processes nonvisual information. It remains a debate whether the cross-modal activation of the occipital cortex is mediated through the modulation of preexisting corticocortical projections or the reorganisation...... of thalamocortical connectivity. Current knowledge on this topic largely stems from anatomical studies in animal models. The aim of this study was to test whether purported changes in thalamocortical connectivity in blindness can be revealed by tractography based on diffusion-weighted magnetic resonance imaging...... network between congenitally blind individuals, late blind individuals, and normal sighted controls, diffusion tensor imaging (DTI) indices revealed significant microstructural changes within thalamic clusters of both blind groups. Furthermore, we find a significant decrease in fractional anisotropy (FA...

  18. Connectivity in the early life history of sandeel inferred from otolith microchemistry

    Science.gov (United States)

    Gibb, Fiona M.; Régnier, Thomas; Donald, Kirsty; Wright, Peter J.

    2017-01-01

    Connectivity is a central issue in the development, sustainability and effectiveness of networks of Marine Protected Areas (MPAs). In populations with site attached adults, connectivity is limited to dispersal in the pelagic larval stage. While biophysical models have been widely used to infer early dispersal, empirical evidence through sources such as otolith microchemistry can provide a means of evaluating model predictions. In the present study, connectivity in the lesser sandeel, Ammodytes marinus, was investigated using LA-ICP-MS otolith microchemistry. Otoliths from juveniles (age 0) were examined from four Scottish spawning areas predicted to differ in terms of larval retention rates and connectivity based on past biophysical models. There were significant spatial differences in otolith post-settled juvenile chemistry among locations at a scale of 100-400 km. Differences in near core chemistry pointed to three chemically distinct natal sources, as identified by a cluster analysis, contributing to settlement locations.

  19. Predictive Brain Mechanisms in Sound-to-Meaning Mapping during Speech Processing.

    Science.gov (United States)

    Lyu, Bingjiang; Ge, Jianqiao; Niu, Zhendong; Tan, Li Hai; Gao, Jia-Hong

    2016-10-19

    Spoken language comprehension relies not only on the identification of individual words, but also on the expectations arising from contextual information. A distributed frontotemporal network is known to facilitate the mapping of speech sounds onto their corresponding meanings. However, how prior expectations influence this efficient mapping at the neuroanatomical level, especially in terms of individual words, remains unclear. Using fMRI, we addressed this question in the framework of the dual-stream model by scanning native speakers of Mandarin Chinese, a language highly dependent on context. We found that, within the ventral pathway, the violated expectations elicited stronger activations in the left anterior superior temporal gyrus and the ventral inferior frontal gyrus (IFG) for the phonological-semantic prediction of spoken words. Functional connectivity analysis showed that expectations were mediated by both top-down modulation from the left ventral IFG to the anterior temporal regions and enhanced cross-stream integration through strengthened connections between different subregions of the left IFG. By further investigating the dynamic causality within the dual-stream model, we elucidated how the human brain accomplishes sound-to-meaning mapping for words in a predictive manner. In daily communication via spoken language, one of the core processes is understanding the words being used. Effortless and efficient information exchange via speech relies not only on the identification of individual spoken words, but also on the contextual information giving rise to expected meanings. Despite the accumulating evidence for the bottom-up perception of auditory input, it is still not fully understood how the top-down modulation is achieved in the extensive frontotemporal cortical network. Here, we provide a comprehensive description of the neural substrates underlying sound-to-meaning mapping and demonstrate how the dual-stream model functions in the modulation of

  20. Intra-hemispheric intrinsic connectivity asymmetry and its relationships with handedness and language Lateralization.

    Science.gov (United States)

    Joliot, M; Tzourio-Mazoyer, N; Mazoyer, B

    2016-12-01

    Asymmetry in intra-hemispheric intrinsic connectivity, and its association with handedness and hemispheric dominance for language, were investigated in a sample of 290 healthy volunteers enriched in left-handers (52.7%). From the resting-state FMRI data of each participant, we derived an intra-hemispheric intrinsic connectivity asymmetry (HICA) matrix as the difference between the left and right intra-hemispheric matrices of intrinsic correlation computed for each pair of the AICHA atlas ROIs. We defined a similarity measure between the HICA matrices of two individuals as the correlation coefficient of their corresponding elements, and computed for each individual an index of intra-hemispheric intrinsic connectivity asymmetry as the average similarity measure of his HICA matrix to those of the other subjects of the sample (HICAs). Gaussian-mixture modeling of the age-corrected HICAs sample distribution revealed that two types of HICA patterns were present, one (Typical_HICA) including 92.4% of the participants while the other (Atypical_HICA) included only 7.6% of them, mostly left-handers. In addition, we investigated the relationship between asymmetry in intra-hemispheric intrinsic connectivity and language hemispheric dominance, including a potential effect of handedness on this relationship, thanks to an FMRI acquisition during language production from which an hemispheric functional lateralization index for language (HFLI) and a type of hemispheric dominance for language, namely leftward, ambilateral, or rightward, were derived for each individual. There was a significant association between the types of language hemispheric dominance and of intra-hemispheric intrinsic connectivity asymmetry, occurrence of Atypical_HICAs individuals being very high in the group of individuals rightward-lateralized for language (80%), reduced in the ambilateral group (19%) and rare in individuals leftward-lateralized for language (less than 3%). Quantitatively, we found a

  1. Directional connectivity in hydrology and ecology

    Science.gov (United States)

    Larsen, Laurel G.; Choi, Jungyill; Nungesser, Martha K.; Harvey, Judson W.

    2012-01-01

    Quantifying hydrologic and ecological connectivity has contributed to understanding transport and dispersal processes and assessing ecosystem degradation or restoration potential. However, there has been little synthesis across disciplines. The growing field of ecohydrology and recent recognition that loss of hydrologic connectivity is leading to a global decline in biodiversity underscore the need for a unified connectivity concept. One outstanding need is a way to quantify directional connectivity that is consistent, robust to variations in sampling, and transferable across scales or environmental settings. Understanding connectivity in a particular direction (e.g., streamwise, along or across gradient, between sources and sinks, along cardinal directions) provides critical information for predicting contaminant transport, planning conservation corridor design, and understanding how landscapes or hydroscapes respond to directional forces like wind or water flow. Here we synthesize progress on quantifying connectivity and develop a new strategy for evaluating directional connectivity that benefits from use of graph theory in ecology and percolation theory in hydrology. The directional connectivity index (DCI) is a graph-theory based, multiscale metric that is generalizable to a range of different structural and functional connectivity applications. It exhibits minimal sensitivity to image rotation or resolution within a given range and responds intuitively to progressive, unidirectional change. Further, it is linearly related to the integral connectivity scale length—a metric common in hydrology that correlates well with actual fluxes—but is less computationally challenging and more readily comparable across different landscapes. Connectivity-orientation curves (i.e., directional connectivity computed over a range of headings) provide a quantitative, information-dense representation of environmental structure that can be used for comparison or detection of

  2. Directional connectivity in hydrology and ecology.

    Science.gov (United States)

    Larsen, Laurel G; Choi, Jungyill; Nungesser, Martha K; Harvey, Judson W

    2012-12-01

    Quantifying hydrologic and ecological connectivity has contributed to understanding transport and dispersal processes and assessing ecosystem degradation or restoration potential. However, there has been little synthesis across disciplines. The growing field of ecohydrology and recent recognition that loss of hydrologic connectivity is leading to a global decline in biodiversity underscore the need for a unified connectivity concept. One outstanding need is a way to quantify directional connectivity that is consistent, robust to variations in sampling, and transferable across scales or environmental settings. Understanding connectivity in a particular direction (e.g., streamwise, along or across gradient, between sources and sinks, along cardinal directions) provides critical information for predicting contaminant transport, planning conservation corridor design, and understanding how landscapes or hydroscapes respond to directional forces like wind or water flow. Here we synthesize progress on quantifying connectivity and develop a new strategy for evaluating directional connectivity that benefits from use of graph theory in ecology and percolation theory in hydrology. The directional connectivity index (DCI) is a graph-theory based, multiscale metric that is generalizable to a range of different structural and functional connectivity applications. It exhibits minimal sensitivity to image rotation or resolution within a given range and responds intuitively to progressive, unidirectional change. Further, it is linearly related to the integral connectivity scale length--a metric common in hydrology that correlates well with actual fluxes--but is less computationally challenging and more readily comparable across different landscapes. Connectivity-orientation curves (i.e., directional connectivity computed over a range of headings) provide a quantitative, information-dense representation of environmental structure that can be used for comparison or detection of

  3. Predicting arsenic concentrations in groundwater of San Luis Valley, Colorado: implications for individual-level lifetime exposure assessment.

    Science.gov (United States)

    James, Katherine A; Meliker, Jaymie R; Buttenfield, Barbara E; Byers, Tim; Zerbe, Gary O; Hokanson, John E; Marshall, Julie A

    2014-08-01

    Consumption of inorganic arsenic in drinking water at high levels has been associated with chronic diseases. Risk is less clear at lower levels of arsenic, in part due to difficulties in estimating exposure. Herein we characterize spatial and temporal variability of arsenic concentrations and develop models for predicting aquifer arsenic concentrations in the San Luis Valley, Colorado, an area of moderately elevated arsenic in groundwater. This study included historical water samples with total arsenic concentrations from 595 unique well locations. A longitudinal analysis established temporal stability in arsenic levels in individual wells. The mean arsenic levels for a random sample of 535 wells were incorporated into five kriging models to predict groundwater arsenic concentrations at any point in time. A separate validation dataset (n = 60 wells) was used to identify the model with strongest predictability. Findings indicate that arsenic concentrations are temporally stable (r = 0.88; 95 % CI 0.83-0.92 for samples collected from the same well 15-25 years apart) and the spatial model created using ordinary kriging best predicted arsenic concentrations (ρ = 0.72 between predicted and observed validation data). These findings illustrate the value of geostatistical modeling of arsenic and suggest the San Luis Valley is a good region for conducting epidemiologic studies of groundwater metals because of the ability to accurately predict variation in groundwater arsenic concentrations.

  4. Emotion, working memory task demands and individual differences predict behavior, cognitive effort and negative affect.

    Science.gov (United States)

    Storbeck, Justin; Davidson, Nicole A; Dahl, Chelsea F; Blass, Sara; Yung, Edwin

    2015-01-01

    We examined whether positive and negative affect motivates verbal and spatial working memory processes, respectively, which have implications for the expenditure of mental effort. We argue that when emotion promotes cognitive tendencies that are goal incompatible with task demands, greater cognitive effort is required to perform well. We sought to investigate whether this increase in cognitive effort impairs behavioural control over a broad domain of self-control tasks. Moreover, we predicted that individuals with higher behavioural inhibition system (BIS) sensitivities would report more negative affect within the goal incompatible conditions because such individuals report higher negative affect during cognitive challenge. Positive or negative affective states were induced followed by completing a verbal or spatial 2-back working memory task. All participants then completed one of three self-control tasks. Overall, we observed that conditions of emotion and working memory incompatibility (positive/spatial and negative/verbal) performed worse on the self-control tasks, and within the incompatible conditions individuals with higher BIS sensitivities reported more negative affect at the end of the study. The combination of findings suggests that emotion and working memory compatibility reduces cognitive effort and impairs behavioural control.

  5. Individualized prediction of schizophrenia based on the whole-brain pattern of altered white matter tract integrity.

    Science.gov (United States)

    Chen, Yu-Jen; Liu, Chih-Min; Hsu, Yung-Chin; Lo, Yu-Chun; Hwang, Tzung-Jeng; Hwu, Hai-Gwo; Lin, Yi-Tin; Tseng, Wen-Yih Isaac

    2018-01-01

    A schizophrenia diagnosis relies on characteristic symptoms identified by trained physicians, and is thus prone to subjectivity. This study developed a procedure for the individualized prediction of schizophrenia based on whole-brain patterns of altered white matter tract integrity. The study comprised training (108 patients and 144 controls) and testing (60 patients and 60 controls) groups. Male and female participants were comparable in each group and were analyzed separately. All participants underwent diffusion spectrum imaging of the head, and the data were analyzed using the tract-based automatic analysis method to generate a standardized two-dimensional array of white matter tract integrity, called the connectogram. Unique patterns in the connectogram that most accurately identified schizophrenia were systematically reviewed in the training group. Then, the diagnostic performance of the patterns was individually verified in the testing group by using receiver-operating characteristic curve analysis. The performance was high in men (accuracy = 0.85) and satisfactory in women (accuracy = 0.75). In men, the pattern was located in discrete fiber tracts, as has been consistently reported in the literature; by contrast, the pattern was widespread over all tracts in women. These distinct patterns suggest that there is a higher variability in the microstructural alterations in female patients than in male patients. The individualized prediction of schizophrenia is feasible based on the different whole-brain patterns of tract integrity. The optimal masks and their corresponding regions in the fiber tracts could serve as potential imaging biomarkers for schizophrenia. Hum Brain Mapp 39:575-587, 2018. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  6. Estimating population density and connectivity of American mink using spatial capture-recapture.

    Science.gov (United States)

    Fuller, Angela K; Sutherland, Chris S; Royle, J Andrew; Hare, Matthew P

    2016-06-01

    Estimating the abundance or density of populations is fundamental to the conservation and management of species, and as landscapes become more fragmented, maintaining landscape connectivity has become one of the most important challenges for biodiversity conservation. Yet these two issues have never been formally integrated together in a model that simultaneously models abundance while accounting for connectivity of a landscape. We demonstrate an application of using capture-recapture to develop a model of animal density using a least-cost path model for individual encounter probability that accounts for non-Euclidean connectivity in a highly structured network. We utilized scat detection dogs (Canis lupus familiaris) as a means of collecting non-invasive genetic samples of American mink (Neovison vison) individuals and used spatial capture-recapture models (SCR) to gain inferences about mink population density and connectivity. Density of mink was not constant across the landscape, but rather increased with increasing distance from city, town, or village centers, and mink activity was associated with water. The SCR model allowed us to estimate the density and spatial distribution of individuals across a 388 km² area. The model was used to investigate patterns of space usage and to evaluate covariate effects on encounter probabilities, including differences between sexes. This study provides an application of capture-recapture models based on ecological distance, allowing us to directly estimate landscape connectivity. This approach should be widely applicable to provide simultaneous direct estimates of density, space usage, and landscape connectivity for many species.

  7. Estimating population density and connectivity of American mink using spatial capture-recapture

    Science.gov (United States)

    Fuller, Angela K.; Sutherland, Christopher S.; Royle, Andy; Hare, Matthew P.

    2016-01-01

    Estimating the abundance or density of populations is fundamental to the conservation and management of species, and as landscapes become more fragmented, maintaining landscape connectivity has become one of the most important challenges for biodiversity conservation. Yet these two issues have never been formally integrated together in a model that simultaneously models abundance while accounting for connectivity of a landscape. We demonstrate an application of using capture–recapture to develop a model of animal density using a least-cost path model for individual encounter probability that accounts for non-Euclidean connectivity in a highly structured network. We utilized scat detection dogs (Canis lupus familiaris) as a means of collecting non-invasive genetic samples of American mink (Neovison vison) individuals and used spatial capture–recapture models (SCR) to gain inferences about mink population density and connectivity. Density of mink was not constant across the landscape, but rather increased with increasing distance from city, town, or village centers, and mink activity was associated with water. The SCR model allowed us to estimate the density and spatial distribution of individuals across a 388 km2 area. The model was used to investigate patterns of space usage and to evaluate covariate effects on encounter probabilities, including differences between sexes. This study provides an application of capture–recapture models based on ecological distance, allowing us to directly estimate landscape connectivity. This approach should be widely applicable to provide simultaneous direct estimates of density, space usage, and landscape connectivity for many species.

  8. Functional organization of intrinsic connectivity networks in Chinese-chess experts.

    Science.gov (United States)

    Duan, Xujun; Long, Zhiliang; Chen, Huafu; Liang, Dongmei; Qiu, Lihua; Huang, Xiaoqi; Liu, Timon Cheng-Yi; Gong, Qiyong

    2014-04-16

    The functional architecture of the human brain has been extensively described in terms of functional connectivity networks, detected from the low-frequency coherent neuronal fluctuations during a resting state condition. Accumulating evidence suggests that the overall organization of functional connectivity networks is associated with individual differences in cognitive performance and prior experience. Such an association raises the question of how cognitive expertise exerts an influence on the topological properties of large-scale functional networks. To address this question, we examined the overall organization of brain functional networks in 20 grandmaster and master level Chinese-chess players (GM/M) and twenty novice players, by means of resting-state functional connectivity and graph theoretical analyses. We found that, relative to novices, functional connectivity was increased in GM/Ms between basal ganglia, thalamus, hippocampus, and several parietal and temporal areas, suggesting the influence of cognitive expertise on intrinsic connectivity networks associated with learning and memory. Furthermore, we observed economical small-world topology in the whole-brain functional connectivity networks in both groups, but GM/Ms exhibited significantly increased values of normalized clustering coefficient which resulted in increased small-world topology. These findings suggest an association between the functional organization of brain networks and individual differences in cognitive expertise, which might provide further evidence of the mechanisms underlying expert behavior. Copyright © 2014 Elsevier B.V. All rights reserved.

  9. Dynamic fMRI networks predict success in a behavioral weight loss program among older adults.

    Science.gov (United States)

    Mokhtari, Fatemeh; Rejeski, W Jack; Zhu, Yingying; Wu, Guorong; Simpson, Sean L; Burdette, Jonathan H; Laurienti, Paul J

    2018-06-01

    More than one-third of adults in the United States are obese, with a higher prevalence among older adults. Obesity among older adults is a major cause of physical dysfunction, hypertension, diabetes, and coronary heart diseases. Many people who engage in lifestyle weight loss interventions fail to reach targeted goals for weight loss, and most will regain what was lost within 1-2 years following cessation of treatment. This variability in treatment efficacy suggests that there are important phenotypes predictive of success with intentional weight loss that could lead to tailored treatment regimen, an idea that is consistent with the concept of precision-based medicine. Although the identification of biochemical and metabolic phenotypes are one potential direction of research, neurobiological measures may prove useful as substantial behavioral change is necessary to achieve success in a lifestyle intervention. In the present study, we use dynamic brain networks from functional magnetic resonance imaging (fMRI) data to prospectively identify individuals most likely to succeed in a behavioral weight loss intervention. Brain imaging was performed in overweight or obese older adults (age: 65-79 years) who participated in an 18-month lifestyle weight loss intervention. Machine learning and functional brain networks were combined to produce multivariate prediction models. The prediction accuracy exceeded 95%, suggesting that there exists a consistent pattern of connectivity which correctly predicts success with weight loss at the individual level. Connectivity patterns that contributed to the prediction consisted of complex multivariate network components that substantially overlapped with known brain networks that are associated with behavior emergence, self-regulation, body awareness, and the sensory features of food. Future work on independent datasets and diverse populations is needed to corroborate our findings. Additionally, we believe that efforts can begin to

  10. The Biological Basis of Learning and Individuality.

    Science.gov (United States)

    Kandel, Eric R.; Hawkins, Robert D.

    1992-01-01

    Describes the biological basis of learning and individuality. Presents an overview of recent discoveries that suggest learning engages a simple set of rules that modify the strength of connection between neurons in the brain. The changes are cited as playing an important role in making each individual unique. (MCO)

  11. Individual differences in learning predict the return of fear.

    Science.gov (United States)

    Gershman, Samuel J; Hartley, Catherine A

    2015-09-01

    Using a laboratory analogue of learned fear (Pavlovian fear conditioning), we show that there is substantial heterogeneity across individuals in spontaneous recovery of fear following extinction training. We propose that this heterogeneity might stem from qualitative individual differences in the nature of extinction learning. Whereas some individuals tend to form a new memory during extinction, leaving their fear memory intact, others update the original threat association with new safety information, effectively unlearning the fear memory. We formalize this account in a computational model of fear learning and show that individuals who, according to the model, are more likely to form new extinction memories tend to show greater spontaneous recovery compared to individuals who appear to only update a single memory. This qualitative variation in fear and extinction learning may have important implications for understanding vulnerability and resilience to fear-related psychiatric disorders.

  12. Sensing coral reef connectivity pathways from space

    KAUST Repository

    Raitsos, Dionysios E.; Brewin, Robert J. W.; Zhan, Peng; Dreano, Denis; Pradhan, Yaswant; Nanninga, Gerrit B.; Hoteit, Ibrahim

    2017-01-01

    Coral reefs rely on inter-habitat connectivity to maintain gene flow, biodiversity and ecosystem resilience. Coral reef communities of the Red Sea exhibit remarkable genetic homogeneity across most of the Arabian Peninsula coastline, with a genetic break towards the southern part of the basin. While previous studies have attributed these patterns to environmental heterogeneity, we hypothesize that they may also emerge as a result of dynamic circulation flow; yet, such linkages remain undemonstrated. Here, we integrate satellite-derived biophysical observations, particle dispersion model simulations, genetic population data and ship-borne in situ profiles to assess reef connectivity in the Red Sea. We simulated long-term (>20 yrs.) connectivity patterns driven by remotely-sensed sea surface height and evaluated results against estimates of genetic distance among populations of anemonefish, Amphiprion bicinctus, along the eastern Red Sea coastline. Predicted connectivity was remarkably consistent with genetic population data, demonstrating that circulation features (eddies, surface currents) formulate physical pathways for gene flow. The southern basin has lower physical connectivity than elsewhere, agreeing with known genetic structure of coral reef organisms. The central Red Sea provides key source regions, meriting conservation priority. Our analysis demonstrates a cost-effective tool to estimate biophysical connectivity remotely, supporting coastal management in data-limited regions.

  13. Sensing coral reef connectivity pathways from space

    KAUST Repository

    Raitsos, Dionysios E.

    2017-08-18

    Coral reefs rely on inter-habitat connectivity to maintain gene flow, biodiversity and ecosystem resilience. Coral reef communities of the Red Sea exhibit remarkable genetic homogeneity across most of the Arabian Peninsula coastline, with a genetic break towards the southern part of the basin. While previous studies have attributed these patterns to environmental heterogeneity, we hypothesize that they may also emerge as a result of dynamic circulation flow; yet, such linkages remain undemonstrated. Here, we integrate satellite-derived biophysical observations, particle dispersion model simulations, genetic population data and ship-borne in situ profiles to assess reef connectivity in the Red Sea. We simulated long-term (>20 yrs.) connectivity patterns driven by remotely-sensed sea surface height and evaluated results against estimates of genetic distance among populations of anemonefish, Amphiprion bicinctus, along the eastern Red Sea coastline. Predicted connectivity was remarkably consistent with genetic population data, demonstrating that circulation features (eddies, surface currents) formulate physical pathways for gene flow. The southern basin has lower physical connectivity than elsewhere, agreeing with known genetic structure of coral reef organisms. The central Red Sea provides key source regions, meriting conservation priority. Our analysis demonstrates a cost-effective tool to estimate biophysical connectivity remotely, supporting coastal management in data-limited regions.

  14. Prediction models and development of an easy to use open-access tool for measuring lung function of individuals with motor complete spinal cord injury

    NARCIS (Netherlands)

    Mueller, Gabi; de Groot, Sonja; van der Woude, Lucas H.; Perret, Claudio; Michel, Franz; Hopman, Maria T. E.

    Objective: To develop statistical models to predict lung function and respiratory muscle strength from personal and lesion characteristics of individuals with motor complete spinal cord injury. Design: Cross-sectional, multi-centre cohort study. Subjects: A total of 440 individuals with traumatic,

  15. Validation of Individual Non-Linear Predictive Pharmacokinetic ...

    African Journals Online (AJOL)

    3Department of Veterinary Medicine, Faculty of Agriculture, University of Novi Sad, Novi Sad, Republic of Serbia ... Purpose: To evaluate the predictive performance of phenytoin multiple dosing non-linear pharmacokinetic ... status epilepticus affects an estimated 152,000 ..... causal factors, i.e., infection, inflammation, tissue.

  16. The association between resting functional connectivity and dispositional optimism.

    Science.gov (United States)

    Ran, Qian; Yang, Junyi; Yang, Wenjing; Wei, Dongtao; Qiu, Jiang; Zhang, Dong

    2017-01-01

    Dispositional optimism is an individual characteristic that plays an important role in human experience. Optimists are people who tend to hold positive expectations for their future. Previous studies have focused on the neural basis of optimism, such as task response neural activity and brain structure volume. However, the functional connectivity between brain regions of the dispositional optimists are poorly understood. Previous study suggested that the ventromedial prefrontal cortex (vmPFC) are associated with individual differences in dispositional optimism, but it is unclear whether there are other brain regions that combine with the vmPFC to contribute to dispositional optimism. Thus, the present study used the resting-state functional connectivity (RSFC) approach and set the vmPFC as the seed region to examine if differences in functional brain connectivity between the vmPFC and other brain regions would be associated with individual differences in dispositional optimism. The results found that dispositional optimism was significantly positively correlated with the strength of the RSFC between vmPFC and middle temporal gyrus (mTG) and negativly correlated with RSFC between vmPFC and inferior frontal gyrus (IFG). These findings may be suggested that mTG and IFG which associated with emotion processes and emotion regulation also play an important role in the dispositional optimism.

  17. The association between resting functional connectivity and dispositional optimism.

    Directory of Open Access Journals (Sweden)

    Qian Ran

    Full Text Available Dispositional optimism is an individual characteristic that plays an important role in human experience. Optimists are people who tend to hold positive expectations for their future. Previous studies have focused on the neural basis of optimism, such as task response neural activity and brain structure volume. However, the functional connectivity between brain regions of the dispositional optimists are poorly understood. Previous study suggested that the ventromedial prefrontal cortex (vmPFC are associated with individual differences in dispositional optimism, but it is unclear whether there are other brain regions that combine with the vmPFC to contribute to dispositional optimism. Thus, the present study used the resting-state functional connectivity (RSFC approach and set the vmPFC as the seed region to examine if differences in functional brain connectivity between the vmPFC and other brain regions would be associated with individual differences in dispositional optimism. The results found that dispositional optimism was significantly positively correlated with the strength of the RSFC between vmPFC and middle temporal gyrus (mTG and negativly correlated with RSFC between vmPFC and inferior frontal gyrus (IFG. These findings may be suggested that mTG and IFG which associated with emotion processes and emotion regulation also play an important role in the dispositional optimism.

  18. Altered resting-state connectivity within default mode network associated with late chronotype.

    Science.gov (United States)

    Horne, Charlotte Mary; Norbury, Ray

    2018-04-20

    Current evidence suggests late chronotype individuals have an increased risk of developing depression. However, the underlying neural mechanisms of this association are not fully understood. Forty-six healthy, right-handed individuals free of current or previous diagnosis of depression, family history of depression or sleep disorder underwent resting-state functional Magnetic Resonance Imaging (rsFMRI). Using an Independent Component Analysis (ICA) approach, the Default Mode Network (DMN) was identified based on a well validated template. Linear effects of chronotype on DMN connectivity were tested for significance using non-parametric permutation tests (applying 5000 permutations). Sleep quality, age, gender, measures of mood and anxiety, time of scan and cortical grey matter volume were included as covariates in the regression model. A significant positive correlation between chronotype and functional connectivity within nodes of the DMN was observed, including; bilateral PCC and precuneus, such that later chronotype (participants with lower rMEQ scores) was associated with decreased connectivity within these regions. The current results appear consistent with altered DMN connectivity in depressed patients and weighted evidence towards reduced DMN connectivity in other at-risk populations which may, in part, explain the increased vulnerability for depression in late chronotype individuals. The effect may be driven by self-critical thoughts associated with late chronotype although future studies are needed to directly investigate this. Copyright © 2018 Elsevier Ltd. All rights reserved.

  19. The missing link: Predicting connectomes from noisy and partially observed tract tracing data

    DEFF Research Database (Denmark)

    Hinne, Max; Meijers, Annet; Bakker, Rembrandt

    2017-01-01

    a high chance of being connected, while regions far apart are most likely disconnected in the connectome. After learning the latent embedding from the connections that we did observe, the latent space allows us to predict connections that have not been probed previously. We apply the methodology to two....... In this paper, we suggest that instead of probing all possible connections, hitherto unknown connections may be predicted from the data that is already available. Our approach uses a 'latent space model' that embeds the connectivity in an abstract physical space. Regions that are close in the latent space have...... connectivity data sets of the macaque, where we demonstrate that the latent space model is successful in predicting unobserved connectivity, outperforming two baselines and an alternative model in nearly all cases. Furthermore, we show how the latent spatial embedding may be used to integrate multimodal...

  20. Metapopulation responses to patch connectivity and quality are masked by successional habitat dynamics.

    Science.gov (United States)

    Hodgson, Jenny A; Moilanen, Atte; Thomas, Chris D

    2009-06-01

    Many species have to track changes in the spatial distribution of suitable habitat from generation to generation. Understanding the dynamics of such species will likely require spatially explicit models, and patch-based metapopulation models are potentially appropriate. However, relatively little attention has been paid to developing metapopulation models that include habitat dynamics, and very little to testing the predictions of these models. We tested three predictions from theory about the differences between dynamic habitat metapopulations and their static counterparts using long-term survey data from two metapopulations of the butterfly Plebejus argus. As predicted, we showed first that the metapopulation inhabiting dynamic habitat had a lower level of habitat occupancy, which could not be accounted for by other differences between the metapopulations. Secondly, we found that patch occupancy did not significantly increase with increasing patch connectivity in dynamic habitat, whereas there was a strong positive connectivity-occupancy relationship in static habitat. Thirdly, we found no significant relationship between patch occupancy and patch quality in dynamic habitat, whereas there was a strong, positive quality-occupancy relationship in static habitat. Modeling confirmed that the differences in mean patch occupancy and connectivity-occupancy slope could arise without changing the species' metapopulation parameters-importantly, without changing the dependence of colonization upon connectivity. We found that, for a range of landscape scenarios, successional simulations always produced a lower connectivity-occupancy slope than comparable simulations with static patches, whether compared like-for-like or controlling for mean occupancy. We conclude that landscape-scale studies may often underestimate the importance of connectivity for species occurrence and persistence because habitat turnover can obscure the connectivity-occupancy relationship in commonly

  1. Evidence from a rare case-study for Hebbian-like changes in structural connectivity induced by long-term deep brain stimulation

    Directory of Open Access Journals (Sweden)

    Tim J Van Hartevelt

    2015-06-01

    Full Text Available It is unclear whether Hebbian-like learning occurs at the level of long-range white matter connections in humans, i.e. where measurable changes in structural connectivity are correlated with changes in functional connectivity. However, the behavioral changes observed after deep brain stimulation (DBS suggest the existence of such Hebbian-like mechanisms occurring at the structural level with functional consequences. In this rare case study, we obtained the full network of white matter connections of one patient with Parkinson's disease before and after long-term DBS and combined it with a computational model of ongoing activity to investigate the effects of DBS-induced long-term structural changes. The results show that the long-term effects of DBS on resting-state functional connectivity is best obtained in the computational model by changing the structural weights from the subthalamic nucleus to the putamen and the thalamus in a Hebbian-like manner. Moreover, long-term DBS also significantly changed the structural connectivity towards normality in terms of model-based measures of segregation and integration of information processing, two key concepts of brain organization. This novel approach using computational models to model the effects of Hebbian-like changes in structural connectivity allowed us to causally identify the possible underlying neural mechanisms of long-term DBS using rare case study data. In time, this could help predict the efficacy of individual DBS targeting and identify novel DBS targets.

  2. Forecasting Individual Headache Attacks Using Perceived Stress: Development of a Multivariable Prediction Model for Persons With Episodic Migraine.

    Science.gov (United States)

    Houle, Timothy T; Turner, Dana P; Golding, Adrienne N; Porter, John A H; Martin, Vincent T; Penzien, Donald B; Tegeler, Charles H

    2017-07-01

    To develop and validate a prediction model that forecasts future migraine attacks for an individual headache sufferer. Many headache patients and physicians believe that precipitants of headache can be identified and avoided or managed to reduce the frequency of headache attacks. Of the numerous candidate triggers, perceived stress has received considerable attention for its association with the onset of headache in episodic and chronic headache sufferers. However, no evidence is available to support forecasting headache attacks within individuals using any of the candidate headache triggers. This longitudinal cohort with forecasting model development study enrolled 100 participants with episodic migraine with or without aura, and N = 95 contributed 4626 days of electronic diary data and were included in the analysis. Individual headache forecasts were derived from current headache state and current levels of stress using several aspects of the Daily Stress Inventory, a measure of daily hassles that is completed at the end of each day. The primary outcome measure was the presence/absence of any headache attack (head pain > 0 on a numerical rating scale of 0-10) over the next 24 h period. After removing missing data (n = 431 days), participants in the study experienced a headache attack on 1613/4195 (38.5%) days. A generalized linear mixed-effects forecast model using either the frequency of stressful events or the perceived intensity of these events fit the data well. This simple forecasting model possessed promising predictive utility with an AUC of 0.73 (95% CI 0.71-0.75) in the training sample and an AUC of 0.65 (95% CI 0.6-0.67) in a leave-one-out validation sample. This forecasting model had a Brier score of 0.202 and possessed good calibration between forecasted probabilities and observed frequencies but had only low levels of resolution (ie, sharpness). This study demonstrates that future headache attacks can be forecasted for a diverse group of

  3. Brain Network Modularity Predicts Exercise-Related Executive Function Gains in Older Adults.

    Science.gov (United States)

    Baniqued, Pauline L; Gallen, Courtney L; Voss, Michelle W; Burzynska, Agnieszka Z; Wong, Chelsea N; Cooke, Gillian E; Duffy, Kristin; Fanning, Jason; Ehlers, Diane K; Salerno, Elizabeth A; Aguiñaga, Susan; McAuley, Edward; Kramer, Arthur F; D'Esposito, Mark

    2017-01-01

    Recent work suggests that the brain can be conceptualized as a network comprised of groups of sub-networks or modules. The extent of segregation between modules can be quantified with a modularity metric, where networks with high modularity have dense connections within modules and sparser connections between modules. Previous work has shown that higher modularity predicts greater improvements after cognitive training in patients with traumatic brain injury and in healthy older and young adults. It is not known, however, whether modularity can also predict cognitive gains after a physical exercise intervention. Here, we quantified modularity in older adults ( N = 128, mean age = 64.74) who underwent one of the following interventions for 6 months (NCT01472744 on ClinicalTrials.gov): (1) aerobic exercise in the form of brisk walking (Walk), (2) aerobic exercise in the form of brisk walking plus nutritional supplement (Walk+), (3) stretching, strengthening and stability (SSS), or (4) dance instruction. After the intervention, the Walk, Walk+ and SSS groups showed gains in cardiorespiratory fitness (CRF), with larger effects in both walking groups compared to the SSS and Dance groups. The Walk, Walk+ and SSS groups also improved in executive function (EF) as measured by reasoning, working memory, and task-switching tests. In the Walk, Walk+, and SSS groups that improved in EF, higher baseline modularity was positively related to EF gains, even after controlling for age, in-scanner motion and baseline EF. No relationship between modularity and EF gains was observed in the Dance group, which did not show training-related gains in CRF or EF control. These results are consistent with previous studies demonstrating that individuals with a more modular brain network organization are more responsive to cognitive training. These findings suggest that the predictive power of modularity may be generalizable across interventions aimed to enhance aspects of cognition and that

  4. Brain Network Modularity Predicts Exercise-Related Executive Function Gains in Older Adults

    Directory of Open Access Journals (Sweden)

    Pauline L. Baniqued

    2018-01-01

    Full Text Available Recent work suggests that the brain can be conceptualized as a network comprised of groups of sub-networks or modules. The extent of segregation between modules can be quantified with a modularity metric, where networks with high modularity have dense connections within modules and sparser connections between modules. Previous work has shown that higher modularity predicts greater improvements after cognitive training in patients with traumatic brain injury and in healthy older and young adults. It is not known, however, whether modularity can also predict cognitive gains after a physical exercise intervention. Here, we quantified modularity in older adults (N = 128, mean age = 64.74 who underwent one of the following interventions for 6 months (NCT01472744 on ClinicalTrials.gov: (1 aerobic exercise in the form of brisk walking (Walk, (2 aerobic exercise in the form of brisk walking plus nutritional supplement (Walk+, (3 stretching, strengthening and stability (SSS, or (4 dance instruction. After the intervention, the Walk, Walk+ and SSS groups showed gains in cardiorespiratory fitness (CRF, with larger effects in both walking groups compared to the SSS and Dance groups. The Walk, Walk+ and SSS groups also improved in executive function (EF as measured by reasoning, working memory, and task-switching tests. In the Walk, Walk+, and SSS groups that improved in EF, higher baseline modularity was positively related to EF gains, even after controlling for age, in-scanner motion and baseline EF. No relationship between modularity and EF gains was observed in the Dance group, which did not show training-related gains in CRF or EF control. These results are consistent with previous studies demonstrating that individuals with a more modular brain network organization are more responsive to cognitive training. These findings suggest that the predictive power of modularity may be generalizable across interventions aimed to enhance aspects of cognition and

  5. SedInConnect: a stand-alone, free and open source tool for the assessment of sediment connectivity

    Science.gov (United States)

    Crema, Stefano; Cavalli, Marco

    2018-02-01

    There is a growing call, within the scientific community, for solid theoretic frameworks and usable indices/models to assess sediment connectivity. Connectivity plays a significant role in characterizing structural properties of the landscape and, when considered in combination with forcing processes (e.g., rainfall-runoff modelling), can represent a valuable analysis for an improved landscape management. In this work, the authors present the development and application of SedInConnect: a free, open source and stand-alone application for the computation of the Index of Connectivity (IC), as expressed in Cavalli et al. (2013) with the addition of specific innovative features. The tool is intended to have a wide variety of users, both from the scientific community and from the authorities involved in the environmental planning. Thanks to its open source nature, the tool can be adapted and/or integrated according to the users' requirements. Furthermore, presenting an easy-to-use interface and being a stand-alone application, the tool can help management experts in the quantitative assessment of sediment connectivity in the context of hazard and risk assessment. An application to a sample dataset and an overview on up-to-date applications of the approach and of the tool shows the development potential of such analyses. The modelled connectivity, in fact, appears suitable not only to characterize sediment dynamics at the catchment scale but also to integrate prediction models and as a tool for helping geomorphological interpretation.

  6. Brain Connectivity and Visual Attention

    Science.gov (United States)

    Parks, Emily L.

    2013-01-01

    Abstract Emerging hypotheses suggest that efficient cognitive functioning requires the integration of separate, but interconnected cortical networks in the brain. Although task-related measures of brain activity suggest that a frontoparietal network is associated with the control of attention, little is known regarding how components within this distributed network act together or with other networks to achieve various attentional functions. This review considers both functional and structural studies of brain connectivity, as complemented by behavioral and task-related neuroimaging data. These studies show converging results: The frontal and parietal cortical regions are active together, over time, and identifiable frontoparietal networks are active in relation to specific task demands. However, the spontaneous, low-frequency fluctuations of brain activity that occur in the resting state, without specific task demands, also exhibit patterns of connectivity that closely resemble the task-related, frontoparietal attention networks. Both task-related and resting-state networks exhibit consistent relations to behavioral measures of attention. Further, anatomical structure, particularly white matter pathways as defined by diffusion tensor imaging, places constraints on intrinsic functional connectivity. Lastly, connectivity analyses applied to investigate cognitive differences across individuals in both healthy and diseased states suggest that disconnection of attentional networks is linked to deficits in cognitive functioning, and in extreme cases, to disorders of attention. Thus, comprehensive theories of visual attention and their clinical translation depend on the continued integration of behavioral, task-related neuroimaging, and brain connectivity measures. PMID:23597177

  7. Link prediction in multiplex online social networks

    Science.gov (United States)

    Jalili, Mahdi; Orouskhani, Yasin; Asgari, Milad; Alipourfard, Nazanin; Perc, Matjaž

    2017-02-01

    Online social networks play a major role in modern societies, and they have shaped the way social relationships evolve. Link prediction in social networks has many potential applications such as recommending new items to users, friendship suggestion and discovering spurious connections. Many real social networks evolve the connections in multiple layers (e.g. multiple social networking platforms). In this article, we study the link prediction problem in multiplex networks. As an example, we consider a multiplex network of Twitter (as a microblogging service) and Foursquare (as a location-based social network). We consider social networks of the same users in these two platforms and develop a meta-path-based algorithm for predicting the links. The connectivity information of the two layers is used to predict the links in Foursquare network. Three classical classifiers (naive Bayes, support vector machines (SVM) and K-nearest neighbour) are used for the classification task. Although the networks are not highly correlated in the layers, our experiments show that including the cross-layer information significantly improves the prediction performance. The SVM classifier results in the best performance with an average accuracy of 89%.

  8. Anomalous brain functional connectivity contributing to poor adaptive behavior in Down syndrome.

    Science.gov (United States)

    Pujol, Jesus; del Hoyo, Laura; Blanco-Hinojo, Laura; de Sola, Susana; Macià, Dídac; Martínez-Vilavella, Gerard; Amor, Marta; Deus, Joan; Rodríguez, Joan; Farré, Magí; Dierssen, Mara; de la Torre, Rafael

    2015-03-01

    Research in Down syndrome has substantially progressed in the understanding of the effect of gene overexpression at the molecular level, but there is a paucity of information on the ultimate consequences on overall brain functional organization. We have assessed the brain functional status in Down syndrome using functional connectivity MRI. Resting-state whole-brain connectivity degree maps were generated in 20 Down syndrome individuals and 20 control subjects to identify sites showing anomalous synchrony with other areas. A subsequent region-of-interest mapping served to detail the anomalies and to assess their potential contribution to poor adaptive behavior. Down syndrome individuals showed higher regional connectivity in a ventral brain system involving the amygdala/anterior temporal region and the ventral aspect of both the anterior cingulate and frontal cortices. By contrast, lower functional connectivity was identified in dorsal executive networks involving dorsal prefrontal and anterior cingulate cortices and posterior insula. Both functional connectivity increases and decreases contributed to account for patient scoring on adaptive behavior related to communication skills. The data overall suggest a distinctive functional organization with system-specific anomalies associated with reduced adaptive efficiency. Opposite effects were identified on distinct frontal and anterior temporal structures and relative sparing of posterior brain areas, which is generally consistent with Down syndrome cognitive profile. Relevantly, measurable connectivity changes, as a marker of the brain functional anomaly, could have a role in the development of therapeutic strategies addressed to improve the quality of life in Down syndrome individuals. Copyright © 2014 Elsevier Ltd. All rights reserved.

  9. The role of individualism and the Five-Factor Model in the prediction of performance in a leaderless group discussion.

    Science.gov (United States)

    Waldman, David A; Atwater, Leanne E; Davidson, Ronald A

    2004-02-01

    Personality has seen a resurgence in the work performance literature. The Five-Factor Model (FFM) represents a set of personality factors that has received the most attention in recent years. Despite its popularity, the FFM may not be sufficiently comprehensive to account for relevant variation across performance dimensions or tasks. Accordingly, the present study also considers how individualism may predict additional variance in performance beyond the FFM. The study involved 152 undergraduate students who experienced a leaderless group discussion (LGD) exercise. Results showed that while the FFM accounted for variance in students' LGD performance, individualism (independence) accounted for additional, unique variance. Furthermore, analyses of the group compositions revealed curvilinear relationships between the relative amount of extraversion, conscientiousness, and individualism in relation to group-level performance.

  10. Connecting Remote Clusters with ATM

    Energy Technology Data Exchange (ETDEWEB)

    Hu, T.C.; Wyckoff, P.S.

    1998-10-01

    Sandia's entry into utilizing clusters of networked workstations is called Computational Plant or CPlant for short. The design of CPlant uses Ethernet to boot the individual nodes, Myrinet to communicate within a node cluster, and ATM to connect between remote clusters. This SAND document covers the work done to enable the use of ATM on the CPlant nodes in the Fall of 1997.

  11. Wildlife habitat connectivity in the changing climate of New York's Hudson Valley.

    Science.gov (United States)

    Howard, Timothy G; Schlesinger, Matthew D

    2013-09-01

    Maintaining and restoring connectivity are key adaptation strategies for biodiversity conservation under climate change. We present a novel combination of species distribution and connectivity modeling using current and future climate regimes to prioritize connections among populations of 26 rare species in New York's Hudson Valley. We modeled patches for each species for each time period and modeled potential connections among habitat patches by finding the least-cost path for every patch-to-patch connection. Finally, we aggregated these patches and paths to the tax parcel, commonly the primary unit of conservation action. Under future climate regimes, suitable habitat was predicted to contract or appear upslope and farther north. On average, predicted patches were nine times smaller and paths were twice as long under future climate. Parcels within the Hudson Highlands, Shawangunk Ridge, Catskill Mountains, and Harlem Valley had high species overlap, with areas upslope and northward increasing in importance over time. We envision that land managers and conservation planners can use these results to help prioritize parcel-level conservation and management and thus support biodiversity adaptation to climate change. © 2013 New York Academy of Sciences.

  12. Dynamic responses of connections in road safety barriers

    International Nuclear Information System (INIS)

    Bayton, D.A.F.; Long, R.; Fourlaris, G.

    2009-01-01

    Bolted road safety barrier connections utilise slotted holes that are perpendicular to the direction of the safety barrier beam. Due to the clearance between the slotted holes and the bolts, a varying amount of slippage is seen before contact with the edge of the slot is made. The stiffness characteristics of bolted road safety barrier connections have been examined with a representative test coupon that incorporates a full size safety barrier connection slot to industry standard dimensions. Previous research work has successfully determined the stiffness characteristics of the bolted connections at quasi-static strain rates. Representative non-linear finite element models of the bolted test coupons have been constructed. When compared to the laboratory results the initial stiffness, maximum force and displacement of the bolted connections are similar to the finite element model predictions. Current investigations have moved onto strain rates comparable to those observed in actual vehicle crash tests. Explicit dynamic finite element (FE) models have been constructed and validated, using experimental data produced using a series of high strain rate laboratory tests for a number of bolt configurations

  13. Functional Connectivity Bias in the Prefrontal Cortex of Psychopaths.

    Science.gov (United States)

    Contreras-Rodríguez, Oren; Pujol, Jesus; Batalla, Iolanda; Harrison, Ben J; Soriano-Mas, Carles; Deus, Joan; López-Solà, Marina; Macià, Dídac; Pera, Vanessa; Hernández-Ribas, Rosa; Pifarré, Josep; Menchón, José M; Cardoner, Narcís

    2015-11-01

    Psychopathy is characterized by a distinctive interpersonal style that combines callous-unemotional traits with inflexible and antisocial behavior. Traditional emotion-based perspectives link emotional impairment mostly to alterations in amygdala-ventromedial frontal circuits. However, these models alone cannot explain why individuals with psychopathy can regularly benefit from emotional information when placed on their focus of attention and why they are more resistant to interference from nonaffective contextual cues. The present study aimed to identify abnormal or distinctive functional links between and within emotional and cognitive brain systems in the psychopathic brain to characterize further the neural bases of psychopathy. High-resolution anatomic magnetic resonance imaging with a functional sequence acquired in the resting state was used to assess 22 subjects with psychopathy and 22 control subjects. Anatomic and functional connectivity alterations were investigated first using a whole-brain analysis. Brain regions showing overlapping anatomic and functional changes were examined further using seed-based functional connectivity mapping. Subjects with psychopathy showed gray matter reduction involving prefrontal cortex, paralimbic, and limbic structures. Anatomic changes overlapped with areas showing increased degree of functional connectivity at the medial-dorsal frontal cortex. Subsequent functional seed-based connectivity mapping revealed a pattern of reduced functional connectivity of prefrontal areas with limbic-paralimbic structures and enhanced connectivity within the dorsal frontal lobe in subjects with psychopathy. Our results suggest that a weakened link between emotional and cognitive domains in the psychopathic brain may combine with enhanced functional connections within frontal executive areas. The identified functional alterations are discussed in the context of potential contributors to the inflexible behavior displayed by individuals with

  14. The vulnerability to suicidal behavior is associated with reduced connectivity strength

    Directory of Open Access Journals (Sweden)

    Stijn eBijttebier

    2015-11-01

    Full Text Available Suicidal behavior constitutes a major public health problem. Based on the stress–diathesis model, biological correlates of a diathesis might help to predict risk after stressor-exposure. Structural changes in cortical and subcortical areas and their connections have increasingly been linked with the diathesis. The current study identified structural network changes associated with a diathesis using a whole-brain approach by examining the structural connectivity between regions in euthymic suicide attempters. In addition, the association between connectivity measures, clinical and genetic characteristics was investigated. We hypothesized that suicide attempters showed lower connectivity strength, associated with an increased severity of general clinical characteristics and an elevated expression of short alleles in serotonin polymorphisms.Thirteen euthymic suicide attempters (SA were compared with fifteen euthymic non-attempters and seventeen healthy controls. Clinical characteristics and three serotonin-related genetic polymorphisms were assessed. Diffusion MRI together with anatomical scans were administered. Preprocessing was performed using Explore DTI. Whole brain tractography of the diffusion-weighted images was followed by a number of streamlines-weighted network analysis using NBS.The network analysis revealed decreased connectivity strength in SA in the connections between the left olfactory cortex and left anterior cingulate gyrus. Furthermore, SA had increased suicidal ideation, hopelessness and self-reported depression, but did not show any differences for the genetic polymorphisms. Finally, lower connectivity strength between the right calcarine fissure and the left middle occipital gyrus was associated with increased trait anxiety severity (rs=-0.78, p<0.01 and hopelessness (rs=-0.76, p<0.01.SA showed differences in white matter network connectivity strength associated with clinical characteristics. Together, these variables could

  15. Prediction of individual mandibular changes induced by functional jaw orthopedics followed by fixed appliances in Class II patients.

    Science.gov (United States)

    Franchi, Lorenzo; Baccetti, Tiziano

    2006-11-01

    To identify pretreatment cephalometric variables for the prediction of individual mandibular outcomes of functional jaw orthopedics (FJO) followed by fixed appliances in Class II patients treated at the peak in mandibular growth. The study was performed on 51 subjects (24 females, 27 males) with Class II malocclusion. First-phase therapy was accomplished with a twin block in 16 subjects, a stainless steel crown Herbst in 15 subjects, and an acrylic splint Herbst in 20 subjects. Lateral cephalograms were available at the start of treatment with FJO and at the completion of fixed appliance therapy. All subjects received FJO at the peak in mandibular growth (CS 3 at T1). Individual responsiveness to Class II treatment including FJO was defined on the basis of the T2-T1 increment in total mandibular length (Co-Gn) when compared with untreated Class II subjects. Discriminant analysis identified a single predictive parameter (Co-Go-Me degrees) with a classification power of 80%. Pretreatment vertical and sagittal parameters were not able to improve the prediction based upon the mandibular angle. A Class II patient at the peak in skeletal maturation (CS 3) with a pretreatment Co-Go-Me degrees smaller than 125.5 degrees is expected to respond favorably to treatment including FJO. A Class II patient at CS 3 with a pretreatment value for Co-Go-Me degrees greater than 125.5 degrees is expected to respond poorly to treatment including FJO.

  16. Ventral Striatum Functional Connectivity as a Predictor of Adolescent Depressive Disorder in a Longitudinal Community-Based Sample.

    Science.gov (United States)

    Pan, Pedro Mario; Sato, João R; Salum, Giovanni A; Rohde, Luis A; Gadelha, Ary; Zugman, Andre; Mari, Jair; Jackowski, Andrea; Picon, Felipe; Miguel, Eurípedes C; Pine, Daniel S; Leibenluft, Ellen; Bressan, Rodrigo A; Stringaris, Argyris

    2017-11-01

    Previous studies have implicated aberrant reward processing in the pathogenesis of adolescent depression. However, no study has used functional connectivity within a distributed reward network, assessed using resting-state functional MRI (fMRI), to predict the onset of depression in adolescents. This study used reward network-based functional connectivity at baseline to predict depressive disorder at follow-up in a community sample of adolescents. A total of 637 children 6-12 years old underwent resting-state fMRI. Discovery and replication analyses tested intrinsic functional connectivity (iFC) among nodes of a putative reward network. Logistic regression tested whether striatal node strength, a measure of reward-related iFC, predicted onset of a depressive disorder at 3-year follow-up. Further analyses investigated the specificity of this prediction. Increased left ventral striatum node strength predicted increased risk for future depressive disorder (odds ratio=1.54, 95% CI=1.09-2.18), even after excluding participants who had depressive disorders at baseline (odds ratio=1.52, 95% CI=1.05-2.20). Among 11 reward-network nodes, only the left ventral striatum significantly predicted depression. Striatal node strength did not predict other common adolescent psychopathology, such as anxiety, attention deficit hyperactivity disorder, and substance use. Aberrant ventral striatum functional connectivity specifically predicts future risk for depressive disorder. This finding further emphasizes the need to understand how brain reward networks contribute to youth depression.