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Sample records for neural regions including

  1. A Neural Region of Abstract Working Memory

    Science.gov (United States)

    Cowan, Nelson; Li, Dawei; Moffitt, Amanda; Becker, Theresa M.; Martin, Elizabeth A.; Saults, J. Scott; Christ, Shawn E.

    2011-01-01

    Over 350 years ago, Descartes proposed that the neural basis of consciousness must be a brain region in which sensory inputs are combined. Using fMRI, we identified at least one such area for working memory, the limited information held in mind, described by William James as the trailing edge of consciousness. Specifically, a region in the left…

  2. Polycrystalline-Diamond MEMS Biosensors Including Neural Microelectrode-Arrays.

    Science.gov (United States)

    Varney, Michael W; Aslam, Dean M; Janoudi, Abed; Chan, Ho-Yin; Wang, Donna H

    2011-08-15

    Diamond is a material of interest due to its unique combination of properties, including its chemical inertness and biocompatibility. Polycrystalline diamond (poly-C) has been used in experimental biosensors that utilize electrochemical methods and antigen-antibody binding for the detection of biological molecules. Boron-doped poly-C electrodes have been found to be very advantageous for electrochemical applications due to their large potential window, low background current and noise, and low detection limits (as low as 500 fM). The biocompatibility of poly-C is found to be comparable, or superior to, other materials commonly used for implants, such as titanium and 316 stainless steel. We have developed a diamond-based, neural microelectrode-array (MEA), due to the desirability of poly-C as a biosensor. These diamond probes have been used for in vivo electrical recording and in vitro electrochemical detection. Poly-C electrodes have been used for electrical recording of neural activity. In vitro studies indicate that the diamond probe can detect norepinephrine at a 5 nM level. We propose a combination of diamond micro-machining and surface functionalization for manufacturing diamond pathogen-microsensors.

  3. Polycrystalline-Diamond MEMS Biosensors Including Neural Microelectrode-Arrays

    Directory of Open Access Journals (Sweden)

    Donna H. Wang

    2011-08-01

    Full Text Available Diamond is a material of interest due to its unique combination of properties, including its chemical inertness and biocompatibility. Polycrystalline diamond (poly-C has been used in experimental biosensors that utilize electrochemical methods and antigen-antibody binding for the detection of biological molecules. Boron-doped poly-C electrodes have been found to be very advantageous for electrochemical applications due to their large potential window, low background current and noise, and low detection limits (as low as 500 fM. The biocompatibility of poly-C is found to be comparable, or superior to, other materials commonly used for implants, such as titanium and 316 stainless steel. We have developed a diamond-based, neural microelectrode-array (MEA, due to the desirability of poly-C as a biosensor. These diamond probes have been used for in vivo electrical recording and in vitro electrochemical detection. Poly-C electrodes have been used for electrical recording of neural activity. In vitro studies indicate that the diamond probe can detect norepinephrine at a 5 nM level. We propose a combination of diamond micro-machining and surface functionalization for manufacturing diamond pathogen-microsensors.

  4. New Neural Network Methods for Forecasting Regional Employment

    NARCIS (Netherlands)

    Patuelli, R.; Reggiani, A; Nijkamp, P.; Blien, U.

    2006-01-01

    In this paper, a set of neural network (NN) models is developed to compute short-term forecasts of regional employment patterns in Germany. Neural networks are modern statistical tools based on learning algorithms that are able to process large amounts of data. Neural networks are enjoying

  5. Neural Correlates of Animacy Attribution Include Neocerebellum in Healthy Adults

    Science.gov (United States)

    Jack, Allison; Pelphrey, Kevin A.

    2015-01-01

    Recent work suggests that biological motion perception is supported by interactions between posterior superior temporal sulcus (pSTS) and regions of the posterior lobe of the cerebellum. However, insufficient attention has been given to cerebellar contributions to most other social cognitive functions, including ones that rely upon the use of biological motion cues for making mental inferences. Here, using adapted Heider and Simmel stimuli in a passive-viewing paradigm, we present functional magnetic resonance imaging evidence detailing cerebellar contributions to animacy attribution processes in healthy adults. We found robust cerebellar activity associated with viewing animate versus random movement in hemispheric lobule VII bilaterally as well as in vermal and paravermal lobule IX. Stronger activity in left Crus I and lobule VI was associated with a greater tendency to describe the stimuli in social-affective versus motion-related terms. Psychophysiological interaction analysis indicated preferential effective connectivity between right pSTS and left Crus II during the viewing of animate than random stimuli, controlling for individual variance in social attributions. These findings indicate that lobules VI, VII, and IX participate in social functions even when no active response is required. This cerebellar activity may also partially explain individual differences in animacy attribution. PMID:24981794

  6. Neural regions essential for writing verbs.

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    Hillis, Argye E; Wityk, Robert J; Barker, Peter B; Caramazza, Alfonso

    2003-01-01

    Functional imaging data collected during cognitive tasks show which brain regions are active during those tasks, but do not necessarily indicate which regions are essential for those tasks. Here, in a study of two cases of selectively impaired written naming of verbs after focal brain ischemia, we combined imaging and behavioral testing to unambiguously identify brain regions that are crucial for a specific cognitive process. We used magnetic resonance perfusion imaging to show that the selective impairment in each case was due to hypoperfusion (low blood flow) in left posterior inferior frontal gyrus (PIFG) and precentral gyrus (PrG); the impairment was immediately reversed when blood flow was restored to these regions, indicating that parts of the left frontal lobe are crucial for representing and processing verbs.

  7. Interpersonal liking modulates motor-related neural regions.

    Directory of Open Access Journals (Sweden)

    Mona Sobhani

    Full Text Available Observing someone perform an action engages brain regions involved in motor planning, such as the inferior frontal, premotor, and inferior parietal cortices. Recent research suggests that during action observation, activity in these neural regions can be modulated by membership in an ethnic group defined by physical differences. In this study we expanded upon previous research by matching physical similarity of two different social groups and investigating whether likability of an outgroup member modulates activity in neural regions involved in action observation. Seventeen Jewish subjects were familiarized with biographies of eight individuals, half of the individuals belonged to Neo-Nazi groups (dislikable and half of which did not (likable. All subjects and actors in the stimuli were Caucasian and physically similar. The subjects then viewed videos of actors portraying the characters performing simple motor actions (e.g. grasping a water bottle and raising it to the lips, while undergoing fMRI. Using multivariate pattern analysis (MVPA, we found that a classifier trained on brain activation patterns successfully discriminated between the likable and dislikable action observation conditions within the right ventral premotor cortex. These data indicate that the spatial pattern of activity in action observation related neural regions is modulated by likability even when watching a simple action such as reaching for a cup. These findings lend further support for the notion that social factors such as interpersonal liking modulate perceptual processing in motor-related cortices.

  8. Regional Computation of TEC Using a Neural Network Model

    Science.gov (United States)

    Leandro, R. F.; Santos, M. C.

    2004-05-01

    One of the main sources of errors of GPS measurements is the ionosphere refraction. As a dispersive medium, the ionosphere allow its influence to be computed by using dual frequency receivers. In the case of single frequency receivers it is necessary to use models that tell us how big the ionospheric refraction is. The GPS broadcast message carries parameters of this model, namely Klobuchar model. Dual frequency receivers allow to estimate the influence of ionosphere in the GPS signal by the computation of TEC (Total Electron Content) values, that have a direct relationship with the magnitude of the delay caused by the ionosphere. One alternative is to create a regional model based on a network of dual frequency receivers. In this case, the regional behaviour of ionosphere is modelled in a way that it is possible to estimate the TEC values into or near this region. This regional model can be based on polynomials, for example. In this work we will present a Neural Network-based model to the regional computation of TEC. The advantage of using a Neural Network is that it is not necessary to have a great knowledge on the behaviour of the modelled surface due to the adaptation capability of neural networks training process, that is an iterative adjust of the synaptic weights in function of residuals, using the training parameters. Therefore, the previous knowledge of the modelled phenomena is important to define what kind of and how many parameters are needed to train the neural network so that reasonable results are obtained from the estimations. We have used data from the GPS tracking network in Brazil, and we have tested the accuracy of the new model to all locations where there is a station, accessing the efficiency of the model everywhere. TEC values were computed for each station of the network. After that the training parameters data set for the test station was formed, with the TEC values of all others (all stations, except the test one). The Neural Network was

  9. Regional frequency analysis using Growing Neural Gas network

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    Abdi, Amin; Hassanzadeh, Yousef; Ouarda, Taha B. M. J.

    2017-07-01

    The delineation of hydrologically homogeneous regions is an important issue in regional hydrological frequency analysis. In the present study, an application of the Growing Neural Gas (GNG) network for hydrological data clustering is presented. The GNG is an incremental and unsupervised neural network, which is able to adapt its structure during the training procedure without using a prior knowledge of the size and shape of the network. In the GNG algorithm, the Minimum Description Length (MDL) measure as the cluster validity index is utilized for determining the optimal number of clusters (sub-regions). The capability of the proposed algorithm is illustrated by regionalizing drought severities for 40 synoptic weather stations in Iran. To fulfill this aim, first a clustering method is applied to form the sub-regions and then a heterogeneity measure is used to test the degree of heterogeneity of the delineated sub-regions. According to the MDL measure and considering two different indices namely CS and Davies-Bouldin (DB) in the GNG network, the entire study area is subdivided in two sub-regions located in the eastern and western sides of Iran. In order to evaluate the performance of the GNG algorithm, a number of other commonly used clustering methods, like K-means, fuzzy C-means, self-organizing map and Ward method are utilized in this study. The results of the heterogeneity measure based on the L-moments approach reveal that only the GNG algorithm successfully yields homogeneous sub-regions in comparison to the other methods.

  10. Improved exponential convergence result for generalized neural networks including interval time-varying delayed signals.

    Science.gov (United States)

    Rajchakit, G; Saravanakumar, R; Ahn, Choon Ki; Karimi, Hamid Reza

    2017-02-01

    This article examines the exponential stability analysis problem of generalized neural networks (GNNs) including interval time-varying delayed states. A new improved exponential stability criterion is presented by establishing a proper Lyapunov-Krasovskii functional (LKF) and employing new analysis theory. The improved reciprocally convex combination (RCC) and weighted integral inequality (WII) techniques are utilized to obtain new sufficient conditions to ascertain the exponential stability result of such delayed GNNs. The superiority of the obtained results is clearly demonstrated by numerical examples. Copyright © 2016 Elsevier Ltd. All rights reserved.

  11. Maintenance of neural stem cell regional identity in culture.

    Science.gov (United States)

    Delgado, Ryan N; Lu, Changqing; Lim, Daniel A

    2016-01-01

    Neural stem cells (NSCs) are distributed throughout the ventricular-subventricular zone (V-SVZ) in the adult mouse brain. NSCs located in spatially distinct regions of the V-SVZ generate different types of olfactory bulb (OB) neurons, and the regional expression of specific transcription factors correlates with these differences in NSC developmental potential. In a recent article, we show that Nkx2.1-expressing embryonic precursors give rise to NKX2.1+ NSCs located in the ventral V-SVZ of adult mice. Here we characterize a V-SVZ monolayer culture system that retains regional gene expression and neurogenic potential of NSCs from the dorsal and ventral V-SVZ. In particular, we find that Nkx2.1-lineage V-SVZ NSCs maintain Nkx2.1 expression through serial passage and can generate new neurons in vitro. Thus, V-SVZ NSCs retain key aspects of their in vivo regional identity in culture, providing new experimental opportunities for understanding how such developmental patterns are established and maintained during development.

  12. Petroleum geology of Falkland Islands region, including Malvinas basin

    Energy Technology Data Exchange (ETDEWEB)

    Clarke, J.W.; Masters, C.D.

    1986-05-01

    The Falkland Islands are on continental crust that is part of the South American lithospheric plate. Two prospective areas for oil and gas are Malvinas basin south of the Falklands extending onto Burdwood Bank, and Falkland Plateau east and northeast of the Falklands. To evaluate this area, workers must understand the paleogeography at the time the Atlantic Ocean opened, during the Jurassic and Cretaceous, because this environment controlled deposition of euxinic muds, which are the prime source beds for oil and gas. Thick Upper Jurassic source beds have been penetrated by the drill on the Falkland Plateau and in the Magallanes basin. The seismic profiles across the Malvinas basin, Burdwood Bank, and Falkland Plateau suggest that the Jurassic-Cretaceous source beds may be continuous throughout the entire area. Now the question is, does the euxinic claystone facies extend throughout the same area. This question remains, as do those concerning traps, seals, and structures. The authors surmise that block faulting will have disturbed the sedimentary pile sufficiently for traps to have formed. In the eastern part of Malvinas basin, which includes Burdwood Bank, the thick (up to 7000 m) sedimentary section is favorable for hydrocarbon accumulations. However, the lower part of this section may have generated gas, which in turn may have displaced most of the trapped oil.

  13. Response of neural reward regions to food cues in autism spectrum disorders

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    Cascio Carissa J

    2012-05-01

    Full Text Available Abstract Background One hypothesis for the social deficits that characterize autism spectrum disorders (ASD is diminished neural reward response to social interaction and attachment. Prior research using established monetary reward paradigms as a test of non-social reward to compare with social reward may involve confounds in the ability of individuals with ASD to utilize symbolic representation of money and the abstraction required to interpret monetary gains. Thus, a useful addition to our understanding of neural reward circuitry in ASD includes a characterization of the neural response to primary rewards. Method We asked 17 children with ASD and 18 children without ASD to abstain from eating for at least four hours before an MRI scan in which they viewed images of high-calorie foods. We assessed the neural reward network for increases in the blood oxygenation level dependent (BOLD signal in response to the food images Results We found very similar patterns of increased BOLD signal to these images in the two groups; both groups showed increased BOLD signal in the bilateral amygdala, as well as in the nucleus accumbens, orbitofrontal cortex, and insula. Direct group comparisons revealed that the ASD group showed a stronger response to food cues in bilateral insula along the anterior-posterior gradient and in the anterior cingulate cortex than the control group, whereas there were no neural reward regions that showed higher activation for controls than for ASD. Conclusion These results suggest that neural response to primary rewards is not diminished but in fact shows an aberrant enhancement in children with ASD.

  14. Differentiation of Human Embryonic Stem Cells to Regional Specific Neural Precursors in Chemically Defined Medium Conditions

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    Erceg, Slaven; Laínez, Sergio; Ronaghi, Mohammad; Stojkovic, Petra; Pérez-Aragó, Maria Amparo; Moreno-Manzano, Victoria; Moreno-Palanques, Rubén; Planells-Cases, Rosa; Stojkovic, Miodrag

    2008-01-01

    Background Human embryonic stem cells (hESC) provide a unique model to study early events in human development. The hESC-derived cells can potentially be used to replace or restore different tissues including neuronal that have been damaged by disease or injury. Methodology and Principal Findings The cells of two different hESC lines were converted to neural rosettes using adherent and chemically defined conditions. The progenitor cells were exposed to retinoic acid (RA) or to human recombinant basic fibroblast growth factor (bFGF) in the late phase of the rosette formation. Exposing the progenitor cells to RA suppressed differentiation to rostral forebrain dopamine neural lineage and promoted that of spinal neural tissue including motor neurons. The functional characteristics of these differentiated neuronal precursors under both, rostral (bFGF) and caudalizing (RA) signals were confirmed by patch clamp analysis. Conclusions/Significance These findings suggest that our differentiation protocol has the capacity to generate region-specific and electrophysiologically active neurons under in vitro conditions without embryoid body formation, co-culture with stromal cells and without presence of cells of mesodermal or endodermal lineages. PMID:18461168

  15. Differentiation of human embryonic stem cells to regional specific neural precursors in chemically defined medium conditions.

    Directory of Open Access Journals (Sweden)

    Slaven Erceg

    Full Text Available BACKGROUND: Human embryonic stem cells (hESC provide a unique model to study early events in human development. The hESC-derived cells can potentially be used to replace or restore different tissues including neuronal that have been damaged by disease or injury. METHODOLOGY AND PRINCIPAL FINDINGS: The cells of two different hESC lines were converted to neural rosettes using adherent and chemically defined conditions. The progenitor cells were exposed to retinoic acid (RA or to human recombinant basic fibroblast growth factor (bFGF in the late phase of the rosette formation. Exposing the progenitor cells to RA suppressed differentiation to rostral forebrain dopamine neural lineage and promoted that of spinal neural tissue including motor neurons. The functional characteristics of these differentiated neuronal precursors under both, rostral (bFGF and caudalizing (RA signals were confirmed by patch clamp analysis. CONCLUSIONS/SIGNIFICANCE: These findings suggest that our differentiation protocol has the capacity to generate region-specific and electrophysiologically active neurons under in vitro conditions without embryoid body formation, co-culture with stromal cells and without presence of cells of mesodermal or endodermal lineages.

  16. Classification of bifurcations regions in IVOCT images using support vector machine and artificial neural network models

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    Porto, C. D. N.; Costa Filho, C. F. F.; Macedo, M. M. G.; Gutierrez, M. A.; Costa, M. G. F.

    2017-03-01

    Studies in intravascular optical coherence tomography (IV-OCT) have demonstrated the importance of coronary bifurcation regions in intravascular medical imaging analysis, as plaques are more likely to accumulate in this region leading to coronary disease. A typical IV-OCT pullback acquires hundreds of frames, thus developing an automated tool to classify the OCT frames as bifurcation or non-bifurcation can be an important step to speed up OCT pullbacks analysis and assist automated methods for atherosclerotic plaque quantification. In this work, we evaluate the performance of two state-of-the-art classifiers, SVM and Neural Networks in the bifurcation classification task. The study included IV-OCT frames from 9 patients. In order to improve classification performance, we trained and tested the SVM with different parameters by means of a grid search and different stop criteria were applied to the Neural Network classifier: mean square error, early stop and regularization. Different sets of features were tested, using feature selection techniques: PCA, LDA and scalar feature selection with correlation. Training and test were performed in sets with a maximum of 1460 OCT frames. We quantified our results in terms of false positive rate, true positive rate, accuracy, specificity, precision, false alarm, f-measure and area under ROC curve. Neural networks obtained the best classification accuracy, 98.83%, overcoming the results found in literature. Our methods appear to offer a robust and reliable automated classification of OCT frames that might assist physicians indicating potential frames to analyze. Methods for improving neural networks generalization have increased the classification performance.

  17. Multiscale approach including microfibril scale to assess elastic constants of cortical bone based on neural network computation and homogenization method

    CERN Document Server

    Barkaoui, Abdelwahed; Tarek, Merzouki; Hambli, Ridha; Ali, Mkaddem

    2014-01-01

    The complexity and heterogeneity of bone tissue require a multiscale modelling to understand its mechanical behaviour and its remodelling mechanisms. In this paper, a novel multiscale hierarchical approach including microfibril scale based on hybrid neural network computation and homogenisation equations was developed to link nanoscopic and macroscopic scales to estimate the elastic properties of human cortical bone. The multiscale model is divided into three main phases: (i) in step 0, the elastic constants of collagen-water and mineral-water composites are calculated by averaging the upper and lower Hill bounds; (ii) in step 1, the elastic properties of the collagen microfibril are computed using a trained neural network simulation. Finite element (FE) calculation is performed at nanoscopic levels to provide a database to train an in-house neural network program; (iii) in steps 2 to 10 from fibril to continuum cortical bone tissue, homogenisation equations are used to perform the computation at the higher s...

  18. A biologically inspired neural model for visual and proprioceptive integration including sensory training.

    Science.gov (United States)

    Saidi, Maryam; Towhidkhah, Farzad; Gharibzadeh, Shahriar; Lari, Abdolaziz Azizi

    2013-12-01

    Humans perceive the surrounding world by integration of information through different sensory modalities. Earlier models of multisensory integration rely mainly on traditional Bayesian and causal Bayesian inferences for single causal (source) and two causal (for two senses such as visual and auditory systems), respectively. In this paper a new recurrent neural model is presented for integration of visual and proprioceptive information. This model is based on population coding which is able to mimic multisensory integration of neural centers in the human brain. The simulation results agree with those achieved by casual Bayesian inference. The model can also simulate the sensory training process of visual and proprioceptive information in human. Training process in multisensory integration is a point with less attention in the literature before. The effect of proprioceptive training on multisensory perception was investigated through a set of experiments in our previous study. The current study, evaluates the effect of both modalities, i.e., visual and proprioceptive training and compares them with each other through a set of new experiments. In these experiments, the subject was asked to move his/her hand in a circle and estimate its position. The experiments were performed on eight subjects with proprioception training and eight subjects with visual training. Results of the experiments show three important points: (1) visual learning rate is significantly more than that of proprioception; (2) means of visual and proprioceptive errors are decreased by training but statistical analysis shows that this decrement is significant for proprioceptive error and non-significant for visual error, and (3) visual errors in training phase even in the beginning of it, is much less than errors of the main test stage because in the main test, the subject has to focus on two senses. The results of the experiments in this paper is in agreement with the results of the neural model

  19. Neural Mechanisms Underlying Musical Pitch Perception and Clinical Applications Including Developmental Dyslexia.

    Science.gov (United States)

    Yuskaitis, Christopher J; Parviz, Mahsa; Loui, Psyche; Wan, Catherine Y; Pearl, Phillip L

    2015-08-01

    Music production and perception invoke a complex set of cognitive functions that rely on the integration of sensorimotor, cognitive, and emotional pathways. Pitch is a fundamental perceptual attribute of sound and a building block for both music and speech. Although the cerebral processing of pitch is not completely understood, recent advances in imaging and electrophysiology have provided insight into the functional and anatomical pathways of pitch processing. This review examines the current understanding of pitch processing and behavioral and neural variations that give rise to difficulties in pitch processing, and potential applications of music education for language processing disorders such as dyslexia.

  20. Neural Mechanisms Underlying Musical Pitch Perception and Clinical Applications including Developmental Dyselxia

    Science.gov (United States)

    Yuskaitis, Christopher J.; Parviz, Mahsa; Loui, Psyche; Wan, Catherine Y.; Pearl, Phillip L.

    2017-01-01

    Music production and perception invoke a complex set of cognitive functions that rely on the integration of sensory-motor, cognitive, and emotional pathways. Pitch is a fundamental perceptual attribute of sound and a building block for both music and speech. Although the cerebral processing of pitch is not completely understood, recent advances in imaging and electrophysiology have provided insight into the functional and anatomical pathways of pitch processing. This review examines the current understanding of pitch processing, behavioral and neural variations that give rise to difficulties in pitch processing, and potential applications of music education for language processing disorders such as dyslexia. PMID:26092314

  1. Co-speech gestures influence neural activity in brain regions associated with processing semantic information.

    Science.gov (United States)

    Dick, Anthony Steven; Goldin-Meadow, Susan; Hasson, Uri; Skipper, Jeremy I; Small, Steven L

    2009-11-01

    Everyday communication is accompanied by visual information from several sources, including co-speech gestures, which provide semantic information listeners use to help disambiguate the speaker's message. Using fMRI, we examined how gestures influence neural activity in brain regions associated with processing semantic information. The BOLD response was recorded while participants listened to stories under three audiovisual conditions and one auditory-only (speech alone) condition. In the first audiovisual condition, the storyteller produced gestures that naturally accompany speech. In the second, the storyteller made semantically unrelated hand movements. In the third, the storyteller kept her hands still. In addition to inferior parietal and posterior superior and middle temporal regions, bilateral posterior superior temporal sulcus and left anterior inferior frontal gyrus responded more strongly to speech when it was further accompanied by gesture, regardless of the semantic relation to speech. However, the right inferior frontal gyrus was sensitive to the semantic import of the hand movements, demonstrating more activity when hand movements were semantically unrelated to the accompanying speech. These findings show that perceiving hand movements during speech modulates the distributed pattern of neural activation involved in both biological motion perception and discourse comprehension, suggesting listeners attempt to find meaning, not only in the words speakers produce, but also in the hand movements that accompany speech.

  2. Extracting Neural Oscillation Signatures of Laser-Induced Nociception in Pain-Related Regions in Rats

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    Xuezhu Li

    2017-10-01

    Full Text Available Previous studies have shown that multiple brain regions are involved in pain perception and pain-related neural processes by forming a functionally connected pain network. It is still unclear how these pain-related brain areas actively work together to generate the experience of pain. To get a better insight into the pain network, we implanted electrodes in four pain-related areas of rats including the anterior cingulate cortex (ACC, orbitofrontal cortex (OFC, primary somatosensory cortex (S1 and periaqueductal gray (PAG. We analyzed the pattern of local field potential (LFP oscillations under noxious laser stimulations and innoxious laser stimulations. A high-dimensional feature matrix was built based on the LFP characters for both experimental conditions. Generalized linear models (GLMs were trained to classify recorded LFPs under noxious vs. innoxious condition. We found a general power decrease in α and β bands and power increase in γ band in the recorded areas under noxious condition. After noxious laser stimulation, there was a consistent change in LFP power and correlation in all four brain areas among all 13 rats. With GLM classifiers, noxious laser trials were distinguished from innoxious laser trials with high accuracy (86% using high-dimensional LFP features. This work provides a basis for further research to examine which aspects (e.g., sensory, motor or affective processes of noxious stimulation should drive distinct neural activity across the pain network.

  3. What does gamma coherence tell us about inter-regional neural communication?

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    Buzsáki, György; Schomburg, Erik W

    2015-04-01

    Neural oscillations have been measured and interpreted in multitudinous ways, with a variety of hypothesized functions in physiology, information processing and cognition. Much attention has been paid in recent years to gamma-band (30-100 Hz) oscillations and synchrony, with an increasing interest in 'high gamma' (>100 Hz) signals as mesoscopic measures of inter-regional communication. The biophysical origins of the measured variables are often difficult to precisely identify, however, making their interpretation fraught with pitfalls. Here we discuss how measurements of inter-regional gamma coherence can be prone to misinterpretation and suggest strategies for deciphering the roles that synchronized oscillations across brain networks may play in neural function.

  4. [Gateway Reflex, a regulator of the inflammation feedback loop by regional neural activation].

    Science.gov (United States)

    Arima, Yasunobu; Kamimura, Daisuke; Atsumi, Toru; Murakami, Masaaki

    2015-04-01

    Inflammation is observed in many diseases and disorders. We discovered a key machinery of inflammation, the inflammation amplifier, which is induced by the simultaneous activation of NFκB and STAT3 followed by the hyper-activation of NFκB in non-immune cells, including endothelial cells and fibroblasts. Since that discovery, we found the Gateway Reflex, which describes regional neural activations that enhance the inflammation amplifier to create a gateway for immune cells to bypass the blood-brain barrier. In addition, we have identified over 1,000 positive regulators and over 500 targets of the inflammation amplifier, which include a significant numbers of human disease-associated genes. In parallel, we performed a comprehensive analysis of human disease samples and found that the inflammation amplifier was activated during the development of chronic inflammation. Thus, we concluded that the inflammation amplifier is associated with various human diseases and disorders, including autoimmune diseases, metabolic syndromes, neurodegenerative diseases, and other inflammatory diseases. We are now attempting drug discovery for inflammatory diseases and disorders based on the inflammation amplifier and Gateway Reflex. In this review, we discuss the Gateway Reflex as an example for the neuro-immune interaction in vivo.

  5. Effect of orthography over neural regions in bilinguals: a view from neuroimaging.

    Science.gov (United States)

    Kumar, Uttam

    2014-09-19

    Effects of written script processing on neural regions have been explored using fMRI in a group of bilingual population that has not so far been studied, namely, Urdu-Hindi skilled bilinguals. Hindi and Urdu languages are very similar at the spoken level but differ greatly in scripts; Hindi is a highly transparent script, whereas Urdu is more opaque. The common regions (conjunction analyses) observed for Urdu-Hindi bilingual readers are left inferior frontal gyrus (IFG) (BA 44/45), bilateral middle temporal (BA 22), left fusiform gyrus (BA 37) and bilateral middle occipital regions. The distinct regions for Urdu words were found in left superior frontal and left middle frontal regions whereas; no distinct region was found for Hindi words. Imaging result suggests that middle and superior frontal regions are crucial for the orthography and graphemic complexity of Urdu script. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  6. Soy sauce classification by geographic region and fermentation based on artificial neural network and genetic algorithm.

    Science.gov (United States)

    Xu, Libin; Li, Yang; Xu, Ning; Hu, Yong; Wang, Chao; He, Jianjun; Cao, Yueze; Chen, Shigui; Li, Dongsheng

    2014-12-24

    This work demonstrated the possibility of using artificial neural networks to classify soy sauce from China. The aroma profiles of different soy sauce samples were differentiated using headspace solid-phase microextraction. The soy sauce samples were analyzed by gas chromatography-mass spectrometry, and 22 and 15 volatile aroma compounds were selected for sensitivity analysis to classify the samples by fermentation and geographic region, respectively. The 15 selected samples can be classified by fermentation and geographic region with a prediction success rate of 100%. Furans and phenols represented the variables with the greatest contribution in classifying soy sauce samples by fermentation and geographic region, respectively.

  7. Emission-line Diagnostics of Nearby HII Regions Including Supernova Hosts

    Science.gov (United States)

    Xiao, Lin; Eldridge, J. J.; Stanway, Elizabeth; Galbany, L.

    2017-11-01

    We present a new model of the optical nebular emission from HII regions by combining the results of the Binary Population and Spectral Synthesis (bpass) code with the photoionization code cloudy (Ferland et al. 1998). We explore a variety of emission-line diagnostics of these star-forming HII regions and examine the effects of metallicity and interacting binary evolution on the nebula emission-line production. We compare the line emission properties of HII regions with model stellar populations, and provide new constraints on their stellar populations and supernova progenitors. We find that models including massive binary stars can successfully match all the observational constraints and provide reasonable age and mass estimation of the HII regions and supernova progenitors.

  8. Modelling innovation performance of European regions using multi-output neural networks.

    Science.gov (United States)

    Hajek, Petr; Henriques, Roberto

    2017-01-01

    Regional innovation performance is an important indicator for decision-making regarding the implementation of policies intended to support innovation. However, patterns in regional innovation structures are becoming increasingly diverse, complex and nonlinear. To address these issues, this study aims to develop a model based on a multi-output neural network. Both intra- and inter-regional determinants of innovation performance are empirically investigated using data from the 4th and 5th Community Innovation Surveys of NUTS 2 (Nomenclature of Territorial Units for Statistics) regions. The results suggest that specific innovation strategies must be developed based on the current state of input attributes in the region. Thus, it is possible to develop appropriate strategies and targeted interventions to improve regional innovation performance. We demonstrate that support of entrepreneurship is an effective instrument of innovation policy. We also provide empirical support that both business and government R&D activity have a sigmoidal effect, implying that the most effective R&D support should be directed to regions with below-average and average R&D activity. We further show that the multi-output neural network outperforms traditional statistical and machine learning regression models. In general, therefore, it seems that the proposed model can effectively reflect both the multiple-output nature of innovation performance and the interdependency of the output attributes.

  9. Modelling innovation performance of European regions using multi-output neural networks.

    Directory of Open Access Journals (Sweden)

    Petr Hajek

    Full Text Available Regional innovation performance is an important indicator for decision-making regarding the implementation of policies intended to support innovation. However, patterns in regional innovation structures are becoming increasingly diverse, complex and nonlinear. To address these issues, this study aims to develop a model based on a multi-output neural network. Both intra- and inter-regional determinants of innovation performance are empirically investigated using data from the 4th and 5th Community Innovation Surveys of NUTS 2 (Nomenclature of Territorial Units for Statistics regions. The results suggest that specific innovation strategies must be developed based on the current state of input attributes in the region. Thus, it is possible to develop appropriate strategies and targeted interventions to improve regional innovation performance. We demonstrate that support of entrepreneurship is an effective instrument of innovation policy. We also provide empirical support that both business and government R&D activity have a sigmoidal effect, implying that the most effective R&D support should be directed to regions with below-average and average R&D activity. We further show that the multi-output neural network outperforms traditional statistical and machine learning regression models. In general, therefore, it seems that the proposed model can effectively reflect both the multiple-output nature of innovation performance and the interdependency of the output attributes.

  10. What does gamma coherence tell us about inter-regional neural communication?

    OpenAIRE

    Buzsáki, György; Schomburg, Erik W

    2015-01-01

    Neural oscillations have been measured and interpreted in multitudinous ways, with a variety of hypothesized functions in physiology, information processing and cognition. Much attention has been paid in recent years to gamma-band (30–100 Hz) oscillations and synchrony, with an increasing interest in ‘high gamma’ (>100 Hz) signals as mesoscopic measures of inter-regional communication. The biophysical origins of the measured variables are often difficult to precisely identify, however, making...

  11. A density-functional theory-based neural network potential for water clusters including van der Waals corrections.

    Science.gov (United States)

    Morawietz, Tobias; Behler, Jörg

    2013-08-15

    The fundamental importance of water for many chemical processes has motivated the development of countless efficient but approximate water potentials for large-scale molecular dynamics simulations, from simple empirical force fields to very sophisticated flexible water models. Accurate and generally applicable water potentials should fulfill a number of requirements. They should have a quality close to quantum chemical methods, they should explicitly depend on all degrees of freedom including all relevant many-body interactions, and they should be able to describe molecular dissociation and recombination. In this work, we present a high-dimensional neural network (NN) potential for water clusters based on density-functional theory (DFT) calculations, which is constructed using clusters containing up to 10 monomers and is in principle able to meet all these requirements. We investigate the reliability of specific parametrizations employing two frequently used generalized gradient approximation (GGA) exchange-correlation functionals, PBE and RPBE, as reference methods. We find that the binding energy errors of the NN potentials with respect to DFT are significantly lower than the typical uncertainties of DFT calculations arising from the choice of the exchange-correlation functional. Further, we examine the role of van der Waals interactions, which are not properly described by GGA functionals. Specifically, we incorporate the D3 scheme suggested by Grimme (J. Chem. Phys. 2010, 132, 154104) in our potentials and demonstrate that it can be applied to GGA-based NN potentials in the same way as to DFT calculations without modification. Our results show that the description of small water clusters provided by the RPBE functional is significantly improved if van der Waals interactions are included, while in case of the PBE functional, which is well-known to yield stronger binding than RPBE, van der Waals corrections lead to overestimated binding energies.

  12. A Revised Genome Assembly of the Region 5' to Canine SOX9 Includes the RevSex Orthologous Region.

    Science.gov (United States)

    Rossi, Elena; Radi, Orietta; De Lorenzi, Lisa; Iannuzzi, Alessandra; Camerino, Giovanna; Zuffardi, Orsetta; Parma, Pietro

    2015-01-01

    The SOX gene family includes many genes that play a determinant role in several developmental pathways. The SOX9 gene has been identified as a major factor in testis development in mammals after it is activated by the SRY gene. However, duplication of the gene itself in some mammalian species, or of a well-delimited upstream 'RevSex' region in humans, has been shown to result in testis development in the absence of the SRY gene. In the current study, we present an accurate analysis of the genomic organization of the SOX9 locus in dogs by both in silico and FISH approaches. Contrary to what is observed in the current dog genome assembly, we found that the genomic organization is quite similar to that reported in humans and other mammalian species, including the position of the RevSex region in respect to SOX9. The analysis of the conserved sequences within this region in 7 mammalian species facilitated the highlighting of a consensus sequence for SRY binding. This new information could help in the identification of evolutionarily conserved elements relevant for SOX9 gene regulation, and could provide valid targets for mutation analysis in XY DSD patients. © 2015 S. Karger AG, Basel.

  13. The gateway theory: how regional neural activation creates a gateway for immune cells via an inflammation amplifier.

    Science.gov (United States)

    Ogura, Hideki; Arima, Yasunobu; Kamimura, Daisuke; Murakami, Masaaki

    2013-01-01

    The inflammation amplifier, a nuclear factor-kappa B (NF-kB)feedback loop in non-immune cells including fibroblasts and endothelial cells, describes how NF-kB-mediated transcriptions are enhanced to induce the inflammation in the presence of signal Tranducer and Activator of Transcription 3 (STAT3) activation. It was originally discovered in rheumatoid arthritis and multiple sclerosis mouse models and has since been shown to be associated with various human diseases and disorders including autoimmune diseases, metabolic syndromes, neurodegenerative diseases, and other inflammatory diseases. The amplifier begins with IL-17, which acts as the main signal to express NF-kB-mediated transcriptions, and IL-6, an NF-kB target, which functions as a fuel for the inflammation amplifier. Indeed, other NF-kB targets including various chemokines also act as effector molecules that cause local accumulation of various immune cells and subsequent inflammation. Through extensive studies in the multiple sclerosis model experimental autoimmune encephalomyelitis, we recently demonstrated that regional neural activation induces excess activation of the inflammation amplifier at specific blood vessels in the fifth lumbar cord, creating a gateway for immune cells to enter the central nervous system (CNS). We thus propose the gateway theory to describe how regional neural activation enables immune cells to enter the CNS from the blood and argue that this theory might provide novel therapeutic targets for inflammatory diseases and disorders.

  14. Regional cerebral glucose metabolic changes in oculopalatal myoclonus: implication for neural pathways, underlying the disorder

    Energy Technology Data Exchange (ETDEWEB)

    Cho, Sang Soo; Moon, So Young; Kim, Ji Soo; Kim, Sang Eun [College of Medicine, Seoul National University, Seoul (Korea, Republic of)

    2004-07-01

    Palatal myoclonus (PM) is characterized by rhythmic involuntary jerky movements of the soft palate of the throat. When associated with eye movements, it is called oculopalatal myoclonus (OPM). Ordinary PM is characterized by hypertrophic olivary degeneration, a trans-synaptic degeneration following loss of neuronal input to the inferior olivary nucleus due to an interruption of the Guillain-Mollaret triangle usually by a hemorrhage. However, the neural pathways underlying the disorder are uncertain. In an attempt to understand the pathologic neural pathways, we examined the metabolic correlates of this tremulous condition. Brain FDG PET scans were acquired in 8 patients with OPM (age, 49.9{+-}4.6 y: all males: 7 with pontine hemorrhage, 1 with diffuse brainstem infarction) and age-matched 50 healthy males (age, 50.7{+-} 9.0) and the regional glucose metabolism compared using SPM99. For group analysis, the hemispheres containing lesions were assigned to the right side of the brain. Patients with OPM had significant hypometabolism in the ipsilateral (to the lesion) brainstem and superior temporal and parahippocampal gyri (P < 0.05 corrected, k = 100). By contrast, there was significant hypermetabolism in the contralateral middle and inferior temporal gyri, thalamus, middle frontal gyrus and precuneus (P < 0.05 corrected, k=l00). Our data demonstrate the distinct metabolic changes between several ipsilateral and contralateral brain regions (hypometabolism vs. hypermetabolism) in patients with OPM. This may provide clues for understanding the neural pathways underlying the disorder.

  15. An ArcGIS approach to include tectonic structures in point data regionalization.

    Science.gov (United States)

    Darsow, Andreas; Schafmeister, Maria-Theresia; Hofmann, Thilo

    2009-01-01

    Point data derived from drilling logs must often be regionalized. However, aquifers may show discontinuous surface structures, such as the offset of an aquitard caused by tectonic faults. One main challenge has been to incorporate these structures into the regionalization process of point data. We combined ordinary kriging and inverse distance weighted (IDW) interpolation to account for neotectonic structures in the regionalization process. The study area chosen to test this approach is the largest porous aquifer in Austria. It consists of three basins formed by neotectonic events and delimited by steep faults with a vertical offset of the aquitard up to 70 m within very short distances. First, ordinary kriging was used to incorporate the characteristic spatial variability of the aquitard location by means of a variogram. The tectonic faults could be included into the regionalization process by using breaklines with buffer zones. All data points inside the buffer were deleted. Last, IDW was performed, resulting in an aquitard map representing the discontinuous surface structures. This approach enables one to account for such surfaces using the standard software package ArcGIS; therefore, it could be adopted in many practical applications.

  16. Characteristics of regional seismic waves from large explosive events including Korean nuclear explosions

    Science.gov (United States)

    Jo, Eunyoung; Lee, Ha-sung

    2015-04-01

    Three North Korean underground nuclear explosion (UNE) tests were conducted in 2006, 2009 and 2013. Discrimination of explosions from natural earthquakes is important in monitoring the seismic activity in the Korean Peninsula. The UNEs were well recorded by dense regional seismic networks in South Korea. The UNEs provide unique regional seismic waveforms with high signal-to-noise ratios. However, the continental crust in the Korean Peninsula changes abruptly into a transitional structure between continental and oceanic crusts across the eastern coast. The complex geological and tectonic structures around the Korean Peninsula cause significant variations in regional waveforms. Outstanding question is whether conventional discrimination techniques can be applicable for explosions including the North Korean UNEs. P/S amplitude ratios are widely used for seismic discrimination. To understand the regional shear-energy composition, we analyze the frequency contents of waveforms. The shear-energy contents for the UNEs are compared with those for natural earthquakes with comparable magnitudes. The result shows that the UNEs are successfully discriminated from earthquakes in the Korean Peninsula. We also analyze the explosive events from North Korean not UNEs to test the applicability of the discrimination technique. The result of high frequency Pn/Sn regional discrimination in the explosions show that as magnitude of event is smaller, available distance of discrimination is decreased particularly in high frequency range. The poor signal to noise ratio of Pn phase in the explosions, and inefficient propagation of Sn phase in the Western part of the peninsula frustrate Pn/Sn discriminant, while the UNEs show good performance using both discriminants because of propagation path effects in the eastern part of the peninsula.

  17. Neural correlates of envy: Regional homogeneity of resting-state brain activity predicts dispositional envy.

    Science.gov (United States)

    Xiang, Yanhui; Kong, Feng; Wen, Xue; Wu, Qihan; Mo, Lei

    2016-11-15

    Envy differs from common negative emotions across cultures. Although previous studies have explored the neural basis of episodic envy via functional magnetic resonance imaging (fMRI), little is known about the neural processes associated with dispositional envy. In the present study, we used regional homogeneity (ReHo) as an index in resting-state fMRI (rs-fMRI) to identify brain regions involved in individual differences in dispositional envy, as measured by the Dispositional Envy Scale (DES). Results showed that ReHo in the inferior/middle frontal gyrus (IFG/MFG) and dorsomedial prefrontal cortex (DMPFC) positively predicted dispositional envy. Moreover, of all the personality traits measured by the Revised NEO Personality Inventory (NEO-PI-R), only neuroticism was significantly associated with dispositional envy. Furthermore, neuroticism mediated the underlying association between the ReHo of the IFG/MFG and dispositional envy. Hence, to the best of our knowledge, this study provides the first evidence that spontaneous brain activity in multiple regions related to self-evaluation, social perception, and social emotion contributes to dispositional envy. In addition, our findings reveal that neuroticism may play an important role in the cognitive processing of dispositional envy. Copyright © 2016 Elsevier Inc. All rights reserved.

  18. Bioimpedance Harmonic Analysis as a Diagnostic Tool to Assess Regional Circulation and Neural Activity

    Science.gov (United States)

    Mudraya, I. S.; Revenko, S. V.; Khodyreva, L. A.; Markosyan, T. G.; Dudareva, A. A.; Ibragimov, A. R.; Romich, V. V.; Kirpatovsky, V. I.

    2013-04-01

    The novel technique based on harmonic analysis of bioimpedance microvariations with original hard- and software complex incorporating a high-resolution impedance converter was used to assess the neural activity and circulation in human urinary bladder and penis in patients with pelvic pain, erectile dysfunction, and overactive bladder. The therapeutic effects of shock wave therapy and Botulinum toxin detrusor injections were evaluated quantitatively according to the spectral peaks at low 0.1 Hz frequency (M for Mayer wave), respiratory (R) and cardiac (C) rhythms with their harmonics. Enhanced baseline regional neural activity identified according to M and R peaks was found to be presumably sympathetic in pelvic pain patients, and parasympathetic - in patients with overactive bladder. Total pulsatile activity and pulsatile resonances found in the bladder as well as in the penile spectrum characterised regional circulation and vascular tone. The abnormal spectral parameters characteristic of the patients with genitourinary diseases shifted to the norm in the cases of efficient therapy. Bioimpedance harmonic analysis seems to be a potent tool to assess regional peculiarities of circulatory and autonomic nervous activity in the course of patient treatment.

  19. [Functional results of cryosurgical procedures in rhegmatogenous retinal detachment including macula region - our experience].

    Science.gov (United States)

    Chrapek, O; Sín, M; Jirková, B; Jarkovský, J; Rehák, J

    2013-10-01

    Aim of this study is to evaluate retrospectively functional results of cryosurgical treatment of uncomplicated, idiopathic rhegmatogenous retinal detachment including macula region in phakic patients operated on at the Department of Ophthalmology, Faculty Hospital, Palacký University, Olomouc, Czech Republic, E.U., during the period 2002 -2013, and to evaluate the significance of the macula detachment duration for the final visual acuity. In the study group were included 56 eyes of 56 patients operated in the years 2003 - 2012 at the Department of Ophthalmology, Faculty Hospital, Palacký University, Olomouc. All patients were phakic and in all of them, the retinal detachment including the macula region was diagnosed. The mean follow-up period of the patients was 8,75 months. The initial and final visual acuity testing were performed. Comparing the initial and final visual acuity we rated the level of the visual acuity change. The result was stated as improved, if the visual acuity improved by 1 or more lines on the ETDRS chart. The result was rated as stabilized, if the visual acuity remained the same or it changed by 1 line of the ETDRS chart only. The result was evaluated as worsened, if the visual acuity decreased by 1 or more lines of the ETDRS chart. In the followed-up group, the authors compared visual acuity levels in patients with the macula detachment duration 10 days and 11 days. For the statistical evaluation of achieved results, the Mann - Whitney U test was used. The visual acuity improved in 49 (87 %), did not changed in 5 (9 %) and worsened in 2 (4 %) patients. The patients with macula detachment duration 10 days achieved statistically significant better visual acuity than patients with macula detachment duration 11 days. Patients with macula detachment duration 10 days have better prognosis for functional result than patients with macula detachment duration 11 days.

  20. A regional neural network model for predicting mean daily river water temperature

    Science.gov (United States)

    Wagner, Tyler; DeWeber, Jefferson Tyrell

    2014-01-01

    Water temperature is a fundamental property of river habitat and often a key aspect of river resource management, but measurements to characterize thermal regimes are not available for most streams and rivers. As such, we developed an artificial neural network (ANN) ensemble model to predict mean daily water temperature in 197,402 individual stream reaches during the warm season (May–October) throughout the native range of brook trout Salvelinus fontinalis in the eastern U.S. We compared four models with different groups of predictors to determine how well water temperature could be predicted by climatic, landform, and land cover attributes, and used the median prediction from an ensemble of 100 ANNs as our final prediction for each model. The final model included air temperature, landform attributes and forested land cover and predicted mean daily water temperatures with moderate accuracy as determined by root mean squared error (RMSE) at 886 training sites with data from 1980 to 2009 (RMSE = 1.91 °C). Based on validation at 96 sites (RMSE = 1.82) and separately for data from 2010 (RMSE = 1.93), a year with relatively warmer conditions, the model was able to generalize to new stream reaches and years. The most important predictors were mean daily air temperature, prior 7 day mean air temperature, and network catchment area according to sensitivity analyses. Forest land cover at both riparian and catchment extents had relatively weak but clear negative effects. Predicted daily water temperature averaged for the month of July matched expected spatial trends with cooler temperatures in headwaters and at higher elevations and latitudes. Our ANN ensemble is unique in predicting daily temperatures throughout a large region, while other regional efforts have predicted at relatively coarse time steps. The model may prove a useful tool for predicting water temperatures in sampled and unsampled rivers under current conditions and future projections of climate

  1. A regional neural network ensemble for predicting mean daily river water temperature

    Science.gov (United States)

    DeWeber, Jefferson Tyrell; Wagner, Tyler

    2014-09-01

    Water temperature is a fundamental property of river habitat and often a key aspect of river resource management, but measurements to characterize thermal regimes are not available for most streams and rivers. As such, we developed an artificial neural network (ANN) ensemble model to predict mean daily water temperature in 197,402 individual stream reaches during the warm season (May-October) throughout the native range of brook trout Salvelinus fontinalis in the eastern U.S. We compared four models with different groups of predictors to determine how well water temperature could be predicted by climatic, landform, and land cover attributes, and used the median prediction from an ensemble of 100 ANNs as our final prediction for each model. The final model included air temperature, landform attributes and forested land cover and predicted mean daily water temperatures with moderate accuracy as determined by root mean squared error (RMSE) at 886 training sites with data from 1980 to 2009 (RMSE = 1.91 °C). Based on validation at 96 sites (RMSE = 1.82) and separately for data from 2010 (RMSE = 1.93), a year with relatively warmer conditions, the model was able to generalize to new stream reaches and years. The most important predictors were mean daily air temperature, prior 7 day mean air temperature, and network catchment area according to sensitivity analyses. Forest land cover at both riparian and catchment extents had relatively weak but clear negative effects. Predicted daily water temperature averaged for the month of July matched expected spatial trends with cooler temperatures in headwaters and at higher elevations and latitudes. Our ANN ensemble is unique in predicting daily temperatures throughout a large region, while other regional efforts have predicted at relatively coarse time steps. The model may prove a useful tool for predicting water temperatures in sampled and unsampled rivers under current conditions and future projections of climate and land use

  2. Detection and classification of the breast abnormalities in digital mammograms via regional Convolutional Neural Network.

    Science.gov (United States)

    Al-Masni, M A; Al-Antari, M A; Park, J M; Gi, G; Kim, T Y; Rivera, P; Valarezo, E; Han, S-M; Kim, T-S

    2017-07-01

    Automatic detection and classification of the masses in mammograms are still a big challenge and play a crucial role to assist radiologists for accurate diagnosis. In this paper, we propose a novel computer-aided diagnose (CAD) system based on one of the regional deep learning techniques: a ROI-based Convolutional Neural Network (CNN) which is called You Only Look Once (YOLO). Our proposed YOLO-based CAD system contains four main stages: mammograms preprocessing, feature extraction utilizing multi convolutional deep layers, mass detection with confidence model, and finally mass classification using fully connected neural network (FC-NN). A set of training mammograms with the information of ROI masses and their types are used to train YOLO. The trained YOLO-based CAD system detects the masses and classifies their types into benign or malignant. Our results show that the proposed YOLO-based CAD system detects the mass location with an overall accuracy of 96.33%. The system also distinguishes between benign and malignant lesions with an overall accuracy of 85.52%. Our proposed system seems to be feasible as a CAD system capable of detection and classification at the same time. It also overcomes some challenging breast cancer cases such as the mass existing in the pectoral muscles or dense regions.

  3. REGION OF NON-INTEREST BASED DIGITAL IMAGE WATERMARKING USING NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    Bibi Isac

    2011-11-01

    Full Text Available Copyrights protection of digital data become inevitable in current world. Digital watermarks have been recently proposed as secured scheme for copyright protection, authentication, source tracking, and broadcast monitoring of video, audio, text data and digital images. In this paper a method to embed a watermark in region of non-interest (RONI and a method for adaptive calculation of strength factor using neural network are proposed. The embedding and extraction processes are carried out in the transform domain by using Discrete Wavelet Transform (DWT. Finally, the algorithm robustness is tested against noise addition attacks and geometric distortion attacks. The results authenticate that the proposed watermarking algorithm does not degrade the quality of cover image as the watermark is inserted only in region of non-interest and is resistive to attacks.

  4. A hybrid neural network system for prediction and recognition of promoter regions in human genome.

    Science.gov (United States)

    Chen, Chuan-Bo; Li, Tao

    2005-05-01

    This paper proposes a high specificity and sensitivity algorithm called PromPredictor for recognizing promoter regions in the human genome. PromPredictor extracts compositional features and CpG islands information from genomic sequence, feeding these features as input for a hybrid neural network system (HNN) and then applies the HNN for prediction. It combines a novel promoter recognition model, coding theory, feature selection and dimensionality reduction with machine learning algorithm. Evaluation on Human chromosome 22 was approximately 66% in sensitivity and approximately 48% in specificity. Comparison with two other systems revealed that our method had superior sensitivity and specificity in predicting promoter regions. PromPredictor is written in MATLAB and requires Matlab to run. PromPredictor is freely available at http://www.whtelecom.com/Prompredictor.htm.

  5. Adaptive Steganalysis Based on Selection Region and Combined Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Donghui Hu

    2017-01-01

    Full Text Available Digital image steganalysis is the art of detecting the presence of information hiding in carrier images. When detecting recently developed adaptive image steganography methods, state-of-art steganalysis methods cannot achieve satisfactory detection accuracy, because the adaptive steganography methods can adaptively embed information into regions with rich textures via the guidance of distortion function and thus make the effective steganalysis features hard to be extracted. Inspired by the promising success which convolutional neural network (CNN has achieved in the fields of digital image analysis, increasing researchers are devoted to designing CNN based steganalysis methods. But as for detecting adaptive steganography methods, the results achieved by CNN based methods are still far from expected. In this paper, we propose a hybrid approach by designing a region selection method and a new CNN framework. In order to make the CNN focus on the regions with complex textures, we design a region selection method by finding a region with the maximal sum of the embedding probabilities. To evolve more diverse and effective steganalysis features, we design a new CNN framework consisting of three separate subnets with independent structure and configuration parameters and then merge and split the three subnets repeatedly. Experimental results indicate that our approach can lead to performance improvement in detecting adaptive steganography.

  6. Acute stress evokes sexually dimorphic, stressor-specific patterns of neural activation across multiple limbic brain regions in adult rats.

    Science.gov (United States)

    Sood, Ankit; Chaudhari, Karina; Vaidya, Vidita A

    2018-03-01

    Stress enhances the risk for psychiatric disorders such as anxiety and depression. Stress responses vary across sex and may underlie the heightened vulnerability to psychopathology in females. Here, we examined the influence of acute immobilization stress (AIS) and a two-day short-term forced swim stress (FS) on neural activation in multiple cortical and subcortical brain regions, implicated as targets of stress and in the regulation of neuroendocrine stress responses, in male and female rats using Fos as a neural activity marker. AIS evoked a sex-dependent pattern of neural activation within the cingulate and infralimbic subdivisions of the medial prefrontal cortex (mPFC), lateral septum (LS), habenula, and hippocampal subfields. The degree of neural activation in the mPFC, LS, and habenula was higher in males. Female rats exhibited reduced Fos positive cell numbers in the dentate gyrus hippocampal subfield, an effect not observed in males. We addressed whether the sexually dimorphic neural activation pattern noted following AIS was also observed with the short-term stress of FS. In the paraventricular nucleus of the hypothalamus and the amygdala, FS similar to AIS resulted in robust increases in neural activation in both sexes. The pattern of neural activation evoked by FS was distinct across sexes, with a heightened neural activation noted in the prelimbic mPFC subdivision and hippocampal subfields in females and differed from the pattern noted with AIS. This indicates that the sex differences in neural activation patterns observed within stress-responsive brain regions are dependent on the nature of stressor experience.

  7. Estimating Full Regional Skeletal Muscle Fibre Orientation from B-Mode Ultrasound Images Using Convolutional, Residual, and Deconvolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Ryan Cunningham

    2018-01-01

    Full Text Available This paper presents an investigation into the feasibility of using deep learning methods for developing arbitrary full spatial resolution regression analysis of B-mode ultrasound images of human skeletal muscle. In this study, we focus on full spatial analysis of muscle fibre orientation, since there is an existing body of work with which to compare results. Previous attempts to automatically estimate fibre orientation from ultrasound are not adequate, often requiring manual region selection, feature engineering, providing low-resolution estimations (one angle per muscle and deep muscles are often not attempted. We build upon our previous work in which automatic segmentation was used with plain convolutional neural network (CNN and deep residual convolutional network (ResNet architectures, to predict a low-resolution map of fibre orientation in extracted muscle regions. Here, we use deconvolutions and max-unpooling (DCNN to regularise and improve predicted fibre orientation maps for the entire image, including deep muscles, removing the need for automatic segmentation and we compare our results with the CNN and ResNet, as well as a previously established feature engineering method, on the same task. Dynamic ultrasound images sequences of the calf muscles were acquired (25 Hz from 8 healthy volunteers (4 male, ages: 25–36, median 30. A combination of expert annotation and interpolation/extrapolation provided labels of regional fibre orientation for each image. Neural networks (CNN, ResNet, DCNN were then trained both with and without dropout using leave one out cross-validation. Our results demonstrated robust estimation of full spatial fibre orientation within approximately 6° error, which was an improvement on previous methods.

  8. Ear Detection under Uncontrolled Conditions with Multiple Scale Faster Region-Based Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Yi Zhang

    2017-04-01

    Full Text Available Ear detection is an important step in ear recognition approaches. Most existing ear detection techniques are based on manually designing features or shallow learning algorithms. However, researchers found that the pose variation, occlusion, and imaging conditions provide a great challenge to the traditional ear detection methods under uncontrolled conditions. This paper proposes an efficient technique involving Multiple Scale Faster Region-based Convolutional Neural Networks (Faster R-CNN to detect ears from 2D profile images in natural images automatically. Firstly, three regions of different scales are detected to infer the information about the ear location context within the image. Then an ear region filtering approach is proposed to extract the correct ear region and eliminate the false positives automatically. In an experiment with a test set of 200 web images (with variable photographic conditions, 98% of ears were accurately detected. Experiments were likewise conducted on the Collection J2 of University of Notre Dame Biometrics Database (UND-J2 and University of Beira Interior Ear dataset (UBEAR, which contain large occlusion, scale, and pose variations. Detection rates of 100% and 98.22%, respectively, demonstrate the effectiveness of the proposed approach.

  9. Expanding the North American Breeding Bird Survey analysis to include additional species and regions

    Science.gov (United States)

    Sauer, John; Niven, Daniel; Pardieck, Keith L.; Ziolkowski, David; Link, William

    2017-01-01

    The North American Breeding Bird Survey (BBS) contains data for >700 bird species, but analyses often focus on a core group of ∼420 species. We analyzed data for 122 species of North American birds for which data exist in the North American Breeding Bird Survey (BBS) database but are not routinely analyzed on the BBS Summary and Analysis Website. Many of these species occur in the northern part of the continent, on routes that fall outside the core survey area presently analyzed in the United States and southern Canada. Other species not historically analyzed occur in the core survey area with very limited data but have large portions of their ranges in Mexico and south. A third group of species not historically analyzed included species thought to be poorly surveyed by the BBS, such as rare, coastal, or nocturnal species. For 56 species found primarily in regions north of the core survey area, we expanded the scope of the analysis, using data from 1993 to 2014 during which ≥3 survey routes had been sampled in 6 northern strata (Bird Conservation regions in Alaska, Yukon, and Newfoundland and Labrador) and fitting log-linear hierarchical models for an augmented BBS survey area that included both the new northern strata and the core survey area. We also applied this model to 168 species historically analyzed in the BBS that had data from these additional northern strata. For both groups of species we calculated survey-wide trends for the both core and augmented survey areas from 1993 to 2014; for species that did not occur in the newly defined strata, we computed trends from 1966 to 2014. We evaluated trend estimates in terms of established credibility criteria for BBS results, screening for imprecise trends, small samples, and low relative abundance. Inclusion of data from the northern strata permitted estimation of trend for 56 species not historically analyzed, but only 4 of these were reasonably monitored and an additional 13 were questionably monitored; 39

  10. Fluid region segmentation in OCT images based on convolution neural network

    Science.gov (United States)

    Liu, Dong; Liu, Xiaoming; Fu, Tianyu; Yang, Zhou

    2017-07-01

    In the retinal image, characteristics of fluid have great significance for diagnosis in eye disease. In the clinical, the segmentation of fluid is usually conducted manually, but is time-consuming and the accuracy is highly depend on the expert's experience. In this paper, we proposed a segmentation method based on convolution neural network (CNN) for segmenting the fluid from fundus image. The B-scans of OCT are segmented into layers, and patches from specific region with annotation are used for training. After the data set being divided into training set and test set, network training is performed and a good segmentation result is obtained, which has a significant advantage over traditional methods such as threshold method.

  11. A method for locating regions containing neural activation at a given confidence level from MEG data

    Energy Technology Data Exchange (ETDEWEB)

    Schmidt, D.M.; George, J.S.

    1996-02-01

    The MEG inverse problem does not have a general, unique solution. Unless restrictive model assumptions are made, there are generally many more free parameters than measurements and there exist silent sources - current distributions which produce no external magnetic field. By weighting solutions according to how well each fits our prior notion about what properties good solutions should have, it may be possible to obtain a single current distribution that best fits the data and our expectations. However, in general there will still exist a number of different current distributions which fit both the data and our prior expectations sufficiently well. For example, a simulated data set based on a single or several dipoles can generally be fit equally well by a distributed current minimum-norm reconstruction. In experimental data it is often possible to find a relatively small number of dipoles which both fit the data and have a norm not much larger than that of the minimum-norm solution. Moreover, the few-dipole solutions often have currents in different regions than the corresponding minimum-norm solution. Because there exist well-fitting current distributions which may have current in significantly different locations, it can be misleading to infer locations of stimulus-correlated neural activity based on a single, best-fitting current distribution. we demonstrate here a method for inferring the location and number of regions containing neural activation by considering all possible current distributions within a given model (not just the most likely one) weighted according to how well each fits both the data and our prior expectations.

  12. LOAD FORECASTING FOR POWER SYSTEM PLANNING AND OPERATION USING ARTIFICIAL NEURAL NETWORK AT AL BATINAH REGION OMAN

    Directory of Open Access Journals (Sweden)

    HUSSEIN A. ABDULQADER

    2012-08-01

    Full Text Available Load forecasting is essential part for the power system planning and operation. In this paper the modeling and design of artificial neural network for load forecasting is carried out in a particular region of Oman. Neural network approach helps to reduce the problem associated with conventional method and has the advantage of learning directly from the historical data. The neural network here uses data such as past load; weather information like humidity and temperatures. Once the neural network is trained for the past set of data it can give a prediction of future load. This reduces the capital investment reducing the equipments to be installed. The actual data are taken from the Mazoon Electrical Company, Oman. The data of load for the year 2007, 2008 and 2009 are collected for a particular region called Al Batinah in Oman and trained using neural networks to forecast the future. The main objective is to forecast the amount of electricity needed for better load distribution in the areas of this region in Oman. The load forecasting is done for the year 2010 and is validated for the accuracy.

  13. Regionally-specified second trimester fetal neural stem cells reveals differential neurogenic programming.

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    Yiping Fan

    Full Text Available Neural stem/progenitor cells (NSC have the potential for treatment of a wide range of neurological diseases such as Parkinson Disease and multiple sclerosis. Currently, NSC have been isolated only from hippocampus and subventricular zone (SVZ of the adult brain. It is not known whether NSC can be found in all parts of the developing mid-trimester central nervous system (CNS when the brain undergoes massive transformation and growth. Multipotent NSC from the mid-trimester cerebra, thalamus, SVZ, hippocampus, thalamus, cerebellum, brain stem and spinal cord can be derived and propagated as clonal neurospheres with increasing frequencies with increasing gestations. These NSC can undergo multi-lineage differentiation both in vitro and in vivo, and engraft in a developmental murine model. Regionally-derived NSC are phenotypically distinct, with hippocampal NSC having a significantly higher neurogenic potential (53.6% over other sources (range of 0%-27.5%, p<0.004. Whole genome expression analysis showed differential gene expression between these regionally-derived NSC, which involved the Notch, epidermal growth factor as well as interleukin pathways. We have shown the presence of phenotypically-distinct regionally-derived NSC from the mid-trimester CNS, which may reflect the ontological differences occurring within the CNS. Aside from informing on the role of such cells during fetal growth, they may be useful for different cellular therapy applications.

  14. Evolvable synthetic neural system

    Science.gov (United States)

    Curtis, Steven A. (Inventor)

    2009-01-01

    An evolvable synthetic neural system includes an evolvable neural interface operably coupled to at least one neural basis function. Each neural basis function includes an evolvable neural interface operably coupled to a heuristic neural system to perform high-level functions and an autonomic neural system to perform low-level functions. In some embodiments, the evolvable synthetic neural system is operably coupled to one or more evolvable synthetic neural systems in a hierarchy.

  15. Region stability analysis and tracking control of memristive recurrent neural network.

    Science.gov (United States)

    Bao, Gang; Zeng, Zhigang; Shen, Yanjun

    2018-02-01

    Memristor is firstly postulated by Leon Chua and realized by Hewlett-Packard (HP) laboratory. Research results show that memristor can be used to simulate the synapses of neurons. This paper presents a class of recurrent neural network with HP memristors. Firstly, it shows that memristive recurrent neural network has more compound dynamics than the traditional recurrent neural network by simulations. Then it derives that n dimensional memristive recurrent neural network is composed of [Formula: see text] sub neural networks which do not have a common equilibrium point. By designing the tracking controller, it can make memristive neural network being convergent to the desired sub neural network. At last, two numerical examples are given to verify the validity of our result. Copyright © 2017 Elsevier Ltd. All rights reserved.

  16. Is variation management included in regional healthcare governance systems? Some proposals from Italy.

    Science.gov (United States)

    Nuti, Sabina; Seghieri, Chiara

    2014-01-01

    The Italian National Health System, which follows a Beveridge model, provides universal healthcare coverage through general taxation. Universal coverage provides uniform healthcare access to citizens and is the characteristic usually considered the added value of a welfare system financed by tax revenues. Nonetheless, wide differences in practice patterns, health outcomes and regional usages of resources that cannot be justified by differences in patient needs have been demonstrated to exist. Beginning with the experience of the health care system of the Tuscany region (Italy), this study describes the first steps of a long-term approach to proactively address the issue of geographic variation in healthcare. In particular, the study highlights how the unwarranted variation management has been addressed in a region with a high degree of managerial control over the delivery of health care and a consolidated performance evaluation system, by first, considering it a high priority objective and then by actively integrating it into the regional planning and control mechanism. The implications of this study can be useful to policy makers, professionals and managers, and will contribute to the understanding of how the management of variation can be implemented with performance measurements and financial incentives. Copyright © 2013 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

  17. A regional GNSS-VTEC model over Nigeria using neural networks: A novel approach

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    Daniel Okoh

    2016-01-01

    Full Text Available A neural network model of the Global Navigation Satellite System – vertical total electron content (GNSS-VTEC over Nigeria is developed. A new approach that has been utilized in this work is the consideration of the International Reference Ionosphere's (IRI's critical plasma frequency (foF2 parameter as an additional neuron for the network's input layer. The work also explores the effects of using various other input layer neurons like disturbance storm time (DST and sunspot number. All available GNSS data from the Nigerian Permanent GNSS Network (NIGNET were used, and these cover the period from 2011 to 2015, for 14 stations. Asides increasing the learning accuracy of the networks, the inclusion of the IRI's foF2 parameter as an input neuron is ideal for making the networks to learn long-term solar cycle variations. This is important especially for regions, like in this work, where the GNSS data is available for less than the period of a solar cycle. The neural network model developed in this work has been tested for time-varying and spatial performances. The latest 10% of the GNSS observations from each of the stations were used to test the forecasting ability of the networks, while data from 2 of the stations were entirely used for spatial performance testing. The results show that root-mean-squared-errors were generally less than 8.5 TEC units for all modes of testing performed using the optimal network. When compared to other models, the model developed in this work was observed to reduce the prediction errors to about half those of the NeQuick and the IRI model.

  18. Mapping regional forest fire probability using artificial neural network model in a Mediterranean forest ecosystem

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    Onur Satir

    2016-09-01

    Full Text Available Forest fires are one of the most important factors in environmental risk assessment and it is the main cause of forest destruction in the Mediterranean region. Forestlands have a number of known benefits such as decreasing soil erosion, containing wild life habitats, etc. Additionally, forests are also important player in carbon cycle and decreasing the climate change impacts. This paper discusses forest fire probability mapping of a Mediterranean forestland using a multiple data assessment technique. An artificial neural network (ANN method was used to map forest fire probability in Upper Seyhan Basin (USB in Turkey. Multi-layer perceptron (MLP approach based on back propagation algorithm was applied in respect to physical, anthropogenic, climate and fire occurrence datasets. Result was validated using relative operating characteristic (ROC analysis. Coefficient of accuracy of the MLP was 0.83. Landscape features input to the model were assessed statistically to identify the most descriptive factors on forest fire probability mapping using the Pearson correlation coefficient. Landscape features like elevation (R = −0.43, tree cover (R = 0.93 and temperature (R = 0.42 were strongly correlated with forest fire probability in the USB region.

  19. Neural activity in the posterior superior temporal region during eye contact perception correlates with autistic traits.

    Science.gov (United States)

    Hasegawa, Naoya; Kitamura, Hideaki; Murakami, Hiroatsu; Kameyama, Shigeki; Sasagawa, Mutsuo; Egawa, Jun; Endo, Taro; Someya, Toshiyuki

    2013-08-09

    The present study investigated the relationship between neural activity associated with gaze processing and autistic traits in typically developed subjects using magnetoencephalography. Autistic traits in 24 typically developed college students with normal intelligence were assessed using the Autism Spectrum Quotient (AQ). The Minimum Current Estimates method was applied to estimate the cortical sources of magnetic responses to gaze stimuli. These stimuli consisted of apparent motion of the eyes, displaying direct or averted gaze motion. Results revealed gaze-related brain activations in the 150-250 ms time window in the right posterior superior temporal sulcus (pSTS), and in the 150-450 ms time window in medial prefrontal regions. In addition, the mean amplitude in the 150-250 ms time window in the right pSTS region was modulated by gaze direction, and its activity in response to direct gaze stimuli correlated with AQ score. pSTS activation in response to direct gaze is thought to be related to higher-order social processes. Thus, these results suggest that brain activity linking eye contact and social signals is associated with autistic traits in a typical population. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  20. Coupled modeling of land hydrology–regional climate including human carbon emission and water exploitation

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    Zheng-Hui Xie

    2017-06-01

    Full Text Available Carbon emissions and water use are two major kinds of human activities. To reveal whether these two activities can modify the hydrological cycle and climate system in China, we conducted two sets of numerical experiments using regional climate model RegCM4. In the first experiment used to study the climatic responses to human carbon emissions, the model were configured over entire China because the impacts of carbon emissions can be detected across the whole country. Results from the first experiment revealed that near-surface air temperature may significantly increase from 2007 to 2059 at a rate exceeding 0.1 °C per decade in most areas across the country; southwestern and southeastern China also showed increasing trends in summer precipitation, with rates exceeding 10 mm per decade over the same period. In summer, only northern China showed an increasing trend of evapotranspiration, with increase rates ranging from 1 to 5 mm per decade; in winter, increase rates ranging from 1 to 5 mm per decade were observed in most regions. These effects are believed to be caused by global warming from human carbon emissions. In the second experiment used to study the effects of human water use, the model were configured over a limited region—Haihe River Basin in the northern China, because compared with the human carbon emissions, the effects of human water use are much more local and regional, and the Haihe River Basin is the most typical region in China that suffers from both intensive human groundwater exploitation and surface water diversion. We incorporated a scheme of human water regulation into RegCM4 and conducted the second experiment. Model outputs showed that the groundwater table severely declined by ∼10 m in 1971–2000 through human groundwater over-exploitation in the basin; in fact, current conditions are so extreme that even reducing the pumping rate by half cannot eliminate the groundwater depletion cones observed in the area

  1. Landslide susceptibility assessment of SE Bartin (West Black Sea region, Turkey by artificial neural networks

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    M. Ercanoglu

    2005-01-01

    Full Text Available Landslides are significant natural hazards in Turkey, second only to earthquakes with respect to economic losses and casualties. The West Black Sea region of Turkey is known as one of the most landslide-prone regions in the country. The work presented in this paper is aimed at evaluating landslide susceptibility in a selected area in the West Black Sea region using Artificial Neural Network (ANN method. A total of 317 landslides were identified and mapped in the area by extensive field work and by use of air photo interpretations to build a landslide inventory map. A landslide database was then derived automatically from the landslide inventory map. To evaluate landslide susceptibility, six input parameters (slope angle, slope aspect, topographical elevation, topographical shape, wetness index, and vegetation index were used. To obtain maps of these parameters, Digital Elevation Model (DEM and ASTER satellite imagery of the study area were used. At the first stage, all data were normalized in [0, 1] interval, and parameter effects on landslide occurrence were expressed using Statistical Index values (Wi. Then, landslide susceptibility analyses were performed using an ANN. Finally, performance of the resulting map and the applied methodology is discussed relative to performance indicators, such as predicted areal extent of landslides and the strength of relation (rij value. Much of the areal extents of the landslides (87.2% were classified as susceptible to landsliding, and rij value of 0.85 showed a high degree of similarity. In addition to these, at the final stage, an independent validation strategy was followed by dividing the landslide data set into two parts and 82.5% of the validation data set was found to be correctly classified as landslide susceptible areas. According to these results, it is concluded that the map produced by the ANN is reliable and methodology applied in the study produced high performance, and satisfactory results.

  2. A Deep Convolutional Neural Network for segmenting and classifying epithelial and stromal regions in histopathological images.

    Science.gov (United States)

    Xu, Jun; Luo, Xiaofei; Wang, Guanhao; Gilmore, Hannah; Madabhushi, Anant

    2016-05-26

    Epithelial (EP) and stromal (ST) are two types of tissues in histological images. Automated segmentation or classification of EP and ST tissues is important when developing computerized system for analyzing the tumor microenvironment. In this paper, a Deep Convolutional Neural Networks (DCNN) based feature learning is presented to automatically segment or classify EP and ST regions from digitized tumor tissue microarrays (TMAs). Current approaches are based on handcraft feature representation, such as color, texture, and Local Binary Patterns (LBP) in classifying two regions. Compared to handcrafted feature based approaches, which involve task dependent representation, DCNN is an end-to-end feature extractor that may be directly learned from the raw pixel intensity value of EP and ST tissues in a data driven fashion. These high-level features contribute to the construction of a supervised classifier for discriminating the two types of tissues. In this work we compare DCNN based models with three handcraft feature extraction based approaches on two different datasets which consist of 157 Hematoxylin and Eosin (H&E) stained images of breast cancer and 1376 immunohistological (IHC) stained images of colorectal cancer, respectively. The DCNN based feature learning approach was shown to have a F1 classification score of 85%, 89%, and 100%, accuracy (ACC) of 84%, 88%, and 100%, and Matthews Correlation Coefficient (MCC) of 86%, 77%, and 100% on two H&E stained (NKI and VGH) and IHC stained data, respectively. Our DNN based approach was shown to outperform three handcraft feature extraction based approaches in terms of the classification of EP and ST regions.

  3. Probabilistic seismic hazard assessment including site effects for Evansville, Indiana, and the surrounding region

    Science.gov (United States)

    Haase, Jennifer S.; Bowling, Tim; Nowack, Robert L.; Choi, Yoon S.; Cramer, Chris H.; Boyd, Oliver S.; Bauer, Robert A.

    2011-01-01

    We provide a probabilistic seismic hazard assessment for the Evansville, Indiana region incorporating information from new surficial geologic mapping efforts on the part of the U.S. Geological Survey (USGS) and the Kentucky and Indiana State Geological Surveys, as well as information on the thickness and properties of near surface soils and their associated uncertainties. The subsurface information has been compiled to determine bedrock elevation and reference depth-dependent shear-wave velocity models for the different soil types. The probabilistic seismic hazard calculation applied here follows the method used for the 2008 U.S. Geological Survey National Seismic Hazard Maps, with modifications to incorporate estimates of local site conditions and their uncertainties, in a completely probabilistic manner. The resulting analysis shows strong local variations of acceleration with 2 percent probability of exceedance in 50 years, particularly for 0.2-second (s) period spectral acceleration (SA), that are clearly correlated with variations in the thickness of unconsolidated soils above bedrock. These values are much greater than the USGS national seismic hazard map values, which assume B/C site conditions. When compared to the national maps with an assumed uniform site D class amplification factor applied, the high-resolution seismic hazard maps have higher amplitudes for peak ground acceleration and 0.2-s SA for most of the map region. However, deamplification relative to the D class national seismic hazard maps appears to play an important role within the limits of the ancient bedrock valley underlying Evansville where soils are thickest. For 1.0-s SA, the new high-resolution seismic hazard maps show levels consistent with D class site response within the limits of this ancient bedrock valley, but levels consistent with B/C site conditions outside.

  4. Regional neural response differences in the determination of faces or houses positioned in a wide visual field.

    Science.gov (United States)

    Wang, Bin; Yan, Tianyi; Wu, Jinglong; Chen, Kewei; Imajyo, Satoshi; Ohno, Seiichiro; Kanazawa, Susumu

    2013-01-01

    In human visual cortex, the primary visual cortex (V1) is considered to be essential for visual information processing; the fusiform face area (FFA) and parahippocampal place area (PPA) are considered as face-selective region and places-selective region, respectively. Recently, a functional magnetic resonance imaging (fMRI) study showed that the neural activity ratios between V1 and FFA were constant as eccentricities increasing in central visual field. However, in wide visual field, the neural activity relationships between V1 and FFA or V1 and PPA are still unclear. In this work, using fMRI and wide-view present system, we tried to address this issue by measuring neural activities in V1, FFA and PPA for the images of faces and houses aligning in 4 eccentricities and 4 meridians. Then, we further calculated ratio relative to V1 (RRV1) as comparing the neural responses amplitudes in FFA or PPA with those in V1. We found V1, FFA, and PPA showed significant different neural activities to faces and houses in 3 dimensions of eccentricity, meridian, and region. Most importantly, the RRV1s in FFA and PPA also exhibited significant differences in 3 dimensions. In the dimension of eccentricity, both FFA and PPA showed smaller RRV1s at central position than those at peripheral positions. In meridian dimension, both FFA and PPA showed larger RRV1s at upper vertical positions than those at lower vertical positions. In the dimension of region, FFA had larger RRV1s than PPA. We proposed that these differential RRV1s indicated FFA and PPA might have different processing strategies for encoding the wide field visual information from V1. These different processing strategies might depend on the retinal position at which faces or houses are typically observed in daily life. We posited a role of experience in shaping the information processing strategies in the ventral visual cortex.

  5. Junctional neurulation: a unique developmental program shaping a discrete region of the spinal cord highly susceptible to neural tube defects.

    Science.gov (United States)

    Dady, Alwyn; Havis, Emmanuelle; Escriou, Virginie; Catala, Martin; Duband, Jean-Loup

    2014-09-24

    In higher vertebrates, the primordium of the nervous system, the neural tube, is shaped along the rostrocaudal axis through two consecutive, radically different processes referred to as primary and secondary neurulation. Failures in neurulation lead to severe anomalies of the nervous system, called neural tube defects (NTDs), which are among the most common congenital malformations in humans. Mechanisms causing NTDs in humans remain ill-defined. Of particular interest, the thoracolumbar region, which encompasses many NTD cases in the spine, corresponds to the junction between primary and secondary neurulations. Elucidating which developmental processes operate during neurulation in this region is therefore pivotal to unraveling the etiology of NTDs. Here, using the chick embryo as a model, we show that, at the junction, the neural tube is elaborated by a unique developmental program involving concerted movements of elevation and folding combined with local cell ingression and accretion. This process ensures the topological continuity between the primary and secondary neural tubes while supplying all neural progenitors of both the junctional and secondary neural tubes. Because it is distinct from the other neurulation events, we term this phenomenon junctional neurulation. Moreover, the planar-cell-polarity member, Prickle-1, is recruited specifically during junctional neurulation and its misexpression within a limited time period suffices to cause anomalies that phenocopy lower spine NTDs in human. Our study thus provides a molecular and cellular basis for understanding the causality of NTD prevalence in humans and ascribes to Prickle-1 a critical role in lower spinal cord formation. Copyright © 2014 the authors 0270-6474/14/3413208-14$15.00/0.

  6. Net analyte signal-based simultaneous determination of antazoline and naphazoline using wavelength region selection by experimental design-neural networks.

    Science.gov (United States)

    Hemmateenejad, Bahram; Ghavami, Raoof; Miri, Ramin; Shamsipur, Majtaba

    2006-02-15

    Net analyte signal (NAS)-based multivariate calibration methods were employed for simultaneous determination of anthazoline and naphazoline. The NAS vectors calculated from the absorbance data of the drugs mixture were used as input for classical least squares (CLS), principal component and partial least squares regression PCR and PLS methods. A wavelength selection strategy was used to find the best wavelength region for each drug separately. As a new procedure, we proposed an experimental design-neural network strategy for wavelength region optimization. By use of a full factorial design method, some different wavelength regions were selected by taking into account different spectral parameters including the starting wavelength, the ending wavelength and the wavelength interval. The performance of all the multivariate calibration methods, in all selected wavelength regions for both drugs, was evaluated by calculating a fitness function based on the root mean square error of calibration and validation. A three-layered feed-forward artificial neural network (ANN) model with back-propagation learning algorithm was employed to model the nonlinear relationship between the spectral parameters and fitness of each regression method. From the resulted ANN models, the spectral regions in which lowest fitness could be obtained were chosen. Comparison of the results revealed that the net NAS-PLS resulted in lower prediction error than the other models. The proposed NAS-based calibration method was successfully applied to the simultaneous analyses of anthazoline and naphazoline in a commercial eye drop sample.

  7. Estimates of global and regional prevalence of neural tube defects for 2015: a systematic analysis.

    Science.gov (United States)

    Blencowe, Hannah; Kancherla, Vijaya; Moorthie, Sowmiya; Darlison, Matthew W; Modell, Bernadette

    2018-01-24

    Neural tube defects (NTDs) are associated with substantial mortality, morbidity, disability, and psychological and economic costs. Many are preventable with folic acid, and access to appropriate services for those affected can improve survival and quality of life. We used a compartmental model to estimate global and regional birth prevalence of NTDs (live births, stillbirths, and elective terminations of pregnancy) and subsequent under-5 mortality. Data were identified through web-based reviews of birth defect registry databases and systematic literature reviews. Meta-analyses were undertaken where appropriate. For 2015, our model estimated 260,100 (uncertainty interval (UI): 213,800-322,000) NTD-affected birth outcomes worldwide (prevalence 18.6 (15.3-23.0)/10,000 live births). Approximately 50% of cases were elective terminations of pregnancy for fetal anomalies (UI: 59,300 (47,900-74,500)) or stillbirths (57,800 (UI: 35,000-88,600)). Of NTD-affected live births, 117,900 (∼75%) (UI: 105,500-186,600) resulted in under-5 deaths. Our systematic review showed a paucity of high-quality data in the regions of the world with the highest burden. Despite knowledge about prevention, NTDs remain highly prevalent worldwide. Lack of surveillance and incomplete ascertainment of affected pregnancies make NTDs invisible to policy makers. Improved surveillance of all adverse outcomes is needed to improve the robustness of total NTD prevalence estimation, evaluate effectiveness of prevention through folic acid fortification, and improve outcomes through care and rehabilitation. © 2018 The Authors. Annals of the New York Academy of Sciences published by Wiley Periodicals Inc. on behalf of The New York Academy of Sciences.

  8. Including capabilities of local actors in regional economic development: Empirical results of local seaweed industries in Sulawesi

    Directory of Open Access Journals (Sweden)

    Mark T.J. Vredegoor

    2013-10-01

    Full Text Available Stimson, et al. (2009 developed one of the most relevant and well known model for Regional Eco- nomic Development. This model covers the most important factors related to economic develop- ment question. However, this model excludes the social components of development. Local com- munity should be included in terms of the development of a region. This paper introduced to the Stimson model “Skills” and “Knowledge” at the individual level for local actors indicating the ca- pabilities at the individual level and introduced “Human Coordination” for the capabilities at the collective level. In our empirical research we looked at the Indonesian seaweed market with a spe- cific focus on the region of Baubau. This region was chosen because there are hardly any economic developments. Furthermore this study focuses on the poorer community who are trying to improve their situation by the cultivation of Seaweed. Eighteen local informants was interviewed besides additional interviews of informants from educational and governmental institutions in the cities of Jakarta, Bandung and Yogyakarta. The informants selected had a direct or indirect relationship with the region of Baubau. With the support of the empirical data from this region we can confirm that it is worthwhile to include the local community in the model for regional economic develop- ment. The newly added variables: at the individual level; Skills and Knowledge and at the level of the collective: Human Coordination was supported by the empirical material. It is an indication that including the new variables can give regional economic an extra dimension. In this way we think that it becomes more explicit that “endogenous” means that the people, or variables closely related to them, should be more explicitly included in models trying to capture Regional Economic Develop- ment or rephrased as Local Economic Development

  9. The siRNA-mediated knockdown of GluN3A in 46C-derived neural stem cells affects mRNA expression levels of neural genes, including known iGluR interactors

    Science.gov (United States)

    Eilebrecht, Elke; Schöneborn, Hendrik; Neumann, Sebastian; Benecke, Arndt G.; Hollmann, Michael

    2018-01-01

    For years, GluN3A was solely considered to be a dominant-negative modulator of NMDARs, since its incorporation into receptors alters hallmark features of conventional NMDARs composed of GluN1/GluN2 subunits. Only recently, increasing evidence has accumulated that GluN3A plays a more diversified role. It is considered to be critically involved in the maturation of glutamatergic synapses, and it might act as a molecular brake to prevent premature synaptic strengthening. Its expression pattern supports a putative role during neural development, since GluN3A is predominantly expressed in early pre- and postnatal stages. In this study, we used RNA interference to efficiently knock down GluN3A in 46C-derived neural stem cells (NSCs) both at the mRNA and at the protein level. Global gene expression profiling upon GluN3A knockdown revealed significantly altered expression of a multitude of neural genes, including genes encoding small GTPases, retinal proteins, and cytoskeletal proteins, some of which have been previously shown to interact with GluN3A or other iGluR subunits. Canonical pathway enrichment studies point at important roles of GluN3A affecting key cellular pathways involved in cell growth, proliferation, motility, and survival, such as the mTOR pathway. This study for the first time provides insights into transcriptome changes upon the specific knockdown of an NMDAR subunit in NSCs, which may help to identify additional functions and downstream pathways of GluN3A and GluN3A-containing NMDARs. PMID:29438442

  10. Classifying region of interests from mammograms with breast cancer into BIRADS using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Estefanía D. Avalos-Rivera

    2017-05-01

    Full Text Available Breast cancer is one of the most common cancers among female diseases all over the world. Early diagnosis and treatment is particularly important in reducing the mortality rate. This research is focused on the prevention of breast cancer, therefore it is important to detect micro-calcifications (MCs which are a sign of early stage breast cancer. Micro-calcifications are tiny deposits of calcium which are visible on mammograms as they present as tiny white spots. A computer-aided diagnosis system (CAD is created with the development of computer technology that way radiologists are aided improving their diagnostics while using CAD as a second reader. We are aiming to classify into BIRADS 2, 3 and 4 which are the stages when the cancer can be prevented and a fourth category called No lesion which are veins and tissue that our high pass Gaussian filter detects. This research focuses on classification using ANN (Artificial Neural Network. Experimenting with the categories to classify into using ANN, the results were the following: into the four mentioned before an overall accuracy of 71% was obtained, then joining categories BIRADS 2 and 3 into one and classifying into 3 categories gave an 80% of accuracy. Joining this two categories was the result of analizing the ROC curve and observation of the ROI images of the MCs as the regions measured are very alike in this two categories and variation is that MCs are more present in BIRADS 3 than in BIRADS 2. Data matrix was reduced using PCA (Principal Component Analysis but it did not gave better results so it was discarded as the ANN accuracy to classify was reduced to a 69.8%.

  11. A novel lead design enables selective deep brain stimulation of neural populations in the subthalamic region

    Science.gov (United States)

    van Dijk, Kees J.; Verhagen, Rens; Chaturvedi, Ashutosh; McIntyre, Cameron C.; Bour, Lo J.; Heida, Ciska; Veltink, Peter H.

    2015-08-01

    Objective. The clinical effects of deep brain stimulation (DBS) of the subthalamic nucleus (STN-DBS) as a treatment for Parkinson’s disease are sensitive to the location of the DBS lead within the STN. New high density (HD) lead designs have been created which are hypothesized to provide additional degrees of freedom in shaping the stimulating electric field. The objective of this study is to compare the performances of a new HD lead with a conventional cylindrical contact (CC) lead. Approach. A computational model, consisting of a finite element electric field model combined with multi-compartment neuron and axon models representing different neural populations in the subthalamic region, was used to evaluate the two leads. We compared ring-mode and steering-mode stimulation with the HD lead to single contact stimulation with the CC lead. These stimulation modes were tested for the lead: (1) positioned in the centroid of the STN, (2) shifted 1 mm towards the internal capsule (IC), and (3) shifted 2 mm towards the IC. Under these conditions, we quantified the number of STN neurons that were activated without activating IC fibers, which are known to cause side-effects. Main results. The modeling results show that the HD lead is able to mimic the stimulation effect of the CC lead. Additionally, in steering-mode stimulation there was a significant increase of activated STN neurons compared to the CC mode. Significance. From the model simulations we conclude that the HD lead in steering-mode with optimized stimulation parameter selection can stimulate more STN cells. Next, the clinical impact of the increased number of activated STN cells should be tested and balanced across the increased complexity of identifying the optimized stimulation parameter settings for the HD lead.

  12. Regional differences in the expression of laminin isoforms during mouse neural tube development

    Science.gov (United States)

    Copp, Andrew J.; Carvalho, Rita; Wallace, Adam; Sorokin, Lydia; Sasaki, Takako; Greene, Nicholas D.E.; Ybot-Gonzalez, Patricia

    2013-01-01

    Many significant human birth defects originate around the time of neural tube closure or early during post-closure nervous system development. For example, failure of the neural tube to close generates anencephaly and spina bifida, faulty cell cycle progression is implicated in primary microcephaly, while defective migration of neuroblasts can lead to neuronal migration disorders such as lissencephaly. At the stage of neural tube closure, basement membranes are becoming organised around the neuroepithelium, and beneath the adjacent non-neural surface ectoderm. While there is circumstantial evidence to implicate basement membrane dynamics in neural tube and surface ectodermal development, we have an incomplete understanding of the molecular composition of basement membranes at this stage. In the present study, we examined the developing basement membranes of the mouse embryo at mid-gestation (embryonic day 9.5), with particular reference to laminin composition. We performed in situ hybridization to detect the mRNAs of all eleven individual laminin chains, and immunohistochemistry to identify which laminin chains are present in the basement membranes. From this information, we inferred the likely laminin variants and their tissues of origin: that is, whether a given basement membrane laminin is contributed by epithelium, mesenchyme, or both. Our findings reveal major differences in basement composition along the body axis, with the rostral neural tube (at mandibular arch and heart levels) exhibiting many distinct laminin variants, while the lumbar level where the neural tube is just closing shows a much simpler laminin profile. Moreover, there appears to be a marked difference in the extent to which the mesenchyme contributes laminin variants to the basement membrane, with potential contribution of several laminins rostrally, but no contribution caudally. This information paves the way towards a mechanistic analysis of basement membrane laminin function during early

  13. Potential Habitat Modelling of Ferula ovina Using Artificial Neural Network in Fereydunshahr Region, Isfahan Province

    Directory of Open Access Journals (Sweden)

    Z. Rahmati

    2015-06-01

    Full Text Available Species distribution maps have been widely developed based on ecological niche theory together with statistical and geographical information system in plant ecology. The current study aimed to evaluate Artificial Neural Network (ANN in mapping potential habitat of Ferula ovina Boiss in Ferydunshar rangelands, Isfahan. This is known as valuable forage and medicinal species. Environmental data (independent variables and species occurrence data (dependent variable were required to determine potential habitat of a given species. Some physical and chemical soil properties, climate and physiographic variables were mapped for the entire studied area using krigging and inverse distance weighting methods. F. ovina occurrence data were collected from 278 sites including 137 presence and 141 absence sites. The relationships between the studied environmental variables and F. ovina occurrence data were explored using ANN method. According to the sensitivity analysis, occurrence of F. ovina mostly correlated with silt and sand percentage, elevation slope, and organic matter. Model evaluation based on Kappa coefficient (0.66 and Receiver operating characteristic (ROC=0.9 showed good model fitness in relation to reality on local scales. The ANN technique enables managers to identify appropriate areas for rehabilitation practices such as direct seeding and planting.                       

  14. 77 FR 62535 - Hydro Aluminum North America, Inc., Midwest Region, Including On-Site Leased Workers From...

    Science.gov (United States)

    2012-10-15

    ... Employment and Training Administration Hydro Aluminum North America, Inc., Midwest Region, Including On- Site Leased Workers From Employment Group, Aerotek, and Manpower, Kalamazoo, Michigan; Hydro Aluminum North... and former workers of Hydro Aluminum North America, Inc., Kalamazoo, Michigan. The subject worker...

  15. Generation of Regionally Specified Neural Progenitors and Functional Neurons from Human Embryonic Stem Cells under Defined Conditions

    Directory of Open Access Journals (Sweden)

    Agnete Kirkeby

    2012-06-01

    Full Text Available To model human neural-cell-fate specification and to provide cells for regenerative therapies, we have developed a method to generate human neural progenitors and neurons from human embryonic stem cells, which recapitulates human fetal brain development. Through the addition of a small molecule that activates canonical WNT signaling, we induced rapid and efficient dose-dependent specification of regionally defined neural progenitors ranging from telencephalic forebrain to posterior hindbrain fates. Ten days after initiation of differentiation, the progenitors could be transplanted to the adult rat striatum, where they formed neuron-rich and tumor-free grafts with maintained regional specification. Cells patterned toward a ventral midbrain (VM identity generated a high proportion of authentic dopaminergic neurons after transplantation. The dopamine neurons showed morphology, projection pattern, and protein expression identical to that of human fetal VM cells grafted in parallel. VM-patterned but not forebrain-patterned neurons released dopamine and reversed motor deficits in an animal model of Parkinson's disease.

  16. Application of artificial neural network for vapor liquid equilibrium calculation of ternary system including ionic liquid: Water, ethanol and 1-butyl-3-methylimidazolium acetate

    Energy Technology Data Exchange (ETDEWEB)

    Fazlali, Alireza; Koranian, Parvaneh [Arak University, Arak (Iran, Islamic Republic of); Beigzadeh, Reza [Islamic Azad University, Kermanshah (Iran, Islamic Republic of); Rahimi, Masoud [Razi University, Kermanshah (Iran, Islamic Republic of)

    2013-09-15

    A feed forward three-layer artificial neural network (ANN) model was developed for VLE prediction of ternary systems including ionic liquid (IL) (water+ethanol+1-butyl-3- methyl-imidazolium acetate), in a relatively wide range of IL mass fractions up to 0.8, with the mole fractions of ethanol on IL-free basis fixed separately at 0.1, 0.2, 0.4, 0.6, 0.8, and 0.98. The output results of the ANN were the mole fraction of ethanol in vapor phase and the equilibrium temperature. The validity of the model was evaluated through a test data set, which were not employed in the training case of the network. The performance of the ANN model for estimating the mole fraction and temperature in the ternary system including IL was compared with the non-random-two-liquid (NRTL) and electrolyte non-random-two- liquid (eNRTL) models. The results of this comparison show that the ANN model has a superior performance in predicting the VLE of ternary systems including ionic liquid.

  17. Self-Organizing Maps Neural Networks Applied to the Classification of Ethanol Samples According to the Region of Commercialization

    Directory of Open Access Journals (Sweden)

    Aline Regina Walkoff

    2017-10-01

    Full Text Available Physical-chemical analysis data were collected, from 998 ethanol samples of automotive ethanol commercialized in the northern, midwestern and eastern regions of the state of Paraná. The data presented self-organizing maps (SOM neural networks, which classified them according to those regions. The self-organizing maps best configuration had a 45 x 45 topology and 5000 training epochs, with a final learning rate of 6.7x10-4, a final neighborhood relationship of 3x10-2 and a mean quantization error of 2x10-2. This neural network provided a topological map depicting three separated groups, each one corresponding to samples of a same region of commercialization. Four maps of weights, one for each parameter, were presented. The network established the pH was the most important variable for classification and electrical conductivity the least one. The self-organizing maps application allowed the segmentation of alcohol samples, therefore identifying them according to the region of commercialization. DOI: http://dx.doi.org/10.17807/orbital.v9i4.982

  18. The role of the anterior neural ridge and Fgf-8 in early forebrain patterning and regionalization in Xenopus laevis.

    Science.gov (United States)

    Eagleson, Gerald W; Dempewolf, Ryan D

    2002-05-01

    The tissue, cellular and molecular mechanisms that regulate early regional specification of the vertebrate forebrain are largely unknown. We studied the expression patterns of Xbf-1, an anterior (and telencephalon) neural-specific winged helix transcription factor and Fgf-8, an early-secreted factor. This study looked at Xbf-1 and Fgf-8 expression in combination with embryonic grafting experiments and also used beads containing the recombinant Fgf-8 protein to determine these factors' effects upon forebrain patterning events. We provide evidence that additional Fgf-8 displaces Xbf-1 expression posteriorly, suggesting a concentration dependence of Fgf-8 for the early distinct regionalization of the telencephalic primordia. Also, additional stage 15 mid-anterior neural ridge (mANR) transplants inhibited telencephalon development, whereas lateral ANR transplants facilitated increased areas of telencephalon development. In both cases, these transplantations promoted ectopic expression of Xbf-1. These studies suggested that the distinct regionalization of the forebrain primordia involves the inhibitory actions of the mANR towards a telencephalon development and maintaining bilateral telencephali. These telencephalic primordia are initially localized by optimal Fgf-8 expression. The anterior mANR will eventually become the anterior and rostral diencephalic tissue. This in vivo study demonstrated Fgf-8 and the mANR are important in forebrain regionalization.

  19. Deep neural networks show an equivalent and often superior performance to dermatologists in onychomycosis diagnosis: Automatic construction of onychomycosis datasets by region-based convolutional deep neural network

    Science.gov (United States)

    Lim, Woohyung; Kim, Myoung Shin; Na, Jung Im; Park, Ilwoo

    2018-01-01

    Although there have been reports of the successful diagnosis of skin disorders using deep learning, unrealistically large clinical image datasets are required for artificial intelligence (AI) training. We created datasets of standardized nail images using a region-based convolutional neural network (R-CNN) trained to distinguish the nail from the background. We used R-CNN to generate training datasets of 49,567 images, which we then used to fine-tune the ResNet-152 and VGG-19 models. The validation datasets comprised 100 and 194 images from Inje University (B1 and B2 datasets, respectively), 125 images from Hallym University (C dataset), and 939 images from Seoul National University (D dataset). The AI (ensemble model; ResNet-152 + VGG-19 + feedforward neural networks) results showed test sensitivity/specificity/ area under the curve values of (96.0 / 94.7 / 0.98), (82.7 / 96.7 / 0.95), (92.3 / 79.3 / 0.93), (87.7 / 69.3 / 0.82) for the B1, B2, C, and D datasets. With a combination of the B1 and C datasets, the AI Youden index was significantly (p = 0.01) higher than that of 42 dermatologists doing the same assessment manually. For B1+C and B2+ D dataset combinations, almost none of the dermatologists performed as well as the AI. By training with a dataset comprising 49,567 images, we achieved a diagnostic accuracy for onychomycosis using deep learning that was superior to that of most of the dermatologists who participated in this study. PMID:29352285

  20. Abnormal regional spontaneous neural activity in visual pathway in retinal detachment patients: a resting-state functional MRI study

    Directory of Open Access Journals (Sweden)

    Huang X

    2017-11-01

    Full Text Available Xin Huang,1,2,* Dan Li,3,* Hai-Jun Li,3 Yu-Lin Zhong,1 Shelby Freeberg,4 Jing Bao,1 Xian-Jun Zeng,3 Yi Shao1 1Department of Ophthalmology, The First Affiliated Hospital of Nanchang University, Jiangxi Province Clinical Ophthalmology Institute, Nanchang, Jiangxi, People’s Republic of China; 2Department of Ophthalmology, Eye Center, Renmin Hospital of Wuhan University, Wuhan University, Wuhan, Hubei, People’s Republic of China; 3Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, People’s Republic of China; 4Department of Ophthalmology, University of Florida, Gainesville, FL, USA *These authors contributed equally to this work Objective: The aim of the study was to investigate changes of brain neural homogeneity in retinal detachment (RD patients using the regional homogeneity (ReHo method to understand their relationships with clinical features. Materials and methods: A total of 30 patients with RD (16 men and 14 women, and 30 healthy controls (HCs (16 men and 14 women closely matched in age and sex were recruited. Resting-state functional magnetic resonance imaging scans were performed for all subjects. The ReHo method was used to investigate the brain regional neural homogeneity. Patients with RD were distinguished from HCs by receiver operating characteristic curve. The relationships between the mean ReHo signal values in many brain regions and clinical features in RD patients were calculated by Pearson correlation analysis. Results: Compared with HCs, RD patients had significantly decreased ReHo values in the right occipital lobe, right superior temporal gyrus, bilateral cuneus and left middle frontal gyrus. Moreover, we found that the mean ReHo signal of the bilateral cuneus showed positive relationships with the duration of the RD (r=0.392, P=0.032. Conclusion: The RD patients showed brain neural homogeneity dysfunction in the visual pathway, which may underline the pathological mechanism

  1. The Artificial Neural Network Estimation for Daily and Hourly Rice Evapotranspiration in the Region of Red Soil, South China

    Science.gov (United States)

    Jing, Yuanshu; Ruthaikarn, Buaphean; Jin, Xinyi; Pang, Bo

    The evapotranspiration estimation is a key item for irrigation program. It has the important practical significance for high stable yield and water-saving in the region of red soil, South China. Penman-Monteith equation, recommended by FAO, is verified to be the most effective calculation to actual evaporation in many regions of the world. The only default is it has to use complete meteorological factors. To solve this problem, we are trying to find out a artificial neural network model (ANN) which can easily get its information and easy to calculate as well as guaranteed accuracy. A Bowen ratio energy balance (BREB) system and automatic weather station were employed for simultaneous measurement of actual evapotranspiration above the rice field. The frequency of 20-min recording provided the possibility for the estimation of daily and hourly evapotranspiration. The determined coefficient from the artificial neural network model on daily scale R2 is 0.9642, while hourly scale R2 is 0.9880. The reason was that the hourly scale training samples was greater than the daily scale measures. In general, the model gives an effective and feasible way for the evaluation of paddy rice evapotranspiration by the conventional parameters.

  2. Potential for Expanding the Near Real Time ForWarn Regional Forest Monitoring System to Include Alaska

    Science.gov (United States)

    Spruce, Joseph P.; Gasser, Gerald; Hargrove, William; Smoot, James; Kuper, Philip D.

    2014-01-01

    The on-line near real time (NRT) ForWarn system is currently deployed to monitor regional forest disturbances within the conterminous United States (CONUS), using daily MODIS Aqua and Terra NDVI data to derive monitoring products. The Healthy Forest Restoration Act of 2003 mandated such a system. Work on ForWarn began in 2006 with development and validation of retrospective MODIS NDVI-based forest monitoring products. Subsequently, NRT forest disturbance monitoring products were demonstrated, leading to the actual system deployment in 2010. ForWarn provides new CONUS forest disturbance monitoring products every 8 days, using USGS eMODIS data for current NDVI. ForWarn currently does not cover Alaska, which includes extensive forest lands at risk to multiple biotic and abiotic threats. This poster discusses a case study using Alaska eMODIS Terra data to derive ForWarn like forest change products during the 2010 growing season. The eMODIS system provides current MODIS Terra NDVI products for Alaska. Resulting forest change products were assessed with ground, aerial, and Landsat reference data. When cloud and snow free, these preliminary products appeared to capture regional forest disturbances from insect defoliation and fires; however, more work is needed to mitigate cloud and snow contamination, including integration of eMODIS Aqua data.

  3. Prediction of oxidation parameters of purified Kilka fish oil including gallic acid and methyl gallate by adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network.

    Science.gov (United States)

    Asnaashari, Maryam; Farhoosh, Reza; Farahmandfar, Reza

    2016-10-01

    As a result of concerns regarding possible health hazards of synthetic antioxidants, gallic acid and methyl gallate may be introduced as natural antioxidants to improve oxidative stability of marine oil. Since conventional modelling could not predict the oxidative parameters precisely, artificial neural network (ANN) and neuro-fuzzy inference system (ANFIS) modelling with three inputs, including type of antioxidant (gallic acid and methyl gallate), temperature (35, 45 and 55 °C) and concentration (0, 200, 400, 800 and 1600 mg L(-1) ) and four outputs containing induction period (IP), slope of initial stage of oxidation curve (k1 ) and slope of propagation stage of oxidation curve (k2 ) and peroxide value at the IP (PVIP ) were performed to predict the oxidation parameters of Kilka oil triacylglycerols and were compared to multiple linear regression (MLR). The results showed ANFIS was the best model with high coefficient of determination (R(2)  = 0.99, 0.99, 0.92 and 0.77 for IP, k1 , k2 and PVIP , respectively). So, the RMSE and MAE values for IP were 7.49 and 4.92 in ANFIS model. However, they were to be 15.95 and 10.88 and 34.14 and 3.60 for the best MLP structure and MLR, respectively. So, MLR showed the minimum accuracy among the constructed models. Sensitivity analysis based on the ANFIS model suggested a high sensitivity of oxidation parameters, particularly the induction period on concentrations of gallic acid and methyl gallate due to their high antioxidant activity to retard oil oxidation and enhanced Kilka oil shelf life. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

  4. Structured Regions of Alpha-synuclein Fibrils Include the Early Onset Parkinson's Disease Mutation Sites

    Energy Technology Data Exchange (ETDEWEB)

    Comellas Canal, Gemma; Lemkau, Luisel R.; Nieuwkoop, Andrew J.; Kloepper, Kathryn D.; Ladror, Daniel T.; Ebisu, Reika; Woods, Wendy S.; Lipton, Andrew S.; George, Julia M.; Rienstra, Chad M.

    2011-08-26

    Alpha-Synuclein (AS) fibrils constitute the major proteinaceous component of Lewy bodies (LBs), the pathological hallmark of Parkinson’s disease (PD) and other neurodegenerative diseases. Three single point mutations in the AS gene, as well as multiplication of the wild-type (WT) AS allele, have been previously identified in families with early-onset PD. Although AS fibrils have been the subject of intense study, critical details about their structure including the precise location of the B-strands and the extent of the core, the three-dimensional structure and the effects of the mutations—remain unknown. Here, we have used magic-angle spinning solid-state NMR spectroscopy to present a detailed characterization of the full-length WT AS fibrils. With improved sample preparations, isotopic labeling patterns and NMR experiments, we have confidently assigned more than 90% of the 13C and 15N backbone and sidechain chemical shifts of the detected residues from residue 39 to 97, and quantified the conformational dynamics throughout this region. Our results demonstrate that the core of AS fibrils extends with a repeated motif and that residues 30, 46 and 53-the early-onset PD mutant sites-are located in structured regions of AS fibrils.

  5. Neural bases of accented speech perception

    OpenAIRE

    Patti eAdank; Nuttall, Helen E.; Briony eBanks; Dan eKennedy-Higgins

    2015-01-01

    The recognition of unfamiliar regional and foreign accents represents a challenging task for the speech perception system (Adank, Evans, Stuart-Smith, & Scott, 2009; Floccia, Goslin, Girard, & Konopczynski, 2006). Despite the frequency with which we encounter such accents, the neural mechanisms supporting successful perception of accented speech are poorly understood. Nonetheless, candidate neural substrates involved in processing speech in challenging listening conditions, including accented...

  6. Effectiveness of Folic Acid Fortified Flour for Prevention of Neural Tube Defects in a High Risk Region.

    Science.gov (United States)

    Wang, Haochen; De Steur, Hans; Chen, Gong; Zhang, Xiaotian; Pei, Lijun; Gellynck, Xavier; Zheng, Xiaoying

    2016-03-09

    Despite efforts to tackle folate deficiency and Neural Tube Defects (NTDs) through folic acid fortification, its implementation is still lacking where it is needed most, highlighting the need for studies that evaluate the effectiveness of folic acid fortified wheat flour in a poor, rural, high-risk, NTD region of China. One of the most affected regions, Shanxi Province, was selected as a case study. A community intervention was carried out in which 16,648 women of child-bearing age received fortified flour (eight villages) and a control group received ordinary flour (three villages). NTD birth prevalence and biological indicators were measured two years after program initiation at endline only. The effect on the NTD burden was calculated using the disability-adjusted life years (DALYs) method. In the intervention group, serum folate level was higher than in the control group. NTDs in the intervention group were 68.2% lower than in the control group (OR = 0.313, 95% CI = 0.207-0473, p < 0.001). In terms of DALYs, burden in intervention group was approximately 58.5% lower than in the control group. Flour fortification was associated with lower birth prevalence and burden of NTDs in economically developing regions with a high risk of NTDs. The positive findings confirm the potential of fortification when selecting an appropriate food vehicle and target region. As such, this study provides support for decision makers aiming for the implementation of (mandatory) folic acid fortification in China.

  7. Effectiveness of Folic Acid Fortified Flour for Prevention of Neural Tube Defects in a High Risk Region

    Directory of Open Access Journals (Sweden)

    Haochen Wang

    2016-03-01

    Full Text Available Despite efforts to tackle folate deficiency and Neural Tube Defects (NTDs through folic acid fortification, its implementation is still lacking where it is needed most, highlighting the need for studies that evaluate the effectiveness of folic acid fortified wheat flour in a poor, rural, high-risk, NTD region of China. One of the most affected regions, Shanxi Province, was selected as a case study. A community intervention was carried out in which 16,648 women of child-bearing age received fortified flour (eight villages and a control group received ordinary flour (three villages. NTD birth prevalence and biological indicators were measured two years after program initiation at endline only. The effect on the NTD burden was calculated using the disability-adjusted life years (DALYs method. In the intervention group, serum folate level was higher than in the control group. NTDs in the intervention group were 68.2% lower than in the control group (OR = 0.313, 95% CI = 0.207–0473, p < 0.001. In terms of DALYs, burden in intervention group was approximately 58.5% lower than in the control group. Flour fortification was associated with lower birth prevalence and burden of NTDs in economically developing regions with a high risk of NTDs. The positive findings confirm the potential of fortification when selecting an appropriate food vehicle and target region. As such, this study provides support for decision makers aiming for the implementation of (mandatory folic acid fortification in China.

  8. A Kohonen neural network description of scoliosis fused regions and their corresponding Lenke classification.

    Science.gov (United States)

    Mezghani, N; Phan, P; Mitiche, A; Labelle, H; de Guise, J A

    2012-03-01

    Surgical instrumentation for adolescent idiopathic scoliosis (AIS) is a complex procedure where selection of the appropriate curve segment to fuse, i.e., fusion region, is a challenging decision in scoliosis surgery. Currently, the Lenke classification model is used for fusion region evaluation and surgical planning. Retrospective evaluation of Lenke classification and fusion region results was performed. Using a database of 1,776 surgically treated AIS cases, we investigated a topologically ordered self organizing Kohonen network, trained using Cobb angle measurements, to determine the relationship between the Lenke class and the fusion region selection. Specifically, the purpose was twofold (1) produce two spatially matched maps, one of Lenke classes and the other of fusion regions, and (2) associate these two maps to determine where the Lenke classes correlate with the fused spine regions. Topologically ordered maps obtained using a multi-center database of surgically treated AIS cases, show that the recommended fusion region agrees with the Lenke class except near boundaries between Lenke map classes. Overall agreement was 88%. The Lenke classification and fusion region agree in the majority of adolescent idiopathic scoliosis when reviewed retrospectively. The results indicate the need for spinal fixation instrumentation variation associated with the Lenke classification.

  9. A longitudinal analysis of neural regions involved in reading the mind in the eyes.

    Science.gov (United States)

    Overgaauw, Sandy; van Duijvenvoorde, Anna C K; Gunther Moor, Bregtje; Crone, Eveline A

    2015-05-01

    The ability to perceive social intentions from people's eyes is present from an early age, yet little is known about whether this skill is fully developed in childhood or that subtle changes may still occur across adolescence. This fMRI study investigated the ability to read mental states by using an adapted version of the Reading the Mind in the Eyes task within adolescents (aged 12-19 years) over a 2-year test-retest interval. This longitudinal setup provides the opportunity to study both stability over time as well as age-related changes. The behavioral results showed that participants who performed well in the mental state condition at the first measurement also performed well at the second measurement. fMRI results revealed positive test-retest correlations of neural activity in the right superior temporal sulcus and right inferior frontal gyrus for the contrast mental state > control, suggesting stability within individuals over time. Besides stability of activation, dorsal medial prefrontal cortex showed a dip in mid-adolescence for the mental state > control condition and right inferior frontal gyrus decreased linearly with age for the mental state > control condition. These findings underline changes in the slope of the developmental pattern depending on age, even in the existence of relatively stable activation in the social brain network. © The Author (2014). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  10. Generation of Integration-free and Region-Specific Neural Progenitors from Primate Fibroblasts

    Directory of Open Access Journals (Sweden)

    Jianfeng Lu

    2013-05-01

    Full Text Available Postnatal and adult human and monkey fibroblasts were infected with Sendai virus containing the Yamanaka factors for 24 hr, then they were cultured in a chemically defined medium containing leukemia inhibitory factor (LIF, transforming growth factor (TGF-β inhibitor SB431542, and glycogen synthase kinase (GSK-3β inhibitor CHIR99021 at 39°C for inactivation of the virus. Induced neural progenitor (iNP colonies appeared as early as day 13 and can be expanded for >20 passages. Under the same defined condition, no induced pluripotent stem cell (iPSC colonies formed at either 37°C or 39°C. The iNPs predominantly express hindbrain genes and differentiate into hindbrain neurons, and when caudalized, they produced an enriched population of spinal motor neurons. Following transplantation into the forebrain, the iNP-derived cells retained the hindbrain identity. The ability to generate defined, integration-free iNPs from adult primate fibroblasts under a defined condition with predictable fate choices will facilitate disease modeling and therapeutic development.

  11. Bird species and numbers of birds in oak savannas of the Southwestern Borderlands region including effects of burning

    Science.gov (United States)

    Peter F. Ffolliott; Hui Chen; Gerald J. Gottfried

    2011-01-01

    Oak savannas of the Southwestern Borderlands region provide food, cover, and sites for nesting, roosting, and perching for a diversity of bird species. The results of a five-year (2003-2007) study of bird species, numbers of birds, and their diversities in the naturally occurring (unburned) oak savannas of the region are reported in this paper. Effects of cool-season...

  12. Nodes and networks in the neural architecture for language: Broca's region and beyond

    NARCIS (Netherlands)

    Hagoort, Peter

    2014-01-01

    Current views on the neurobiological underpinnings of language are discussed that deviate in a number of ways from the classical Wernicke-Lichtheim-Geschwind model. More areas than Broca's and Wernicke's region are involved in language. Moreover, a division along the axis of language production and

  13. Individual Differences in Neural Regions Functionally Related to Real and Imagined Stuttering

    Science.gov (United States)

    Wymbs, Nicholas F.; Ingham, Roger J.; Ingham, Janis C.; Paolini, Katherine E.; Grafton, Scott T.

    2013-01-01

    Recent brain imaging investigations of developmental stuttering show considerable disagreement regarding which regions are related to stuttering. These divergent findings have been mainly derived from group studies. To investigate functional neurophysiology with improved precision, an individual-participant approach (N = 4) using event-related…

  14. Visual motion imagery neurofeedback based on the hMT+/V5 complex: evidence for a feedback-specific neural circuit involving neocortical and cerebellar regions

    Science.gov (United States)

    Banca, Paula; Sousa, Teresa; Catarina Duarte, Isabel; Castelo-Branco, Miguel

    2015-12-01

    Objective. Current approaches in neurofeedback/brain-computer interface research often focus on identifying, on a subject-by-subject basis, the neural regions that are best suited for self-driven modulation. It is known that the hMT+/V5 complex, an early visual cortical region, is recruited during explicit and implicit motion imagery, in addition to real motion perception. This study tests the feasibility of training healthy volunteers to regulate the level of activation in their hMT+/V5 complex using real-time fMRI neurofeedback and visual motion imagery strategies. Approach. We functionally localized the hMT+/V5 complex to further use as a target region for neurofeedback. An uniform strategy based on motion imagery was used to guide subjects to neuromodulate hMT+/V5. Main results. We found that 15/20 participants achieved successful neurofeedback. This modulation led to the recruitment of a specific network as further assessed by psychophysiological interaction analysis. This specific circuit, including hMT+/V5, putative V6 and medial cerebellum was activated for successful neurofeedback runs. The putamen and anterior insula were recruited for both successful and non-successful runs. Significance. Our findings indicate that hMT+/V5 is a region that can be modulated by focused imagery and that a specific cortico-cerebellar circuit is recruited during visual motion imagery leading to successful neurofeedback. These findings contribute to the debate on the relative potential of extrinsic (sensory) versus intrinsic (default-mode) brain regions in the clinical application of neurofeedback paradigms. This novel circuit might be a good target for future neurofeedback approaches that aim, for example, the training of focused attention in disorders such as ADHD.

  15. Including local rainfall dynamics and uncertain boundary conditions into a 2-D regional-local flood modelling cascade

    Science.gov (United States)

    Bermúdez, María; Neal, Jeffrey C.; Bates, Paul D.; Coxon, Gemma; Freer, Jim E.; Cea, Luis; Puertas, Jerónimo

    2016-04-01

    Flood inundation models require appropriate boundary conditions to be specified at the limits of the domain, which commonly consist of upstream flow rate and downstream water level. These data are usually acquired from gauging stations on the river network where measured water levels are converted to discharge via a rating curve. Derived streamflow estimates are therefore subject to uncertainties in this rating curve, including extrapolating beyond the maximum observed ratings magnitude. In addition, the limited number of gauges in reach-scale studies often requires flow to be routed from the nearest upstream gauge to the boundary of the model domain. This introduces additional uncertainty, derived not only from the flow routing method used, but also from the additional lateral rainfall-runoff contributions downstream of the gauging point. Although generally assumed to have a minor impact on discharge in fluvial flood modeling, this local hydrological input may become important in a sparse gauge network or in events with significant local rainfall. In this study, a method to incorporate rating curve uncertainty and the local rainfall-runoff dynamics into the predictions of a reach-scale flood inundation model is proposed. Discharge uncertainty bounds are generated by applying a non-parametric local weighted regression approach to stage-discharge measurements for two gauging stations, while measured rainfall downstream from these locations is cascaded into a hydrological model to quantify additional inflows along the main channel. A regional simplified-physics hydraulic model is then applied to combine these inputs and generate an ensemble of discharge and water elevation time series at the boundaries of a local-scale high complexity hydraulic model. Finally, the effect of these rainfall dynamics and uncertain boundary conditions are evaluated on the local-scale model. Improvements in model performance when incorporating these processes are quantified using observed

  16. Metastatic potential to regional lymph nodes with Gleason score ≤7, including tertiary pattern 5, at radical prostatectomy.

    Science.gov (United States)

    Diolombi, Mairo L; Epstein, Jonathan I

    2017-06-01

    To determine the risk of pelvic lymph node (LN) metastases at radical prostatectomy (RP) for Gleason score (GS) ≤7: 3 + 3 = 6 (grade group [GG]1); 3 + 4 = 7 (GG2); 3 + 4 = 7 (GG2) with tertiary pattern 5 (T5); 4 + 3 = 7 (GG3); 4 + 3 = 7 (GG3) with T5, using the 2014 modified Gleason grading system and the novel GG system. We searched our RP database to indentify cases of GS ≤7 prostate cancer with simultaneous pelvic LN dissection (PLND) in the period between 2005 and 2014. Since 2005, we have graded all glomeruloid and cribriform cancer as Gleason pattern 4 and have graded mucinous adenocarcinoma based on the underlying architectural pattern, consistent with the 2014 modified Gleason grading system. All RPs were embedded in entirety, including the PLND. A total of 7 442 cases were identified, of which 73 had at least one positive LN (+LN). The incidence rates for regional LN metastases at RP for 3 + 3 = 6 (GG1), 3 + 4 = 7 (GG2), 3 + 4 = 7 (GG2) with T5, 4 + 3 = 7 (GG3) and 4 + 3 = 7 (GG3) with T5 were 0, 0.6, 0.4, 4.3 and 6.3%, respectively. There was a statistically significant difference in risk of +LNs at RP between the grade groups, as defined by the novel GG system. There was no statistically significant difference in risk of +LNs at RP for men with 3 + 4 (GG2) vs 3 + 4 (GG2) with T5 and for men with 4 + 3 (GG3) vs 4 + 3 (GG3) with T5. Non-pelvic LN involvement was identified in 0.2% of all RP cases. Two patients with GS 3 + 4 = 7 with Gleason grading system where 3 + 3 with T4 (2005 modified grading system) is now considered 3 + 4 (GG2), with a comment on percent pattern 4, because Gleason grading system. © 2016 The Authors BJU International © 2016 BJU International Published by John Wiley & Sons Ltd.

  17. DNA methylation aberrations rather than polymorphisms of FZD3 gene increase the risk of spina bifida in a high-risk region for neural tube defects.

    Science.gov (United States)

    Shangguan, Shaofang; Wang, Li; Chang, Shaoyan; Lu, Xiaoling; Wang, Zhen; Wu, Lihua; Wang, Jianhua; Wang, Xiuwei; Guan, Zhen; Bao, Yihua; Zhao, Huizhi; Zou, Jizhen; Niu, Bo; Zhang, Ting

    2015-01-01

    Animal models of neural tube defects (NTDs) have indicated roles for the Fzd3 gene and the planar cell polarity signaling pathway in convergent extension. We investigated the involvement of FZD3 in genetic and epigenetic mechanisms associated with human NTDs, especially spina bifida. We explored the effects of variants spanning the FZD3 gene in NTDs and examined the role of aberrant methylation of the FZD3 promoter on gene expression in brain tissue in spina bifida. Six FZD3 single nucleotide polymorphisms were genotyped using a MassARRAY system in tissue from 165 NTD fetuses and 152 controls. DNA methylation aberrations in the FZD3 promoter region were detected using a MassARRAY EpiTYPER (17 CpG units from -500 to -2400 bp from the transcription start site) in brain tissue from 77 spina bifida and 74 control fetuses. None of the six single nucleotide polymorphisms evaluated were significantly associated with spina bifida, but the mean methylation level was significantly higher in spina bifida samples (13.70%) compared with control samples (10.91%) (p = 0.001). In terms of specific sites, DNA methylation levels were significantly higher in the spina bifida samples at 14 of the 17 CpG units, which mostly included in R2 region. FZD3 mRNA expression was negatively correlated with methylation of the FZD3 promoter region, especially the R2 region (R = 0.970; p = 0.001) in HeLa cells. The results of this study suggest that DNA methylation plays an important role in FZD3 gene expression regulation and may be associated with an increased risk of spina bifida. © 2014 Wiley Periodicals, Inc.

  18. Neural activity in the reward-related brain regions predicts implicit self-esteem: A novel validity test of psychological measures using neuroimaging.

    Science.gov (United States)

    Izuma, Keise; Kennedy, Kate; Fitzjohn, Alexander; Sedikides, Constantine; Shibata, Kazuhisa

    2018-03-01

    Self-esteem, arguably the most important attitudes an individual possesses, has been a premier research topic in psychology for more than a century. Following a surge of interest in implicit attitude measures in the 90s, researchers have tried to assess self-esteem implicitly to circumvent the influence of biases inherent in explicit measures. However, the validity of implicit self-esteem measures remains elusive. Critical tests are often inconclusive, as the validity of such measures is examined in the backdrop of imperfect behavioral measures. To overcome this serious limitation, we tested the neural validity of the most widely used implicit self-esteem measure, the implicit association test (IAT). Given the conceptualization of self-esteem as attitude toward the self, and neuroscience findings that the reward-related brain regions represent an individual's attitude or preference for an object when viewing its image, individual differences in implicit self-esteem should be associated with neural signals in the reward-related regions during passive-viewing of self-face (the most obvious representation of the self). Using multi-voxel pattern analysis (MVPA) on functional MRI (fMRI) data, we demonstrate that the neural signals in the reward-related regions were robustly associated with implicit (but not explicit) self-esteem, thus providing unique evidence for the neural validity of the self-esteem IAT. In addition, both implicit and explicit self-esteem were related, although differently, to neural signals in regions involved in self-processing. Our finding highlights the utility of neuroscience methods in addressing fundamental psychological questions and providing unique insights into important psychological constructs. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

  19. Learning Oriented Region-based Convolutional Neural Networks for Building Detection in Satellite Remote Sensing Images

    Directory of Open Access Journals (Sweden)

    C. Chen

    2017-05-01

    Full Text Available The automated building detection in aerial images is a fundamental problem encountered in aerial and satellite images analysis. Recently, thanks to the advances in feature descriptions, Region-based CNN model (R-CNN for object detection is receiving an increasing attention. Despite the excellent performance in object detection, it is problematic to directly leverage the features of R-CNN model for building detection in single aerial image. As we know, the single aerial image is in vertical view and the buildings possess significant directional feature. However, in R-CNN model, direction of the building is ignored and the detection results are represented by horizontal rectangles. For this reason, the detection results with horizontal rectangle cannot describe the building precisely. To address this problem, in this paper, we proposed a novel model with a key feature related to orientation, namely, Oriented R-CNN (OR-CNN. Our contributions are mainly in the following two aspects: 1 Introducing a new oriented layer network for detecting the rotation angle of building on the basis of the successful VGG-net R-CNN model; 2 the oriented rectangle is proposed to leverage the powerful R-CNN for remote-sensing building detection. In experiments, we establish a complete and bran-new data set for training our oriented R-CNN model and comprehensively evaluate the proposed method on a publicly available building detection data set. We demonstrate State-of-the-art results compared with the previous baseline methods.

  20. Learning Oriented Region-based Convolutional Neural Networks for Building Detection in Satellite Remote Sensing Images

    Science.gov (United States)

    Chen, C.; Gong, W.; Hu, Y.; Chen, Y.; Ding, Y.

    2017-05-01

    The automated building detection in aerial images is a fundamental problem encountered in aerial and satellite images analysis. Recently, thanks to the advances in feature descriptions, Region-based CNN model (R-CNN) for object detection is receiving an increasing attention. Despite the excellent performance in object detection, it is problematic to directly leverage the features of R-CNN model for building detection in single aerial image. As we know, the single aerial image is in vertical view and the buildings possess significant directional feature. However, in R-CNN model, direction of the building is ignored and the detection results are represented by horizontal rectangles. For this reason, the detection results with horizontal rectangle cannot describe the building precisely. To address this problem, in this paper, we proposed a novel model with a key feature related to orientation, namely, Oriented R-CNN (OR-CNN). Our contributions are mainly in the following two aspects: 1) Introducing a new oriented layer network for detecting the rotation angle of building on the basis of the successful VGG-net R-CNN model; 2) the oriented rectangle is proposed to leverage the powerful R-CNN for remote-sensing building detection. In experiments, we establish a complete and bran-new data set for training our oriented R-CNN model and comprehensively evaluate the proposed method on a publicly available building detection data set. We demonstrate State-of-the-art results compared with the previous baseline methods.

  1. The active principle region of Buyang Huanwu decoction induced differentiation of bone marrow-derived mesenchymal stem cells into neural-like cells

    Science.gov (United States)

    Zheng, Jinghui; Wan, Yi; Chi, Jianhuai; Shen, Dekai; Wu, Tingting; Li, Weimin; Du, Pengcheng

    2012-01-01

    The present study induced in vitro-cultured passage 4 bone marrow-derived mesenchymal stem cells to differentiate into neural-like cells with a mixture of alkaloid, polysaccharide, aglycone, glycoside, essential oils, and effective components of Buyang Huanwu decoction (active principle region of decoction for invigorating yang for recuperation). After 28 days, nestin and neuron-specific enolase were expressed in the cytoplasm. Reverse transcription-PCR and western blot analyses showed that nestin and neuron-specific enolase mRNA and protein expression was greater in the active principle region group compared with the original formula group. Results demonstrated that the active principle region of Buyang Huanwu decoction induced greater differentiation of rat bone marrow-derived mesenchymal stem cells into neural-like cells in vitro than the original Buyang Huanwu decoction formula. PMID:25806066

  2. Tennessee Valley region study: potential year 2000 radiological dose to population resulting from nuclear facility operations. [Includes glossary

    Energy Technology Data Exchange (ETDEWEB)

    None

    1978-06-01

    A companion report, DOE/ET-0064/1, presents a geographic, cultural, and demographic profile of the Tennessee Valley Region study area. This report describes the calculations of radionuclide release and transport and of the resultant dose to the regional population, assuming a projected installed capacity of 220,000 MW in the year 2000, of which 144,000 MW would be nuclear. All elements of the fuel cycle were assumed to be in operation. The radiological dose was calculated as a one-year dose based on ingestion of 35 different food types as well as for nine non-food pathways, and was reported as dose to the total body and for six specific organs for each of four age groups (infant, child, teen, and adult). Results indicate that the average individual would receive an incremental dose of 7 x 10/sup -4/ millirems in the year 2000 from the operation of nuclear facilities within and adjacent to the region, five orders of magnitude smaller than the dose from naturally occurring radiation in the area. The major contributor to dose was found to be tritium, and the most significant pathways were immersion in air, inhalation of air, transpiration of tritium (absorption through the skin), and exposure radionuclide-containing soil. 60 references.

  3. Estimation of monthly global solar radiation in the eastern Mediterranean region in Turkey by using artificial neural networks

    Science.gov (United States)

    Sahan, Muhittin; Yakut, Emre

    2016-11-01

    In this study, an artificial neural network (ANN) model was used to estimate monthly average global solar radiation on a horizontal surface for selected 5 locations in Mediterranean region for period of 18 years (1993-2010). Meteorological and geographical data were taken from Turkish State Meteorological Service. The ANN architecture designed is a feed-forward back-propagation model with one-hidden layer containing 21 neurons with hyperbolic tangent sigmoid as the transfer function and one output layer utilized a linear transfer function (purelin). The training algorithm used in ANN model was the Levenberg Marquand back propagation algorith (trainlm). Results obtained from ANN model were compared with measured meteorological values by using statistical methods. A correlation coefficient of 97.97 ( 98%) was obtained with root mean square error (RMSE) of 0.852 MJ/m2, mean square error (MSE) of 0.725 MJ/m2, mean absolute bias error (MABE) 10.659MJ/m2, and mean absolute percentage error (MAPE) of 4.8%. Results show good agreement between the estimated and measured values of global solar radiation. We suggest that the developed ANN model can be used to predict solar radiation another location and conditions.

  4. Interevent times estimation of major and continuous earthquakes in Hormozgan region based on radial basis function neural network

    Directory of Open Access Journals (Sweden)

    M.R. Mosavi

    2016-01-01

    Full Text Available This paper presents a new method to estimate the time of important earthquakes in Hormozgan region with magnitude greater than 5.5 based on the Radial Basis Function (RBF Neural Network (NN models. Input vector to the network is composed of different seismicity rates between main events that are calculated in convenient and reliable way to create optimized training methods. It helps network with a limited number of training data to estimation. It is common for earthquakes modeling by data-driven methods in this case. In addition, the proposed method is combined with Rosenberg cluster method to remove aftershocks events from the history of catalog for NN to better process the data. The results show that created RBF model successfully estimates the interevent times between large and sequence earthquakes that can be used as a tool to predict earthquake, so that comparison with other NN structure, for example Multi-Layer Perceptron (MLP NN, reveals the superiority of the proposed method. Because of superiority proposed method has higher accuracy, lower costs and simpler network structure.

  5. Estimation of monthly global solar radiation in the eastern Mediterranean region in Turkey by using artificial neural networks

    Directory of Open Access Journals (Sweden)

    Sahan Muhittin

    2016-01-01

    Full Text Available In this study, an artificial neural network (ANN model was used to estimate monthly average global solar radiation on a horizontal surface for selected 5 locations in Mediterranean region for period of 18 years (1993-2010. Meteorological and geographical data were taken from Turkish State Meteorological Service. The ANN architecture designed is a feed-forward back-propagation model with one-hidden layer containing 21 neurons with hyperbolic tangent sigmoid as the transfer function and one output layer utilized a linear transfer function (purelin. The training algorithm used in ANN model was the Levenberg Marquand back propagation algorith (trainlm. Results obtained from ANN model were compared with measured meteorological values by using statistical methods. A correlation coefficient of 97.97 (~98% was obtained with root mean square error (RMSE of 0.852 MJ/m2, mean square error (MSE of 0.725 MJ/m2, mean absolute bias error (MABE 10.659MJ/m2, and mean absolute percentage error (MAPE of 4.8%. Results show good agreement between the estimated and measured values of global solar radiation. We suggest that the developed ANN model can be used to predict solar radiation another location and conditions.

  6. Construction of hazard maps of Hantavirus contagion using Remote Sensing, logistic regression and Artificial Neural Networks: case Araucan\\'ia Region, Chile

    CERN Document Server

    Alvarez, G; Salinas, R

    2016-01-01

    In this research, methods and computational results based on statistical analysis and mathematical modelling, data collection in situ in order to make a hazard map of Hanta Virus infection in the region of Araucania, Chile are presented. The development of this work involves several elements such as Landsat satellite images, biological information regarding seropositivity of Hanta Virus and information concerning positive cases of infection detected in the region. All this information has been processed to find a function that models the danger of contagion in the region, through logistic regression analysis and Artificial Neural Networks

  7. Regional variations in certain cellular characteristics in human lumbar intervertebral discs, including the presence of alpha-smooth muscle actin.

    Science.gov (United States)

    Hastreiter, D; Ozuna, R M; Spector, M

    2001-07-01

    An evaluation of the regional variation of certain cellular features in the human intervertebral disc (IVD) could lead to a better understanding of site-specific properties relative to degradation, response to injury, and healing processes. The objective of this study was to determine how cell density, cell morphology, cell grouping, and expression of a specific actin isoform varied with location and degeneration in the human disc. A total of 41 human L4-L5 and L5-S1 discs removed postmortem from 21 individuals were analyzed. The discs were graded for degeneration based on the Thompson scale and processed for evaluation. Microtomed sections from paraffin-embedded specimens were stained with hematoxylin and eosin or a monoclonal antibody to alpha-smooth muscle actin (alpha-SMA), an actin isoform often associated with contraction. A significant regional dependence was found for most of the measured parameters. A fourfold increase in cell density was found in proceeding from the nucleus pulposus (NP) to the outer annulus (OA) of the IVD. Approximately 30% of the cells in the NP were present in groups. Virtually all of the cells in the NP and 40% of those in the OA were round. Moreover, notable percentages (12-15%) of the cells in the NP and inner annulus (IA) contained alpha-SMA. Only pair density was found to be correlated with Thompson grade, with more degenerated specimens having higher values. A greater effect was also observed on the percentage of cells in groups. These findings provide the basis for future work to investigate the importance of cells in groups, the role of alpha-SMA in the disc, and the changes in these cellular characteristics in pathological disc conditions.

  8. Neural Networks: Implementations and Applications

    NARCIS (Netherlands)

    Vonk, E.; Veelenturf, L.P.J.; Jain, L.C.

    1996-01-01

    Artificial neural networks, also called neural networks, have been used successfully in many fields including engineering, science and business. This paper presents the implementation of several neural network simulators and their applications in character recognition and other engineering areas

  9. The Neural Crest in Cardiac Congenital Anomalies

    Science.gov (United States)

    Keyte, Anna; Hutson, Mary Redmond

    2012-01-01

    This review discusses the function of neural crest as they relate to cardiovascular defects. The cardiac neural crest cells are a subpopulation of cranial neural crest discovered nearly 30 years ago by ablation of premigratory neural crest. The cardiac neural crest cells are necessary for normal cardiovascular development. We begin with a description of the crest cells in normal development, including their function in remodeling the pharyngeal arch arteries, outflow tract septation, valvulogenesis, and development of the cardiac conduction system. The cells are also responsible for modulating signaling in the caudal pharynx, including the second heart field. Many of the molecular pathways that are known to influence specification, migration, patterning and final targeting of the cardiac neural crest cells are reviewed. The cardiac neural crest cells play a critical role in the pathogenesis of various human cardiocraniofacial syndromes such as DiGeorge, Velocardiofacial, CHARGE, Fetal Alcohol, Alagille, LEOPARD, and Noonan syndromes, as well as Retinoic Acid Embryopathy. The loss of neural crest cells or their dysfunction may not always directly cause abnormal cardiovascular development, but are involved secondarily because crest cells represent a major component in the complex tissue interactions in the head, pharynx and outflow tract. Thus many of the human syndromes linking defects in the heart, face and brain can be better understood when considered within the context of a single cardiocraniofacial developmental module with the neural crest being a key cell type that interconnects the regions. PMID:22595346

  10. Menstrual cycle-dependent neural plasticity in the adult human brain is hormone, task, and region specific.

    NARCIS (Netherlands)

    Fernandez, G.S.E.; Weis, S.; Stoffel-Wagner, B.; Tendolkar, I.; Reuber, M.; Beyenburg, S.; Klaver, P.; Fell, J.; Greiff, A. de; Ruhlmann, J.; Reul, J.; Elger, C.E.

    2003-01-01

    In rodents, cyclically fluctuating levels of gonadal steroid hormones modulate neural plasticity by altering synaptic transmission and synaptogenesis. Alterations of mood and cognition observed during the menstrual cycle suggest that steroid-related plasticity also occurs in humans. Cycle

  11. Mapping the brain's orchestration during speech comprehension: task-specific facilitation of regional synchrony in neural networks

    Directory of Open Access Journals (Sweden)

    Keil Andreas

    2004-10-01

    Full Text Available Abstract Background How does the brain convert sounds and phonemes into comprehensible speech? In the present magnetoencephalographic study we examined the hypothesis that the coherence of electromagnetic oscillatory activity within and across brain areas indicates neurophysiological processes linked to speech comprehension. Results Amplitude-modulated (sinusoidal 41.5 Hz auditory verbal and nonverbal stimuli served to drive steady-state oscillations in neural networks involved in speech comprehension. Stimuli were presented to 12 subjects in the following conditions (a an incomprehensible string of words, (b the same string of words after being introduced as a comprehensible sentence by proper articulation, and (c nonverbal stimulations that included a 600-Hz tone, a scale, and a melody. Coherence, defined as correlated activation of magnetic steady state fields across brain areas and measured as simultaneous activation of current dipoles in source space (Minimum-Norm-Estimates, increased within left- temporal-posterior areas when the sound string was perceived as a comprehensible sentence. Intra-hemispheric coherence was larger within the left than the right hemisphere for the sentence (condition (b relative to all other conditions, and tended to be larger within the right than the left hemisphere for nonverbal stimuli (condition (c, tone and melody relative to the other conditions, leading to a more pronounced hemispheric asymmetry for nonverbal than verbal material. Conclusions We conclude that coherent neuronal network activity may index encoding of verbal information on the sentence level and can be used as a tool to investigate auditory speech comprehension.

  12. Neural-network-based prediction techniques for single station modeling and regional mapping of the foF2 and M(3000F2 ionospheric characteristics

    Directory of Open Access Journals (Sweden)

    T. D. Xenos

    2002-01-01

    Full Text Available In this work, Neural-Network-based single-station hourly daily foF2 and M(3000F2 modelling of 15 European ionospheric stations is investigated. The data used are neural networks and hourly daily values from the period 1964- 1988 for training the neural networks and from the period 1989-1994 for checking the prediction accuracy. Two types of models are presented for the F2-layer critical frequency prediction and two for the propagation factor M(3000F2. The first foF2 model employs the E-layer local noon calculated daily critical frequency (foE12 and the local noon F2- layer critical frequency of the previous day. The second foF2 model, which introduces a new regional mapping technique, employs the Juliusruh neural network model and uses the E-layer local noon calculated daily critical frequency (foE12, and the previous day F2-layer critical frequency measured at Juliusruh at noon. The first M(3000F2 model employs the E-layer local noon calculated daily critical frequency (foE12, its ± 3 h deviations and the local noon cosine of the solar zenith angle (cos c12. The second model, which introduces a new M(3000F2 mapping technique, employs Juliusruh neural network model and uses the E-layer local noon calculated daily critical frequency (foE12, and the previous day F2-layer critical frequency measured at Juliusruh at noon.

  13. Pax7 lineage contributions to the mammalian neural crest.

    Directory of Open Access Journals (Sweden)

    Barbara Murdoch

    Full Text Available Neural crest cells are vertebrate-specific multipotent cells that contribute to a variety of tissues including the peripheral nervous system, melanocytes, and craniofacial bones and cartilage. Abnormal development of the neural crest is associated with several human maladies including cleft/lip palate, aggressive cancers such as melanoma and neuroblastoma, and rare syndromes, like Waardenburg syndrome, a complex disorder involving hearing loss and pigment defects. We previously identified the transcription factor Pax7 as an early marker, and required component for neural crest development in chick embryos. In mammals, Pax7 is also thought to play a role in neural crest development, yet the precise contribution of Pax7 progenitors to the neural crest lineage has not been determined.Here we use Cre/loxP technology in double transgenic mice to fate map the Pax7 lineage in neural crest derivates. We find that Pax7 descendants contribute to multiple tissues including the cranial, cardiac and trunk neural crest, which in the cranial cartilage form a distinct regional pattern. The Pax7 lineage, like the Pax3 lineage, is additionally detected in some non-neural crest tissues, including a subset of the epithelial cells in specific organs.These results demonstrate a previously unappreciated widespread distribution of Pax7 descendants within and beyond the neural crest. They shed light regarding the regionally distinct phenotypes observed in Pax3 and Pax7 mutants, and provide a unique perspective into the potential roles of Pax7 during disease and development.

  14. An Approach for Identifying of Fusarium Infected Maize Grains by Spectral Analysis in the Visible and Near Infrared Region, SIMCA Models, Parametric and Neural Classifiers

    Directory of Open Access Journals (Sweden)

    Tsvetelina Draganova

    2010-08-01

    Full Text Available An approach for identifying of Fusarium infected single maize grains based on diffuse reflectance in visible and near infrared region is proposed in the paper. Spectral characteristics were collected in the range 400-2500 nm in steps of 2 nm. Soft independent modeling of class analogy (SIMCA is used for data processing. Maize grains classification is based on SIMCA classifier and Probabilistic neural network (PNN. Recognition accuracy which is achieved for both classes of grains is respectively 99.89% for healthy, and 93.7% for infected.

  15. Data from a survey of Clostridium perfringens and Clostridium difficile shedding by dogs and cats in the Madrid region (Spain, including phenotypic and genetic characteristics of recovered isolates

    Directory of Open Access Journals (Sweden)

    Sergio Álvarez-Pérez

    2017-10-01

    Full Text Available This article contains information related to a recent survey of the prevalence of fecal shedding of Clostridium perfringens and C. difficile by dogs and cats attended in veterinary clinics located in the Madrid region (Spain. Specifically, we provide detailed information about the clinics that participated in the survey, the demographic and clinic characteristics of recruited animals and the genetic and phenotypic characteristics (including antimicrobial susceptibility data, of recovered bacterial isolates.

  16. Gonorrhoea and gonococcal antimicrobial resistance surveillance networks in the WHO European Region, including the independent countries of the former Soviet Union.

    Science.gov (United States)

    Unemo, Magnus; Ison, Catherine A; Cole, Michelle; Spiteri, Gianfranco; van de Laar, Marita; Khotenashvili, Lali

    2013-12-01

    Antimicrobial resistance (AMR) in Neisseria gonorrhoeae has emerged for essentially all antimicrobials following their introduction into clinical practice. During the latest decade, susceptibility to the last remaining options for antimicrobial monotherapy, the extended-spectrum cephalosporins (ESC), has markedly decreased internationally and treatment failures with these ESCs have been verified. In response to this developing situation, WHO and the European Centre for Disease Prevention and Control (ECDC) have published global and region-specific response plans, respectively. One main component of these action/response plans is to enhance the surveillance of AMR and treatment failures. This paper describes the perspectives from the diverse WHO European Region (53 countries), including the independent countries of the former Soviet Union, regarding gonococcal AMR surveillance networks. The WHO European Region has a high prevalence of resistance to all previously recommended antimicrobials, and most of the first strictly verified treatment failures with cefixime and ceftriaxone were also reported from Europe. In the European Union/European Economic Area (EU/EEA), the European gonococcal antimicrobial surveillance programme (Euro-GASP) funded by the ECDC is running. In 2011, the Euro-GASP included 21/31 (68%) EU/EEA countries, and the programme is further strengthened annually. However, in the non-EU/EEA countries, internationally reported and quality assured gonococcal AMR data are lacking in 87% of the countries and, worryingly, appropriate support for establishment of a GASP is still lacking. Accordingly, national and international support, including political and financial commitment, for gonococcal AMR surveillance in the non-EU/EEA countries of the WHO European Region is essential.

  17. Neural bases of accented speech perception

    Directory of Open Access Journals (Sweden)

    Patti eAdank

    2015-10-01

    Full Text Available The recognition of unfamiliar regional and foreign accents represents a challenging task for the speech perception system (Adank, Evans, Stuart-Smith, & Scott, 2009; Floccia, Goslin, Girard, & Konopczynski, 2006. Despite the frequency with which we encounter such accents, the neural mechanisms supporting successful perception of accented speech are poorly understood. Nonetheless, candidate neural substrates involved in processing speech in challenging listening conditions, including accented speech, are beginning to be identified. This review will outline neural bases associated with perception of accented speech in the light of current models of speech perception, and compare these data to brain areas associated with processing other speech distortions. We will subsequently evaluate competing models of speech processing with regards to neural processing of accented speech. See Cristia et al. (2012 for an in-depth overview of behavioural aspects of accent processing.

  18. Neural bases of accented speech perception.

    Science.gov (United States)

    Adank, Patti; Nuttall, Helen E; Banks, Briony; Kennedy-Higgins, Daniel

    2015-01-01

    The recognition of unfamiliar regional and foreign accents represents a challenging task for the speech perception system (Floccia et al., 2006; Adank et al., 2009). Despite the frequency with which we encounter such accents, the neural mechanisms supporting successful perception of accented speech are poorly understood. Nonetheless, candidate neural substrates involved in processing speech in challenging listening conditions, including accented speech, are beginning to be identified. This review will outline neural bases associated with perception of accented speech in the light of current models of speech perception, and compare these data to brain areas associated with processing other speech distortions. We will subsequently evaluate competing models of speech processing with regards to neural processing of accented speech. See Cristia et al. (2012) for an in-depth overview of behavioral aspects of accent processing.

  19. Inversion of Schlumberger resistivity sounding data from the critically dynamic Koyna region using the Hybrid Monte Carlo-based neural network approach

    Directory of Open Access Journals (Sweden)

    S. Maiti

    2011-03-01

    Full Text Available Koyna region is well-known for its triggered seismic activities since the hazardous earthquake of M=6.3 occurred around the Koyna reservoir on 10 December 1967. Understanding the shallow distribution of resistivity pattern in such a seismically critical area is vital for mapping faults, fractures and lineaments. However, deducing true resistivity distribution from the apparent resistivity data lacks precise information due to intrinsic non-linearity in the data structures. Here we present a new technique based on the Bayesian neural network (BNN theory using the concept of Hybrid Monte Carlo (HMC/Markov Chain Monte Carlo (MCMC simulation scheme. The new method is applied to invert one and two-dimensional Direct Current (DC vertical electrical sounding (VES data acquired around the Koyna region in India. Prior to apply the method on actual resistivity data, the new method was tested for simulating synthetic signal. In this approach the objective/cost function is optimized following the Hybrid Monte Carlo (HMC/Markov Chain Monte Carlo (MCMC sampling based algorithm and each trajectory was updated by approximating the Hamiltonian differential equations through a leapfrog discretization scheme. The stability of the new inversion technique was tested in presence of correlated red noise and uncertainty of the result was estimated using the BNN code. The estimated true resistivity distribution was compared with the results of singular value decomposition (SVD-based conventional resistivity inversion results. Comparative results based on the HMC-based Bayesian Neural Network are in good agreement with the existing model results, however in some cases, it also provides more detail and precise results, which appears to be justified with local geological and structural details. The new BNN approach based on HMC is faster and proved to be a promising inversion scheme to interpret complex and non-linear resistivity problems. The HMC-based BNN results

  20. Non-Linearity Explanation in Artificial Neural Network Application with a Case Study of Fog Forecast Over Delhi Region

    Science.gov (United States)

    Saurabh, K.; Dimri, A. P.

    2016-05-01

    Fog affects human life in a number of ways by reducing the visibility, hence affecting critical infrastructure, transportation, tourism or by the formation of frost, thus harming the standing crops. Smog is becoming a regular phenomenon in urban areas which is highly toxic to humans. Delhi was chosen as the area of study as it encounters all these hazards of fog stated apart from other political and economic reasons. The complex relationship behind the parameters and processes behind the formation of fog makes it extremely difficult to model and forecast it accurately. It is attempted to forecast the fog and understand its dynamics through a statistical downscaling technique of artificial neural network which is deemed accurate for short-term forecasting and usually outperform time-series models. The backpropagation neural network, which is a gradient descent algorithm where the network weights are moved along the negative of the gradient of the performance function, has been used for our analysis. Indian Meteorological Department (IMD) supported National Oceanic and Atmospheric Administration (NOAA) data had been used for carrying out the simulations. The model was found to have high accuracy but lacking in skill. An attempt has been made to present the data in a binary form by determining a threshold by the contingency table approach followed by its critical analysis. It is found that the calculation of an optimum threshold was also difficult to fix as the parameters of fog formation on which the model has been has been trained had shown some changes in their trend over a period of time.

  1. Monitoring Regional Forest Disturbances across the US with Near Real Time MODIS NDVI Products included in the ForWarn Forest Threat Early Warning System

    Science.gov (United States)

    Spruce, J.; Hargrove, W. W.; Gasser, J.; Norman, S. P.

    2013-12-01

    Forest threats across the US have become increasingly evident in recent years. These include regionally extensive disturbances (e.g., from drought, bark beetle outbreaks, and wildfires) that can occur across multiyear durations and result in extensive forest mortality. In addition, forests can be subject to ephemeral, sometimes yearly defoliation from various insects and types of storm damage. After prolonged severe disturbance, signs of forest recovery can vary in terms of satellite-based Normalized Difference Vegetation Index (NDVI) values. The increased extent and threat of forest disturbances in part led to the enactment of the 2003 Healthy Forest Restoration Act, which mandated that a national forest threat Early Warning System (EWS) be deployed. In response, the US Forest Service collaborated with NASA, DOE Oak Ridge National Laboratory, and the USGS Eros Data Center to build the near real time ForWarn forest threat EWS for monitoring regionally evident forest disturbances, starting on-line operations in 2010. Given the diversity of disturbance types, severities, and durations, ForWarn employs multiple historical baselines used with current NDVI to derive a suite of six nationwide 'weekly' forest change products. ForWarn uses daily 232 meter MODIS Aqua and Terra satellite NDVI data, including MOD13 products for deriving historical baseline NDVIs and eMODIS products for compiling current NDVI. Separately pre-processing the current and historical NDVIs, the Time Series Product Tool and the Phenological Parameters Estimation Tool are used to temporally reduce noise, fuse, and aggregate MODIS NDVIs into 24 day composites refreshed every 8 days with 46 dates of forest change products per year. The 24 day compositing interval typically enables new disturbances to be detected, while minimizing the frequency of residual atmospheric contamination. ForWarn's three standard forest change products compare current NDVI to that from the previous year, previous 3 years, and

  2. Detection and Localization of Robotic Tools in Robot-Assisted Surgery Videos Using Deep Neural Networks for Region Proposal and Detection.

    Science.gov (United States)

    Sarikaya, Duygu; Corso, Jason J; Guru, Khurshid A

    2017-07-01

    Video understanding of robot-assisted surgery (RAS) videos is an active research area. Modeling the gestures and skill level of surgeons presents an interesting problem. The insights drawn may be applied in effective skill acquisition, objective skill assessment, real-time feedback, and human-robot collaborative surgeries. We propose a solution to the tool detection and localization open problem in RAS video understanding, using a strictly computer vision approach and the recent advances of deep learning. We propose an architecture using multimodal convolutional neural networks for fast detection and localization of tools in RAS videos. To the best of our knowledge, this approach will be the first to incorporate deep neural networks for tool detection and localization in RAS videos. Our architecture applies a region proposal network (RPN) and a multimodal two stream convolutional network for object detection to jointly predict objectness and localization on a fusion of image and temporal motion cues. Our results with an average precision of 91% and a mean computation time of 0.1 s per test frame detection indicate that our study is superior to conventionally used methods for medical imaging while also emphasizing the benefits of using RPN for precision and efficiency. We also introduce a new data set, ATLAS Dione, for RAS video understanding. Our data set provides video data of ten surgeons from Roswell Park Cancer Institute, Buffalo, NY, USA, performing six different surgical tasks on the daVinci Surgical System (dVSS) with annotations of robotic tools per frame.

  3. Comparison Study on the Estimation of the Spatial Distribution of Regional Soil Metal(loid)s Pollution Based on Kriging Interpolation and BP Neural Network.

    Science.gov (United States)

    Jia, Zhenyi; Zhou, Shenglu; Su, Quanlong; Yi, Haomin; Wang, Junxiao

    2017-12-26

    Soil pollution by metal(loid)s resulting from rapid economic development is a major concern. Accurately estimating the spatial distribution of soil metal(loid) pollution has great significance in preventing and controlling soil pollution. In this study, 126 topsoil samples were collected in Kunshan City and the geo-accumulation index was selected as a pollution index. We used Kriging interpolation and BP neural network methods to estimate the spatial distribution of arsenic (As) and cadmium (Cd) pollution in the study area. Additionally, we introduced a cross-validation method to measure the errors of the estimation results by the two interpolation methods and discussed the accuracy of the information contained in the estimation results. The conclusions are as follows: data distribution characteristics, spatial variability, and mean square errors (MSE) of the different methods showed large differences. Estimation results from BP neural network models have a higher accuracy, the MSE of As and Cd are 0.0661 and 0.1743, respectively. However, the interpolation results show significant skewed distribution, and spatial autocorrelation is strong. Using Kriging interpolation, the MSE of As and Cd are 0.0804 and 0.2983, respectively. The estimation results have poorer accuracy. Combining the two methods can improve the accuracy of the Kriging interpolation and more comprehensively represent the spatial distribution characteristics of metal(loid)s in regional soil. The study may provide a scientific basis and technical support for the regulation of soil metal(loid) pollution.

  4. Comparison Study on the Estimation of the Spatial Distribution of Regional Soil Metal(loids Pollution Based on Kriging Interpolation and BP Neural Network

    Directory of Open Access Journals (Sweden)

    Zhenyi Jia

    2017-12-01

    Full Text Available Soil pollution by metal(loids resulting from rapid economic development is a major concern. Accurately estimating the spatial distribution of soil metal(loid pollution has great significance in preventing and controlling soil pollution. In this study, 126 topsoil samples were collected in Kunshan City and the geo-accumulation index was selected as a pollution index. We used Kriging interpolation and BP neural network methods to estimate the spatial distribution of arsenic (As and cadmium (Cd pollution in the study area. Additionally, we introduced a cross-validation method to measure the errors of the estimation results by the two interpolation methods and discussed the accuracy of the information contained in the estimation results. The conclusions are as follows: data distribution characteristics, spatial variability, and mean square errors (MSE of the different methods showed large differences. Estimation results from BP neural network models have a higher accuracy, the MSE of As and Cd are 0.0661 and 0.1743, respectively. However, the interpolation results show significant skewed distribution, and spatial autocorrelation is strong. Using Kriging interpolation, the MSE of As and Cd are 0.0804 and 0.2983, respectively. The estimation results have poorer accuracy. Combining the two methods can improve the accuracy of the Kriging interpolation and more comprehensively represent the spatial distribution characteristics of metal(loids in regional soil. The study may provide a scientific basis and technical support for the regulation of soil metal(loid pollution.

  5. Transcriptomic analysis across nasal, temporal, and macular regions of human neural retina and RPE/choroid by RNA-Seq.

    Science.gov (United States)

    Whitmore, S Scott; Wagner, Alex H; DeLuca, Adam P; Drack, Arlene V; Stone, Edwin M; Tucker, Budd A; Zeng, Shemin; Braun, Terry A; Mullins, Robert F; Scheetz, Todd E

    2014-12-01

    Proper spatial differentiation of retinal cell types is necessary for normal human vision. Many retinal diseases, such as Best disease and male germ cell associated kinase (MAK)-associated retinitis pigmentosa, preferentially affect distinct topographic regions of the retina. While much is known about the distribution of cell types in the retina, the distribution of molecular components across the posterior pole of the eye has not been well-studied. To investigate regional difference in molecular composition of ocular tissues, we assessed differential gene expression across the temporal, macular, and nasal retina and retinal pigment epithelium (RPE)/choroid of human eyes using RNA-Seq. RNA from temporal, macular, and nasal retina and RPE/choroid from four human donor eyes was extracted, poly-A selected, fragmented, and sequenced as 100 bp read pairs. Digital read files were mapped to the human genome and analyzed for differential expression using the Tuxedo software suite. Retina and RPE/choroid samples were clearly distinguishable at the transcriptome level. Numerous transcription factors were differentially expressed between regions of the retina and RPE/choroid. Photoreceptor-specific genes were enriched in the peripheral samples, while ganglion cell and amacrine cell genes were enriched in the macula. Within the RPE/choroid, RPE-specific genes were upregulated at the periphery while endothelium associated genes were upregulated in the macula. Consistent with previous studies, BEST1 expression was lower in macular than extramacular regions. The MAK gene was expressed at lower levels in macula than in extramacular regions, but did not exhibit a significant difference between nasal and temporal retina. The regional molecular distinction is greatest between macula and periphery and decreases between different peripheral regions within a tissue. Datasets such as these can be used to prioritize candidate genes for possible involvement in retinal diseases with

  6. Bifurcation Scenarios of Neural Firing Patterns across Two Separated Chaotic Regions as Indicated by Theoretical and Biological Experimental Models

    Directory of Open Access Journals (Sweden)

    Huaguang Gu

    2013-01-01

    Full Text Available Nonlinear dynamics can be used to identify relationships between different firing patterns, which play important roles in the information processing. The present study provides novel biological experimental findings regarding complex bifurcation scenarios from period-1 bursting to period-1 spiking with chaotic firing patterns. These bifurcations were found to be similar to those simulated using the Hindmarsh-Rose model across two separated chaotic regions. One chaotic region lay between period-1 and period-2 burstings. This region has not attracted much attention. The other region is a well-known comb-shaped chaotic region, and it appears after period-2 bursting. After period-2 bursting, the chaotic firings lay in a period-adding bifurcation scenario or in a period-doubling bifurcation cascade. The deterministic dynamics of the chaotic firing patterns were identified using a nonlinear prediction method. These results provided details regarding the processes and dynamics of bifurcation containing the chaotic bursting between period-1 and period-2 burstings and other chaotic firing patterns within the comb-shaped chaotic region. They also provided details regarding the relationships between different firing patterns in parameter space.

  7. Genomic scan for quantitative trait loci of chemical and physical body composition and deposition on pig chromosome X including the pseudoautosomal region of males

    Directory of Open Access Journals (Sweden)

    Kalm Ernst

    2009-03-01

    Full Text Available Abstract A QTL analysis of pig chromosome X (SSCX was carried out using an approach that accurately takes into account the specific features of sex chromosomes i.e. their heterogeneity, the presence of a pseudoautosomal region and the dosage compensation phenomenon. A three-generation full-sib population of 386 animals was created by crossing Pietrain sires with a crossbred dam line. Phenotypic data on 72 traits were recorded for at least 292 and up to 315 F2 animals including chemical body composition measured on live animals at five target weights ranging from 30 to 140 kg, daily gain and feed intake measured throughout growth, and carcass characteristics obtained at slaughter weight (140 kg. Several significant and suggestive QTL were detected on pig chromosome X: (1 in the pseudoautosomal region of SSCX, a QTL for entire loin weight, which showed paternal imprinting, (2 closely linked to marker SW2456, a suggestive QTL for feed intake at which Pietrain alleles were found to be associated with higher feed intake, which is unexpected for a breed known for its low feed intake capacity, (3 at the telomeric end of the q arm of SSCX, QTL for jowl weight and lipid accretion and (4 suggestive QTL for chemical body composition at 30 kg. These results indicate that SSCX is important for physical and chemical body composition and accretion as well as feed intake regulation.

  8. Daily global solar radiation modelling using multi-layer perceptron neural networks in semi-arid region

    Directory of Open Access Journals (Sweden)

    Mawloud GUERMOUI

    2016-07-01

    Full Text Available Accurate estimation of Daily Global Solar Radiation (DGSR has been a major goal for solar energy application. However, solar radiation measurements are not a simple task for several reasons. In the cases where data are not available, it is very common the use of computational models to estimate the missing data, which are based mainly of the search for relationships between weather variables, such as temperature, humidity, sunshine duration, etc. In this respect, the present study focuses on the development of artificial neural network (ANN model for estimation of daily global solar radiation on horizontal surface in Ghardaia city (South Algeria. In this analysis back-propagation algorithm is applied. Daily mean air temperature, relative humidity and sunshine duration was used as climatic inputs parameters, while the daily global solar radiation (DGSR was the only output of the ANN. We have evaluated Multi-Layer Perceptron (MLP models to estimate DGSR using three year of measurement (2005-2008. It was found that MLP-model based on sunshine duration and mean air temperature give accurate results in term of Mean Absolute Bias Error, Root Mean Square Error, Relative Square Error and Correlation Coefficient. The obtained values of these indicators are 0.67 MJ/m², 1.28 MJ/m², 6.12%and 98.18%, respectively which shows that MLP is highly qualified for DGSR estimation in semi-arid climates.

  9. Neural networks combined with region growing techniques for tumor detection in [18F]-fluorothymidine dynamic positron emission tomography breast cancer studies

    Science.gov (United States)

    Cseh, Zoltan; Kenny, Laura; Swingland, James; Bose, Subrata; Turheimer, Federico E.

    2013-03-01

    Early detection and precise localization of malignant tumors has been a primary challenge in medical imaging in recent years. Functional modalities play a continuously increasing role in these efforts. Image segmentation algorithms which enable automatic, accurate tumor visualization and quantification on noisy positron emission tomography (PET) images would significantly improve the quality of treatment planning processes and in turn, the success of treatments. In this work a novel multistep method has been applied in order to identify tumor regions in 4D dynamic [18F] fluorothymidine (FLT) PET studies of patients with locally advanced breast cancer. In order to eliminate the effect of inherently detectable high inhomogeneity inside tumors, specific voxel-kinetic classes were initially introduced by finding characteristic FLT-uptake curves with K-means algorithm on a set of voxels collected from each tumor. Image voxel sets were then split based on voxel time-activity curve (TAC) similarities, and models were generated separately on each voxel set. At first, artificial neural networks, in comparison with linear classification algorithms were applied to distinguish tumor and healthy regions relying on the characteristics of TACs of the individual voxels. The outputs of the best model with very high specificity were then used as input seeds for region shrinking and growing techniques, the application of which considerably enhanced the sensitivity and specificity (78.65% +/- 0.65% and 98.98% +/- 0.03%, respectively) of the final image segmentation model.

  10. Remote sensing of harmful algal events in optically complex waters using regionally specific neural network-based algorithms for MERIS data

    Science.gov (United States)

    Gonzalez Vilas, L.; Castro Fernandez, M.; Spyrakos, E.; Torres Palenzuela, J.

    2013-08-01

    In typical case 2 waters an accurate remote sensing retrieval of chlorophyll a (chla) is still challenging. There is a widespread understanding that universally applicable water constituent retrieval algorithms are currently not feasible, shifting the research focus to regionally specific implementations of powerful inversion methods. This study takes advantage of regionally specific chlorophyll a (chla) algorithms, which were developed by the authors of this abstract in previous works, and the characteristics of Medium Resolution Imaging Spectrometer (MERIS) in order to study harmful algal events in the optically complex waters of the Galician Rias (NW). Harmful algal events are a frequent phenomenon in this area with direct and indirect impacts to the mussel production that constitute a very important economic activity for the local community. More than 240 106 kg of mussel per year are produced in these highly primary productive upwelling systems. A MERIS archive from nine years (2003-2012) was analysed using regionally specific chla algorithms. The latter were developed based on Multilayer perceptron (MLP) artificial neural networks and fuzzy c-mean clustering techniques (FCM). FCM specifies zones (based on water leaving reflectances) where the retrieval algorithms normally provide more reliable results. Monthly chla anomalies and other statistics were calculated for the nine years MERIS archive. These results were then related to upwelling indices and other associated measurements to determine the driver forces for specific phytoplankton blooms. The distribution and changes of chla are also discussed.

  11. Keypoint Density-Based Region Proposal for Fine-Grained Object Detection and Classification Using Regions with Convolutional Neural Network Features

    Science.gov (United States)

    2015-12-15

    2014), with Regions of Interest (ROIs) and the final convolutional filters being given as input to the ROI pooling layer . The 13 convolutional layers ...gave rise to specialized part models that operate by transfer of likely locations (Goering et al., 2014), which achieved high classification and...pipeline. The existing evaluations and applications of R-CNN have focused on standard coarse (rather than fine-grained) detection and classification tasks

  12. In a non-human primate model, aging disrupts the neural control of intestinal smooth muscle contractility in a region-specific manner.

    Science.gov (United States)

    Tran, L; Greenwood-Van Meerveld, B

    2014-03-01

    Incidences of gastrointestinal (GI) motility disorders increase with age. However, there is a paucity of knowledge about the aging mechanisms leading to GI dysmotility. Motility in the GI tract is a function of smooth muscle contractility, which is modulated in part by the enteric nervous system (ENS). Evidence suggests that aging impairs the ENS, thus we tested the hypothesis that senescence in the GI tract precipitates abnormalities in smooth muscle and neurally mediated contractility in a region-specific manner. Jejunal and colonic circular muscle strips were isolated from young (4-10 years) and old (18+ years) baboons. Myogenic responses were investigated using potassium chloride (KCl) and carbachol (CCh). Neurally mediated contractile responses were evoked by electrical field stimulation (EFS) and were recorded in the absence and presence of atropine (1 μM) or NG-Nitro-l-arginine methyl ester (l-NAME; 100 μM). The myogenic responses to KCl in the jejunum and colon were unaffected by age. In the colon, but not the jejunum, CCh-induced contractile responses were reduced in aged animals. Compared to young baboons, there was enhanced EFS-induced contractility of old baboon jejunal smooth muscle in contrast to the reduced contractility in the colon. The effect of atropine on the EFS response was lower in aged colonic tissue, suggesting reduced participation of acetylcholine. In aged jejunal tissue, higher contractile responses to EFS were found to be due to reduced nitregic inhibition. These findings provide key evidence for the importance of intestinal smooth muscle and ENS senescence in age-associated GI motility disorders. © 2014 The Authors. Neurogastroenterology & Motility published by John Wiley & Sons Ltd.

  13. Neural substrates of decision-making.

    Science.gov (United States)

    Broche-Pérez, Y; Herrera Jiménez, L F; Omar-Martínez, E

    2016-06-01

    Decision-making is the process of selecting a course of action from among 2 or more alternatives by considering the potential outcomes of selecting each option and estimating its consequences in the short, medium and long term. The prefrontal cortex (PFC) has traditionally been considered the key neural structure in decision-making process. However, new studies support the hypothesis that describes a complex neural network including both cortical and subcortical structures. The aim of this review is to summarise evidence on the anatomical structures underlying the decision-making process, considering new findings that support the existence of a complex neural network that gives rise to this complex neuropsychological process. Current evidence shows that the cortical structures involved in decision-making include the orbitofrontal cortex (OFC), anterior cingulate cortex (ACC), and dorsolateral prefrontal cortex (DLPFC). This process is assisted by subcortical structures including the amygdala, thalamus, and cerebellum. Findings to date show that both cortical and subcortical brain regions contribute to the decision-making process. The neural basis of decision-making is a complex neural network of cortico-cortical and cortico-subcortical connections which includes subareas of the PFC, limbic structures, and the cerebellum. Copyright © 2014 Sociedad Española de Neurología. Published by Elsevier España, S.L.U. All rights reserved.

  14. Electrophysiological heterogeneity of pacemaker cells in rabbit intercaval region, including SA node: insights from recording multiple ion currents in each cell.

    Science.gov (United States)

    Monfredi, Oliver; Tsutsui, Kenta; Ziman, Bruce D; Stern, Michael D; Lakatta, Edward G; Maltsev, Victor A

    2017-09-15

    Cardiac pacemaker cells, including cells of the sinoatrial node, are heterogeneous in size, morphology and electrophysiological characteristics. The exact extent to which these cells differ electrophysiologically is unclear, yet is critical to understanding their functioning. We examined major ionic currents in individual intercaval pacemaker cells (IPC) sampled from the para-cristal, inter-caval region (including the sinoatrial node) that were spontaneously beating after enzymatic isolation from rabbit hearts. Beating rate was measured at baseline and following inhibition of the Ca2+ pump with cyclopiazonic acid. Thereafter, in each cell, we consecutively measured density of If, IK (a surrogate of repolarization capacity) and ICa,L using whole-cell patch-clamp. The ionic current densities varied to a greater extent than previously appreciated, with some IPCs demonstrating very small or zero If Density of none of the currents was correlated with cell size, while ICa,L and If densities were related to baseline beating rates. If density was correlated with IK density, but not with that of ICa,L Inhibition of Ca2+ cycling had a greater beating rate slowing effect in IPCs with lower If densities. Our numerical model simulation indicated that 1) IPC with small (or zero) If or small ICa,L can operate via a major contribution of Ca2+-clock, 2) If-Ca2+-clock interplay could be important for robust pacemaking function, 3) coupled If-IK function could regulate maximum diastolic potential. Thus, we have demonstrated marked electrophysiological heterogeneity of intercaval pacemaker cells. This heterogeneity is manifested in basal beating rate and response to interference of Ca2+ cycling, which is linked to If. Copyright © 2016, American Journal of Physiology-Heart and Circulatory Physiology.

  15. Radioactive fallout and neural tube defects

    African Journals Online (AJOL)

    Nejat Akar

    2015-07-10

    Jul 10, 2015 ... Neural tube defects;. Anencephaly;. Spina bifida. Abstract Possible link between radioactivity and the occurrence of neural tube defects is a long lasting debate ... Neural tube defects, are one of the common congenital mal- formations ... ent cities of Turkey (˙Izmir/Aegean Region; Trabzon/Black Sea region ...

  16. Monitoring Regional Forest Disturbances across the US with Near Real Time MODIS NDVI Products included in the ForWarn Forest Threat Early Warning System

    Science.gov (United States)

    Spruce, Joseph; Hargrove, William W.; Gasser, Gerald; Norman, Steve

    2013-01-01

    U.S. forests occupy approx.1/3 of total land area (approx. 304 million ha). Since 2000, a growing number of regionally evident forest disturbances have occurred due to abiotic and biotic agents. Regional forest disturbances can threaten human life and property, bio-diversity and water supplies. Timely regional forest disturbance monitoring products are needed to aid forest health management work. Near Real Time (NRT) twice daily MODIS NDVI data provide a means to monitor U.S. regional forest disturbances every 8 days. Since 2010, these NRT forest change products have been produced and posted on the US Forest Service ForWarn Early Warning System for Forest Threats.

  17. Review of haploporid (Trematoda) genera with ornate muscularisation in the region of the oral sucker, including four new species and a new genus.

    Science.gov (United States)

    Pulis, Eric E; Overstreet, Robin M

    2013-02-01

    Species of the Haploporidae Nicoll, 1914 with elaborate muscularisation of the oral sucker belong in three trematode genera, including three new species and a new genus from the intestine of fishes in Australian waters. Spiritestis Nagaty, 1948 is resurrected and S. herveyensis n. sp. is described from the mullet Moolgarda seheli (Forsskål) collected in Hervey Bay, Queensland, Australia; the latter differs from S. arabii Nagaty, 1948 in that the position of the genital pore is pharyngeal rather than post-pharyngeal and the geographical range is off Australia rather than the Red Sea. A new genus is proposed for two new species, with a uniquely ornamented oral sucker, which infect Australian scatophagids. Members of Capitimitta n. g. are distinguished from Waretrema Srivastava, 1937, species of which have a simple oral sucker with six radially arranged anterior muscular lobes, in that their oral sucker is V-shaped with six embedded muscular finger-like structures in the anteroventral portion. The relatively small C. darwinensis n. sp., collected from Selenotoca multifasciata (Richardson) at Darwin, Northern Territory, Australia, is distinguished from C. costata n. sp., collected from Scatophagus argus (Linnaeus) in the same locality and S. multifasciata off Brisbane, Australia, and by having smaller eggs, a vitellarium commencing at a level close to the ventral sucker rather than at greater than one ovarian length posterior to the ventral sucker, and shorter tegumental body spines. Sequence data of a c.2,500 bp region of the 3' end of 18S, the entire ITS region and the 5' end of the 28S revealed that Spiritestis and Capitimitta are not as closely related as some morphological features would suggest and are probably not the closest relative of each other. What has been reported as Waretrema piscicolum Srivastava, 1937 probably consists of several species, some in different genera, and one, based on material collected by Dr Masaaki Machida, is proposed as Spiritestis

  18. Lhx1 in the proximal region of the optic vesicle permits neural retina development in the chicken

    Directory of Open Access Journals (Sweden)

    Takumi Kawaue

    2012-08-01

    How the eye forms has been one of the fundamental issues in developmental biology. The retinal anlage first appears as the optic vesicle (OV evaginating from the forebrain. Subsequently, its distal portion invaginates to form the two-walled optic cup, which develops into the outer pigmented and inner neurosensory layers of the retina. Recent work has shown that this optic-cup morphogenesis proceeds as a self-organizing activity without any extrinsic molecules. However, intrinsic factors that regulate this process have not been elucidated. Here we show that a LIM-homeobox gene, Lhx1, normally expressed in the proximal region of the nascent OV, induces a second neurosensory retina formation from the outer pigmented retina when overexpressed in the chicken OV. Lhx2, another LIM-homeobox gene supposed to be involved in early OV formation, could not substitute this function of Lhx1, while Lhx5, closely related to Lhx1, could replace it. Conversely, knockdown of Lhx1 expression by RNA interference resulted in the formation of a small or pigmented vesicle. These results suggest that the proximal region demarcated by Lhx1 expression permits OV development, eventually dividing the two retinal domains.

  19. Restricted spontaneous in vitro differentiation and region-specific migration of long-term expanded fetal human neural precursor cells after transplantation into the adult rat brain.

    Science.gov (United States)

    Maciaczyk, Jaroslaw; Singec, Ilyas; Maciaczyk, Donata; Klein, Alexander; Nikkhah, Guido

    2009-09-01

    Human fetal neural stem/progenitor cells (hNSCs) are investigated for their potential as a cell source for cell-based therapies in neurodegenerative diseases. However, the limited availability of fetal tissue and insufficient understanding of the lineage-dependent pattern of survival, migration, and differentiation following engraftment are still unresolved issues. In the current study hNSCs derived from different brain regions were long-term expanded in vitro to yield proliferating neurospheres giving rise to neurons, astro-, and oligodendroglial cells and assessed for their potential for migration, differentiation, and anatomical integration following intracerebral grafting into rats. hNSCs isolated from neocortex, striatum, midbrain, and spinal cord (SC) proliferated following in vitro differentiation, and showed a significant decrease of newly formed neurons along the rostrocaudal axis of the developing central nervous system (CNS). Most of the mature neurons were positive for the neurotransmitter GABA. In vivo all cell types survived up to 9 weeks posttransplantation. Intrastriatally grafted hNSCs migrated extensively along white matter tracts reaching both rostral (forceps minor) and caudal (midbrain, cerebral peduncle) brain regions. The majority of migratory cells expressed the stem cell marker, nestin. A fraction of grafted cells acquired a neuronal phenotype expressing doublecortin, beta-III-tubulin, or GABA. These data demonstrate efficient in vitro propagation, region-specific long-term survival, long-distance migration, and neuronal differentiation of hNSCs after transplantation into the adult rat brain. The availability of a large pool of in vitro expanded nestin-positive cells offers the possibility for further ex vivo manipulations and the recruitment of different neuronal phenotypes for cell replacement strategies for CNS disorders.

  20. [Effects of therapeutic complexes including balneoradonokinesitherapy, electromyostimulation and low-frequency magnetotherapy on regional blood flow in patients with postrraumatic gonarthritis].

    Science.gov (United States)

    Raspopova, E A; Udartsev, E Iu

    2006-01-01

    Balneoradonokinesitherapy alone and its combination with electrostimulation and low-frequency magnetotherapy were used for the treatment of regional blood flow disorders in 76 patients with posttraumatic gonarthritis. Balneoradonokinesitherapy in combination with electromyostimulation improved blood circulation. When low-frequency magnetotherapy was added to the latter complex, the regress of regional blood flow disorders of a damaged extremity was most significant.

  1. Conducting Polymers for Neural Prosthetic and Neural Interface Applications

    Science.gov (United States)

    2015-01-01

    Neural interfacing devices are an artificial mechanism for restoring or supplementing the function of the nervous system lost as a result of injury or disease. Conducting polymers (CPs) are gaining significant attention due to their capacity to meet the performance criteria of a number of neuronal therapies including recording and stimulating neural activity, the regeneration of neural tissue and the delivery of bioactive molecules for mediating device-tissue interactions. CPs form a flexible platform technology that enables the development of tailored materials for a range of neuronal diagnostic and treatment therapies. In this review the application of CPs for neural prostheses and other neural interfacing devices are discussed, with a specific focus on neural recording, neural stimulation, neural regeneration, and therapeutic drug delivery. PMID:26414302

  2. The centrodistal joint interosseous ligament region in the tarsus of the horse: Normal appearance, abnormalities and possible association with other tarsal lesions, including osteoarthritis.

    Science.gov (United States)

    Skelly-Smith, E; Ireland, J; Dyson, S

    2016-07-01

    There have been no detailed descriptions of the radiological appearance of the centrodistal joint interosseous ligament region in horses with and without distal tarsal joint pain. To describe the normal radiological appearance of the centrodistal joint interosseous ligament region; to determine the prevalence of mineralisation or ossification of the interosseous ligament; and to describe radiological abnormalities surrounding the interosseous space and concurrent radiological abnormalities in the tarsus. The association between interosseous ligament region abnormalities and radiological evidence of osteoarthritis of the centrodistal joint was assessed. Retrospective study. Case records and radiographs of all horses/ponies (n = 700) that underwent radiographic examination of one/both tarsi over 7 years were reviewed. Case history, height, bodyweight and cause(s) of lameness were recorded. Factors associated with abnormalities of the centrodistal interosseous ligament region were assessed using logistic regression analysis. The normal interosseous space was an oval or circular-shaped radiolucent area bordered proximally and distally by a rim of bone of uniform opacity and thickness, which varied in thickness among animals. Abnormalities of the interosseous ligament region of the lame(r) limb were evident in 121/700 (17.3%; 95% confidence interval 14.5-20.1%) animals. Increasing bodyweight was associated with decreased odds of interosseous ligament region abnormalities. Forty-seven animals (6.7%; 95% confidence interval 4.9-8.6%) had radiological evidence of osteoarthritis of the centrodistal joint. A greater proportion of animals with interosseous ligament region abnormalities (36.4%) had radiological evidence of osteoarthritis of the centrodistal joint, compared to those with normal interosseous ligament regions (0.5%; Pregion abnormalities and osteoarthritis of the centrodistal joint were not necessarily associated with distal tarsal joint pain. There is an

  3. Global, regional, and national consumption levels of dietary fats and oils in 1990 and 2010: a systematic analysis including 266 country-specific nutrition surveys.

    Science.gov (United States)

    Micha, Renata; Khatibzadeh, Shahab; Shi, Peilin; Fahimi, Saman; Lim, Stephen; Andrews, Kathryn G; Engell, Rebecca E; Powles, John; Ezzati, Majid; Mozaffarian, Dariush

    2014-04-15

    To quantify global consumption of key dietary fats and oils by country, age, and sex in 1990 and 2010. Data were identified, obtained, and assessed among adults in 16 age- and sex-specific groups from dietary surveys worldwide on saturated, omega 6, seafood omega 3, plant omega 3, and trans fats, and dietary cholesterol. We included 266 surveys in adults (83% nationally representative) comprising 1,630,069 unique individuals, representing 113 of 187 countries and 82% of the global population. A multilevel hierarchical Bayesian model accounted for differences in national and regional levels of missing data, measurement incomparability, study representativeness, and sampling and modelling uncertainty. Global adult population, by age, sex, country, and time. In 2010, global saturated fat consumption was 9.4%E (95%UI=9.2 to 9.5); country-specific intakes varied dramatically from 2.3 to 27.5%E; in 75 of 187 countries representing 61.8% of the world's adult population, the mean intake was fat; 97 to 440 mg/day (228 mg/day) for dietary cholesterol; 5 to 3,886 mg/day (163 mg/day) for seafood omega 3; and fat ≥ 5%E; corresponding proportions meeting optimal intakes were 0.6% for trans fat (≤ 0.5%E); 87.6% for dietary cholesterol (fat (≥ 250 mg/day); and 43.9% for plant omega 3 fat (≥ 1,100 mg/day). Trans fat intakes were generally higher at younger ages; and dietary cholesterol and seafood omega 3 fats generally higher at older ages. Intakes were similar by sex. Between 1990 and 2010, global saturated fat, dietary cholesterol, and trans fat intakes remained stable, while omega 6, seafood omega 3, and plant omega 3 fat intakes each increased. These novel global data on dietary fats and oils identify dramatic diversity across nations and inform policies and priorities for improving global health.

  4. Neural mechanisms of social dominance

    Directory of Open Access Journals (Sweden)

    Noriya eWatanabe

    2015-06-01

    Full Text Available In a group setting, individuals’ perceptions of their own level of dominance or of the dominance level of others, and the ability to adequately control their behavior based on these perceptions are crucial for living within a social environment. Recent advances in neural imaging and molecular technology have enabled researchers to investigate the neural substrates that support the perception of social dominance and the formation of a social hierarchy in humans. At the systems’ level, recent studies showed that dominance perception is represented in broad brain regions which include the amygdala, hippocampus, striatum, and various cortical networks such as the prefrontal, and parietal cortices. Additionally, neurotransmitter systems such as the dopaminergic and serotonergic systems, modulate and are modulated by the formation of the social hierarchy in a group. While these monoamine systems have a wide distribution and multiple functions, it was recently found that the Neuropeptide B/W contributes to the perception of dominance and is present in neurons that have a limited projection primarily to the amygdala. The present review discusses the specific roles of these neural regions and neurotransmitter systems in the perception of dominance and in hierarchy formation.

  5. Logistic regression and artificial neural network models for mapping of regional-scale landslide susceptibility in volcanic mountains of West Java (Indonesia)

    Science.gov (United States)

    Ngadisih, Bhandary, Netra P.; Yatabe, Ryuichi; Dahal, Ranjan K.

    2016-05-01

    West Java Province is the most landslide risky area in Indonesia owing to extreme geo-morphological conditions, climatic conditions and densely populated settlements with immense completed and ongoing development activities. So, a landslide susceptibility map at regional scale in this province is a fundamental tool for risk management and land-use planning. Logistic regression and Artificial Neural Network (ANN) models are the most frequently used tools for landslide susceptibility assessment, mainly because they are capable of handling the nature of landslide data. The main objective of this study is to apply logistic regression and ANN models and compare their performance for landslide susceptibility mapping in volcanic mountains of West Java Province. In addition, the model application is proposed to identify the most contributing factors to landslide events in the study area. The spatial database built in GIS platform consists of landslide inventory, four topographical parameters (slope, aspect, relief, distance to river), three geological parameters (distance to volcano crater, distance to thrust and fault, geological formation), and two anthropogenic parameters (distance to road, land use). The logistic regression model in this study revealed that slope, geological formations, distance to road and distance to volcano are the most influential factors of landslide events while, the ANN model revealed that distance to volcano crater, geological formation, distance to road, and land-use are the most important causal factors of landslides in the study area. Moreover, an evaluation of the model showed that the ANN model has a higher accuracy than the logistic regression model.

  6. Nucleotide sequence of the promoter-distal region of the tra operon of plasmid R100, including traI (DNA helicase I) and traD genes.

    Science.gov (United States)

    Yoshioka, Y; Fujita, Y; Ohtsubo, E

    1990-07-05

    The nucleotide sequence of the promoter-distal region of the tra operon of R100 was determined. There are five open reading frames in the region between traT and finO, and their protein products were identified. Nucleotide sequences of plasmid F corresponding to the junction regions among the open reading frames seen in R100 were also determined. Comparison of these nucleotide sequences revealed strong homology in the regions containing traD, traI and an open reading frame (named orfD). The TraD protein (83,899 Da) contains three hydrophobic regions, of which two are located near the amino-terminal region. This protein also contains a possible ATP-binding consensus sequence at the amino-terminal region and a characteristic repeated peptide sequence (Gln-Gln-Pro)10 at the carboxy-terminal region. The TraI protein (191,679 Da) contains the sequence motif conserved in an ATP-dependent DNA helicase superfamily in its carboxy-terminal region. The protein product of orfD, which is probably a new tra gene (named traX), contains 65% hydrophobic amino acids, especially rich in alanine and leucine. There exist non-homologous regions between R100 and F that could be represented as four I-D (insertion or deletion) loops in heteroduplex molecules. Assignment of each loop to the strand of R100 or F was , however, found to be the reverse from that previously assumed. The three I-D loops that were located between traT and traD, between traD and traI, and between traI and finO had no terminal inverted repeat sequences nor had they any homology with known insertion sequences, while the fourth was IS3, located within the finO gene of F. The sequences in the I-D loops, except IS3, may also code for proteins that are, however, likely to be nonessential for transfer of plasmids.

  7. Functional Network Architecture of Reading-Related Regions across Development

    Science.gov (United States)

    Vogel, Alecia C.; Church, Jessica A.; Power, Jonathan D.; Miezin, Fran M.; Petersen, Steven E.; Schlaggar, Bradley L.

    2013-01-01

    Reading requires coordinated neural processing across a large number of brain regions. Studying relationships between reading-related regions informs the specificity of information processing performed in each region. Here, regions of interest were defined from a meta-analysis of reading studies, including a developmental study. Relationships…

  8. ANT Advanced Neural Tool

    Energy Technology Data Exchange (ETDEWEB)

    Labrador, I.; Carrasco, R.; Martinez, L.

    1996-07-01

    This paper describes a practical introduction to the use of Artificial Neural Networks. Artificial Neural Nets are often used as an alternative to the traditional symbolic manipulation and first order logic used in Artificial Intelligence, due the high degree of difficulty to solve problems that can not be handled by programmers using algorithmic strategies. As a particular case of Neural Net a Multilayer Perception developed by programming in C language on OS9 real time operating system is presented. A detailed description about the program structure and practical use are included. Finally, several application examples that have been treated with the tool are presented, and some suggestions about hardware implementations. (Author) 15 refs.

  9. Rapid generation of sub-type, region-specific neurons and neural networks from human pluripotent stem cell-derived neurospheres

    Directory of Open Access Journals (Sweden)

    Aynun N. Begum

    2015-11-01

    Full Text Available Stem cell-based neuronal differentiation has provided a unique opportunity for disease modeling and regenerative medicine. Neurospheres are the most commonly used neuroprogenitors for neuronal differentiation, but they often clump in culture, which has always represented a challenge for neurodifferentiation. In this study, we report a novel method and defined culture conditions for generating sub-type or region-specific neurons from human embryonic and induced pluripotent stem cells derived neurosphere without any genetic manipulation. Round and bright-edged neurospheres were generated in a supplemented knockout serum replacement medium (SKSRM with 10% CO2, which doubled the expression of the NESTIN, PAX6 and FOXG1 genes compared with those cultured with 5% CO2. Furthermore, an additional step (AdSTEP was introduced to fragment the neurospheres and facilitate the formation of a neuroepithelial-type monolayer that we termed the “neurosphederm”. The large neural tube-type rosette (NTTR structure formed from the neurosphederm, and the NTTR expressed higher levels of the PAX6, SOX2 and NESTIN genes compared with the neuroectoderm-derived neuroprogenitors. Different layers of cortical, pyramidal, GABAergic, glutamatergic, cholinergic neurons appeared within 27 days using the neurosphederm, which is a shorter period than in traditional neurodifferentiation-protocols (42–60 days. With additional supplements and timeline dopaminergic and Purkinje neurons were also generated in culture too. Furthermore, our in vivo results indicated that the fragmented neurospheres facilitated significantly better neurogenesis in severe combined immunodeficiency (SCID mouse brains compared with the non-fragmented neurospheres. Therefore, this neurosphere-based neurodifferentiation protocol is a valuable tool for studies of neurodifferentiation, neuronal transplantation and high throughput screening assays.

  10. An alternative approach for estimating above ground biomass using Resourcesat-2 satellite data and artificial neural network in Bundelkhand region of India.

    Science.gov (United States)

    Deb, Dibyendu; Singh, J P; Deb, Shovik; Datta, Debajit; Ghosh, Arunava; Chaurasia, R S

    2017-10-20

    Determination of above ground biomass (AGB) of any forest is a longstanding scientific endeavor, which helps to estimate net primary productivity, carbon stock and other biophysical parameters of that forest. With advancement of geospatial technology in last few decades, AGB estimation now can be done using space-borne and airborne remotely sensed data. It is a well-established, time saving and cost effective technique with high precision and is frequently applied by the scientific community. It involves development of allometric equations based on correlations of ground-based forest biomass measurements with vegetation indices derived from remotely sensed data. However, selection of the best-fit and explanatory models of biomass estimation often becomes a difficult proposition with respect to the image data resolution (spatial and spectral) as well as the sensor platform position in space. Using Resourcesat-2 satellite data and Normalized Difference Vegetation Index (NDVI), this pilot scale study compared traditional linear and nonlinear models with an artificial intelligence-based non-parametric technique, i.e. artificial neural network (ANN) for formulation of the best-fit model to determine AGB of forest of the Bundelkhand region of India. The results confirmed the superiority of ANN over other models in terms of several statistical significance and reliability assessment measures. Accordingly, this study proposed the use of ANN instead of traditional models for determination of AGB and other bio-physical parameters of any dry deciduous forest of tropical sub-humid or semi-arid area. In addition, large numbers of sampling sites with different quadrant sizes for trees, shrubs, and herbs as well as application of LiDAR data as predictor variable were recommended for very high precision modelling in ANN for a large scale study.

  11. DC parameter extraction of equivalent circuit model in InGaAsSb heterojunction bipolar transistors including non-ideal effects in the base region

    Science.gov (United States)

    Chang, Yang-Hua; Cheng, Zong-Tai

    2011-07-01

    This paper presents the DC parameter extraction of the equivalent circuit model in an InP-InGaAsSb double heterojunction bipolar transistor (HBT). The non-ideal collector current is modeled by a non-ideal doping distribution in the base region. Then several consequent non-ideal effects, which have always been neglected in typical HBTs, are studied using Medici device simulator. Moreover, the associated DC parameters of VBIC model are extracted accordingly. The equivalent circuit model is in good agreement with the measured data in I C- V CE characteristics.

  12. Report of Increasing Overdose Deaths that include Acetyl Fentanyl in Multiple Counties of the Southwestern Region of the Commonwealth of Pennsylvania in 2015-2016.

    Science.gov (United States)

    Dwyer, Jessica B; Janssen, Jennifer; Luckasevic, Todd M; Williams, Karl E

    2018-01-01

    Acetyl fentanyl is a Schedule I controlled synthetic opioid that is becoming an increasingly detected "designer drug." Routine drug screening procedures in local forensic toxicology laboratories identified a total of 41 overdose deaths associated with acetyl fentanyl within multiple counties of the southwestern region of the state of Pennsylvania. The range, median, mean, and standard deviation of blood acetyl fentanyl concentrations for these 41 cases were 0.13-2100 ng/mL, 11 ng/mL, 169.3 ng/mL, and 405.3 ng/mL, respectively. Thirty-six individuals (88%) had a confirmed history of substance abuse, and all but one case (96%) were ruled multiple drug toxicities. This report characterizes this localized trend of overdose deaths associated with acetyl fentanyl and provides further evidence supporting an alarmingly concentrated opiate and opioid epidemic of both traditional and novel drugs within this region of the United States. © 2017 American Academy of Forensic Sciences.

  13. Two new species of Pergalumna (Acari, Oribatida, Galumnidae from Costa Rica, including a key to all species of the genus from the Neotropical region

    Directory of Open Access Journals (Sweden)

    Sergey Ermilov

    2014-08-01

    Full Text Available Two new species of oribatid mites of the genus Pergalumna (Oribatida, Galumnidae, P. elongatiporosa sp. n. and P. striatiprodorsum sp. n., are described from leaf litter of a secondary forest in Costa Rica. Pergalumna elongatiporosa sp. n. is most similar morphologically to P. horvathorum P. Balogh, 1997 and P. sura P. Balogh, 1997, however, it differs from both by the body size, body surface ornamentation and morphology of notogastral porose areas A1 and A3. Pergalumna striatiprodorsum sp. n. is most similar morphologically to P. hawaiiensis hawaiiensis (Jacot, 1934 and P. strigulata Mahunka, 1978, however, it differs from P. hawaiiensis by the length of interlamellar setae and surface ornamentation of the prodorsum; from P. strigulata by the surface of ornamentation of the notogaster, length of interlamellar setae and morphology of bothridial setae. An identification key to known species of Pergalumna from the Neotropical region is given.

  14. Neural overlap in processing music and speech

    Science.gov (United States)

    Peretz, Isabelle; Vuvan, Dominique; Lagrois, Marie-Élaine; Armony, Jorge L.

    2015-01-01

    Neural overlap in processing music and speech, as measured by the co-activation of brain regions in neuroimaging studies, may suggest that parts of the neural circuitries established for language may have been recycled during evolution for musicality, or vice versa that musicality served as a springboard for language emergence. Such a perspective has important implications for several topics of general interest besides evolutionary origins. For instance, neural overlap is an important premise for the possibility of music training to influence language acquisition and literacy. However, neural overlap in processing music and speech does not entail sharing neural circuitries. Neural separability between music and speech may occur in overlapping brain regions. In this paper, we review the evidence and outline the issues faced in interpreting such neural data, and argue that converging evidence from several methodologies is needed before neural overlap is taken as evidence of sharing. PMID:25646513

  15. Reduced-folate carrier (RFC is expressed in placenta and yolk sac, as well as in cells of the developing forebrain, hindbrain, neural tube, craniofacial region, eye, limb buds and heart

    Directory of Open Access Journals (Sweden)

    Prasad Puttur

    2003-07-01

    Full Text Available Abstract Background Folate is essential for cellular proliferation and tissue regeneration. As mammalian cells cannot synthesize folates de novo, tightly regulated cellular uptake processes have evolved to sustain sufficient levels of intracellular tetrahydrofolate cofactors to support biosynthesis of purines, pyrimidines, and some amino acids (serine, methionine. Though reduced-folate carrier (RFC is one of the major proteins mediating folate transport, knowledge of the developmental expression of RFC is lacking. We utilized in situ hybridization and immunolocalization to determine the developmental distribution of RFC message and protein, respectively. Results In the mouse, RFC transcripts and protein are expressed in the E10.0 placenta and yolk sac. In the E9.0 to E11.5 mouse embryo RFC is widely detectable, with intense signal localized to cell populations in the neural tube, craniofacial region, limb buds and heart. During early development, RFC is expressed throughout the eye, but by E12.5, RFC protein becomes localized to the retinal pigment epithelium (RPE. Conclusions Clinical studies show a statistical decrease in the number of neural tube defects, craniofacial abnormalities, cardiovascular defects and limb abnormalities detected in offspring of female patients given supplementary folate during pregnancy. The mechanism, however, by which folate supplementation ameliorates the occurrence of developmental defects is unclear. The present work demonstrates that RFC is present in placenta and yolk sac and provides the first evidence that it is expressed in the neural tube, craniofacial region, limb buds and heart during organogenesis. These findings suggest that rapidly dividing cells in the developing neural tube, craniofacial region, limb buds and heart may be particularly susceptible to folate deficiency.

  16. Neural plasticity and its initiating conditions in tinnitus.

    Science.gov (United States)

    Roberts, L E

    2017-12-12

    Deafferentation caused by cochlear pathology (which can be hidden from the audiogram) activates forms of neural plasticity in auditory pathways, generating tinnitus and its associated conditions including hyperacusis. This article discusses tinnitus mechanisms and suggests how these mechanisms may relate to those involved in normal auditory information processing. Research findings from animal models of tinnitus and from electromagnetic imaging of tinnitus patients are reviewed which pertain to the role of deafferentation and neural plasticity in tinnitus and hyperacusis. Auditory neurons compensate for deafferentation by increasing their input/output functions (gain) at multiple levels of the auditory system. Forms of homeostatic plasticity are believed to be responsible for this neural change, which increases the spontaneous and driven activity of neurons in central auditory structures in animals expressing behavioral evidence of tinnitus. Another tinnitus correlate, increased neural synchrony among the affected neurons, is forged by spike-timing-dependent neural plasticity in auditory pathways. Slow oscillations generated by bursting thalamic neurons verified in tinnitus animals appear to modulate neural plasticity in the cortex, integrating tinnitus neural activity with information in brain regions supporting memory, emotion, and consciousness which exhibit increased metabolic activity in tinnitus patients. The latter process may be induced by transient auditory events in normal processing but it persists in tinnitus, driven by phantom signals from the auditory pathway. Several tinnitus therapies attempt to suppress tinnitus through plasticity, but repeated sessions will likely be needed to prevent tinnitus activity from returning owing to deafferentation as its initiating condition.

  17. Late-glacial and Holocene Vegetation and Climate Variability, Including Major Droughts, in the Sky Lakes Region of Southeastern New York State

    Science.gov (United States)

    Menking, Kirsten M.; Peteet, Dorothy M.; Anderson, Roger Y.

    2012-01-01

    Sediment cores from Lakes Minnewaska and Mohonk in the Shawangunk Mountains of southeastern New York were analyzed for pollen, plantmacrofossils, macroscopic charcoal, organic carbon content, carbon isotopic composition, carbon/nitrogen ratio, and lithologic changes to determine the vegetation and landscape history of the greater Catskill Mountain region since deglaciation. Pollen stratigraphy generally matches the New England pollen zones identified by Deevey (1939) and Davis (1969), with boreal genera (Picea, Abies) present during the late Pleistocene yielding to a mixed Pinus, Quercus and Tsuga forest in the early Holocene. Lake Minnewaska sediments record the Younger Dryas and possibly the 8.2 cal kyr BP climatic events in pollen and sediment chemistry along with an 1400 cal yr interval of wet conditions (increasing Tsuga and declining Quercus) centered about 6400 cal yr BP. BothMinnewaska andMohonk reveal a protracted drought interval in themiddle Holocene, 5700-4100 cal yr BP, during which Pinus rigida colonized the watershed, lake levels fell, and frequent fires led to enhanced hillslope erosion. Together, the records show at least three wet-dry cycles throughout the Holocene and both similarities and differences to climate records in New England and central New York. Drought intervals raise concerns for water resources in the New York City metropolitan area and may reflect a combination of enhanced La Niña, negative phase NAO, and positive phase PNA climatic patterns and/or northward shifts of storm tracks.

  18. Matrix attachment regions included in a bicistronic vector enhances and stabilizes follistatin gene expressions in both transgenic cells and transgenic mice

    Directory of Open Access Journals (Sweden)

    Xiaoming HU,Jing GUO,Chunling BAI,Zhuying WEI,Li GAO,Tingmao HU,Shorgan BOU,Guangpeng LI

    2016-03-01

    Full Text Available In the present study, follistatin (FST gene expression vectors with either a bicistronic gene transfer cassette alone, or a bicistron gene cassette carrying a matrix attachment region (MAR were constructed and transfected to bovine fetal fibroblasts. Evaluations of both the integration and expression of exogenous FST indicated that the pMAR-CAG-FST-IRES-AcGFP1-polyA-MAR (pMAR-FST vector had higher capacity to form monoclonal transgenic cells than the vector without MAR, though transient transfection and integration efficiency were similar with either construct. Remarkably, protein expression in transgenic cells with the pMAR-FST vector was significantly higher than that from the bicistronic vector. Exogenous FST was expressed in all of the pMAR-FST transgenic mice at F0, F1 and F2. Total muscle growth in F0 mice was significantly greater than in wild-type mice, with larger muscles in fore and hind limbs of transgenic mice. pMAR-FST transgenic mice were also found with more evenly distributed muscle bundles and thinner spaces between sarcolemma, which suggests a correlation between transgene expression-associated muscle development and the trend of muscle growth. In conclusion, a pMAR-FST vector, which excluded the resistant genes and frame structure, enhances and stabilizes FST gene expressions in both transfected cells and transgenic mice.

  19. Influence of neural adaptation on dynamics and equilibrium state of neural activities in a ring neural network

    Science.gov (United States)

    Takiyama, Ken

    2017-12-01

    How neural adaptation affects neural information processing (i.e. the dynamics and equilibrium state of neural activities) is a central question in computational neuroscience. In my previous works, I analytically clarified the dynamics and equilibrium state of neural activities in a ring-type neural network model that is widely used to model the visual cortex, motor cortex, and several other brain regions. The neural dynamics and the equilibrium state in the neural network model corresponded to a Bayesian computation and statistically optimal multiple information integration, respectively, under a biologically inspired condition. These results were revealed in an analytically tractable manner; however, adaptation effects were not considered. Here, I analytically reveal how the dynamics and equilibrium state of neural activities in a ring neural network are influenced by spike-frequency adaptation (SFA). SFA is an adaptation that causes gradual inhibition of neural activity when a sustained stimulus is applied, and the strength of this inhibition depends on neural activities. I reveal that SFA plays three roles: (1) SFA amplifies the influence of external input in neural dynamics; (2) SFA allows the history of the external input to affect neural dynamics; and (3) the equilibrium state corresponds to the statistically optimal multiple information integration independent of the existence of SFA. In addition, the equilibrium state in a ring neural network model corresponds to the statistically optimal integration of multiple information sources under biologically inspired conditions, independent of the existence of SFA.

  20. Global, regional and national consumption of major food groups in 1990 and 2010: a systematic analysis including 266 country-specific nutrition surveys worldwide.

    Science.gov (United States)

    Micha, Renata; Khatibzadeh, Shahab; Shi, Peilin; Andrews, Kathryn G; Engell, Rebecca E; Mozaffarian, Dariush

    2015-09-24

    To quantify global intakes of key foods related to non-communicable diseases in adults by region (n=21), country (n=187), age and sex, in 1990 and 2010. We searched and obtained individual-level intake data in 16 age/sex groups worldwide from 266 surveys across 113 countries. We combined these data with food balance sheets available in all nations and years. A hierarchical Bayesian model estimated mean food intake and associated uncertainty for each age-sex-country-year stratum, accounting for differences in intakes versus availability, survey methods and representativeness, and sampling and modelling uncertainty. Global adult population, by age, sex, country and time. In 2010, global fruit intake was 81.3 g/day (95% uncertainty interval 78.9-83.7), with country-specific intakes ranging from 19.2-325.1 g/day; in only 2 countries (representing 0.4% of the world's population), mean intakes met recommended targets of ≥300 g/day. Country-specific vegetable intake ranged from 34.6-493.1 g/day (global mean=208.8 g/day); corresponding values for nuts/seeds were 0.2-152.7 g/day (8.9 g/day); for whole grains, 1.3-334.3 g/day (38.4 g/day); for seafood, 6.0-87.6 g/day (27.9 g/day); for red meats, 3.0-124.2 g/day (41.8 g/day); and for processed meats, 2.5-66.1 g/day (13.7 g/day). Mean national intakes met recommended targets in countries representing 0.4% of the global population for vegetables (≥400 g/day); 9.6% for nuts/seeds (≥4 (28.35 g) servings/week); 7.6% for whole grains (≥2.5 (50 g) servings/day); 4.4% for seafood (≥3.5 (100 g) servings/week); 20.3% for red meats (≤1 (100 g) serving/week); and 38.5% for processed meats (≤1 (50 g) serving/week). Intakes of healthful foods were generally higher and of less healthful foods generally lower at older ages. Intakes were generally similar by sex. Vegetable, seafood and processed meat intakes were stable over time; fruits, nuts/seeds and red meat, increased; and whole

  1. Neural Based Orthogonal Data Fitting The EXIN Neural Networks

    CERN Document Server

    Cirrincione, Giansalvo

    2008-01-01

    Written by three leaders in the field of neural based algorithms, Neural Based Orthogonal Data Fitting proposes several neural networks, all endowed with a complete theory which not only explains their behavior, but also compares them with the existing neural and traditional algorithms. The algorithms are studied from different points of view, including: as a differential geometry problem, as a dynamic problem, as a stochastic problem, and as a numerical problem. All algorithms have also been analyzed on real time problems (large dimensional data matrices) and have shown accurate solutions. Wh

  2. Gene expression profiling in the stress control brain region hypothalamic paraventricular nucleus reveals a novel gene network including Amyloid beta Precursor Protein

    Directory of Open Access Journals (Sweden)

    Deussing Jan M

    2010-10-01

    Full Text Available Abstract Background The pivotal role of stress in the precipitation of psychiatric diseases such as depression is generally accepted. This study aims at the identification of genes that are directly or indirectly responding to stress. Inbred mouse strains that had been evidenced to differ in their stress response as well as in their response to antidepressant treatment were chosen for RNA profiling after stress exposure. Gene expression and regulation was determined by microarray analyses and further evaluated by bioinformatics tools including pathway and cluster analyses. Results Forced swimming as acute stressor was applied to C57BL/6J and DBA/2J mice and resulted in sets of regulated genes in the paraventricular nucleus of the hypothalamus (PVN, 4 h or 8 h after stress. Although the expression changes between the mouse strains were quite different, they unfolded in phases over time in both strains. Our search for connections between the regulated genes resulted in potential novel signalling pathways in stress. In particular, Guanine nucleotide binding protein, alpha inhibiting 2 (GNAi2 and Amyloid β (A4 precursor protein (APP were detected as stress-regulated genes, and together with other genes, seem to be integrated into stress-responsive pathways and gene networks in the PVN. Conclusions This search for stress-regulated genes in the PVN revealed its impact on interesting genes (GNAi2 and APP and a novel gene network. In particular the expression of APP in the PVN that is governing stress hormone balance, is of great interest. The reported neuroprotective role of this molecule in the CNS supports the idea that a short acute stress can elicit positive adaptational effects in the brain.

  3. The neural basis of emotions varies over time: different regions go with onset- and offset-bound processes underlying emotion intensity.

    Science.gov (United States)

    Résibois, Maxime; Verduyn, Philippe; Delaveau, Pauline; Rotgé, Jean-Yves; Kuppens, Peter; Van Mechelen, Iven; Fossati, Philippe

    2017-08-01

    According to theories of emotion dynamics, emotions unfold across two phases in which different types of processes come to the fore: emotion onset and emotion offset. Differences in onset-bound processes are reflected by the degree of explosiveness or steepness of the response at onset, and differences in offset-bound processes by the degree of accumulation or intensification of the subsequent response. Whether onset- and offset-bound processes have distinctive neural correlates and, hence, whether the neural basis of emotions varies over time, still remains unknown. In the present fMRI study, we address this question using a recently developed paradigm that allows to disentangle explosiveness and accumulation. Thirty-one participants were exposed to neutral and negative social feedback, and asked to reflect on its contents. Emotional intensity while reading and thinking about the feedback was measured with an intensity profile tracking approach. Using non-negative matrix factorization, the resulting profile data were decomposed in explosiveness and accumulation components, which were subsequently entered as continuous regressors of the BOLD response. It was found that the neural basis of emotion intensity shifts as emotions unfold over time with emotion explosiveness and accumulation having distinctive neural correlates. © The Author (2017). Published by Oxford University Press.

  4. A neural measure of behavioral engagement: Task-residual low frequency blood oxygenation level dependent activity in the precuneus

    OpenAIRE

    Zhang, Sheng; Li, Chiang-shan Ray

    2009-01-01

    Brain imaging has provided a useful tool to examine the neural processes underlying human cognition. A critical question is whether and how task engagement influences the observed regional brain activations. Here we highlighted this issue and derived a neural measure of task engagement from the task-residual low frequency blood oxygenation level dependent (BOLD) activity in the precuneus. Using independent component analysis, we identified brain regions in the default circuit – including the ...

  5. Discrete Neural Signatures of Basic Emotions.

    Science.gov (United States)

    Saarimäki, Heini; Gotsopoulos, Athanasios; Jääskeläinen, Iiro P; Lampinen, Jouko; Vuilleumier, Patrik; Hari, Riitta; Sams, Mikko; Nummenmaa, Lauri

    2016-06-01

    Categorical models of emotions posit neurally and physiologically distinct human basic emotions. We tested this assumption by using multivariate pattern analysis (MVPA) to classify brain activity patterns of 6 basic emotions (disgust, fear, happiness, sadness, anger, and surprise) in 3 experiments. Emotions were induced with short movies or mental imagery during functional magnetic resonance imaging. MVPA accurately classified emotions induced by both methods, and the classification generalized from one induction condition to another and across individuals. Brain regions contributing most to the classification accuracy included medial and inferior lateral prefrontal cortices, frontal pole, precentral and postcentral gyri, precuneus, and posterior cingulate cortex. Thus, specific neural signatures across these regions hold representations of different emotional states in multimodal fashion, independently of how the emotions are induced. Similarity of subjective experiences between emotions was associated with similarity of neural patterns for the same emotions, suggesting a direct link between activity in these brain regions and the subjective emotional experience. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  6. Positive mood enhances reward-related neural activity.

    Science.gov (United States)

    Young, Christina B; Nusslock, Robin

    2016-06-01

    Although behavioral research has shown that positive mood leads to desired outcomes in nearly every major life domain, no studies have directly examined the effects of positive mood on the neural processes underlying reward-related affect and goal-directed behavior. To address this gap, participants in the present fMRI study experienced either a positive (n = 20) or neutral (n = 20) mood induction and subsequently completed a monetary incentive delay task that assessed reward and loss processing. Consistent with prediction, positive mood elevated activity specifically during reward anticipation in corticostriatal neural regions that have been implicated in reward processing and goal-directed behavior, including the nucleus accumbens, caudate, lateral orbitofrontal cortex and putamen, as well as related paralimbic regions, including the anterior insula and ventromedial prefrontal cortex. These effects were not observed during reward outcome, loss anticipation or loss outcome. Critically, this is the first study to report that positive mood enhances reward-related neural activity. Our findings have implications for uncovering the neural mechanisms by which positive mood enhances goal-directed behavior, understanding the malleability of reward-related neural activity, and developing targeted treatments for psychiatric disorders characterized by deficits in reward processing. © The Author (2016). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  7. Optical modulator including grapene

    Energy Technology Data Exchange (ETDEWEB)

    Liu, Ming; Yin, Xiaobo; Zhang, Xiang

    2016-06-07

    The present invention provides for a one or more layer graphene optical modulator. In a first exemplary embodiment the optical modulator includes an optical waveguide, a nanoscale oxide spacer adjacent to a working region of the waveguide, and a monolayer graphene sheet adjacent to the spacer. In a second exemplary embodiment, the optical modulator includes at least one pair of active media, where the pair includes an oxide spacer, a first monolayer graphene sheet adjacent to a first side of the spacer, and a second monolayer graphene sheet adjacent to a second side of the spacer, and at least one optical waveguide adjacent to the pair.

  8. Anomalous neural circuit function in schizophrenia during a virtual Morris water task.

    Science.gov (United States)

    Folley, Bradley S; Astur, Robert; Jagannathan, Kanchana; Calhoun, Vince D; Pearlson, Godfrey D

    2010-02-15

    Previous studies have reported learning and navigation impairments in schizophrenia patients during virtual reality allocentric learning tasks. The neural bases of these deficits have not been explored using functional MRI despite well-explored anatomic characterization of these paradigms in non-human animals. Our objective was to characterize the differential distributed neural circuits involved in virtual Morris water task performance using independent component analysis (ICA) in schizophrenia patients and controls. Additionally, we present behavioral data in order to derive relationships between brain function and performance, and we have included a general linear model-based analysis in order to exemplify the incremental and differential results afforded by ICA. Thirty-four individuals with schizophrenia and twenty-eight healthy controls underwent fMRI scanning during a block design virtual Morris water task using hidden and visible platform conditions. Independent components analysis was used to deconstruct neural contributions to hidden and visible platform conditions for patients and controls. We also examined performance variables, voxel-based morphometry and hippocampal subparcellation, and regional BOLD signal variation. Independent component analysis identified five neural circuits. Mesial temporal lobe regions, including the hippocampus, were consistently task-related across conditions and groups. Frontal, striatal, and parietal circuits were recruited preferentially during the visible condition for patients, while frontal and temporal lobe regions were more saliently recruited by controls during the hidden platform condition. Gray matter concentrations and BOLD signal in hippocampal subregions were associated with task performance in controls but not patients. Patients exhibited impaired performance on the hidden and visible conditions of the task, related to negative symptom severity. While controls showed coupling between neural circuits, regional

  9. Neural network modeling of emotion

    Science.gov (United States)

    Levine, Daniel S.

    2007-03-01

    This article reviews the history and development of computational neural network modeling of cognitive and behavioral processes that involve emotion. The exposition starts with models of classical conditioning dating from the early 1970s. Then it proceeds toward models of interactions between emotion and attention. Then models of emotional influences on decision making are reviewed, including some speculative (not and not yet simulated) models of the evolution of decision rules. Through the late 1980s, the neural networks developed to model emotional processes were mainly embodiments of significant functional principles motivated by psychological data. In the last two decades, network models of these processes have become much more detailed in their incorporation of known physiological properties of specific brain regions, while preserving many of the psychological principles from the earlier models. Most network models of emotional processes so far have dealt with positive and negative emotion in general, rather than specific emotions such as fear, joy, sadness, and anger. But a later section of this article reviews a few models relevant to specific emotions: one family of models of auditory fear conditioning in rats, and one model of induced pleasure enhancing creativity in humans. Then models of emotional disorders are reviewed. The article concludes with philosophical statements about the essential contributions of emotion to intelligent behavior and the importance of quantitative theories and models to the interdisciplinary enterprise of understanding the interactions of emotion, cognition, and behavior.

  10. Neural Networks

    Directory of Open Access Journals (Sweden)

    Schwindling Jerome

    2010-04-01

    Full Text Available This course presents an overview of the concepts of the neural networks and their aplication in the framework of High energy physics analyses. After a brief introduction on the concept of neural networks, the concept is explained in the frame of neuro-biology, introducing the concept of multi-layer perceptron, learning and their use as data classifer. The concept is then presented in a second part using in more details the mathematical approach focussing on typical use cases faced in particle physics. Finally, the last part presents the best way to use such statistical tools in view of event classifers, putting the emphasis on the setup of the multi-layer perceptron. The full article (15 p. corresponding to this lecture is written in french and is provided in the proceedings of the book SOS 2008.

  11. Profile of the biodiesel B100 commercialized in the region of Londrina: application of artificial neural networks of the type self organizing maps

    Directory of Open Access Journals (Sweden)

    Vilson Machado de Campos Filho

    2015-10-01

    Full Text Available The 97 samples were grouped according to the year of analysis. For each year, letters from A to D were attributed, between 2010 and 2013; A (33 B (25 C (24 and D (15. The parameters of compliance previously analyzed are those established by the National Agency of Petroleum, Natural Gas and Biofuels (ANP, through resolution ANP 07/2008. The parameters analyzed were density, flash point, peroxide and acid value. The observed values were presented to Artificial Neural Network (ANN Self Organizing MAP (SOM in order to classify, by physical-chemical properties, each sample from year of production. The ANN was trained on different days and randomly divided samples into two groups, training and test set. It was found that SOM network differentiated samples by the year and the compliance parameters, allowing to identify that the density and the flash point were the most significant compliance parameters, so good for the distinction and classification of these samples.

  12. Neural correlates of eating disorders: translational potential

    Directory of Open Access Journals (Sweden)

    McAdams CJ

    2015-09-01

    Full Text Available Carrie J McAdams,1,2 Whitney Smith1 1University of Texas at Southwestern Medical Center, 2Department of Psychiatry, Texas Health Presbyterian Hospital of Dallas, Dallas, TX, USA Abstract: Eating disorders are complex and serious psychiatric illnesses whose etiology includes psychological, biological, and social factors. Treatment of eating disorders is challenging as there are few evidence-based treatments and limited understanding of the mechanisms that result in sustained recovery. In the last 20 years, we have begun to identify neural pathways that are altered in eating disorders. Consideration of how these pathways may contribute to an eating disorder can provide an understanding of expected responses to treatments. Eating disorder behaviors include restrictive eating, compulsive overeating, and purging behaviors after eating. Eating disorders are associated with changes in many neural systems. In this targeted review, we focus on three cognitive processes associated with neurocircuitry differences in subjects with eating disorders such as reward, decision-making, and social behavior. We briefly examine how each of these systems function in healthy people, using Neurosynth meta-analysis to identify key regions commonly implicated in these circuits. We review the evidence for disruptions of these regions and systems in eating disorders. Finally, we describe psychiatric and psychological treatments that are likely to function by impacting these regions. Keywords: anorexia nervosa, bulimia nervosa, social cognition, reward processing, decision-making

  13. Rare Deleterious PARD3 Variants in the aPKC-Binding Region are Implicated in the Pathogenesis of Human Cranial Neural Tube Defects Via Disrupting Apical Tight Junction Formation.

    Science.gov (United States)

    Chen, Xiaoli; An, Yu; Gao, Yonghui; Guo, Liu; Rui, Lei; Xie, Hua; Sun, Mei; Lam Hung, Siv; Sheng, Xiaoming; Zou, Jizhen; Bao, Yihua; Guan, Hongyan; Niu, Bo; Li, Zandong; Finnell, Richard H; Gusella, James F; Wu, Bai-Lin; Zhang, Ting

    2017-04-01

    Increasing evidence that mutation of planar cell polarity (PCP) genes contributes to human cranial neural tube defect (NTD) susceptibility prompted us to hypothesize that rare variants of genes in the core apical-basal polarity (ABP) pathway are risk factors for cranial NTDs. In this study, we screened for rare genomic variation of PARD3 in 138 cranial NTD cases and 274 controls. Overall, the rare deleterious variants of PARD3 were significantly associated with increased risk for cranial NTDs (11/138 vs.7/274, P analysis in HEK293T and MDCK cells confirmed abnormal aPKC binding or interaction for two PARD3 variants (p.P913Q and p.D783G), resulting in defective tight junction formation via disrupted aPKC binding. Functional analysis in human neural progenitor cells and chick embryos revealed that PARD3 knockdown gave rise to abnormal cell polarity and compromised the polarization process of neuroepithelial tissue. Our studies suggest that rare deleterious variants of PARD3 in the aPKC-binding region contribute to human cranial NTDs, possibly by disrupting apical tight junction formation and subsequent polarization process of the neuroepithelium. © 2016 WILEY PERIODICALS, INC.

  14. Zebrafish homologs of genes within 16p11.2, a genomic region associated with brain disorders, are active during brain development, and include two deletion dosage sensor genes.

    Science.gov (United States)

    Blaker-Lee, Alicia; Gupta, Sunny; McCammon, Jasmine M; De Rienzo, Gianluca; Sive, Hazel

    2012-11-01

    Deletion or duplication of one copy of the human 16p11.2 interval is tightly associated with impaired brain function, including autism spectrum disorders (ASDs), intellectual disability disorder (IDD) and other phenotypes, indicating the importance of gene dosage in this copy number variant region (CNV). The core of this CNV includes 25 genes; however, the number of genes that contribute to these phenotypes is not known. Furthermore, genes whose functional levels change with deletion or duplication (termed 'dosage sensors'), which can associate the CNV with pathologies, have not been identified in this region. Using the zebrafish as a tool, a set of 16p11.2 homologs was identified, primarily on chromosomes 3 and 12. Use of 11 phenotypic assays, spanning the first 5 days of development, demonstrated that this set of genes is highly active, such that 21 out of the 22 homologs tested showed loss-of-function phenotypes. Most genes in this region were required for nervous system development - impacting brain morphology, eye development, axonal density or organization, and motor response. In general, human genes were able to substitute for the fish homolog, demonstrating orthology and suggesting conserved molecular pathways. In a screen for 16p11.2 genes whose function is sensitive to hemizygosity, the aldolase a (aldoaa) and kinesin family member 22 (kif22) genes were identified as giving clear phenotypes when RNA levels were reduced by ∼50%, suggesting that these genes are deletion dosage sensors. This study leads to two major findings. The first is that the 16p11.2 region comprises a highly active set of genes, which could present a large genetic target and might explain why multiple brain function, and other, phenotypes are associated with this interval. The second major finding is that there are (at least) two genes with deletion dosage sensor properties among the 16p11.2 set, and these could link this CNV to brain disorders such as ASD and IDD.

  15. The neural basis of task switching changes with skill acquisition.

    Science.gov (United States)

    Jimura, Koji; Cazalis, Fabienne; Stover, Elena R S; Poldrack, Russell A

    2014-01-01

    Learning novel skills involves reorganization and optimization of cognitive processing involving a broad network of brain regions. Previous work has shown asymmetric costs of switching to a well-trained task vs. a poorly-trained task, but the neural basis of these differential switch costs is unclear. The current study examined the neural signature of task switching in the context of acquisition of new skill. Human participants alternated randomly between a novel visual task (mirror-reversed word reading) and a highly practiced one (plain word reading), allowing the isolation of task switching and skill set maintenance. Two scan sessions were separated by 2 weeks, with behavioral training on the mirror reading task in between the two sessions. Broad cortical regions, including bilateral prefrontal, parietal, and extrastriate cortices, showed decreased activity associated with learning of the mirror reading skill. In contrast, learning to switch to the novel skill was associated with decreased activity in a focal subcortical region in the dorsal striatum. Switching to the highly practiced task was associated with a non-overlapping set of regions, suggesting substantial differences in the neural substrates of switching as a function of task skill. Searchlight multivariate pattern analysis also revealed that learning was associated with decreased pattern information for mirror vs. plain reading tasks in fronto-parietal regions. Inferior frontal junction and posterior parietal cortex showed a joint effect of univariate activation and pattern information. These results suggest distinct learning mechanisms task performance and executive control as a function of learning.

  16. The neural basis of task switching changes with skill acquisition

    Directory of Open Access Journals (Sweden)

    Koji eJimura

    2014-05-01

    Full Text Available Learning novel skills involves reorganization and optimization of cognitive processing involving a broad network of brain regions. Previous work has shown asymmetric costs of switching to a well-trained task versus a poorly-trained task, but the neural basis of these differential switch costs is unclear. The current study examined the neural signature of task switching in the context of acquisition of new skill. Human participants alternated randomly between a novel visual task (mirror-reversed word reading and a highly practiced one (plain word reading, allowing the isolation of task switching and skill set maintenance. Two scan sessions were separated by two weeks, with behavioral training on the mirror reading task in between the two sessions. Broad cortical regions, including bilateral prefrontal, parietal, and extrastriate cortices, showed decreased activity associated with learning of the mirror reading skill. In contrast, learning to switch to the novel skill was associated with decreased activity in a focal subcortical region in the dorsal striatum. Switching to the highly practiced task was associated with a non-overlapping set of regions, suggesting substantial differences in the neural substrates of switching as a function of task skill. Searchlight multivariate pattern analysis also revealed that learning was associated with decreased pattern information for mirror versus plain reading tasks in fronto-parietal regions. Inferior frontal junction and posterior parietal cortex showed a joint effect of univariate activation and pattern information. These results suggest distinct learning mechanisms task performance and executive control as a function of learning.

  17. Neural Tube Defects

    Science.gov (United States)

    ... vitamin, before and during pregnancy prevents most neural tube defects. Neural tube defects are usually diagnosed before the infant is ... or imaging tests. There is no cure for neural tube defects. The nerve damage and loss of function ...

  18. Neural Activations of Guided Imagery and Music in Negative Emotional Processing: A Functional MRI Study.

    Science.gov (United States)

    Lee, Sang Eun; Han, Yeji; Park, HyunWook

    2016-01-01

    The Bonny Method of Guided Imagery and Music uses music and imagery to access and explore personal emotions associated with episodic memories. Understanding the neural mechanism of guided imagery and music (GIM) as combined stimuli for emotional processing informs clinical application. We performed functional magnetic resonance imaging (fMRI) to demonstrate neural mechanisms of GIM for negative emotional processing when personal episodic memory is recalled and re-experienced through GIM processes. Twenty-four healthy volunteers participated in the study, which used classical music and verbal instruction stimuli to evoke negative emotions. To analyze the neural mechanism, activated regions associated with negative emotional and episodic memory processing were extracted by conducting volume analyses for the contrast between GIM and guided imagery (GI) or music (M). The GIM stimuli showed increased activation over the M-only stimuli in five neural regions associated with negative emotional and episodic memory processing, including the left amygdala, left anterior cingulate gyrus, left insula, bilateral culmen, and left angular gyrus (AG). Compared with GI alone, GIM showed increased activation in three regions associated with episodic memory processing in the emotional context, including the right posterior cingulate gyrus, bilateral parahippocampal gyrus, and AG. No neural regions related to negative emotional and episodic memory processing showed more activation for M and GI than for GIM. As a combined multimodal stimulus, GIM may increase neural activations related to negative emotions and episodic memory processing. Findings suggest a neural basis for GIM with personal episodic memories affecting cortical and subcortical structures and functions. © the American Music Therapy Association 2016. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  19. Neural mechanisms for voice recognition

    NARCIS (Netherlands)

    Andics, A.V.; McQueen, J.M.; Petersson, K.M.; Gal, V.; Rudas, G.; Vidnyanszky, Z.

    2010-01-01

    We investigated neural mechanisms that support voice recognition in a training paradigm with fMRI. The same listeners were trained on different weeks to categorize the mid-regions of voice-morph continua as an individual's voice. Stimuli implicitly defined a voice-acoustics space, and training

  20. Activity in part of the neural correlates of consciousness reflects integration.

    Science.gov (United States)

    Eriksson, Johan

    2017-10-01

    Integration is commonly viewed as a key process for generating conscious experiences. Accordingly, there should be increased activity within the neural correlates of consciousness when demands on integration increase. We used fMRI and "informational masking" to isolate the neural correlates of consciousness and measured how the associated brain activity changed as a function of required integration. Integration was manipulated by comparing the experience of hearing simple reoccurring tones to hearing harmonic tone triplets. The neural correlates of auditory consciousness included superior temporal gyrus, lateral and medial frontal regions, cerebellum, and also parietal cortex. Critically, only activity in left parietal cortex increased significantly as a function of increasing demands on integration. We conclude that integration can explain part of the neural activity associated with the generation conscious experiences, but that much of associated brain activity apparently reflects other processes. Copyright © 2017 Elsevier Inc. All rights reserved.

  1. Day-Ahead Natural Gas Demand Forecasting Using Optimized ABC-Based Neural Network with Sliding Window Technique: The Case Study of Regional Basis in Turkey

    Directory of Open Access Journals (Sweden)

    Mustafa Akpinar

    2017-06-01

    Full Text Available The increase of energy consumption in the world is reflected in the consumption of natural gas. However, this increment requires additional investment. This effect leads imbalances in terms of demand forecasting, such as applying penalties in the case of error rates occurring beyond the acceptable limits. As the forecasting errors increase, penalties increase exponentially. Therefore, the optimal use of natural gas as a scarce resource is important. There are various demand forecast ranges for natural gas and the most difficult range among these demands is the day-ahead forecasting, since it is hard to implement and makes predictions with low error rates. The objective of this study is stabilizing gas tractions on day-ahead demand forecasting using low-consuming subscriber data for minimizing error using univariate artificial bee colony-based artificial neural networks (ANN-ABC. For this purpose, households and low-consuming commercial users’ four-year consumption data between the years of 2011–2014 are gathered in daily periods. Previous consumption values are used to forecast day-ahead consumption values with sliding window technique and other independent variables are not taken into account. Dataset is divided into two parts. First, three-year daily consumption values are used with a seven day window for training the networks, while the last year is used for the day-ahead demand forecasting. Results show that ANN-ABC is a strong, stable, and effective method with a low error rate of 14.9 mean absolute percentage error (MAPE for training utilizing MAPE with a univariate sliding window technique.

  2. Neural correlates of stimulus reportability.

    Science.gov (United States)

    Hulme, Oliver J; Friston, Karl F; Zeki, Semir

    2009-08-01

    Most experiments on the "neural correlates of consciousness" employ stimulus reportability as an operational definition of what is consciously perceived. The interpretation of such experiments therefore depends critically on understanding the neural basis of stimulus reportability. Using a high volume of fMRI data, we investigated the neural correlates of stimulus reportability using a partial report object detection paradigm. Subjects were presented with a random array of circularly arranged disc-stimuli and were cued, after variable delays (following stimulus offset), to report the presence or absence of a disc at the cued location, using variable motor actions. By uncoupling stimulus processing, decision, and motor response, we were able to use signal detection theory to deconstruct the neural basis of stimulus reportability. We show that retinotopically specific responses in the early visual cortex correlate with stimulus processing but not decision or report; a network of parietal/temporal regions correlates with decisions but not stimulus presence, whereas classical motor regions correlate with report. These findings provide a basic framework for understanding the neural basis of stimulus reportability without the theoretical burden of presupposing a relationship between reportability and consciousness.

  3. Complex-Valued Neural Networks

    CERN Document Server

    Hirose, Akira

    2012-01-01

    This book is the second enlarged and revised edition of the first successful monograph on complex-valued neural networks (CVNNs) published in 2006, which lends itself to graduate and undergraduate courses in electrical engineering, informatics, control engineering, mechanics, robotics, bioengineering, and other relevant fields. In the second edition the recent trends in CVNNs research are included, resulting in e.g. almost a doubled number of references. The parametron invented in 1954 is also referred to with discussion on analogy and disparity. Also various additional arguments on the advantages of the complex-valued neural networks enhancing the difference to real-valued neural networks are given in various sections. The book is useful for those beginning their studies, for instance, in adaptive signal processing for highly functional sensing and imaging, control in unknown and changing environment, robotics inspired by human neural systems, and brain-like information processing, as well as interdisciplina...

  4. CHARGEd with neural crest defects.

    Science.gov (United States)

    Pauli, Silke; Bajpai, Ruchi; Borchers, Annette

    2017-10-30

    Neural crest cells are highly migratory pluripotent cells that give rise to diverse derivatives including cartilage, bone, smooth muscle, pigment, and endocrine cells as well as neurons and glia. Abnormalities in neural crest-derived tissues contribute to the etiology of CHARGE syndrome, a complex malformation disorder that encompasses clinical symptoms like coloboma, heart defects, atresia of the choanae, retarded growth and development, genital hypoplasia, ear anomalies, and deafness. Mutations in the chromodomain helicase DNA-binding protein 7 (CHD7) gene are causative of CHARGE syndrome and loss-of-function data in different model systems have firmly established a role of CHD7 in neural crest development. Here, we will summarize our current understanding of the function of CHD7 in neural crest development and discuss possible links of CHARGE syndrome to other developmental disorders. © 2017 Wiley Periodicals, Inc.

  5. Altered neural activation during prepotent response inhibition in breast cancer survivors treated with chemotherapy: an fMRI study.

    Science.gov (United States)

    Kam, Julia W Y; Boyd, Lara A; Hsu, Chun L; Liu-Ambrose, Teresa; Handy, Todd C; Lim, Howard J; Hayden, Sherri; Campbell, Kristin L

    2016-09-01

    While impairments in executive functions have been reported in breast cancer survivors (BCS) who have undergone adjuvant chemotherapy, only a limited number of functional neuroimaging studies have associated alterations in cerebral activity with executive functions deficits in BCS. Using fMRI, the current study assessed the neural basis underlying a specific facet of executive function, namely prepotent response inhibition. 12 BCS who self-reported cognitive problems up to 3 years following cancer treatment and 12 female healthy comparisons (HC) performed the Stroop task. We compared their neural activation between the incongruent and neutral experimental conditions. Relative to the HC group, BCS showed lower blood-oxygen level dependent signal in several frontal regions, including the anterior cingulate cortex, a region critical for response inhibition. Our data indicates reduced neural activation in BCS during a prepotent response inhibition task, providing support for the prevailing notion of neural alterations observed in BCS treated with chemotherapy.

  6. Exploring the neural correlates of visual creativity

    Science.gov (United States)

    Liew, Sook-Lei; Dandekar, Francesco

    2013-01-01

    Although creativity has been called the most important of all human resources, its neural basis is still unclear. In the current study, we used fMRI to measure neural activity in participants solving a visuospatial creativity problem that involves divergent thinking and has been considered a canonical right hemisphere task. As hypothesized, both the visual creativity task and the control task as compared to rest activated a variety of areas including the posterior parietal cortex bilaterally and motor regions, which are known to be involved in visuospatial rotation of objects. However, directly comparing the two tasks indicated that the creative task more strongly activated left hemisphere regions including the posterior parietal cortex, the premotor cortex, dorsolateral prefrontal cortex (DLPFC) and the medial PFC. These results demonstrate that even in a task that is specialized to the right hemisphere, robust parallel activity in the left hemisphere supports creative processing. Furthermore, the results support the notion that higher motor planning may be a general component of creative improvisation and that such goal-directed planning of novel solutions may be organized top-down by the left DLPFC and by working memory processing in the medial prefrontal cortex. PMID:22349801

  7. Zebrafish homologs of genes within 16p11.2, a genomic region associated with brain disorders, are active during brain development, and include two deletion dosage sensor genes

    Directory of Open Access Journals (Sweden)

    Alicia Blaker-Lee

    2012-11-01

    Deletion or duplication of one copy of the human 16p11.2 interval is tightly associated with impaired brain function, including autism spectrum disorders (ASDs, intellectual disability disorder (IDD and other phenotypes, indicating the importance of gene dosage in this copy number variant region (CNV. The core of this CNV includes 25 genes; however, the number of genes that contribute to these phenotypes is not known. Furthermore, genes whose functional levels change with deletion or duplication (termed ‘dosage sensors’, which can associate the CNV with pathologies, have not been identified in this region. Using the zebrafish as a tool, a set of 16p11.2 homologs was identified, primarily on chromosomes 3 and 12. Use of 11 phenotypic assays, spanning the first 5 days of development, demonstrated that this set of genes is highly active, such that 21 out of the 22 homologs tested showed loss-of-function phenotypes. Most genes in this region were required for nervous system development – impacting brain morphology, eye development, axonal density or organization, and motor response. In general, human genes were able to substitute for the fish homolog, demonstrating orthology and suggesting conserved molecular pathways. In a screen for 16p11.2 genes whose function is sensitive to hemizygosity, the aldolase a (aldoaa and kinesin family member 22 (kif22 genes were identified as giving clear phenotypes when RNA levels were reduced by ∼50%, suggesting that these genes are deletion dosage sensors. This study leads to two major findings. The first is that the 16p11.2 region comprises a highly active set of genes, which could present a large genetic target and might explain why multiple brain function, and other, phenotypes are associated with this interval. The second major finding is that there are (at least two genes with deletion dosage sensor properties among the 16p11.2 set, and these could link this CNV to brain disorders such as ASD and IDD.

  8. [Neural repair].

    Science.gov (United States)

    Kitada, Masaaki; Dezawa, Mari

    2008-05-01

    Recent progress of stem cell biology gives us the hope for neural repair. We have established methods to specifically induce functional Schwann cells and neurons from bone marrow stromal cells (MSCs). The effectiveness of these induced cells was evaluated by grafting them either into peripheral nerve injury, spinal cord injury, or Parkinson' s disease animal models. MSCs-derived Schwann cells supported axonal regeneration and re-constructed myelin to facilitate the functional recovery in peripheral and spinal cord injury. MSCs-derived dopaminergic neurons integrated into host striatum and contributed to behavioral repair. In this review, we introduce the differentiation potential of MSCs and finally discuss about their benefits and drawbacks of these induction systems for cell-based therapy in neuro-traumatic and neuro-degenerative diseases.

  9. The neural correlates of temporal reward discounting.

    Science.gov (United States)

    Scheres, Anouk; de Water, Erik; Mies, Gabry W

    2013-09-01

    Temporal reward discounting (TD) refers to the decrease in subjective value of a reward when the delay to that reward increases. In recent years, a growing number of studies on the neural correlates of temporal reward discounting have been conducted. This article focuses on functional magnetic resonance imaging (fMRI) studies on TD in humans. First, we describe the different types of tasks (also from behavioral studies) and the dependent variables. Subsequently, we discuss the evidence for three neurobiological models of TD: the dual-systems model, the single-system model and the self-control model. Further, studies in which nontraditional tasks (e.g., with nonmonetary rewards) were used to study TD are reviewed. Finally, we discuss the neural correlates of individual differences in discounting, and its development across the lifespan. We conclude that the evidence for each of the three neurobiological models of TD is mixed, in that all models receive (partial) support, and several studies provide support for multiple models. Because of large differences between studies in task design and analytical approach, it is difficult to draw a firm conclusion regarding which model provides the best explanation of the neural correlates of temporal discounting. We propose that some components of these models can complement each other, and future studies should test the predictions offered by different models against each other. Several future research directions are suggested, including studying the connectivity between brain regions in relation to discounting, and directly comparing the neural mechanisms involved in discounting of monetary and primary rewards. WIREs Cogn Sci 2013, 4:523-545. doi: 10.1002/wcs.1246 CONFLICT OF INTEREST: The authors have declared no conflicts of interest for this article. For further resources related to this article, please visit the WIREs website. © 2013 John Wiley & Sons, Ltd.

  10. Different neural manifestations of two slow frequency bands in resting functional magnetic resonance imaging: a systemic survey at regional, interregional, and network levels.

    Science.gov (United States)

    Xue, Shao-Wei; Li, Da; Weng, Xu-Chu; Northoff, Georg; Li, Dian-Wen

    2014-05-01

    Temporal and spectral perspectives are two fundamental facets in deciphering fluctuating signals. In resting state, the dynamics of blood oxygen level-dependent (BOLD) signals recorded by functional magnetic resonance imaging (fMRI) have been proven to be strikingly informative (0.01-0.1 Hz). The distinction between slow-4 (0.027-0.073 Hz) and slow-5 (0.01-0.027 Hz) has been described, but the pertinent data have never been systematically investigated. This study used fMRI to measure spontaneous brain activity and to explore the different spectral characteristics of slow-4 and slow-5 at regional, interregional, and network levels, respectively assessed by regional homogeneity (ReHo) and mean amplitude of low-frequency fluctuation (mALFF), functional connectivity (FC) patterns, and graph theory. Results of paired t-tests supported/replicated recent research dividing low-frequency BOLD fluctuation into slow-4 and slow-5 for ReHo and mALFF. Interregional analyses showed that for brain regions reaching statistical significance, FC strengths at slow-4 were always weaker than those at slow-5. Community detection algorithm was applied to FC data and unveiled two modules sensitive to frequency effects: one comprised sensorimotor structure, and the other encompassed limbic/paralimbic system. Graph theoretical analysis verified that slow-4 and slow-5 differed in local segregation measures. Although the manifestation of frequency differences seemed complicated, the associated brain regions can be grossly categorized into limbic/paralimbic, midline, and sensorimotor systems. Our results suggest that future resting fMRI research addressing the three above systems either from neuropsychiatric or psychological perspectives may consider using spectrum-specific analytical strategies.

  11. NeuralWISP: A Wirelessly Powered Neural Interface With 1-m Range.

    Science.gov (United States)

    Yeager, D J; Holleman, J; Prasad, R; Smith, J R; Otis, B P

    2009-12-01

    We present the NeuralWISP, a wireless neural interface operating from far-field radio-frequency RF energy. The NeuralWISP is compatible with commercial RF identification readers and operates at a range up to 1 m. It includes a custom low-noise, low-power amplifier integrated circuit for processing the neural signal and an analog spike detection circuit for reducing digital computational requirements and communications bandwidth. Our system monitors the neural signal and periodically transmits the spike density in a user-programmable time window. The entire system draws an average 20 muA from the harvested 1.8-V supply.

  12. NeuroMEMS: Neural Probe Microtechnologies

    Directory of Open Access Journals (Sweden)

    Sam Musallam

    2008-10-01

    Full Text Available Neural probe technologies have already had a significant positive effect on our understanding of the brain by revealing the functioning of networks of biological neurons. Probes are implanted in different areas of the brain to record and/or stimulate specific sites in the brain. Neural probes are currently used in many clinical settings for diagnosis of brain diseases such as seizers, epilepsy, migraine, Alzheimer’s, and dementia. We find these devices assisting paralyzed patients by allowing them to operate computers or robots using their neural activity. In recent years, probe technologies were assisted by rapid advancements in microfabrication and microelectronic technologies and thus are enabling highly functional and robust neural probes which are opening new and exciting avenues in neural sciences and brain machine interfaces. With a wide variety of probes that have been designed, fabricated, and tested to date, this review aims to provide an overview of the advances and recent progress in the microfabrication techniques of neural probes. In addition, we aim to highlight the challenges faced in developing and implementing ultralong multi-site recording probes that are needed to monitor neural activity from deeper regions in the brain. Finally, we review techniques that can improve the biocompatibility of the neural probes to minimize the immune response and encourage neural growth around the electrodes for long term implantation studies.

  13. 34 CFR 303.15 - Include; including.

    Science.gov (United States)

    2010-07-01

    ... 34 Education 2 2010-07-01 2010-07-01 false Include; including. 303.15 Section 303.15 Education Regulations of the Offices of the Department of Education (Continued) OFFICE OF SPECIAL EDUCATION AND REHABILITATIVE SERVICES, DEPARTMENT OF EDUCATION EARLY INTERVENTION PROGRAM FOR INFANTS AND TODDLERS WITH...

  14. Neural crest contributions to the lamprey head

    Science.gov (United States)

    McCauley, David W.; Bronner-Fraser, Marianne

    2003-01-01

    The neural crest is a vertebrate-specific cell population that contributes to the facial skeleton and other derivatives. We have performed focal DiI injection into the cranial neural tube of the developing lamprey in order to follow the migratory pathways of discrete groups of cells from origin to destination and to compare neural crest migratory pathways in a basal vertebrate to those of gnathostomes. The results show that the general pathways of cranial neural crest migration are conserved throughout the vertebrates, with cells migrating in streams analogous to the mandibular and hyoid streams. Caudal branchial neural crest cells migrate ventrally as a sheet of cells from the hindbrain and super-pharyngeal region of the neural tube and form a cylinder surrounding a core of mesoderm in each pharyngeal arch, similar to that seen in zebrafish and axolotl. In addition to these similarities, we also uncovered important differences. Migration into the presumptive caudal branchial arches of the lamprey involves both rostral and caudal movements of neural crest cells that have not been described in gnathostomes, suggesting that barriers that constrain rostrocaudal movement of cranial neural crest cells may have arisen after the agnathan/gnathostome split. Accordingly, neural crest cells from a single axial level contributed to multiple arches and there was extensive mixing between populations. There was no apparent filling of neural crest derivatives in a ventral-to-dorsal order, as has been observed in higher vertebrates, nor did we find evidence of a neural crest contribution to cranial sensory ganglia. These results suggest that migratory constraints and additional neural crest derivatives arose later in gnathostome evolution.

  15. High Frequency Deep Brain Stimulation and Neural Rhythms in Parkinson's Disease.

    Science.gov (United States)

    Blumenfeld, Zack; Brontë-Stewart, Helen

    2015-12-01

    High frequency (HF) deep brain stimulation (DBS) is an established therapy for the treatment of Parkinson's disease (PD). It effectively treats the cardinal motor signs of PD, including tremor, bradykinesia, and rigidity. The most common neural target is the subthalamic nucleus, located within the basal ganglia, the region most acutely affected by PD pathology. Using chronically-implanted DBS electrodes, researchers have been able to record underlying neural rhythms from several nodes in the PD network as well as perturb it using DBS to measure the ensuing neural and behavioral effects, both acutely and over time. In this review, we provide an overview of the PD neural network, focusing on the pathophysiological signals that have been recorded from PD patients as well as the mechanisms underlying the therapeutic benefits of HF DBS. We then discuss evidence for the relationship between specific neural oscillations and symptoms of PD, including the aberrant relationships potentially underlying functional connectivity in PD as well as the use of different frequencies of stimulation to more specifically target certain symptoms. Finally, we briefly describe several current areas of investigation and how the ability to record neural data in ecologically-valid settings may allow researchers to explore the relationship between brain and behavior in an unprecedented manner, culminating in the future automation of neurostimulation therapy for the treatment of a variety of neuropsychiatric diseases.

  16. Practical neural network recipies in C++

    CERN Document Server

    Masters

    2014-01-01

    This text serves as a cookbook for neural network solutions to practical problems using C++. It will enable those with moderate programming experience to select a neural network model appropriate to solving a particular problem, and to produce a working program implementing that network. The book provides guidance along the entire problem-solving path, including designing the training set, preprocessing variables, training and validating the network, and evaluating its performance. Though the book is not intended as a general course in neural networks, no background in neural works is assum

  17. Fin-and-tube condenser performance evaluation using neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Zhao, Ling-Xiao [Institute of Refrigeration and Cryogenics, Shanghai Jiaotong University, Shanghai 200240 (China); Zhang, Chun-Lu [China R and D Center, Carrier Corporation, No. 3239 Shen Jiang Road, Shanghai 201206 (China)

    2010-05-15

    The paper presents neural network approach to performance evaluation of the fin-and-tube air-cooled condensers which are widely used in air-conditioning and refrigeration systems. Inputs of the neural network include refrigerant and air-flow rates, refrigerant inlet temperature and saturated temperature, and entering air dry-bulb temperature. Outputs of the neural network consist of the heating capacity and the pressure drops on both refrigerant and air sides. The multi-input multi-output (MIMO) neural network is separated into multi-input single-output (MISO) neural networks for training. Afterwards, the trained MISO neural networks are combined into a MIMO neural network, which indicates that the number of training data sets is determined by the biggest MISO neural network not the whole MIMO network. Compared with a validated first-principle model, the standard deviations of neural network models are less than 1.9%, and all errors fall into {+-}5%. (author)

  18. Getting the word out: neural correlates of enthusiastic message propagation.

    Science.gov (United States)

    Falk, Emily B; O'Donnell, Matthew Brook; Lieberman, Matthew D

    2012-01-01

    What happens in the mind of a person who first hears a potentially exciting idea?We examined the neural precursors of spreading ideas with enthusiasm, and dissected enthusiasm into component processes that can be identified through automated linguistic analysis, gestalt human ratings of combined linguistic and non-verbal cues, and points of convergence/divergence between the two. We combined tools from natural language processing (NLP) with data gathered using fMRI to link the neurocognitive mechanisms that are set in motion during initial exposure to ideas and subsequent behaviors of these message communicators outside of the scanner. Participants' neural activity was recorded as they reviewed ideas for potential television show pilots. Participants' language from video-taped interviews collected post-scan was transcribed and given to an automated linguistic sentiment analysis (SA) classifier, which returned ratings for evaluative language (evaluative vs. descriptive) and valence (positive vs. negative). Separately, human coders rated the enthusiasm with which participants transmitted each idea. More positive sentiment ratings by the automated classifier were associated with activation in neural regions including medial prefrontal cortex; MPFC, precuneus/posterior cingulate cortex; PC/PCC, and medial temporal lobe; MTL. More evaluative, positive, descriptions were associated exclusively with neural activity in temporal-parietal junction (TPJ). Finally, human ratings indicative of more enthusiastic sentiment were associated with activation across these regions (MPFC, PC/PCC, DMPFC, TPJ, and MTL) as well as in ventral striatum (VS), inferior parietal lobule and premotor cortex. Taken together, these data demonstrate novel links between neural activity during initial idea encoding and the enthusiasm with which the ideas are subsequently delivered. This research lays the groundwork to use machine learning and neuroimaging data to study word of mouth communication and

  19. Getting the word out: Neural correlates of enthusiastic message propagation

    Directory of Open Access Journals (Sweden)

    Emily eFalk

    2012-11-01

    Full Text Available What happens in the mind of a person who first hears a potentially exciting idea? We examined the neural precursors of spreading ideas with enthusiasm, and dissect enthusiasm into component processes that can be identified through automated linguistic analysis, gestalt human ratings of combined linguistic and non-verbal cues, and points of convergence/divergence between the two. We combined tools from natural language processing with data gathered using fMRI, to link the neurocognitive mechanisms that are set in motion during initial exposure to ideas and subsequent behaviors of these message communicators outside of the scanner. Participants’ neural activity was recorded as they reviewed ideas for potential television show pilots. Participants’ language from video-taped interviews collected post-scan was transcribed and given to an automated linguistic sentiment analysis classifier, which returned ratings for evaluative language (evaluative vs. descriptive and valence (positive vs. negative. Separately, human coders rated the enthusiasm with which participants transmitted each idea. More positive sentiment ratings by the automated classifier were associated with activation in neural regions including medial prefrontal cortex; MPFC, precuneus/posterior cingulate cortex; PC/PCC, and medial temporal lobe; MTL. More evaluative, positive, descriptions were associated exclusively with neural activity in temporal parietal junction (TPJ. Finally, human ratings indicative of more enthusiastic sentiment were associated with activation across these regions (MPFC, PC/PCC, DMPFC, TPJ, MTL as well as in ventral striatum, inferior parietal lobule and premotor cortex. Taken together, these data demonstrate novel links between neural activity during initial idea encoding and the enthusiasm with which the ideas are subsequently delivered. These data also demonstrate the novel use of machine learning tools to link natural language data to neuroimaging data.

  20. Neural networks and statistical learning

    CERN Document Server

    Du, Ke-Lin

    2014-01-01

    Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop a practical working understanding of the content. Each of the twenty-five chapters includes state-of-the-art descriptions and important research results on the respective topics. The broad coverage includes the multilayer perceptron, the Hopfield network, associative memory models, clustering models and algorithms, the radial basis function network, recurrent neural networks, principal component analysis, nonnegative matrix factorization, independent component analysis, discriminant analysis, support vector machines, kernel methods, reinforcement learning, probabilistic and Bayesian networks, data fusion and ensemble learning, fuzzy sets and logic, neurofuzzy models, hardw...

  1. Functional neural correlates of fluid and crystallized analogizing.

    Science.gov (United States)

    Geake, John G; Hansen, Peter C

    2010-02-15

    The main aim of this study was to characterize neural correlates of analogizing as a cognitive contributor to fluid and crystallized intelligence. In a previous fMRI study which employed fluid analogy letter strings as criteria in a multiple plausibility design (Geake and Hansen, 2005), two frontal ROIs associated with working memory (WM) load (within BA 9 and BA 45/46) were identified as regions in which BOLD increase correlated positively with a crystallized measure of (verbal) IQ. In this fMRI study we used fluid letter, number and polygon strings to further investigate the role of analogizing in fluid (transformation string completion) and non fluid or crystallized (unique symbol counting) cognitive tasks. The multi stimulus type (letter, number, polygon) design of the analogy strings enabled investigation of a secondary research question concerning the generalizability of fluid analogizing at a neural level. A selective psychometric battery, including the Raven's Progressive Matrices (RPM), measured individual cognitive abilities. Neural activations for the effect of task-fluid analogizing (string transformation plausibility) vs. crystallized analogizing (unique symbol counting)-included bilateral frontal and parietal areas associated with WM load and fronto parietal models of general intelligence. Neural activations for stimulus type differences were mainly confined to visually specific posterior regions. ROI covariate analyses of the psychometric measures failed to find consistent co-relationships between fluid analogizing and the RPM and other subtests, except for the WAIS Digit Symbol subtest in a group of bilateral frontal cortical regions associated with the maintenance of WM load. Together, these results support claims for separate developmental trajectories for fluid cognition and general intelligence as assessed by these psychometric subtests. Copyright 2009 Elsevier Inc. All rights reserved.

  2. Classification of Urinary Calculi using Feed-Forward Neural Networks

    African Journals Online (AJOL)

    In this work the results of classification of these types of calculi (using their infrared spectra in the region 1450–450 cm–1) by feed-forward neural networks are presented. Genetic algorithms were used for optimization of neural networks and for selection of the spectral regions most suitable for classification purposes.

  3. Multilayer perceptron neural network for downscaling rainfall in arid ...

    Indian Academy of Sciences (India)

    Multilayer perceptron neural network for downscaling rainfall in arid region: A case study of Baluchistan, Pakistan ... A multilayer perceptron (MLP) neural network has been proposed in the present study for the downscaling of rainfall in the data scarce arid region of Baluchistan province of Pakistan, which is considered as ...

  4. Neural mechanisms of discourse comprehension: a human lesion study.

    Science.gov (United States)

    Barbey, Aron K; Colom, Roberto; Grafman, Jordan

    2014-01-01

    Discourse comprehension is a hallmark of human social behaviour and refers to the act of interpreting a written or spoken message by constructing mental representations that integrate incoming language with prior knowledge and experience. Here, we report a human lesion study (n = 145) that investigates the neural mechanisms underlying discourse comprehension (measured by the Discourse Comprehension Test) and systematically examine its relation to a broad range of psychological factors, including psychometric intelligence (measured by the Wechsler Adult Intelligence Scale), emotional intelligence (measured by the Mayer, Salovey, Caruso Emotional Intelligence Test), and personality traits (measured by the Neuroticism-Extraversion-Openness Personality Inventory). Scores obtained from these factors were submitted to voxel-based lesion-symptom mapping to elucidate their neural substrates. Stepwise regression analyses revealed that working memory and extraversion reliably predict individual differences in discourse comprehension: higher working memory scores and lower extraversion levels predict better discourse comprehension performance. Lesion mapping results indicated that these convergent variables depend on a shared network of frontal and parietal regions, including white matter association tracts that bind these areas into a coordinated system. The observed findings motivate an integrative framework for understanding the neural foundations of discourse comprehension, suggesting that core elements of discourse processing emerge from a distributed network of brain regions that support specific competencies for executive and social function.

  5. Neural Computations in a Dynamical System with Multiple Time Scales

    Science.gov (United States)

    Mi, Yuanyuan; Lin, Xiaohan; Wu, Si

    2016-01-01

    Neural systems display rich short-term dynamics at various levels, e.g., spike-frequency adaptation (SFA) at the single-neuron level, and short-term facilitation (STF) and depression (STD) at the synapse level. These dynamical features typically cover a broad range of time scales and exhibit large diversity in different brain regions. It remains unclear what is the computational benefit for the brain to have such variability in short-term dynamics. In this study, we propose that the brain can exploit such dynamical features to implement multiple seemingly contradictory computations in a single neural circuit. To demonstrate this idea, we use continuous attractor neural network (CANN) as a working model and include STF, SFA and STD with increasing time constants in its dynamics. Three computational tasks are considered, which are persistent activity, adaptation, and anticipative tracking. These tasks require conflicting neural mechanisms, and hence cannot be implemented by a single dynamical feature or any combination with similar time constants. However, with properly coordinated STF, SFA and STD, we show that the network is able to implement the three computational tasks concurrently. We hope this study will shed light on the understanding of how the brain orchestrates its rich dynamics at various levels to realize diverse cognitive functions. PMID:27679569

  6. Foil bearing lubrication theory including compressibility effects

    Science.gov (United States)

    Gorla, Rama Subba Reddy; Catalano, Daniel A.

    1987-01-01

    An analysis is presented to determine the film thickness in a foil bearing. Using the Reynolds equation and including the compressibility effects of the gas, an equation was developed applicable to the film thickness in a foil bearing. The bearing was divided into three regions, namely, the entrance region, middle region and exit region. Solutions are obtained for the film thickness in each region.

  7. Can modular psychological concepts like affect and emotion be assigned to a distinct subset of regional neural circuits?. Comment on "The quartet theory of human emotions: An integrative and neurofunctional model" by S. Koelsch et al.

    Science.gov (United States)

    Fehr, Thorsten; Herrmann, Manfred

    2015-06-01

    regional brain systems or neural modules, but rather suggest highly complex and cross-linked neural networks individually shaped by livelong learning and experience [e.g., 6,7,10,13]. This holds in particular true for complex emotional phenomena such as aggression or empathy in social interaction [8,13]. It thus remains questionable, whether - beyond primary sensory and motor-processing - a small number of modular sub-systems sufficiently cover the organisation of specific phenomenological and social features of perception and behaviour [7,10].

  8. Neural network optimization, components, and design selection

    Science.gov (United States)

    Weller, Scott W.

    1991-01-01

    Neural Networks are part of a revived technology which has received a lot of hype in recent years. As is apt to happen in any hyped technology, jargon and predictions make its assimilation and application difficult. Nevertheless, Neural Networks have found use in a number of areas, working on non-trivial and non-contrived problems. For example, one net has been trained to "read", translating English text into phoneme sequences. Other applications of Neural Networks include data base manipulation and the solving of routing and classification types of optimization problems. It was their use in optimization that got me involved with Neural Networks. As it turned out, "optimization" used in this context was somewhat misleading, because while some network configurations could indeed solve certain kinds of optimization problems, the configuring or "training" of a Neural Network itself is an optimization problem, and most of the literature which talked about Neural Nets and optimization in the same breath did not speak to my goal of using Neural Nets to help solve lens optimization problems. I did eventually apply Neural Network to lens optimization, and I will touch on those results. The application of Neural Nets to the problem of lens selection was much more successful, and those results will dominate this paper.

  9. The neural correlates of reciprocity are sensitive to prior experience of reciprocity.

    Science.gov (United States)

    Cáceda, Ricardo; Prendes-Alvarez, Stefania; Hsu, Jung-Jiin; Tripathi, Shanti P; Kilts, Clint D; James, G Andrew

    2017-08-14

    Reciprocity is central to human relationships and is strongly influenced by multiple factors including the nature of social exchanges and their attendant emotional reactions. Despite recent advances in the field, the neural processes involved in this modulation of reciprocal behavior by ongoing social interaction are poorly understood. We hypothesized that activity within a discrete set of neural networks including a putative moral cognitive neural network is associated with reciprocity behavior. Nineteen healthy adults underwent functional magnetic resonance imaging scanning while playing the trustee role in the Trust Game. Personality traits and moral development were assessed. Independent component analysis was used to identify task-related functional brain networks and assess their relationship to behavior. The saliency network (insula and anterior cingulate) was positively correlated with reciprocity behavior. A consistent array of brain regions supports the engagement of emotional, self-referential and planning processes during social reciprocity behavior. Published by Elsevier B.V.

  10. Artificial Neural Network Analysis of Xinhui Pericarpium Citri ...

    African Journals Online (AJOL)

    Purpose: To develop an effective analytical method to distinguish old peels of Xinhui Pericarpium citri reticulatae (XPCR) stored for > 3 years from new peels stored for < 3 years. Methods: Artificial neural networks (ANN) models, including general regression neural network (GRNN) and multi-layer feedforward neural ...

  11. Analysis of Power Laws, Shape Collapses, and Neural Complexity: New Techniques and MATLAB Support via the NCC Toolbox.

    Science.gov (United States)

    Marshall, Najja; Timme, Nicholas M; Bennett, Nicholas; Ripp, Monica; Lautzenhiser, Edward; Beggs, John M

    2016-01-01

    Neural systems include interactions that occur across many scales. Two divergent methods for characterizing such interactions have drawn on the physical analysis of critical phenomena and the mathematical study of information. Inferring criticality in neural systems has traditionally rested on fitting power laws to the property distributions of "neural avalanches" (contiguous bursts of activity), but the fractal nature of avalanche shapes has recently emerged as another signature of criticality. On the other hand, neural complexity, an information theoretic measure, has been used to capture the interplay between the functional localization of brain regions and their integration for higher cognitive functions. Unfortunately, treatments of all three methods-power-law fitting, avalanche shape collapse, and neural complexity-have suffered from shortcomings. Empirical data often contain biases that introduce deviations from true power law in the tail and head of the distribution, but deviations in the tail have often been unconsidered; avalanche shape collapse has required manual parameter tuning; and the estimation of neural complexity has relied on small data sets or statistical assumptions for the sake of computational efficiency. In this paper we present technical advancements in the analysis of criticality and complexity in neural systems. We use maximum-likelihood estimation to automatically fit power laws with left and right cutoffs, present the first automated shape collapse algorithm, and describe new techniques to account for large numbers of neural variables and small data sets in the calculation of neural complexity. In order to facilitate future research in criticality and complexity, we have made the software utilized in this analysis freely available online in the MATLAB NCC (Neural Complexity and Criticality) Toolbox.

  12. The neural basis of testable and non-testable beliefs.

    Directory of Open Access Journals (Sweden)

    Jonathon R Howlett

    Full Text Available Beliefs about the state of the world are an important influence on both normal behavior and psychopathology. However, understanding of the neural basis of belief processing remains incomplete, and several aspects of belief processing have only recently been explored. Specifically, different types of beliefs may involve fundamentally different inferential processes and thus recruit distinct brain regions. Additionally, neural processing of truth and falsity may differ from processing of certainty and uncertainty. The purpose of this study was to investigate the neural underpinnings of assessment of testable and non-testable propositions in terms of truth or falsity and the level of certainty in a belief. Functional magnetic resonance imaging (fMRI was used to study 14 adults while they rated propositions as true or false and also rated the level of certainty in their judgments. Each proposition was classified as testable or non-testable. Testable propositions activated the DLPFC and posterior cingulate cortex, while non-testable statements activated areas including inferior frontal gyrus, superior temporal gyrus, and an anterior region of the superior frontal gyrus. No areas were more active when a proposition was accepted, while the dorsal anterior cingulate was activated when a proposition was rejected. Regardless of whether a proposition was testable or not, certainty that the proposition was true or false activated a common network of regions including the medial prefrontal cortex, caudate, posterior cingulate, and a region of middle temporal gyrus near the temporo-parietal junction. Certainty in the truth or falsity of a non-testable proposition (a strong belief without empirical evidence activated the insula. The results suggest that different brain regions contribute to the assessment of propositions based on the type of content, while a common network may mediate the influence of beliefs on motivation and behavior based on the level of

  13. Multiple mechanisms of consciousness: the neural correlates of emotional awareness.

    Science.gov (United States)

    Amting, Jayna M; Greening, Steven G; Mitchell, Derek G V

    2010-07-28

    Emotional stimuli, including facial expressions, are thought to gain rapid and privileged access to processing resources in the brain. Despite this access, we are conscious of only a fraction of the myriad of emotion-related cues we face everyday. It remains unclear, therefore, what the relationship is between activity in neural regions associated with emotional representation and the phenomenological experience of emotional awareness. We used functional magnetic resonance imaging and binocular rivalry to delineate the neural correlates of awareness of conflicting emotional expressions in humans. Behaviorally, fearful faces were significantly more likely to be perceived than disgusted or neutral faces. Functionally, increased activity was observed in regions associated with facial expression processing, including the amygdala and fusiform gyrus during emotional awareness. In contrast, awareness of neutral faces and suppression of fearful faces were associated with increased activity in dorsolateral prefrontal and inferior parietal cortices. The amygdala showed increased functional connectivity with ventral visual system regions during fear awareness and increased connectivity with perigenual prefrontal cortex (pgPFC; Brodmann's area 32/10) when fear was suppressed. Despite being prioritized for awareness, emotional items were associated with reduced activity in areas considered critical for consciousness. Contributions to consciousness from bottom-up and top-down neural regions may be additive, such that increased activity in specialized regions within the extended ventral visual system may reduce demands on a frontoparietal system important for awareness. The possibility is raised that interactions between pgPFC and the amygdala, previously implicated in extinction, may also influence whether or not an emotional stimulus is accessible to consciousness.

  14. International Conference on Artificial Neural Networks (ICANN)

    CERN Document Server

    Mladenov, Valeri; Kasabov, Nikola; Artificial Neural Networks : Methods and Applications in Bio-/Neuroinformatics

    2015-01-01

    The book reports on the latest theories on artificial neural networks, with a special emphasis on bio-neuroinformatics methods. It includes twenty-three papers selected from among the best contributions on bio-neuroinformatics-related issues, which were presented at the International Conference on Artificial Neural Networks, held in Sofia, Bulgaria, on September 10-13, 2013 (ICANN 2013). The book covers a broad range of topics concerning the theory and applications of artificial neural networks, including recurrent neural networks, super-Turing computation and reservoir computing, double-layer vector perceptrons, nonnegative matrix factorization, bio-inspired models of cell communities, Gestalt laws, embodied theory of language understanding, saccadic gaze shifts and memory formation, and new training algorithms for Deep Boltzmann Machines, as well as dynamic neural networks and kernel machines. It also reports on new approaches to reinforcement learning, optimal control of discrete time-delay systems, new al...

  15. Effects of oxytocin and vasopressin on the neural response to unreciprocated cooperation within brain regions involved in stress and anxiety in men and women.

    Science.gov (United States)

    Chen, Xu; Hackett, Patrick D; DeMarco, Ashley C; Feng, Chunliang; Stair, Sabrina; Haroon, Ebrahim; Ditzen, Beate; Pagnoni, Giuseppe; Rilling, James K

    2016-06-01

    Anxiety disorders are characterized by hyperactivity in both the amygdala and the anterior insula. Interventions that normalize activity in these areas may therefore be effective in treating anxiety disorders. Recently, there has been significant interest in the potential use of oxytocin (OT), as well as vasopressin (AVP) antagonists, as treatments for anxiety disorders. In this double-blind, placebo-controlled, pharmaco- fMRI study, 153 men and 151 women were randomized to treatment with either 24 IU intranasal OT, 20 IU intranasal AVP, or placebo and imaged with fMRI as they played the iterated Prisoner's Dilemma game with same-sex human and computer partners. In men, OT attenuated the fMRI response to unreciprocated cooperation (CD), a negative social interaction, within the amygdala and anterior insula. This effect was specific to interactions with human partners. In contrast, among women, OT unexpectedly attenuated the amygdala and anterior insula response to unreciprocated cooperation from computer but not human partners. Among women, AVP did not significantly modulate the response to unreciprocated cooperation in either the amygdala or the anterior insula. However, among men, AVP attenuated the BOLD response to CD outcomes with human partners across a relatively large cluster including the amygdala and the anterior insula, which was contrary to expectations. Our results suggest that OT may decrease the stress of negative social interactions among men, whereas these effects were not found in women interacting with human partners. These findings support continued investigation into the possible efficacy of OT as a treatment for anxiety disorders.

  16. Optimizing neural network models: motivation and case studies

    OpenAIRE

    Harp, S A; T. Samad

    2012-01-01

    Practical successes have been achieved  with neural network models in a variety of domains, including energy-related industry. The large, complex design space presented by neural networks is only minimally explored in current practice. The satisfactory results that nevertheless have been obtained testify that neural networks are a robust modeling technology; at the same time, however, the lack of a systematic design approach implies that the best neural network models generally  rem...

  17. Introduction to neural networks

    CERN Document Server

    James, Frederick E

    1994-02-02

    1. Introduction and overview of Artificial Neural Networks. 2,3. The Feed-forward Network as an inverse Problem, and results on the computational complexity of network training. 4.Physics applications of neural networks.

  18. Morphological neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Ritter, G.X.; Sussner, P. [Univ. of Florida, Gainesville, FL (United States)

    1996-12-31

    The theory of artificial neural networks has been successfully applied to a wide variety of pattern recognition problems. In this theory, the first step in computing the next state of a neuron or in performing the next layer neural network computation involves the linear operation of multiplying neural values by their synaptic strengths and adding the results. Thresholding usually follows the linear operation in order to provide for nonlinearity of the network. In this paper we introduce a novel class of neural networks, called morphological neural networks, in which the operations of multiplication and addition are replaced by addition and maximum (or minimum), respectively. By taking the maximum (or minimum) of sums instead of the sum of products, morphological network computation is nonlinear before thresholding. As a consequence, the properties of morphological neural networks are drastically different than those of traditional neural network models. In this paper we consider some of these differences and provide some particular examples of morphological neural network.

  19. Experiencing Past and Future Personal Events: Functional Neuroimaging Evidence on the Neural Bases of Mental Time Travel

    Science.gov (United States)

    Botzung, Anne; Denkova, Ekaterina; Manning, Lilianne

    2008-01-01

    Functional MRI was used in healthy subjects to investigate the existence of common neural structures supporting re-experiencing the past and pre-experiencing the future. Past and future events evocation appears to involve highly similar patterns of brain activation including, in particular, the medial prefrontal cortex, posterior regions and the…

  20. Cognitive Flexibility through Metastable Neural Dynamics Is Disrupted by Damage to the Structural Connectome

    Science.gov (United States)

    Hellyer, Peter J.; Scott, Gregory; Shanahan, Murray; Sharp, David J.

    2015-01-01

    Current theory proposes that healthy neural dynamics operate in a metastable regime, where brain regions interact to simultaneously maximize integration and segregation. Metastability may confer important behavioral properties, such as cognitive flexibility. It is increasingly recognized that neural dynamics are constrained by the underlying structural connections between brain regions. An important challenge is, therefore, to relate structural connectivity, neural dynamics, and behavior. Traumatic brain injury (TBI) is a pre-eminent structural disconnection disorder whereby traumatic axonal injury damages large-scale connectivity, producing characteristic cognitive impairments, including slowed information processing speed and reduced cognitive flexibility, that may be a result of disrupted metastable dynamics. Therefore, TBI provides an experimental and theoretical model to examine how metastable dynamics relate to structural connectivity and cognition. Here, we use complementary empirical and computational approaches to investigate how metastability arises from the healthy structural connectome and relates to cognitive performance. We found reduced metastability in large-scale neural dynamics after TBI, measured with resting-state functional MRI. This reduction in metastability was associated with damage to the connectome, measured using diffusion MRI. Furthermore, decreased metastability was associated with reduced cognitive flexibility and information processing. A computational model, defined by empirically derived connectivity data, demonstrates how behaviorally relevant changes in neural dynamics result from structural disconnection. Our findings suggest how metastable dynamics are important for normal brain function and contingent on the structure of the human connectome. PMID:26085630

  1. Compassion training alters altruism and neural responses to suffering.

    Science.gov (United States)

    Weng, Helen Y; Fox, Andrew S; Shackman, Alexander J; Stodola, Diane E; Caldwell, Jessica Z K; Olson, Matthew C; Rogers, Gregory M; Davidson, Richard J

    2013-07-01

    Compassion is a key motivator of altruistic behavior, but little is known about individuals' capacity to cultivate compassion through training. We examined whether compassion may be systematically trained by testing whether (a) short-term compassion training increases altruistic behavior and (b) individual differences in altruism are associated with training-induced changes in neural responses to suffering. In healthy adults, we found that compassion training increased altruistic redistribution of funds to a victim encountered outside of the training context. Furthermore, increased altruistic behavior after compassion training was associated with altered activation in brain regions implicated in social cognition and emotion regulation, including the inferior parietal cortex and dorsolateral prefrontal cortex (DLPFC), and in DLPFC connectivity with the nucleus accumbens. These results suggest that compassion can be cultivated with training and that greater altruistic behavior may emerge from increased engagement of neural systems implicated in understanding the suffering of other people, executive and emotional control, and reward processing.

  2. Integrative taxonomy of a new species of planarian from the Lake Ohrid basin, including an analysis of biogeographical patterns in freshwater triclads from the Ohrid region (Platyhelminthes, Tricladida, Dugesiidae

    Directory of Open Access Journals (Sweden)

    Giacinta Stocchino

    2013-06-01

    Full Text Available A new species of the genus Dugesia is described from the Lake Ohrid region in the western part of the Balkan Peninsula, forming the first fully documented species description for this genus in the Ohrid area. The morphological species delimitation is supported by complementary molecular, karyological, and cytogenetic data available from the literature. Therefore, species delineation is based on a truly integrative approach. Further, a short account on the degree of freshwater planarian endemicity in the Ohrid region is provided.

  3. Isolating Discriminant Neural Activity in the Presence of Eye Movements and Concurrent Task Demands

    Directory of Open Access Journals (Sweden)

    Jon Touryan

    2017-07-01

    Full Text Available A growing number of studies use the combination of eye-tracking and electroencephalographic (EEG measures to explore the neural processes that underlie visual perception. In these studies, fixation-related potentials (FRPs are commonly used to quantify early and late stages of visual processing that follow the onset of each fixation. However, FRPs reflect a mixture of bottom-up (sensory-driven and top-down (goal-directed processes, in addition to eye movement artifacts and unrelated neural activity. At present there is little consensus on how to separate this evoked response into its constituent elements. In this study we sought to isolate the neural sources of target detection in the presence of eye movements and over a range of concurrent task demands. Here, participants were asked to identify visual targets (Ts amongst a grid of distractor stimuli (Ls, while simultaneously performing an auditory N-back task. To identify the discriminant activity, we used independent components analysis (ICA for the separation of EEG into neural and non-neural sources. We then further separated the neural sources, using a modified measure-projection approach, into six regions of interest (ROIs: occipital, fusiform, temporal, parietal, cingulate, and frontal cortices. Using activity from these ROIs, we identified target from non-target fixations in all participants at a level similar to other state-of-the-art classification techniques. Importantly, we isolated the time course and spectral features of this discriminant activity in each ROI. In addition, we were able to quantify the effect of cognitive load on both fixation-locked potential and classification performance across regions. Together, our results show the utility of a measure-projection approach for separating task-relevant neural activity into meaningful ROIs within more complex contexts that include eye movements.

  4. A dynamic neural field model of temporal order judgments.

    Science.gov (United States)

    Hecht, Lauren N; Spencer, John P; Vecera, Shaun P

    2015-12-01

    Temporal ordering of events is biased, or influenced, by perceptual organization-figure-ground organization-and by spatial attention. For example, within a region assigned figural status or at an attended location, onset events are processed earlier (Lester, Hecht, & Vecera, 2009; Shore, Spence, & Klein, 2001), and offset events are processed for longer durations (Hecht & Vecera, 2011; Rolke, Ulrich, & Bausenhart, 2006). Here, we present an extension of a dynamic field model of change detection (Johnson, Spencer, Luck, & Schöner, 2009; Johnson, Spencer, & Schöner, 2009) that accounts for both the onset and offset performance for figural and attended regions. The model posits that neural populations processing the figure are more active, resulting in a peak of activation that quickly builds toward a detection threshold when the onset of a target is presented. This same enhanced activation for some neural populations is maintained when a present target is removed, creating delays in the perception of the target's offset. We discuss the broader implications of this model, including insights regarding how neural activation can be generated in response to the disappearance of information. (c) 2015 APA, all rights reserved).

  5. Emerging trends in neuro engineering and neural computation

    CERN Document Server

    Lee, Kendall; Garmestani, Hamid; Lim, Chee

    2017-01-01

    This book focuses on neuro-engineering and neural computing, a multi-disciplinary field of research attracting considerable attention from engineers, neuroscientists, microbiologists and material scientists. It explores a range of topics concerning the design and development of innovative neural and brain interfacing technologies, as well as novel information acquisition and processing algorithms to make sense of the acquired data. The book also highlights emerging trends and advances regarding the applications of neuro-engineering in real-world scenarios, such as neural prostheses, diagnosis of neural degenerative diseases, deep brain stimulation, biosensors, real neural network-inspired artificial neural networks (ANNs) and the predictive modeling of information flows in neuronal networks. The book is broadly divided into three main sections including: current trends in technological developments, neural computation techniques to make sense of the neural behavioral data, and application of these technologie...

  6. Application of neural networks in coastal engineering

    Digital Repository Service at National Institute of Oceanography (India)

    Mandal, S.

    methods. That is why it is becoming popular in various fields including coastal engineering. Waves and tides will play important roles in coastal erosion or accretion. This paper briefly describes the back-propagation neural networks and its application...

  7. Neural scaling laws for an uncertain world

    CERN Document Server

    Howard, Marc W

    2016-01-01

    The Weber-Fechner law describes the form of psychological space in many behavioral experiments involving perception of one-dimensional physical quantities. If the physical quantity is expressed using multiple neural receptors, then placing receptive fields evenly along a logarithmic scale naturally leads to the psychological Weber-Fechner law. In the visual system, the spacing and width of extrafoveal receptive fields are consistent with logarithmic scaling. Other sets of neural "receptors" appear to show the same qualitative properties, suggesting that this form of neural scaling reflects a solution to a very general problem. This paper argues that these neural scaling laws enable the brain to represent information about the world efficiently without making any assumptions about the statistics of the world. This analysis suggests that the organization of neural scales to represent one-dimensional quantities, including more abstract quantities such as numerosity, time, and allocentric space, should have a uni...

  8. Neural networks with discontinuous/impact activations

    CERN Document Server

    Akhmet, Marat

    2014-01-01

    This book presents as its main subject new models in mathematical neuroscience. A wide range of neural networks models with discontinuities are discussed, including impulsive differential equations, differential equations with piecewise constant arguments, and models of mixed type. These models involve discontinuities, which are natural because huge velocities and short distances are usually observed in devices modeling the networks. A discussion of the models, appropriate for the proposed applications, is also provided. This book also: Explores questions related to the biological underpinning for models of neural networks\\ Considers neural networks modeling using differential equations with impulsive and piecewise constant argument discontinuities Provides all necessary mathematical basics for application to the theory of neural networks Neural Networks with Discontinuous/Impact Activations is an ideal book for researchers and professionals in the field of engineering mathematics that have an interest in app...

  9. 22nd Italian Workshop on Neural Nets

    CERN Document Server

    Bassis, Simone; Esposito, Anna; Morabito, Francesco

    2013-01-01

    This volume collects a selection of contributions which has been presented at the 22nd Italian Workshop on Neural Networks, the yearly meeting of the Italian Society for Neural Networks (SIREN). The conference was held in Italy, Vietri sul Mare (Salerno), during May 17-19, 2012. The annual meeting of SIREN is sponsored by International Neural Network Society (INNS), European Neural Network Society (ENNS) and IEEE Computational Intelligence Society (CIS). The book – as well as the workshop-  is organized in three main components, two special sessions and a group of regular sessions featuring different aspects and point of views of artificial neural networks and natural intelligence, also including applications of present compelling interest.

  10. Decentralized neural control application to robotics

    CERN Document Server

    Garcia-Hernandez, Ramon; Sanchez, Edgar N; Alanis, Alma y; Ruz-Hernandez, Jose A

    2017-01-01

    This book provides a decentralized approach for the identification and control of robotics systems. It also presents recent research in decentralized neural control and includes applications to robotics. Decentralized control is free from difficulties due to complexity in design, debugging, data gathering and storage requirements, making it preferable for interconnected systems. Furthermore, as opposed to the centralized approach, it can be implemented with parallel processors. This approach deals with four decentralized control schemes, which are able to identify the robot dynamics. The training of each neural network is performed on-line using an extended Kalman filter (EKF). The first indirect decentralized control scheme applies the discrete-time block control approach, to formulate a nonlinear sliding manifold. The second direct decentralized neural control scheme is based on the backstepping technique, approximated by a high order neural network. The third control scheme applies a decentralized neural i...

  11. Medical image analysis with artificial neural networks.

    Science.gov (United States)

    Jiang, J; Trundle, P; Ren, J

    2010-12-01

    Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging. Copyright © 2010 Elsevier Ltd. All rights reserved.

  12. Spatially Nonlinear Interdependence of Alpha-Oscillatory Neural Networks under Chan Meditation

    Directory of Open Access Journals (Sweden)

    Pei-Chen Lo

    2013-01-01

    Full Text Available This paper reports the results of our investigation of the effects of Chan meditation on brain electrophysiological behaviors from the viewpoint of spatially nonlinear interdependence among regional neural networks. Particular emphasis is laid on the alpha-dominated EEG (electroencephalograph. Continuous-time wavelet transform was adopted to detect the epochs containing substantial alpha activities. Nonlinear interdependence quantified by similarity index S(X∣Y, the influence of source signal Y on sink signal X, was applied to the nonlinear dynamical model in phase space reconstructed from multichannel EEG. Experimental group involved ten experienced Chan-Meditation practitioners, while control group included ten healthy subjects within the same age range, yet, without any meditation experience. Nonlinear interdependence among various cortical regions was explored for five local neural-network regions, frontal, posterior, right-temporal, left-temporal, and central regions. In the experimental group, the inter-regional interaction was evaluated for the brain dynamics under three different stages, at rest (stage R, pre-meditation background recording, in Chan meditation (stage M, and the unique Chakra-focusing practice (stage C. Experimental group exhibits stronger interactions among various local neural networks at stages M and C compared with those at stage R. The intergroup comparison demonstrates that Chan-meditation brain possesses better cortical inter-regional interactions than the resting brain of control group.

  13. Spatially Nonlinear Interdependence of Alpha-Oscillatory Neural Networks under Chan Meditation

    Science.gov (United States)

    Chang, Chih-Hao

    2013-01-01

    This paper reports the results of our investigation of the effects of Chan meditation on brain electrophysiological behaviors from the viewpoint of spatially nonlinear interdependence among regional neural networks. Particular emphasis is laid on the alpha-dominated EEG (electroencephalograph). Continuous-time wavelet transform was adopted to detect the epochs containing substantial alpha activities. Nonlinear interdependence quantified by similarity index S(X∣Y), the influence of source signal Y on sink signal X, was applied to the nonlinear dynamical model in phase space reconstructed from multichannel EEG. Experimental group involved ten experienced Chan-Meditation practitioners, while control group included ten healthy subjects within the same age range, yet, without any meditation experience. Nonlinear interdependence among various cortical regions was explored for five local neural-network regions, frontal, posterior, right-temporal, left-temporal, and central regions. In the experimental group, the inter-regional interaction was evaluated for the brain dynamics under three different stages, at rest (stage R, pre-meditation background recording), in Chan meditation (stage M), and the unique Chakra-focusing practice (stage C). Experimental group exhibits stronger interactions among various local neural networks at stages M and C compared with those at stage R. The intergroup comparison demonstrates that Chan-meditation brain possesses better cortical inter-regional interactions than the resting brain of control group. PMID:24489583

  14. Robust adaptive fuzzy neural tracking control for a class of unknown ...

    Indian Academy of Sciences (India)

    In this paper, an adaptive fuzzy neural controller (AFNC) for a class of unknown chaotic systems is proposed. The proposed AFNC is comprised of a fuzzy neural controller and a robust controller. The fuzzy neural controller including a fuzzy neural network identifier (FNNI) is the principal controller. The FNNI is used for ...

  15. Theory of mind: a neural prediction problem.

    Science.gov (United States)

    Koster-Hale, Jorie; Saxe, Rebecca

    2013-09-04

    Predictive coding posits that neural systems make forward-looking predictions about incoming information. Neural signals contain information not about the currently perceived stimulus, but about the difference between the observed and the predicted stimulus. We propose to extend the predictive coding framework from high-level sensory processing to the more abstract domain of theory of mind; that is, to inferences about others' goals, thoughts, and personalities. We review evidence that, across brain regions, neural responses to depictions of human behavior, from biological motion to trait descriptions, exhibit a key signature of predictive coding: reduced activity to predictable stimuli. We discuss how future experiments could distinguish predictive coding from alternative explanations of this response profile. This framework may provide an important new window on the neural computations underlying theory of mind. Copyright © 2013 Elsevier Inc. All rights reserved.

  16. Evolvable Neural Software System

    Science.gov (United States)

    Curtis, Steven A.

    2009-01-01

    The Evolvable Neural Software System (ENSS) is composed of sets of Neural Basis Functions (NBFs), which can be totally autonomously created and removed according to the changing needs and requirements of the software system. The resulting structure is both hierarchical and self-similar in that a given set of NBFs may have a ruler NBF, which in turn communicates with other sets of NBFs. These sets of NBFs may function as nodes to a ruler node, which are also NBF constructs. In this manner, the synthetic neural system can exhibit the complexity, three-dimensional connectivity, and adaptability of biological neural systems. An added advantage of ENSS over a natural neural system is its ability to modify its core genetic code in response to environmental changes as reflected in needs and requirements. The neural system is fully adaptive and evolvable and is trainable before release. It continues to rewire itself while on the job. The NBF is a unique, bilevel intelligence neural system composed of a higher-level heuristic neural system (HNS) and a lower-level, autonomic neural system (ANS). Taken together, the HNS and the ANS give each NBF the complete capabilities of a biological neural system to match sensory inputs to actions. Another feature of the NBF is the Evolvable Neural Interface (ENI), which links the HNS and ANS. The ENI solves the interface problem between these two systems by actively adapting and evolving from a primitive initial state (a Neural Thread) to a complicated, operational ENI and successfully adapting to a training sequence of sensory input. This simulates the adaptation of a biological neural system in a developmental phase. Within the greater multi-NBF and multi-node ENSS, self-similar ENI s provide the basis for inter-NBF and inter-node connectivity.

  17. Localization of neural efficiency of the mathematically gifted brain through a feature subset selection method.

    Science.gov (United States)

    Zhang, Li; Gan, John Q; Wang, Haixian

    2015-10-01

    Based on the neural efficiency hypothesis and task-induced EEG gamma-band response (GBR), this study investigated the brain regions where neural resource could be most efficiently recruited by the math-gifted adolescents in response to varying cognitive demands. In this experiment, various GBR-based mental states were generated with three factors (level of mathematical ability, task complexity, and short-term learning) modulating the level of neural activation. A feature subset selection method based on the sequential forward floating search algorithm was used to identify an "optimal" combination of EEG channel locations, where the corresponding GBR feature subset could obtain the highest accuracy in discriminating pairwise mental states influenced by each experiment factor. The integrative results from multi-factor selections suggest that the right-lateral fronto-parietal system is highly involved in neural efficiency of the math-gifted brain, primarily including the bilateral superior frontal, right inferior frontal, right-lateral central and right temporal regions. By means of the localization method based on single-trial classification of mental states, new GBR features and EEG channel-based brain regions related to mathematical giftedness were identified, which could be useful for the brain function improvement of children/adolescents in mathematical learning through brain-computer interface systems.

  18. Fuzzy logic and neural networks basic concepts & application

    CERN Document Server

    Alavala, Chennakesava R

    2008-01-01

    About the Book: The primary purpose of this book is to provide the student with a comprehensive knowledge of basic concepts of fuzzy logic and neural networks. The hybridization of fuzzy logic and neural networks is also included. No previous knowledge of fuzzy logic and neural networks is required. Fuzzy logic and neural networks have been discussed in detail through illustrative examples, methods and generic applications. Extensive and carefully selected references is an invaluable resource for further study of fuzzy logic and neural networks. Each chapter is followed by a question bank

  19. Identification and characterization of secondary neural tube-derived embryonic neural stem cells in vitro.

    Science.gov (United States)

    Shaker, Mohammed R; Kim, Joo Yeon; Kim, Hyun; Sun, Woong

    2015-05-15

    Secondary neurulation is an embryonic progress that gives rise to the secondary neural tube, the precursor of the lower spinal cord region. The secondary neural tube is derived from aggregated Sox2-expressing neural cells at the dorsal region of the tail bud, which eventually forms rosette or tube-like structures to give rise to neural tissues in the tail bud. We addressed whether the embryonic tail contains neural stem cells (NSCs), namely secondary NSCs (sNSCs), with the potential for self-renewal in vitro. Using in vitro neurosphere assays, neurospheres readily formed at the rosette and neural-tube levels, but less frequently at the tail bud tip level. Furthermore, we identified that sNSC-generated neurospheres were significantly smaller in size compared with cortical neurospheres. Interestingly, various cell cycle analyses revealed that this difference was not due to a reduction in the proliferation rate of NSCs, but rather the neuronal commitment of sNSCs, as sNSC-derived neurospheres contain more committed neuronal progenitor cells, even in the presence of epidermal growth factor (EGF) and basic fibroblast growth factor (bFGF). These results suggest that the higher tendency for sNSCs to spontaneously differentiate into progenitor cells may explain the limited expansion of the secondary neural tube during embryonic development.

  20. Consciousness and neural plasticity

    DEFF Research Database (Denmark)

    In contemporary consciousness studies the phenomenon of neural plasticity has received little attention despite the fact that neural plasticity is of still increased interest in neuroscience. We will, however, argue that neural plasticity could be of great importance to consciousness studies....... If consciousness is related to neural processes it seems, at least prima facie, that the ability of the neural structures to change should be reflected in a theory of this relationship "Neural plasticity" refers to the fact that the brain can change due to its own activity. The brain is not static but rather...... a dynamic entity, which physical structure changes according to its use and environment. This change may take the form of growth of new neurons, the creation of new networks and structures, and change within network structures, that is, changes in synaptic strengths. Plasticity raises questions about...

  1. Acupuncture stimulation on GB34 activates neural responses associated with Parkinson's disease.

    Science.gov (United States)

    Yeo, Sujung; Lim, Sabina; Choe, Il-Hwan; Choi, Yeong-Gon; Chung, Kyung-Cheon; Jahng, Geon-Ho; Kim, Sung-Hoon

    2012-09-01

    Parkinson's disease (PD) is a degenerative brain disorder that is caused by neural defects in the substantia nigra. Numerous studies have reported that acupuncture treatment on GB34 (Yanglingquan) leads to significant improvements in patients with PD and in PD animal models. Studies using functional magnetic resonance imaging (fMRI) have shown that patients with PD, compared to healthy participants, have lower neural responses in extensive brain regions including the putamen, thalamus, and the supplementary motor area. This study investigated the reported association between acupuncture point GB34 and PD. Using fMRI, neural responses of 12 patients with PD and 12 healthy participants were examined before and after acupuncture stimulation. Acupuncture stimulation increased neural responses in regions including the substantia nigra, caudate, thalamus, and putamen, which are impaired caused by PD. Areas associated with PD were activated by the acupuncture stimulation on GB34. This shows that acupuncture treatment on GB34 may be effective in improving the symptoms of PD. Although more randomized controlled trials on the topic will be needed, this study shows that acupuncture may be helpful in the treatment of symptoms involving PD. © 2012 Blackwell Publishing Ltd.

  2. Maternal neural responses to infant cries and faces: relationships with substance use

    Directory of Open Access Journals (Sweden)

    Nicole eLandi

    2011-06-01

    Full Text Available Substance abuse in pregnant and recently postpartum women is a major public health concern because of effects on the infant and on the ability of the adult to care for the infant. In addition to the negative health effects of teratogenic substances on fetal development, substance use can contribute to difficulties associated with the social and behavioral aspects of parenting. Neural circuits associated with parenting behavior overlap with circuits involved in addiction (e.g., frontal, striatal and limbic systems and thus may be co-opted for the craving/reward cycle associated with substance use and abuse and be less available for parenting. The current study investigates the degree to which neural circuits associated with parenting are disrupted in mothers who are substance-using. Specifically, we used functional magnetic resonance imaging to examine the neural response to emotional infant cues (faces and cries in substance-using compared to non-using mothers. In response to both faces (of varying emotional valence and cries (of varying distress levels, substance-using mothers evidenced reduced neural activation in regions that have been previously implicated in reward and motivation as well as regions involved in cognitive control. Specifically, in response to faces, substance users showed reduced activation in prefrontal regions, including the dorsolateral and ventromedial prefrontal cortex, as well as visual processing (occipital lobes and limbic regions (parahippocampus and amygdala. Similarly, in response to infant cries substance-using mothers showed reduced activation relative to non-using mothers in prefrontal regions, auditory sensory processing regions, insula and limbic regions (parahippocampus and amygdala. These findings suggest that infant stimuli may be less salient for substance-using mothers, and such reduced saliency may impair developing infant-caregiver attachment and the ability of mothers to respond appropriately to their

  3. Fuzzy and neural control

    Science.gov (United States)

    Berenji, Hamid R.

    1992-01-01

    Fuzzy logic and neural networks provide new methods for designing control systems. Fuzzy logic controllers do not require a complete analytical model of a dynamic system and can provide knowledge-based heuristic controllers for ill-defined and complex systems. Neural networks can be used for learning control. In this chapter, we discuss hybrid methods using fuzzy logic and neural networks which can start with an approximate control knowledge base and refine it through reinforcement learning.

  4. Spinal Neural Tube Defects in Lagos University Teaching Hospital ...

    African Journals Online (AJOL)

    Background: The incidence of neural tube defects is known to vary among regions. Very little has been reported about the incidence in Sub-Saharan Africa except for the general impression that the prevalent rates are low. Objective: To determine the profile of patients presenting with neural tube defects in Lagos , Nigeria

  5. pen Neural Tube Defects at the National Hospital,

    African Journals Online (AJOL)

    Background: Neural tube defect is the most common congenital abnormality of the central nervous system. Objectives: To document the clinical patterns and neonatal outcome of babies with open neural tube defects at the National Hospital, .... prevalence of these malformations in the region. This is because there are two ...

  6. Neural tumours of the head and neck | Chindia | East African ...

    African Journals Online (AJOL)

    Objective: To document the pattern of occurrence of all primary neural tumours arising in the neck and craniofacial region over the period 1982 to 1991. Design: A retrospective study. Setting: Cancer Registry, Nairobi, Kenya. Results: Out of the 289 cases who were identified to have had whole body neural tumours, ...

  7. Neural Systems for Speech and Song in Autism

    Science.gov (United States)

    Lai, Grace; Pantazatos, Spiro P.; Schneider, Harry; Hirsch, Joy

    2012-01-01

    Despite language disabilities in autism, music abilities are frequently preserved. Paradoxically, brain regions associated with these functions typically overlap, enabling investigation of neural organization supporting speech and song in autism. Neural systems sensitive to speech and song were compared in low-functioning autistic and age-matched…

  8. Visual Impairment, Including Blindness

    Science.gov (United States)

    ... Who Knows What? (log-in required) Select Page Visual Impairment, Including Blindness Mar 31, 2017 Links updated, ... doesn’t wear his glasses. Back to top Visual Impairments in Children Vision is one of our ...

  9. Norwegian trends in numbers of lower extremity revascularisations and amputations including regional trends in endovascular treatments for peripheral arterial disease: a retrospective cross-sectional registry study from 2001 to 2014.

    Science.gov (United States)

    Wendt, Kjersti; Kristiansen, Ronny; Krohg-Sørensen, Kirsten; Gregersen, Fredrik Alexander; Fosse, Erik

    2017-11-14

    The numbers of lower extremity revascularisations and amputations are insufficiently reported in Norway. To support future policy decisions regarding the provision of vascular treatment, knowledge of such trends is important. This retrospective cross-sectional study from 2001 to 2014 used data from the Norwegian Patient Registry. The revascularisation treatments were categorised in multilevel, aortoiliac, femoral to popliteal and popliteal to foot levels and sorted as open, endovascular and hybrid. The sessions in amputations were divided in major (thigh and below knee) and minor (ankle, foot or digit). Incidence rates were assessed per 100 000 for patients in the age group >60 years. The diabetic prevalence was calculated and the endovascular numbers at the South-Eastern, Western, Central and Northern Norway Regional Health Authority were compared. The overall revascularisation rates increased from 308.7 to 366.8 (p=0.02). Open revascularisations decreased from 158.9 to 98.7 (p<0.01) while endovascular revascularisations increased from 142.2 to 243.4 (p<0.01). Hybrid revascularisations increased from 7.4 to 24.8 (p<0.01). Major amputation rates decreased from 87.8 to 48.7 (p<0.01) while minor amputations increased from 12.3 to 19.6 (p=0.01). The diabetic percentages increased from 12.2 to 22.3 (p<0.01) in revascularisations, from 26.5 to 30.8 (p=0.02) in major amputations and from 43.0 to 49.3 (p=0.13) in minor. (p values refer to average annual changes.) The regional trends in endovascular treatments varied within and between the vascular groups. From 2001 to 2014, the revascularisation rates increased due to the rise in endovascular procedures. Open revascularisations and major amputation rates decreased, minor increased. The regional variances in endovascular treatments indicate that the availability of this technology differed between the health regions of Norway. The increase in patients with diabetes requires continued awareness of diabetes and its

  10. Multiscale Convolutional Neural Networks for Hand Detection

    Directory of Open Access Journals (Sweden)

    Shiyang Yan

    2017-01-01

    Full Text Available Unconstrained hand detection in still images plays an important role in many hand-related vision problems, for example, hand tracking, gesture analysis, human action recognition and human-machine interaction, and sign language recognition. Although hand detection has been extensively studied for decades, it is still a challenging task with many problems to be tackled. The contributing factors for this complexity include heavy occlusion, low resolution, varying illumination conditions, different hand gestures, and the complex interactions between hands and objects or other hands. In this paper, we propose a multiscale deep learning model for unconstrained hand detection in still images. Deep learning models, and deep convolutional neural networks (CNNs in particular, have achieved state-of-the-art performances in many vision benchmarks. Developed from the region-based CNN (R-CNN model, we propose a hand detection scheme based on candidate regions generated by a generic region proposal algorithm, followed by multiscale information fusion from the popular VGG16 model. Two benchmark datasets were applied to validate the proposed method, namely, the Oxford Hand Detection Dataset and the VIVA Hand Detection Challenge. We achieved state-of-the-art results on the Oxford Hand Detection Dataset and had satisfactory performance in the VIVA Hand Detection Challenge.

  11. What Is Neural Plasticity?

    Science.gov (United States)

    von Bernhardi, Rommy; Bernhardi, Laura Eugenín-von; Eugenín, Jaime

    2017-01-01

    "Neural plasticity" refers to the capacity of the nervous system to modify itself, functionally and structurally, in response to experience and injury. As the various chapters in this volume show, plasticity is a key component of neural development and normal functioning of the nervous system, as well as a response to the changing environment, aging, or pathological insult. This chapter discusses how plasticity is necessary not only for neural networks to acquire new functional properties, but also for them to remain robust and stable. The article also reviews the seminal proposals developed over the years that have driven experiments and strongly influenced concepts of neural plasticity.

  12. Neural Systems Laboratory

    Data.gov (United States)

    Federal Laboratory Consortium — As part of the Electrical and Computer Engineering Department and The Institute for System Research, the Neural Systems Laboratory studies the functionality of the...

  13. A neural flow estimator

    DEFF Research Database (Denmark)

    Jørgensen, Ivan Harald Holger; Bogason, Gudmundur; Bruun, Erik

    1995-01-01

    is implemented using switched-current technique and is capable of estimating flow in the μl/s range. The neural estimator is built around a multiplierless neural network, containing 96 synaptic weights which are updated using the LMS1-algorithm. An experimental chip has been designed that operates at 5 V......This paper proposes a new way to estimate the flow in a micromechanical flow channel. A neural network is used to estimate the delay of random temperature fluctuations induced in a fluid. The design and implementation of a hardware efficient neural flow estimator is described. The system...

  14. Prevalence of Trichinella spp. in black bears, grizzly bears, and wolves in the Dehcho Region, Northwest Territories, Canada, including the first report of T. nativa in a grizzly bear from Canada.

    Science.gov (United States)

    Larter, Nicholas C; Forbes, Lorry B; Elkin, Brett T; Allaire, Danny G

    2011-07-01

    Samples of muscle from 120 black bears (Ursus americanus), 11 grizzly bears (Ursus arctos), and 27 wolves (Canis lupus) collected in the Dehcho Region of the Northwest Territories from 2001 to 2010 were examined for the presence of Trichinella spp. larvae using a pepsin-HCl digestion assay. Trichinella spp. larvae were found in eight of 11 (73%) grizzly bears, 14 of 27 (52%) wolves, and seven of 120 (5.8%) black bears. The average age of positive grizzly bears, black bears, and wolves was 13.5, 9.9, and approximately 4 yr, respectively. Larvae from 11 wolves, six black bears, and seven grizzly bears were genotyped. Six wolves were infected with T. nativa and five with Trichinella T6, four black bears were infected with T. nativa and two with Trichinella T6, and all seven grizzly bears were infected with Trichinella T6 and one of them had a coinfection with T. nativa. This is the first report of T. nativa in a grizzly bear from Canada. Bears have been linked to trichinellosis outbreaks in humans in Canada, and black bears are a subsistence food source for residents of the Dehcho region. In order to assess food safety risk it is important to monitor the prevalence of Trichinella spp. in both species of bear and their cohabiting mammalian food sources.

  15. The positional identity of iPSC-derived neural progenitor cells along the anterior-posterior axis is controlled in a dosage-dependent manner by bFGF and EGF

    DEFF Research Database (Denmark)

    Zhou, Shuling; Ochalek, Anna; Szczesna, Karolina

    2016-01-01

    Neural rosettes derived from human induced pluripotent stem cells (iPSCs) have been claimed to be a highly robust in vitro cellular model for biomedical application. They are able to propagate in vitro in the presence of mitogens, including basic fibroblast growth factor (bFGF) and epidermal growth...... factor (EGF). However, these two mitogens are also involved in anterior-posterior patterning in a gradient dependent manner along the neural tube axis. Here, we compared the regional identity of neural rosette cells and specific neural subtypes of their progeny propagated with low and high concentrations...... of bFGF and EGF. We observed that low concentrations of bFGF and EGF in the culturing system were able to induce forebrain identity of the neural rosettes and promote subsequent cortical neuronal differentiation. On the contrary, high concentrations of these mitogens stimulate a mid-hindbrain fate...

  16. Non-Linear State Estimation Using Pre-Trained Neural Networks

    DEFF Research Database (Denmark)

    Bayramoglu, Enis; Andersen, Nils Axel; Ravn, Ole

    2010-01-01

    effecting the transformation. This function is approximated by a neural network using offline training. The training is based on monte carlo sampling. A way to obtain parametric distributions of flexible shape to be used easily with these networks is also presented. The method can also be used to improve...... other parametric methods around regions with strong non-linearities by including them inside the network....

  17. Neural correlates of anxiety sensitivity in panic disorder: A functional magnetic resonance imaging study.

    Science.gov (United States)

    Poletti, Sara; Radaelli, Daniele; Cucchi, Michele; Ricci, Liana; Vai, Benedetta; Smeraldi, Enrico; Benedetti, Francesco

    2015-08-30

    Panic disorder has been associated with dysfunctional neuropsychological dimensions, including anxiety sensitivity. Brain-imaging studies of the neural correlates of emotional processing have identified a network of structures that constitute the neural circuitry for emotions. The anterior cingulate cortex (ACC), medial prefrontal cortex (mPFC) and insula, which are part of this network, are also involved in the processing of threat-related stimuli. The aim of the study was to investigate if neural activity in response to emotional stimuli in the cortico-limbic network is associated to anxiety sensitivity in panic disorder. In a sample of 18 outpatients with panic disorder, we studied neural correlates of implicit emotional processing of facial affect expressions with a face-matching paradigm; correlational analyses were performed between brain activations and anxiety sensitivity. The correlational analyses performed showed a positive correlation between anxiety sensitivity and brain activity during emotional processing in regions encompassing the PFC, ACC and insula. Our data seem to confirm that anxiety sensitivity is an important component of panic disorder. Accordingly, the neural underpinnings of anxiety sensitivity could be an interesting focus for treatment and further research. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  18. Learning to Read Words in a New Language Shapes the Neural Organization of the Prior Languages

    Science.gov (United States)

    Mei, Leilei; Xue, Gui; Lu, Zhong-Lin; Chen, Chuansheng; Zhang, Mingxia; He, Qinghua; Wei, Miao; Dong, Qi

    2014-01-01

    Learning a new language entails interactions with one's prior language(s). Much research has shown how native language affects the cognitive and neural mechanisms of a new language, but little is known about whether and how learning a new language shapes the neural mechanisms of prior language(s). In two experiments in the current study, we used an artificial language training paradigm in combination with fMRI to examine (1) the effects of different linguistic components (phonology and semantics) of a new language on the neural process of prior languages (i.e., native and second languages), and (2) whether such effects were modulated by the proficiency level in the new language. Results of Experiment 1 showed that when the training in a new language involved semantics (as opposed to only visual forms and phonology), neural activity during word reading in the native language (Chinese) was reduced in several reading-related regions, including the left pars opercularis, pars triangularis, bilateral inferior temporal gyrus, fusiform gyrus, and inferior occipital gyrus. Results of Experiment 2 replicated the results of Experiment 1 and further found that semantic training also affected neural activity during word reading in the subjects’ second language (English). Furthermore, we found that the effects of the new language were modulated by the subjects’ proficiency level in the new language. These results provide critical imaging evidence for the influence of learning to read words in a new language on word reading in native and second languages. PMID:25447375

  19. Neural speech recognition: continuous phoneme decoding using spatiotemporal representations of human cortical activity

    Science.gov (United States)

    Moses, David A.; Mesgarani, Nima; Leonard, Matthew K.; Chang, Edward F.

    2016-10-01

    Objective. The superior temporal gyrus (STG) and neighboring brain regions play a key role in human language processing. Previous studies have attempted to reconstruct speech information from brain activity in the STG, but few of them incorporate the probabilistic framework and engineering methodology used in modern speech recognition systems. In this work, we describe the initial efforts toward the design of a neural speech recognition (NSR) system that performs continuous phoneme recognition on English stimuli with arbitrary vocabulary sizes using the high gamma band power of local field potentials in the STG and neighboring cortical areas obtained via electrocorticography. Approach. The system implements a Viterbi decoder that incorporates phoneme likelihood estimates from a linear discriminant analysis model and transition probabilities from an n-gram phonemic language model. Grid searches were used in an attempt to determine optimal parameterizations of the feature vectors and Viterbi decoder. Main results. The performance of the system was significantly improved by using spatiotemporal representations of the neural activity (as opposed to purely spatial representations) and by including language modeling and Viterbi decoding in the NSR system. Significance. These results emphasize the importance of modeling the temporal dynamics of neural responses when analyzing their variations with respect to varying stimuli and demonstrate that speech recognition techniques can be successfully leveraged when decoding speech from neural signals. Guided by the results detailed in this work, further development of the NSR system could have applications in the fields of automatic speech recognition and neural prosthetics.

  20. Attenuation of β-Amyloid Deposition and Neurotoxicity by Chemogenetic Modulation of Neural Activity.

    Science.gov (United States)

    Yuan, Peng; Grutzendler, Jaime

    2016-01-13

    Aberrant neural hyperactivity has been observed in early stages of Alzheimer's disease (AD) and may be a driving force in the progression of amyloid pathology. Evidence for this includes the findings that neural activity may modulate β-amyloid (Aβ) peptide secretion and experimental stimulation of neural activity can increase amyloid deposition. However, whether long-term attenuation of neural activity prevents the buildup of amyloid plaques and associated neural pathologies remains unknown. Using viral-mediated delivery of designer receptors exclusively activated by designer drugs (DREADDs), we show in two AD-like mouse models that chronic intermittent increases or reductions of activity have opposite effects on Aβ deposition. Neural activity reduction markedly decreases Aβ aggregation in regions containing axons or dendrites of DREADD-expressing neurons, suggesting the involvement of synaptic and nonsynaptic Aβ release mechanisms. Importantly, activity attenuation is associated with a reduction in axonal dystrophy and synaptic loss around amyloid plaques. Thus, modulation of neural activity could constitute a potential therapeutic strategy for ameliorating amyloid-induced pathology in AD. A novel chemogenetic approach to upregulate and downregulate neuronal activity in Alzheimer's disease (AD) mice was implemented. This led to the first demonstration that chronic intermittent attenuation of neuronal activity in vivo significantly reduces amyloid deposition. The study also demonstrates that modulation of β-amyloid (Aβ) release can occur at both axonal and dendritic fields, suggesting the involvement of synaptic and nonsynaptic Aβ release mechanisms. Activity reductions also led to attenuation of the synaptic pathology associated with amyloid plaques. Therefore, chronic attenuation of neuronal activity could constitute a novel therapeutic approach for AD. Copyright © 2016 the authors 0270-6474/16/360632-10$15.00/0.

  1. Neural, cognitive, and evolutionary foundations of human altruism.

    Science.gov (United States)

    Marsh, Abigail A

    2016-01-01

    This article considers three forms of altruism from both a psychological and a neural perspective, with an emphasis on homologies that can be observed across species and potentially illuminate altruism's evolutionary origins. Kin-based altruism benefits biological relatives and, according to the theory of inclusive fitness, is ultimately beneficial to the altruist from a genetic standpoint. Kin selection adequately explains some altruistic behavior, but it is not applicable to much human altruism. Little is known about the neural processes that support it, but they may include cortical regions involved in processing autobiographical memory and the identities of familiar others. Reciprocity-based altruism is performed in expectation of future rewards and is supported by dopaminergic cortico-striatal networks that guide behavior according to anticipated rewards. Care-based altruism is aimed at improving the well-being of distressed and vulnerable individuals and is closely linked to empathic concern. This form of altruism is thought to rely on the subcortical neural systems that support parental care, particularly structures densely populated with receptors for the hormones oxytocin and vasopressin, including the amygdala, stria terminalis, and striatum. The amygdala may be a particularly important convergence point for care-based altruism because of its dual role in responding both to cues that signal infantile vulnerability and those that signal distress. Research on altruism continues to converge across disciplines, but more research linking molecular-level neural processes to altruistic behavior in humans and other species is needed, as is research on how various forms of altruism intersect. For further resources related to this article, please visit the WIREs website. © 2015 Wiley Periodicals, Inc.

  2. Applications of Pulse-Coupled Neural Networks

    CERN Document Server

    Ma, Yide; Wang, Zhaobin

    2011-01-01

    "Applications of Pulse-Coupled Neural Networks" explores the fields of image processing, including image filtering, image segmentation, image fusion, image coding, image retrieval, and biometric recognition, and the role of pulse-coupled neural networks in these fields. This book is intended for researchers and graduate students in artificial intelligence, pattern recognition, electronic engineering, and computer science. Prof. Yide Ma conducts research on intelligent information processing, biomedical image processing, and embedded system development at the School of Information Sci

  3. A Projection Neural Network for Constrained Quadratic Minimax Optimization.

    Science.gov (United States)

    Liu, Qingshan; Wang, Jun

    2015-11-01

    This paper presents a projection neural network described by a dynamic system for solving constrained quadratic minimax programming problems. Sufficient conditions based on a linear matrix inequality are provided for global convergence of the proposed neural network. Compared with some of the existing neural networks for quadratic minimax optimization, the proposed neural network in this paper is capable of solving more general constrained quadratic minimax optimization problems, and the designed neural network does not include any parameter. Moreover, the neural network has lower model complexities, the number of state variables of which is equal to that of the dimension of the optimization problems. The simulation results on numerical examples are discussed to demonstrate the effectiveness and characteristics of the proposed neural network.

  4. Automated Modeling of Microwave Structures by Enhanced Neural Networks

    Directory of Open Access Journals (Sweden)

    Z. Raida

    2006-12-01

    Full Text Available The paper describes the methodology of the automated creation of neural models of microwave structures. During the creation process, artificial neural networks are trained using the combination of the particle swarm optimization and the quasi-Newton method to avoid critical training problems of the conventional neural nets. In the paper, neural networks are used to approximate the behavior of a planar microwave filter (moment method, Zeland IE3D. In order to evaluate the efficiency of neural modeling, global optimizations are performed using numerical models and neural ones. Both approaches are compared from the viewpoint of CPU-time demands and the accuracy. Considering conclusions, methodological recommendations for including neural networks to the microwave design are formulated.

  5. Neural crest cells: from developmental biology to clinical interventions.

    Science.gov (United States)

    Noisa, Parinya; Raivio, Taneli

    2014-09-01

    Neural crest cells are multipotent cells, which are specified in embryonic ectoderm in the border of neural plate and epiderm during early development by interconnection of extrinsic stimuli and intrinsic factors. Neural crest cells are capable of differentiating into various somatic cell types, including melanocytes, craniofacial cartilage and bone, smooth muscle, and peripheral nervous cells, which supports their promise for cell therapy. In this work, we provide a comprehensive review of wide aspects of neural crest cells from their developmental biology to applicability in medical research. We provide a simplified model of neural crest cell development and highlight the key external stimuli and intrinsic regulators that determine the neural crest cell fate. Defects of neural crest cell development leading to several human disorders are also mentioned, with the emphasis of using human induced pluripotent stem cells to model neurocristopathic syndromes. © 2014 Wiley Periodicals, Inc.

  6. Listening to Include

    Science.gov (United States)

    Veck, Wayne

    2009-01-01

    This paper attempts to make important connections between listening and inclusive education and the refusal to listen and exclusion. Two lines of argument are advanced. First, if educators and learners are to include each other within their educational institutions as unique individuals, then they will need to listen attentively to each other.…

  7. Neural networks and applications tutorial

    Science.gov (United States)

    Guyon, I.

    1991-09-01

    The importance of neural networks has grown dramatically during this decade. While only a few years ago they were primarily of academic interest, now dozens of companies and many universities are investigating the potential use of these systems and products are beginning to appear. The idea of building a machine whose architecture is inspired by that of the brain has roots which go far back in history. Nowadays, technological advances of computers and the availability of custom integrated circuits, permit simulations of hundreds or even thousands of neurons. In conjunction, the growing interest in learning machines, non-linear dynamics and parallel computation spurred renewed attention in artificial neural networks. Many tentative applications have been proposed, including decision systems (associative memories, classifiers, data compressors and optimizers), or parametric models for signal processing purposes (system identification, automatic control, noise canceling, etc.). While they do not always outperform standard methods, neural network approaches are already used in some real world applications for pattern recognition and signal processing tasks. The tutorial is divided into six lectures, that where presented at the Third Graduate Summer Course on Computational Physics (September 3-7, 1990) on Parallel Architectures and Applications, organized by the European Physical Society: (1) Introduction: machine learning and biological computation. (2) Adaptive artificial neurons (perceptron, ADALINE, sigmoid units, etc.): learning rules and implementations. (3) Neural network systems: architectures, learning algorithms. (4) Applications: pattern recognition, signal processing, etc. (5) Elements of learning theory: how to build networks which generalize. (6) A case study: a neural network for on-line recognition of handwritten alphanumeric characters.

  8. Neural patterning of human induced pluripotent stem cells in 3-D cultures for studying biomolecule-directed differential cellular responses.

    Science.gov (United States)

    Yan, Yuanwei; Bejoy, Julie; Xia, Junfei; Guan, Jingjiao; Zhou, Yi; Li, Yan

    2016-09-15

    Appropriate neural patterning of human induced pluripotent stem cells (hiPSCs) is critical to generate specific neural cells/tissues and even mini-brains that are physiologically relevant to model neurological diseases. However, the capacity of signaling factors that regulate 3-D neural tissue patterning in vitro and differential responses of the resulting neural populations to various biomolecules have not yet been fully understood. By tuning neural patterning of hiPSCs with small molecules targeting sonic hedgehog (SHH) signaling, this study generated different 3-D neuronal cultures that were mainly comprised of either cortical glutamatergic neurons or motor neurons. Abundant glutamatergic neurons were observed following the treatment with an antagonist of SHH signaling, cyclopamine, while Islet-1 and HB9-expressing motor neurons were enriched by an SHH agonist, purmorphamine. In neurons derived with different neural patterning factors, whole-cell patch clamp recordings showed similar voltage-gated Na(+)/K(+) currents, depolarization-evoked action potentials and spontaneous excitatory post-synaptic currents. Moreover, these different neuronal populations exhibited differential responses to three classes of biomolecules, including (1) matrix metalloproteinase inhibitors that affect extracellular matrix remodeling; (2) N-methyl-d-aspartate that induces general neurotoxicity; and (3) amyloid β (1-42) oligomers that cause neuronal subtype-specific neurotoxicity. This study should advance our understanding of hiPSC self-organization and neural tissue development and provide a transformative approach to establish 3-D models for neurological disease modeling and drug discovery. Appropriate neural patterning of human induced pluripotent stem cells (hiPSCs) is critical to generate specific neural cells, tissues and even mini-brains that are physiologically relevant to model neurological diseases. However, the capability of sonic hedgehog-related small molecules to tune

  9. Neural correlates of paediatric dysgraphia.

    Science.gov (United States)

    Van Hoorn, Jessika F; Maathuis, Carel G B; Hadders-Algra, Mijna

    2013-11-01

    Writing is an important skill that is related both to school performance and to psychosocial outcomes such as the child's self-esteem. Deficits in handwriting performance are frequently encountered in children with developmental coordination disorder. This review focuses on what is known about the neural correlates of atypical handwriting in children. Knowledge of the neural correlates is derived from studies using clinical case designs (e.g. lesion studies), studies using neuroimaging, and assessment of minor neurological dysfunction. The two functional imaging studies suggest a contribution of cortical areas and the cerebellum. The largest study indicated that cortical areas in all regions of the brain are involved (frontal, temporal, parietal, and occipital). The two lesion studies confirmed cerebellar involvement. The findings of the study on minor neurological dysfunction in children with writing problems correspond to the imaging results. The limited data on the neural substrate of paediatric dysgraphia suggest that at least a subset of the children with dysgraphia have dysfunctions in extensive supraspinal networks. In others, dysfunction may be restricted to either the cerebellum or specific cortical sites. © The Authors. Developmental Medicine & Child Neurology © 2013 Mac Keith Press.

  10. An integrated modelling framework for neural circuits with multiple neuromodulators.

    Science.gov (United States)

    Joshi, Alok; Youssofzadeh, Vahab; Vemana, Vinith; McGinnity, T M; Prasad, Girijesh; Wong-Lin, KongFatt

    2017-01-01

    Neuromodulators are endogenous neurochemicals that regulate biophysical and biochemical processes, which control brain function and behaviour, and are often the targets of neuropharmacological drugs. Neuromodulator effects are generally complex partly owing to the involvement of broad innervation, co-release of neuromodulators, complex intra- and extrasynaptic mechanism, existence of multiple receptor subtypes and high interconnectivity within the brain. In this work, we propose an efficient yet sufficiently realistic computational neural modelling framework to study some of these complex behaviours. Specifically, we propose a novel dynamical neural circuit model that integrates the effective neuromodulator-induced currents based on various experimental data (e.g. electrophysiology, neuropharmacology and voltammetry). The model can incorporate multiple interacting brain regions, including neuromodulator sources, simulate efficiently and easily extendable to large-scale brain models, e.g. for neuroimaging purposes. As an example, we model a network of mutually interacting neural populations in the lateral hypothalamus, dorsal raphe nucleus and locus coeruleus, which are major sources of neuromodulator orexin/hypocretin, serotonin and norepinephrine/noradrenaline, respectively, and which play significant roles in regulating many physiological functions. We demonstrate that such a model can provide predictions of systemic drug effects of the popular antidepressants (e.g. reuptake inhibitors), neuromodulator antagonists or their combinations. Finally, we developed user-friendly graphical user interface software for model simulation and visualization for both fundamental sciences and pharmacological studies. © 2017 The Authors.

  11. The neural and behavioral correlates of social evaluation in childhood

    Directory of Open Access Journals (Sweden)

    Michelle Achterberg

    2017-04-01

    Full Text Available Being accepted or rejected by peers is highly salient for developing social relations in childhood. We investigated the behavioral and neural correlates of social feedback and subsequent aggression in 7–10-year-old children, using the Social Network Aggression Task (SNAT. Participants viewed pictures of peers that gave positive, neutral or negative feedback to the participant’s profile. Next, participants could blast a loud noise towards the peer, as an index of aggression. We included three groups (N = 19, N = 28 and N = 27 and combined the results meta-analytically. Negative social feedback resulted in the most behavioral aggression, with large combined effect-sizes. Whole brain condition effects for each separate sample failed to show robust effects, possibly due to the small samples. Exploratory analyses over the combined test and replication samples confirmed heightened activation in the medial prefrontal cortex (mPFC after negative social feedback. Moreover, meta-analyses of activity in predefined regions of interest showed that negative social feedback resulted in more neural activation in the amygdala, anterior insula and the mPFC/anterior cingulate cortex. Together, the results show that social motivation is already highly salient in middle childhood, and indicate that the SNAT is a valid paradigm for assessing the neural and behavioral correlates of social evaluation in children.

  12. Analytic device including nanostructures

    KAUST Repository

    Di Fabrizio, Enzo M.

    2015-07-02

    A device for detecting an analyte in a sample comprising: an array including a plurality of pixels, each pixel including a nanochain comprising: a first nanostructure, a second nanostructure, and a third nanostructure, wherein size of the first nanostructure is larger than that of the second nanostructure, and size of the second nanostructure is larger than that of the third nanostructure, and wherein the first nanostructure, the second nanostructure, and the third nanostructure are positioned on a substrate such that when the nanochain is excited by an energy, an optical field between the second nanostructure and the third nanostructure is stronger than an optical field between the first nanostructure and the second nanostructure, wherein the array is configured to receive a sample; and a detector arranged to collect spectral data from a plurality of pixels of the array.

  13. Critical Branching Neural Networks

    Science.gov (United States)

    Kello, Christopher T.

    2013-01-01

    It is now well-established that intrinsic variations in human neural and behavioral activity tend to exhibit scaling laws in their fluctuations and distributions. The meaning of these scaling laws is an ongoing matter of debate between isolable causes versus pervasive causes. A spiking neural network model is presented that self-tunes to critical…

  14. Kunstige neurale net

    DEFF Research Database (Denmark)

    Hørning, Annette

    1994-01-01

    Artiklen beskæftiger sig med muligheden for at anvende kunstige neurale net i forbindelse med datamatisk procession af naturligt sprog, specielt automatisk talegenkendelse.......Artiklen beskæftiger sig med muligheden for at anvende kunstige neurale net i forbindelse med datamatisk procession af naturligt sprog, specielt automatisk talegenkendelse....

  15. The neural organization of perception in chess experts.

    Science.gov (United States)

    Krawczyk, Daniel C; Boggan, Amy L; McClelland, M Michelle; Bartlett, James C

    2011-07-20

    The human visual system responds to expertise, and it has been suggested that regions that process faces also process other objects of expertise including chess boards by experts. We tested whether chess and face processing overlap in brain activity using fMRI. Chess experts and novices exhibited face selective areas, but these regions showed no selectivity to chess configurations relative to other stimuli. We next compared neural responses to chess and to scrambled chess displays to isolate areas relevant to expertise. Areas within the posterior cingulate, orbitofrontal cortex, and right temporal cortex were active in this comparison in experts over novices. We also compared chess and face responses within the posterior cingulate and found this area responsive to chess only in experts. These findings indicate that the configurations in chess are not strongly processed by face-selective regions that are selective for faces in individuals who have expertise in both domains. Further, the area most consistently involved in chess did not show overlap with faces. Overall, these results suggest that expert visual processing may be similar at the level of recognition, but need not show the same neural correlates. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

  16. Image inpainting using a neural network

    Directory of Open Access Journals (Sweden)

    Gapon Nikolay

    2017-01-01

    Full Text Available The paper describes a new method of two-dimensional signals reconstruction by restoring static images. A new method of spatial reconstruction of static images based on a geometric model using a neural network is proposed, it is based on the search for similar blocks and copying them into the region of distorted or missing pixel values.

  17. Functional neural anatomy of talent.

    Science.gov (United States)

    Kalbfleisch, M Layne

    2004-03-01

    The terms gifted, talented, and intelligent all have meanings that suggest an individual's highly proficient or exceptional performance in one or more specific areas of strength. Other than Spearman's g, which theorizes about a general elevated level of potential or ability, more contemporary theories of intelligence are based on theoretical models that define ability or intelligence according to a priori categories of specific performance. Recent studies in cognitive neuroscience report on the neural basis of g from various perspectives such as the neural speed theory and the efficiency of prefrontal function. Exceptional talent is the result of interactions between goal-directed behavior and nonvolitional perceptual processes in the brain that have yet to be fully characterized and understood by the fields of psychology and cognitive neuroscience. Some developmental studies report differences in region-specific neural activation, recruitment patterns, and reaction times in subjects who are identified with high IQ scores according to traditional scales of assessment such as the WISC-III or Stanford-Binet. Although as cases of savants and prodigies illustrate, talent is not synonymous with high IQ. This review synthesizes information from the fields of psychometrics and gifted education, with findings from the neurosciences on the neural basis of intelligence, creativity, profiles of expert performers, cognitive function, and plasticity to suggest a paradigm for investigating talent as the maximal and productive use of either or both of one's high level of general intelligence or domain-specific ability. Anat Rec (Part B: New Anat) 277B:21-36, 2004. Copyright 2004 Wiley-Liss, Inc.

  18. Potential utilization of biomass in production of electricity, heat and transportation fuels including energy combines - Regional analyses and examples; Potentiell avsaettning av biomassa foer produktion av el, vaerme och drivmedel inklusive energikombinat - Regionala analyser och raekneexempel

    Energy Technology Data Exchange (ETDEWEB)

    Ericsson, Karin; Boerjesson, Paal

    2008-01-15

    The objective of this study is to analyse how the use of biomass may increase in the next 10-20 years in production of heat, electricity and transportation fuels in Sweden. In these analyses, the biomass is assumed to be used in a resource and cost efficient way. This means for example that the demand for heat determines the potential use of biomass in co-generation of heat and electricity and in energy combines, and that the markets for by-products determine the use of biomass in production of certain transportation fuels. The economic conditions are not analysed in this study. In the heat and electricity production sector, we make regional analyses of the potential use of biomass in production of small-scale heat, district heat, process heat in the forest industry and electricity produced in co-generation with heat in the district heating systems and forest industry. These analyses show that the use of biomass in heat and electricity production could increase from 87 TWh (the use in 2004/2005, excluding small-scale heat production with firewood) to between 113 TWh and 134 TWh, depending on the future expansion of the district heating systems. Geographically, the Stockholm province accounts for a large part of the potential increase owing to the great opportunities for increasing the use of biomass in production of district heat and CHP in this region. In the sector of transportation fuels we applied a partly different approach since we consider the market for biomass-based transportation fuels to be 'unconstrained' within the next 10-20 years. Factors that constrain the production of these fuels are instead the availability of biomass feedstock and the local conditions required for achieving effective production systems. Among the first generation biofuels this report focuses on RME and ethanol from cereals. We estimate that the domestic production of RME and ethanol could amount to up to 1.4 TWh/y and 0.7-3.8 TWh/y, respectively, where the higher

  19. Comparative Analysis of Neural Network Training Methods in Real-time Radiotherapy

    Directory of Open Access Journals (Sweden)

    Nouri S.

    2017-03-01

    Full Text Available Background: The motions of body and tumor in some regions such as chest during radiotherapy treatments are one of the major concerns protecting normal tissues against high doses. By using real-time radiotherapy technique, it is possible to increase the accuracy of delivered dose to the tumor region by means of tracing markers on the body of patients. Objective: This study evaluates the accuracy of some artificial intelligence methods including neural network and those of combination with genetic algorithm as well as particle swarm optimization (PSO estimating tumor positions in real-time radiotherapy. Method: One hundred recorded signals of three external markers were used as input data. The signals from 3 markers thorough 10 breathing cycles of a patient treated via a cyber-knife for a lung tumor were used as data input. Then, neural network method and its combination with genetic or PSO algorithms were applied determining the tumor locations using MATLAB© software program. Results: The accuracies were obtained 0.8%, 12% and 14% in neural network, genetic and particle swarm optimization algorithms, respectively. Conclusion: The internal target volume (ITV should be determined based on the applied neural network algorithm on training steps.

  20. Comparative Analysis of Neural Network Training Methods in Real-time Radiotherapy.

    Science.gov (United States)

    Nouri, S; Hosseini Pooya, S M; Soltani Nabipour, J

    2017-03-01

    The motions of body and tumor in some regions such as chest during radiotherapy treatments are one of the major concerns protecting normal tissues against high doses. By using real-time radiotherapy technique, it is possible to increase the accuracy of delivered dose to the tumor region by means of tracing markers on the body of patients. This study evaluates the accuracy of some artificial intelligence methods including neural network and those of combination with genetic algorithm as well as particle swarm optimization (PSO) estimating tumor positions in real-time radiotherapy. One hundred recorded signals of three external markers were used as input data. The signals from 3 markers thorough 10 breathing cycles of a patient treated via a cyber-knife for a lung tumor were used as data input. Then, neural network method and its combination with genetic or PSO algorithms were applied determining the tumor locations using MATLAB© software program. The accuracies were obtained 0.8%, 12% and 14% in neural network, genetic and particle swarm optimization algorithms, respectively. The internal target volume (ITV) should be determined based on the applied neural network algorithm on training steps.

  1. Screening for Open Neural Tube Defects.

    Science.gov (United States)

    Krantz, David A; Hallahan, Terrence W; Carmichael, Jonathan B

    2016-06-01

    Biochemical prenatal screening was initiated with the use of maternal serum alpha fetoprotein to screen for open neural tube defects. Screening now includes multiple marker and sequential screening protocols involving serum and ultrasound markers to screen for aneuploidy. Recently cell-free DNA screening for aneuploidy has been initiated, but does not screen for neural tube defects. Although ultrasound is highly effective in identifying neural tube defects in high-risk populations, in decentralized health systems maternal serum screening still plays a significant role. Abnormal maternal serum alpha fetoprotein alone or in combination with other markers may indicate adverse pregnancy outcome in the absence of open neural tube defects. Copyright © 2016 Elsevier Inc. All rights reserved.

  2. Being Included and Excluded

    DEFF Research Database (Denmark)

    Korzenevica, Marina

    2016-01-01

    Following the civil war of 1996–2006, there was a dramatic increase in the labor mobility of young men and the inclusion of young women in formal education, which led to the transformation of the political landscape of rural Nepal. Mobility and schooling represent a level of prestige that rural...... politics. It analyzes how formal education and mobility either challenge or reinforce traditional gendered norms which dictate a lowly position for young married women in the household and their absence from community politics. The article concludes that women are simultaneously excluded and included from...... people regard as a prerequisite for participating in local community politics. Based on a fieldwork in two villages of Panchthar district in eastern Nepal, this article explores how these changes strengthen or weaken women’s political agency and how this is reflected in their participation in community...

  3. Central neural pathways for thermoregulation

    Science.gov (United States)

    Morrison, Shaun F.; Nakamura, Kazuhiro

    2010-01-01

    Central neural circuits orchestrate a homeostatic repertoire to maintain body temperature during environmental temperature challenges and to alter body temperature during the inflammatory response. This review summarizes the functional organization of the neural pathways through which cutaneous thermal receptors alter thermoregulatory effectors: the cutaneous circulation for heat loss, the brown adipose tissue, skeletal muscle and heart for thermogenesis and species-dependent mechanisms (sweating, panting and saliva spreading) for evaporative heat loss. These effectors are regulated by parallel but distinct, effector-specific neural pathways that share a common peripheral thermal sensory input. The thermal afferent circuits include cutaneous thermal receptors, spinal dorsal horn neurons and lateral parabrachial nucleus neurons projecting to the preoptic area to influence warm-sensitive, inhibitory output neurons which control thermogenesis-promoting neurons in the dorsomedial hypothalamus that project to premotor neurons in the rostral ventromedial medulla, including the raphe pallidus, that descend to provide the excitation necessary to drive thermogenic thermal effectors. A distinct population of warm-sensitive preoptic neurons controls heat loss through an inhibitory input to raphe pallidus neurons controlling cutaneous vasoconstriction. PMID:21196160

  4. MLPA analysis for a panel of syndromes with mental retardation reveals imbalances in 5.8% of patients with mental retardation and dysmorphic features, including duplications of the Sotos syndrome and Williams-Beuren syndrome regions

    DEFF Research Database (Denmark)

    Kirchhoff, Maria; Bisgaard, Anne-Marie; Bryndorf, Thue

    2007-01-01

    -Beuren, Prader-Willi, Angelman, Miller-Dieker, Smith-Magenis, and 22q11-deletion syndromes). Patients were initially referred for HR-CGH analysis and MRS-MLPA was performed retrospectively. MRS-MLPA analysis revealed imbalances in 15/258 patients (5.8%). Ten deletions were identified, including deletions of 1p36......, 5q35 (Sotos syndrome), 7q11 (Williams-Beuren syndrome), 17p11 (Smith-Magenis syndrome), 15q11 (Angelman syndrome) and 22q11. Duplications were detected in 5q35, 7q11, 17p13, 17p11 and 22q11. We reviewed another 170 patients referred specifically for MRS-MLPA analysis. Eighty of these patients were...

  5. Neural crest development in fetal alcohol syndrome.

    Science.gov (United States)

    Smith, Susan M; Garic, Ana; Flentke, George R; Berres, Mark E

    2014-09-01

    Fetal alcohol spectrum disorder (FASD) is a leading cause of neurodevelopmental disability. Some affected individuals possess distinctive craniofacial deficits, but many more lack overt facial changes. An understanding of the mechanisms underlying these deficits would inform their diagnostic utility. Our understanding of these mechanisms is challenged because ethanol lacks a single receptor when redirecting cellular activity. This review summarizes our current understanding of how ethanol alters neural crest development. Ample evidence shows that ethanol causes the "classic" fetal alcohol syndrome (FAS) face (short palpebral fissures, elongated upper lip, deficient philtrum) because it suppresses prechordal plate outgrowth, thereby reducing neuroectoderm and neural crest induction and causing holoprosencephaly. Prenatal alcohol exposure (PAE) at premigratory stages elicits a different facial appearance, indicating FASD may represent a spectrum of facial outcomes. PAE at this premigratory period initiates a calcium transient that activates CaMKII and destabilizes transcriptionally active β-catenin, thereby initiating apoptosis within neural crest populations. Contributing to neural crest vulnerability are their low antioxidant responses. Ethanol-treated neural crest produce reactive oxygen species and free radical scavengers attenuate their production and prevent apoptosis. Ethanol also significantly impairs neural crest migration, causing cytoskeletal rearrangements that destabilize focal adhesion formation; their directional migratory capacity is also lost. Genetic factors further modify vulnerability to ethanol-induced craniofacial dysmorphology and include genes important for neural crest development, including shh signaling, PDFGA, vangl2, and ribosomal biogenesis. Because facial and brain development are mechanistically and functionally linked, research into ethanol's effects on neural crest also informs our understanding of ethanol's CNS pathologies. © 2014

  6. Genome-wide copy number profiling of mouse neural stem cells during differentiation

    Directory of Open Access Journals (Sweden)

    U. Fischer

    2015-09-01

    Full Text Available There is growing evidence that gene amplifications were present in neural stem and progenitor cells during differentiation. We used array-CGH to discover copy number changes including gene amplifications and deletions during differentiation of mouse neural stem cells using TGF-ß and FCS for differentiation induction. Array data were deposited in GEO (Gene Expression Omnibus, NCBI under accession number GSE35523. Here, we describe in detail the cell culture features and our TaqMan qPCR-experiments to validate the array-CGH analysis. Interpretation of array-CGH experiments regarding gene amplifications in mouse and further detailed analysis of amplified chromosome regions associated with these experiments were published by Fischer and colleagues in Oncotarget (Fischer et al., 2015. We provide additional information on deleted chromosome regions during differentiation and give an impressive overview on copy number changes during differentiation induction at a time line.

  7. The neural changes in connectivity of the voice network during voice pitch perturbation.

    Science.gov (United States)

    Flagmeier, Sabina G; Ray, Kimberly L; Parkinson, Amy L; Li, Karl; Vargas, Robert; Price, Larry R; Laird, Angela R; Larson, Charles R; Robin, Donald A

    2014-05-01

    Voice control is critical to communication. To date, studies have used behavioral, electrophysiological and functional data to investigate the neural correlates of voice control using perturbation tasks, but have yet to examine the interactions of these neural regions. The goal of this study was to use structural equation modeling of functional neuroimaging data to examine network properties of voice with and without perturbation. Results showed that the presence of a pitch shift, which was processed as an error in vocalization, altered connections between right STG and left STG. Other regions that revealed differences in connectivity during error detection and correction included bilateral inferior frontal gyrus, and the primary and pre motor cortices. Results indicated that STG plays a critical role in voice control, specifically, during error detection and correction. Additionally, pitch perturbation elicits changes in the voice network that suggest the right hemisphere is critical to pitch modulation. Published by Elsevier Inc.

  8. Artificial neural networks for stiffness estimation in magnetic resonance elastography.

    Science.gov (United States)

    Murphy, Matthew C; Manduca, Armando; Trzasko, Joshua D; Glaser, Kevin J; Huston, John; Ehman, Richard L

    2017-11-28

    To investigate the feasibility of using artificial neural networks to estimate stiffness from MR elastography (MRE) data. Artificial neural networks were fit using model-based training patterns to estimate stiffness from images of displacement using a patch size of ∼1 cm in each dimension. These neural network inversions (NNIs) were then evaluated in a set of simulation experiments designed to investigate the effects of wave interference and noise on NNI accuracy. NNI was also tested in vivo, comparing NNI results against currently used methods. In 4 simulation experiments, NNI performed as well or better than direct inversion (DI) for predicting the known stiffness of the data. Summary NNI results were also shown to be significantly correlated with DI results in the liver (R 2  = 0.974) and in the brain (R 2  = 0.915), and also correlated with established biological effects including fibrosis stage in the liver and age in the brain. Finally, repeatability error was lower in the brain using NNI compared to DI, and voxel-wise modeling using NNI stiffness maps detected larger effects than using DI maps with similar levels of smoothing. Artificial neural networks represent a new approach to inversion of MRE data. Summary results from NNI and DI are highly correlated and both are capable of detecting biologically relevant signals. Preliminary evidence suggests that NNI stiffness estimates may be more resistant to noise than an algebraic DI approach. Taken together, these results merit future investigation into NNIs to improve the estimation of stiffness in small regions. Magn Reson Med, 2017. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

  9. The functional role of neural oscillations in non-verbal emotional communication

    Directory of Open Access Journals (Sweden)

    Ashley E Symons

    2016-05-01

    Full Text Available Effective interpersonal communication depends on the ability to perceive and interpret nonverbal emotional expressions from multiple sensory modalities. Current theoretical models propose that visual and auditory emotion perception involves a network of brain regions including the primary sensory cortices, the superior temporal sulcus (STS, and orbitofrontal cortex (OFC. However, relatively little is known about how the dynamic interplay between these regions gives rise to the perception of emotions. In recent years, there has been increasing recognition of the importance of neural oscillations in mediating neural communication within and between functional neural networks. Here we review studies investigating changes in oscillatory activity during the perception of visual, auditory, and audiovisual emotional expressions, and aim to characterise the functional role of neural oscillations in nonverbal emotion perception. Findings from the reviewed literature suggest that theta band oscillations most consistently differentiate between emotional and neutral expressions. While early theta synchronisation appears to reflect the initial encoding of emotionally salient sensory information, later fronto-central theta synchronisation may reflect the further integration of sensory information with internal representations. Additionally, gamma synchronisation reflects facilitated sensory binding of emotional expressions within regions such as the OFC, STS, and, potentially, the amygdala. However, the evidence is more ambiguous when it comes to the role of oscillations within the alpha and beta frequencies, which vary as a function of modality (or modalities, presence or absence of predictive information, and attentional or task demands. Thus, the synchronisation of neural oscillations within specific frequency bands mediates the rapid detection, integration, and evaluation of emotional expressions. Moreover, the functional coupling of oscillatory activity

  10. Dynamics of neural cryptography.

    Science.gov (United States)

    Ruttor, Andreas; Kinzel, Wolfgang; Kanter, Ido

    2007-05-01

    Synchronization of neural networks has been used for public channel protocols in cryptography. In the case of tree parity machines the dynamics of both bidirectional synchronization and unidirectional learning is driven by attractive and repulsive stochastic forces. Thus it can be described well by a random walk model for the overlap between participating neural networks. For that purpose transition probabilities and scaling laws for the step sizes are derived analytically. Both these calculations as well as numerical simulations show that bidirectional interaction leads to full synchronization on average. In contrast, successful learning is only possible by means of fluctuations. Consequently, synchronization is much faster than learning, which is essential for the security of the neural key-exchange protocol. However, this qualitative difference between bidirectional and unidirectional interaction vanishes if tree parity machines with more than three hidden units are used, so that those neural networks are not suitable for neural cryptography. In addition, the effective number of keys which can be generated by the neural key-exchange protocol is calculated using the entropy of the weight distribution. As this quantity increases exponentially with the system size, brute-force attacks on neural cryptography can easily be made unfeasible.

  11. Neural processing of reward in adolescent rodents

    Directory of Open Access Journals (Sweden)

    Nicholas W. Simon

    2015-02-01

    Full Text Available Immaturities in adolescent reward processing are thought to contribute to poor decision making and increased susceptibility to develop addictive and psychiatric disorders. Very little is known; however, about how the adolescent brain processes reward. The current mechanistic theories of reward processing are derived from adult models. Here we review recent research focused on understanding of how the adolescent brain responds to rewards and reward-associated events. A critical aspect of this work is that age-related differences are evident in neuronal processing of reward-related events across multiple brain regions even when adolescent rats demonstrate behavior similar to adults. These include differences in reward processing between adolescent and adult rats in orbitofrontal cortex and dorsal striatum. Surprisingly, minimal age related differences are observed in ventral striatum, which has been a focal point of developmental studies. We go on to discuss the implications of these differences for behavioral traits affected in adolescence, such as impulsivity, risk-taking, and behavioral flexibility. Collectively, this work suggests that reward-evoked neural activity differs as a function of age and that regions such as the dorsal striatum that are not traditionally associated with affective processing in adults may be critical for reward processing and psychiatric vulnerability in adolescents.

  12. Birth prevalence of neural tube defects and orofacial clefts in India: a systematic review and meta-analysis.

    Science.gov (United States)

    Allagh, Komal Preet; Shamanna, B R; Murthy, Gudlavalleti V S; Ness, Andy R; Doyle, Pat; Neogi, Sutapa B; Pant, Hira B

    2015-01-01

    In the last two decades, India has witnessed a substantial decrease in infant mortality attributed to infectious disease and malnutrition. However, the mortality attributed to birth defects remains constant. Studies on the prevalence of birth defects such as neural tube defects and orofacial clefts in India have reported inconsistent results. Therefore, we conducted a systematic review of observational studies to document the birth prevalence of neural tube defects and orofacial clefts. A comprehensive literature search for observational studies was conducted in MEDLINE and EMBASE databases using key MeSH terms (neural tube defects OR cleft lip OR cleft palate AND Prevalence AND India). Two reviewers independently reviewed the retrieved studies, and studies satisfying the eligibility were included. The quality of included studies was assessed using selected criteria from STROBE statement. The overall pooled birth prevalence (random effect) of neural tube defects in India is 4.5 per 1000 total births (95% CI 4.2 to 4.9). The overall pooled birth prevalence (random effect) of orofacial clefts is 1.3 per 1000 total births (95% CI 1.1 to 1.5). Subgroup analyses were performed by region, time period, consanguinity, and gender of newborn. The overall prevalence of neural tube defects from India is high compared to other regions of the world, while that of orofacial clefts is similar to other countries. The majority of studies included in the review were hospital based. The quality of these studies ranged from low to moderate. Further well-designed, high quality community-based observational studies are needed to accurately estimate the burden of neural tube defects and orofacial clefts in India.

  13. Birth prevalence of neural tube defects and orofacial clefts in India: a systematic review and meta-analysis.

    Directory of Open Access Journals (Sweden)

    Komal Preet Allagh

    Full Text Available In the last two decades, India has witnessed a substantial decrease in infant mortality attributed to infectious disease and malnutrition. However, the mortality attributed to birth defects remains constant. Studies on the prevalence of birth defects such as neural tube defects and orofacial clefts in India have reported inconsistent results. Therefore, we conducted a systematic review of observational studies to document the birth prevalence of neural tube defects and orofacial clefts.A comprehensive literature search for observational studies was conducted in MEDLINE and EMBASE databases using key MeSH terms (neural tube defects OR cleft lip OR cleft palate AND Prevalence AND India. Two reviewers independently reviewed the retrieved studies, and studies satisfying the eligibility were included. The quality of included studies was assessed using selected criteria from STROBE statement.The overall pooled birth prevalence (random effect of neural tube defects in India is 4.5 per 1000 total births (95% CI 4.2 to 4.9. The overall pooled birth prevalence (random effect of orofacial clefts is 1.3 per 1000 total births (95% CI 1.1 to 1.5. Subgroup analyses were performed by region, time period, consanguinity, and gender of newborn.The overall prevalence of neural tube defects from India is high compared to other regions of the world, while that of orofacial clefts is similar to other countries. The majority of studies included in the review were hospital based. The quality of these studies ranged from low to moderate. Further well-designed, high quality community-based observational studies are needed to accurately estimate the burden of neural tube defects and orofacial clefts in India.

  14. Birth Prevalence of Neural Tube Defects and Orofacial Clefts in India: A Systematic Review and Meta-Analysis

    Science.gov (United States)

    Allagh, Komal Preet; Shamanna, B. R.; Murthy, Gudlavalleti V. S.; Ness, Andy R.; Doyle, Pat; Neogi, Sutapa B.; Pant, Hira B.

    2015-01-01

    Background In the last two decades, India has witnessed a substantial decrease in infant mortality attributed to infectious disease and malnutrition. However, the mortality attributed to birth defects remains constant. Studies on the prevalence of birth defects such as neural tube defects and orofacial clefts in India have reported inconsistent results. Therefore, we conducted a systematic review of observational studies to document the birth prevalence of neural tube defects and orofacial clefts. Methods A comprehensive literature search for observational studies was conducted in MEDLINE and EMBASE databases using key MeSH terms (neural tube defects OR cleft lip OR cleft palate AND Prevalence AND India). Two reviewers independently reviewed the retrieved studies, and studies satisfying the eligibility were included. The quality of included studies was assessed using selected criteria from STROBE statement. Results The overall pooled birth prevalence (random effect) of neural tube defects in India is 4.5 per 1000 total births (95% CI 4.2 to 4.9). The overall pooled birth prevalence (random effect) of orofacial clefts is 1.3 per 1000 total births (95% CI 1.1 to 1.5). Subgroup analyses were performed by region, time period, consanguinity, and gender of newborn. Conclusion The overall prevalence of neural tube defects from India is high compared to other regions of the world, while that of orofacial clefts is similar to other countries. The majority of studies included in the review were hospital based. The quality of these studies ranged from low to moderate. Further well-designed, high quality community-based observational studies are needed to accurately estimate the burden of neural tube defects and orofacial clefts in India. PMID:25768737

  15. Neural Networks for Modeling and Control of Particle Accelerators

    CERN Document Server

    Edelen, A.L.; Chase, B.E.; Edstrom, D.; Milton, S.V.; Stabile, P.

    2016-01-01

    We describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.

  16. Neural reactivation links unconscious thought to decision-making performance.

    Science.gov (United States)

    Creswell, John David; Bursley, James K; Satpute, Ajay B

    2013-12-01

    Brief periods of unconscious thought (UT) have been shown to improve decision making compared with making an immediate decision (ID). We reveal a neural mechanism for UT in decision making using blood oxygen level-dependent (BOLD) functional magnetic resonance imaging. Participants (N = 33) encoded information on a set of consumer products (e.g. 48 attributes describing four different cars), and we manipulated whether participants (i) consciously thought about this information (conscious thought), (ii) completed a difficult 2-back working memory task (UT) or (iii) made an immediate decision about the consumer products (ID) in a within-subjects blocked design. To differentiate UT neural activity from 2-back working memory neural activity, participants completed an independent 2-back task and this neural activity was subtracted from neural activity occurring during the UT 2-back task. Consistent with a neural reactivation account, we found that the same regions activated during the encoding of complex decision information (right dorsolateral prefrontal cortex and left intermediate visual cortex) continued to be activated during a subsequent 2-min UT period. Moreover, neural reactivation in these regions was predictive of subsequent behavioral decision-making performance after the UT period. These results provide initial evidence for post-encoding unconscious neural reactivation in facilitating decision making.

  17. Anger under control: neural correlates of frustration as a function of trait aggression.

    Directory of Open Access Journals (Sweden)

    Christina M Pawliczek

    Full Text Available Antisocial behavior and aggression are prominent symptoms in several psychiatric disorders including antisocial personality disorder. An established precursor to aggression is a frustrating event, which can elicit anger or exasperation, thereby prompting aggressive responses. While some studies have investigated the neural correlates of frustration and aggression, examination of their relation to trait aggression in healthy populations are rare. Based on a screening of 550 males, we formed two extreme groups, one including individuals reporting high (n=21 and one reporting low (n=18 trait aggression. Using functional magnetic resonance imaging (fMRI at 3T, all participants were put through a frustration task comprising unsolvable anagrams of German nouns. Despite similar behavioral performance, males with high trait aggression reported higher ratings of negative affect and anger after the frustration task. Moreover, they showed relatively decreased activation in the frontal brain regions and the dorsal anterior cingulate cortex (dACC as well as relatively less amygdala activation in response to frustration. Our findings indicate distinct frontal and limbic processing mechanisms following frustration modulated by trait aggression. In response to a frustrating event, HA individuals show some of the personality characteristics and neural processing patterns observed in abnormally aggressive populations. Highlighting the impact of aggressive traits on the behavioral and neural responses to frustration in non-psychiatric extreme groups can facilitate further characterization of neural dysfunctions underlying psychiatric disorders that involve abnormal frustration processing and aggression.

  18. Modular representation of layered neural networks.

    Science.gov (United States)

    Watanabe, Chihiro; Hiramatsu, Kaoru; Kashino, Kunio

    2018-01-01

    Layered neural networks have greatly improved the performance of various applications including image processing, speech recognition, natural language processing, and bioinformatics. However, it is still difficult to discover or interpret knowledge from the inference provided by a layered neural network, since its internal representation has many nonlinear and complex parameters embedded in hierarchical layers. Therefore, it becomes important to establish a new methodology by which layered neural networks can be understood. In this paper, we propose a new method for extracting a global and simplified structure from a layered neural network. Based on network analysis, the proposed method detects communities or clusters of units with similar connection patterns. We show its effectiveness by applying it to three use cases. (1) Network decomposition: it can decompose a trained neural network into multiple small independent networks thus dividing the problem and reducing the computation time. (2) Training assessment: the appropriateness of a trained result with a given hyperparameter or randomly chosen initial parameters can be evaluated by using a modularity index. And (3) data analysis: in practical data it reveals the community structure in the input, hidden, and output layers, which serves as a clue for discovering knowledge from a trained neural network. Copyright © 2017 Elsevier Ltd. All rights reserved.

  19. Logarithmic learning for generalized classifier neural network.

    Science.gov (United States)

    Ozyildirim, Buse Melis; Avci, Mutlu

    2014-12-01

    Generalized classifier neural network is introduced as an efficient classifier among the others. Unless the initial smoothing parameter value is close to the optimal one, generalized classifier neural network suffers from convergence problem and requires quite a long time to converge. In this work, to overcome this problem, a logarithmic learning approach is proposed. The proposed method uses logarithmic cost function instead of squared error. Minimization of this cost function reduces the number of iterations used for reaching the minima. The proposed method is tested on 15 different data sets and performance of logarithmic learning generalized classifier neural network is compared with that of standard one. Thanks to operation range of radial basis function included by generalized classifier neural network, proposed logarithmic approach and its derivative has continuous values. This makes it possible to adopt the advantage of logarithmic fast convergence by the proposed learning method. Due to fast convergence ability of logarithmic cost function, training time is maximally decreased to 99.2%. In addition to decrease in training time, classification performance may also be improved till 60%. According to the test results, while the proposed method provides a solution for time requirement problem of generalized classifier neural network, it may also improve the classification accuracy. The proposed method can be considered as an efficient way for reducing the time requirement problem of generalized classifier neural network. Copyright © 2014 Elsevier Ltd. All rights reserved.

  20. Moral transgressions corrupt neural representations of value.

    Science.gov (United States)

    Crockett, Molly J; Siegel, Jenifer Z; Kurth-Nelson, Zeb; Dayan, Peter; Dolan, Raymond J

    2017-06-01

    Moral systems universally prohibit harming others for personal gain. However, we know little about how such principles guide moral behavior. Using a task that assesses the financial cost participants ascribe to harming others versus themselves, we probed the relationship between moral behavior and neural representations of profit and pain. Most participants displayed moral preferences, placing a higher cost on harming others than themselves. Moral preferences correlated with neural responses to profit, where participants with stronger moral preferences had lower dorsal striatal responses to profit gained from harming others. Lateral prefrontal cortex encoded profit gained from harming others, but not self, and tracked the blameworthiness of harmful choices. Moral decisions also modulated functional connectivity between lateral prefrontal cortex and the profit-sensitive region of dorsal striatum. The findings suggest moral behavior in our task is linked to a neural devaluation of reward realized by a prefrontal modulation of striatal value representations.

  1. Neural correlates of forethought in ADHD.

    Science.gov (United States)

    Poissant, Hélène; Mendrek, Adrianna; Senhadji, Noureddine

    2014-04-01

    The purpose of the present investigation was to delineate the neural correlates of forethought in the ADHD children relative to typically developing (TD) children. In all, 21 TD and 23 ADHD adolescents underwent functional magnetic resonance imaging (fMRI) while performing a forethought task. The participants had to identify congruent and incongruent stimuli from cartoon stories representing sequences of action. The findings revealed significantly greater activation in the bilateral prefrontal cortex (PFC) in TD versus ADHD children, and more activation in the cerebellar vermis in the adolescents with ADHD versus TD, during performance of the incongruent relative to congruent condition. The inverse pattern of activation of the PFC and the cerebellar vermis in both groups could reflect a compensatory role played by the cerebellum or suggest the malfunction of the neural network between those regions in ADHD. Further research of the neural correlates of forethought in ADHD is warranted.

  2. Hidden neural networks

    DEFF Research Database (Denmark)

    Krogh, Anders Stærmose; Riis, Søren Kamaric

    1999-01-01

    A general framework for hybrids of hidden Markov models (HMMs) and neural networks (NNs) called hidden neural networks (HNNs) is described. The article begins by reviewing standard HMMs and estimation by conditional maximum likelihood, which is used by the HNN. In the HNN, the usual HMM probability...... parameters are replaced by the outputs of state-specific neural networks. As opposed to many other hybrids, the HNN is normalized globally and therefore has a valid probabilistic interpretation. All parameters in the HNN are estimated simultaneously according to the discriminative conditional maximum...... likelihood criterion. The HNN can be viewed as an undirected probabilistic independence network (a graphical model), where the neural networks provide a compact representation of the clique functions. An evaluation of the HNN on the task of recognizing broad phoneme classes in the TIMIT database shows clear...

  3. [Neural codes for perception].

    Science.gov (United States)

    Romo, R; Salinas, E; Hernández, A; Zainos, A; Lemus, L; de Lafuente, V; Luna, R

    This article describes experiments designed to show the neural codes associated with the perception and processing of tactile information. The results of these experiments have shown the neural activity correlated with tactile perception. The neurones of the primary somatosensory cortex (S1) represent the physical attributes of tactile perception. We found that these representations correlated with tactile perception. By means of intracortical microstimulation we demonstrated the causal relationship between S1 activity and tactile perception. In the motor areas of the frontal lobe is to be found the connection between sensorial and motor representation whilst decisions are being taken. S1 generates neural representations of the somatosensory stimuli which seen to be sufficient for tactile perception. These neural representations are subsequently processed by central areas to S1 and seem useful in perception, memory and decision making.

  4. Neural Oscillators Programming Simplified

    Directory of Open Access Journals (Sweden)

    Patrick McDowell

    2012-01-01

    Full Text Available The neurological mechanism used for generating rhythmic patterns for functions such as swallowing, walking, and chewing has been modeled computationally by the neural oscillator. It has been widely studied by biologists to model various aspects of organisms and by computer scientists and robotics engineers as a method for controlling and coordinating the gaits of walking robots. Although there has been significant study in this area, it is difficult to find basic guidelines for programming neural oscillators. In this paper, the authors approach neural oscillators from a programmer’s point of view, providing background and examples for developing neural oscillators to generate rhythmic patterns that can be used in biological modeling and robotics applications.

  5. Neural correlate of vernier acuity tasks assessed by functional MRI (FMRI).

    Science.gov (United States)

    Sheth, Kevin N; Walker, B Michael; Modestino, Edward J; Miki, Atsushi; Terhune, Kyla P; Francis, Ellie L; Haselgrove, John C; Liu, Grant T

    2007-01-01

    Vernier acuity refers to the ability to discern a small offset within a line. However, while Vernier acuity has been extensively studied psychophysically, its neural correlates are uncertain. Based upon previous psychophysical and electrophysiologic data, we hypothesized that extrastriate areas of the brain would be involved in Vernier acuity tasks, so we designed event-related functional MRI (fMRI) paradigms to identify cortical regions of the brain involved in this behavior. Normal subjects identified suprathreshold and subthreshold Vernier offsets. The results suggest a cortical network including frontal, parietal, occipital, and cerebellar regions subserves the observation, processing, interpretation, and acknowledgment of briefly presented Vernier offsets.

  6. Handbook on neural information processing

    CERN Document Server

    Maggini, Marco; Jain, Lakhmi

    2013-01-01

    This handbook presents some of the most recent topics in neural information processing, covering both theoretical concepts and practical applications. The contributions include:                         Deep architectures                         Recurrent, recursive, and graph neural networks                         Cellular neural networks                         Bayesian networks                         Approximation capabilities of neural networks                         Semi-supervised learning                         Statistical relational learning                         Kernel methods for structured data                         Multiple classifier systems                         Self organisation and modal learning                         Applications to ...

  7. Differences in the neural correlates of frontal lobe tests.

    Science.gov (United States)

    Matsuoka, Teruyuki; Kato, Yuka; Imai, Ayu; Fujimoto, Hiroshi; Shibata, Keisuke; Nakamura, Kaeko; Yamada, Kei; Narumoto, Jin

    2018-01-01

    The Executive Interview (EXIT25), the executive clock-drawing task (CLOX1), and the Frontal Assessment Battery (FAB) are used to assess executive function at the bedside. These tests assess distinct psychometric properties. The aim of this study was to examine differences in the neural correlates of the EXIT25, CLOX1, and FAB based on magnetic resonance imaging. Fifty-eight subjects (30 with Alzheimer's disease, 10 with mild cognitive impairment, and 18 healthy controls) participated in this study. Multiple regression analyses were performed to examine the brain regions correlated with the EXIT25, CLOX1, and FAB scores. Age, gender, and years of education were included as covariates. Statistical thresholds were set to uncorrected P-values of 0.001 at the voxel level and 0.05 at the cluster level. The EXIT25 score correlated inversely with the regional grey matter volume in the left lateral frontal lobe (Brodmann areas 6, 9, 44, and 45). The CLOX1 score correlated positively with the regional grey matter volume in the right orbitofrontal cortex (Brodmann area 11) and the left supramarginal gyrus (Brodmann area 40). The FAB score correlated positively with the regional grey matter volume in the right precentral gyrus (Brodmann area 6). The left lateral frontal lobe (Brodmann area 9) and the right lateral frontal lobe (Brodmann area 46) were identified as common brain regions that showed association with EXIT25, CLOX1, and FAB based only a voxel-level threshold. The results of this study suggest that the EXIT25, CLOX1, and FAB may be associated with the distinct neural correlates of the frontal cortex. © 2018 Japanese Psychogeriatric Society.

  8. Neural cryptography with feedback.

    Science.gov (United States)

    Ruttor, Andreas; Kinzel, Wolfgang; Shacham, Lanir; Kanter, Ido

    2004-04-01

    Neural cryptography is based on a competition between attractive and repulsive stochastic forces. A feedback mechanism is added to neural cryptography which increases the repulsive forces. Using numerical simulations and an analytic approach, the probability of a successful attack is calculated for different model parameters. Scaling laws are derived which show that feedback improves the security of the system. In addition, a network with feedback generates a pseudorandom bit sequence which can be used to encrypt and decrypt a secret message.

  9. Neural cryptography with feedback

    Science.gov (United States)

    Ruttor, Andreas; Kinzel, Wolfgang; Shacham, Lanir; Kanter, Ido

    2004-04-01

    Neural cryptography is based on a competition between attractive and repulsive stochastic forces. A feedback mechanism is added to neural cryptography which increases the repulsive forces. Using numerical simulations and an analytic approach, the probability of a successful attack is calculated for different model parameters. Scaling laws are derived which show that feedback improves the security of the system. In addition, a network with feedback generates a pseudorandom bit sequence which can be used to encrypt and decrypt a secret message.

  10. Neural network applications

    Science.gov (United States)

    Padgett, Mary L.; Desai, Utpal; Roppel, T.A.; White, Charles R.

    1993-01-01

    A design procedure is suggested for neural networks which accommodates the inclusion of such knowledge-based systems techniques as fuzzy logic and pairwise comparisons. The use of these procedures in the design of applications combines qualitative and quantitative factors with empirical data to yield a model with justifiable design and parameter selection procedures. The procedure is especially relevant to areas of back-propagation neural network design which are highly responsive to the use of precisely recorded expert knowledge.

  11. Building Neural Net Software

    OpenAIRE

    Neto, João Pedro; Costa, José Félix

    1999-01-01

    In a recent paper [Neto et al. 97] we showed that programming languages can be translated on recurrent (analog, rational weighted) neural nets. The goal was not efficiency but simplicity. Indeed we used a number-theoretic approach to machine programming, where (integer) numbers were coded in a unary fashion, introducing a exponential slow down in the computations, with respect to a two-symbol tape Turing machine. Implementation of programming languages in neural nets turns to be not only theo...

  12. NEMEFO: NEural MEteorological FOrecast

    Energy Technology Data Exchange (ETDEWEB)

    Pasero, E.; Moniaci, W.; Meindl, T.; Montuori, A. [Polytechnic of Turin (Italy). Dept. of Electronics

    2004-07-01

    Artificial Neural Systems are a well-known technique used to classify and recognize objects. Introducing the time dimension they can be used to forecast numerical series. NEMEFO is a ''nowcasting'' tool, which uses both statistical and neural systems to forecast meteorological data in a restricted area close to a meteorological weather station in a short time range (3 hours). Ice, fog, rain are typical events which can be anticipated by NEMEFO. (orig.)

  13. An overview of Bayesian methods for neural spike train analysis.

    Science.gov (United States)

    Chen, Zhe

    2013-01-01

    Neural spike train analysis is an important task in computational neuroscience which aims to understand neural mechanisms and gain insights into neural circuits. With the advancement of multielectrode recording and imaging technologies, it has become increasingly demanding to develop statistical tools for analyzing large neuronal ensemble spike activity. Here we present a tutorial overview of Bayesian methods and their representative applications in neural spike train analysis, at both single neuron and population levels. On the theoretical side, we focus on various approximate Bayesian inference techniques as applied to latent state and parameter estimation. On the application side, the topics include spike sorting, tuning curve estimation, neural encoding and decoding, deconvolution of spike trains from calcium imaging signals, and inference of neuronal functional connectivity and synchrony. Some research challenges and opportunities for neural spike train analysis are discussed.

  14. An Overview of Bayesian Methods for Neural Spike Train Analysis

    Directory of Open Access Journals (Sweden)

    Zhe Chen

    2013-01-01

    Full Text Available Neural spike train analysis is an important task in computational neuroscience which aims to understand neural mechanisms and gain insights into neural circuits. With the advancement of multielectrode recording and imaging technologies, it has become increasingly demanding to develop statistical tools for analyzing large neuronal ensemble spike activity. Here we present a tutorial overview of Bayesian methods and their representative applications in neural spike train analysis, at both single neuron and population levels. On the theoretical side, we focus on various approximate Bayesian inference techniques as applied to latent state and parameter estimation. On the application side, the topics include spike sorting, tuning curve estimation, neural encoding and decoding, deconvolution of spike trains from calcium imaging signals, and inference of neuronal functional connectivity and synchrony. Some research challenges and opportunities for neural spike train analysis are discussed.

  15. Neural correlates of long-term intense romantic love

    Science.gov (United States)

    Aron, Arthur; Fisher, Helen E.; Brown, Lucy L.

    2012-01-01

    The present study examined the neural correlates of long-term intense romantic love using functional magnetic resonance imaging (fMRI). Ten women and 7 men married an average of 21.4 years underwent fMRI while viewing facial images of their partner. Control images included a highly familiar acquaintance; a close, long-term friend; and a low-familiar person. Effects specific to the intensely loved, long-term partner were found in: (i) areas of the dopamine-rich reward and basal ganglia system, such as the ventral tegmental area (VTA) and dorsal striatum, consistent with results from early-stage romantic love studies; and (ii) several regions implicated in maternal attachment, such as the globus pallidus (GP), substantia nigra, Raphe nucleus, thalamus, insular cortex, anterior cingulate and posterior cingulate. Correlations of neural activity in regions of interest with widely used questionnaires showed: (i) VTA and caudate responses correlated with romantic love scores and inclusion of other in the self; (ii) GP responses correlated with friendship-based love scores; (iii) hypothalamus and posterior hippocampus responses correlated with sexual frequency; and (iv) caudate, septum/fornix, posterior cingulate and posterior hippocampus responses correlated with obsession. Overall, results suggest that for some individuals the reward-value associated with a long-term partner may be sustained, similar to new love, but also involves brain systems implicated in attachment and pair-bonding. PMID:21208991

  16. Neural correlates of cognitive style and flexible cognitive control.

    Science.gov (United States)

    Shin, Gyeonghee; Kim, Chobok

    2015-06-01

    Human abilities of flexible cognitive control are associated with appropriately regulating the amount of cognitive control required in response to contextual demands. In the context of conflicting situations, for instance, the amount of cognitive control increases according to the level of previously experienced conflict, resulting in optimized performance. We explored whether the amount of cognitive control in conflict resolution was related to individual differences in cognitive style that were determined with the Object-Spatial-Verbal cognitive style questionnaire. In this functional magnetic resonance imaging (fMRI) study, a version of the color-word Stroop task, which evokes conflict between color and verbal components, was employed to explore whether individual preferences for distracting information were related to the increases in neural conflict adaptation in cognitive control network regions. The behavioral data revealed that the more the verbal style was preferred, the greater the conflict adaptation effect was observed, especially when the current trial type was congruent. Consistent with the behavioral data, the imaging results demonstrated increased neural conflict adaptation effects in task-relevant network regions, including the left dorsolateral prefrontal cortex, left fusiform gyrus, and left precuneus, as the preference for verbal style increased. These results provide new evidence that flexible cognitive control is closely associated with individuals' preference of cognitive style. Copyright © 2015 Elsevier Inc. All rights reserved.

  17. Common neural substrates for inhibition of spoken and manual responses.

    Science.gov (United States)

    Xue, Gui; Aron, Adam R; Poldrack, Russell A

    2008-08-01

    The inhibition of speech acts is a critical aspect of human executive control over thought and action, but its neural underpinnings are poorly understood. Using functional magnetic resonance imaging and the stop-signal paradigm, we examined the neural correlates of speech control in comparison to manual motor control. Initiation of a verbal response activated left inferior frontal cortex (IFC: Broca's area). Successful inhibition of speech (naming of letters or pseudowords) engaged a region of right IFC (including pars opercularis and anterior insular cortex) as well as presupplementary motor area (pre-SMA); these regions were also activated by successful inhibition of a hand response (i.e., a button press). Moreover, the speed with which subjects inhibited their responses, stop-signal reaction time, was significantly correlated between speech and manual inhibition tasks. These findings suggest a functional dissociation of left and right IFC in initiating versus inhibiting vocal responses, and that manual responses and speech acts share a common inhibitory mechanism localized in the right IFC and pre-SMA.

  18. Neural correlates of choice behavior related to impulsivity and venturesomeness.

    Science.gov (United States)

    Hinvest, Neal S; Elliott, R; McKie, S; Anderson, Ian M

    2011-07-01

    Impulsivity has been associated with several psychiatric disorders including drug addiction and gambling. Impulsive subjects typically have a preference for short-term over long-term rewards and make risky choices. This study used functional magnetic resonance imaging (fMRI) to investigate the neural correlates of self-rated impulsivity and venturesomeness during tasks involving delayed and risky choice. A broader sampling approach was taken by recruiting participants with behaviors that have been linked to impulsivity (gambling N=15, and recreational drug use N=10) and those without these behaviors (N=9). Selection between delayed or probabilistic rewards was associated with activation in fronto-parietal regions in line with previous research. When selecting between delayed rewards, activity within the pregenual anterior cingulate cortex and ventrolateral prefrontal cortex correlated positively with impulsivity scores while activity within the orbitofrontal cortex, subgenual anterior cingulate cortex and caudate correlated positively with venturesomeness scores. Selection between probabilistic rewards revealed no correlation between scores and regional activations. The results from this study provide targets for future research investigating the neural substrates of impulsivity. They also provide targets for the further investigation into the pathophysiology of addiction and impulse-control disorders. Copyright © 2011 Elsevier Ltd. All rights reserved.

  19. Neural correlates of long-term intense romantic love.

    Science.gov (United States)

    Acevedo, Bianca P; Aron, Arthur; Fisher, Helen E; Brown, Lucy L

    2012-02-01

    The present study examined the neural correlates of long-term intense romantic love using functional magnetic resonance imaging (fMRI). Ten women and 7 men married an average of 21.4 years underwent fMRI while viewing facial images of their partner. Control images included a highly familiar acquaintance; a close, long-term friend; and a low-familiar person. Effects specific to the intensely loved, long-term partner were found in: (i) areas of the dopamine-rich reward and basal ganglia system, such as the ventral tegmental area (VTA) and dorsal striatum, consistent with results from early-stage romantic love studies; and (ii) several regions implicated in maternal attachment, such as the globus pallidus (GP), substantia nigra, Raphe nucleus, thalamus, insular cortex, anterior cingulate and posterior cingulate. Correlations of neural activity in regions of interest with widely used questionnaires showed: (i) VTA and caudate responses correlated with romantic love scores and inclusion of other in the self; (ii) GP responses correlated with friendship-based love scores; (iii) hypothalamus and posterior hippocampus responses correlated with sexual frequency; and (iv) caudate, septum/fornix, posterior cingulate and posterior hippocampus responses correlated with obsession. Overall, results suggest that for some individuals the reward-value associated with a long-term partner may be sustained, similar to new love, but also involves brain systems implicated in attachment and pair-bonding.

  20. Transcutaneous parasacral electrical neural stimulation in children with primary monosymptomatic enuresis: a prospective randomized clinical trial.

    Science.gov (United States)

    de Oliveira, Liliana Fajardo; de Oliveira, Dayana Maria; da Silva de Paula, Lidyanne Ilídia; de Figueiredo, André Avarese; de Bessa, José; de Sá, Cacilda Andrade; Bastos Netto, José Murillo

    2013-10-01

    Parasacral transcutaneous electrical neural stimulation is widely used to treat hyperactive bladder in children and adults. Its use in nonmonosymptomatic enuresis has demonstrated improvement in number of dry nights. We assessed the effectiveness of parasacral transcutaneous electrical neural stimulation in the treatment of monosymptomatic primary enuresis. This prospective randomized clinical trial included 29 girls and 16 boys older than 6 years with primary monosymptomatic enuresis. Children were randomly divided into 2 groups consisting of controls, who were treated with behavioral therapy, and an experimental group, who were treated with behavioral therapy plus 10 sessions of parasacral transcutaneous electrical neural stimulation. Neural stimulation was performed with the electrodes placed in the sacral region (S2/S3). Sessions always followed the same pattern, with duration of 20 minutes, frequency of 10 Hz, a generated pulse of 700 μs and intensity determined by the sensitivity threshold of the child. Sessions were done 3 times weekly on alternate days. Patients in both groups were followed at 2-week intervals for the first month and then monthly for 6 consecutive months. Rate of wet nights was 77% in controls and 78.3% in the experimental group at onset of treatment (p = 0.82), and 49.5% and 31.2%, respectively, at the end of treatment (p = 0.02). Analyzing the average rate of improvement, there was a significantly greater increase in dry nights in the group undergoing neural stimulation (61.8%) compared to controls (37.3%, p = 0.0038). At the end of treatment percent improvement in children undergoing electrical stimulation had no relation to gender (p = 0.391) or age (p = 0.911). Treatment of primary monosymptomatic enuresis with 10 sessions of parasacral transcutaneous electrical neural stimulation plus behavioral therapy proved to be effective. However, no patient had complete resolution of symptoms. Copyright © 2013 American Urological Association

  1. Optical-Correlator Neural Network Based On Neocognitron

    Science.gov (United States)

    Chao, Tien-Hsin; Stoner, William W.

    1994-01-01

    Multichannel optical correlator implements shift-invariant, high-discrimination pattern-recognizing neural network based on paradigm of neocognitron. Selected as basic building block of this neural network because invariance under shifts is inherent advantage of Fourier optics included in optical correlators in general. Neocognitron is conceptual electronic neural-network model for recognition of visual patterns. Multilayer processing achieved by iteratively feeding back output of feature correlator to input spatial light modulator and updating Fourier filters. Neural network trained by use of characteristic features extracted from target images. Multichannel implementation enables parallel processing of large number of selected features.

  2. Neural network models: Insights and prescriptions from practical applications

    Energy Technology Data Exchange (ETDEWEB)

    Samad, T. [Honeywell Technology Center, Minneapolis, MN (United States)

    1995-12-31

    Neural networks are no longer just a research topic; numerous applications are now testament to their practical utility. In the course of developing these applications, researchers and practitioners have been faced with a variety of issues. This paper briefly discusses several of these, noting in particular the rich connections between neural networks and other, more conventional technologies. A more comprehensive version of this paper is under preparation that will include illustrations on real examples. Neural networks are being applied in several different ways. Our focus here is on neural networks as modeling technology. However, much of the discussion is also relevant to other types of applications such as classification, control, and optimization.

  3. Liquefaction Microzonation of Babol City Using Artificial Neural Network

    DEFF Research Database (Denmark)

    Farrokhzad, F.; Choobbasti, A.J.; Barari, Amin

    2012-01-01

    that will be less susceptible to damage during earthquakes. The scope of present study is to prepare the liquefaction microzonation map for the Babol city based on Seed and Idriss (1983) method using artificial neural network. Artificial neural network (ANN) is one of the artificial intelligence (AI) approaches...... is proposed in this paper. To meet this objective, an effort is made to introduce a total of 30 boreholes data in an area of 7 km2 which includes the results of field tests into the neural network model and the prediction of artificial neural network is checked in some test boreholes, finally the liquefaction...

  4. The effects of gratitude expression on neural activity.

    Science.gov (United States)

    Kini, Prathik; Wong, Joel; McInnis, Sydney; Gabana, Nicole; Brown, Joshua W

    2016-03-01

    Gratitude is a common aspect of social interaction, yet relatively little is known about the neural bases of gratitude expression, nor how gratitude expression may lead to longer-term effects on brain activity. To address these twin issues, we recruited subjects who coincidentally were entering psychotherapy for depression and/or anxiety. One group participated in a gratitude writing intervention, which required them to write letters expressing gratitude. The therapy-as-usual control group did not perform a writing intervention. After three months, subjects performed a "Pay It Forward" task in the fMRI scanner. In the task, subjects were repeatedly endowed with a monetary gift and then asked to pass it on to a charitable cause to the extent they felt grateful for the gift. Operationalizing gratitude as monetary gifts allowed us to engage the subjects and quantify the gratitude expression for subsequent analyses. We measured brain activity and found regions where activity correlated with self-reported gratitude experience during the task, even including related constructs such as guilt motivation and desire to help as statistical controls. These were mostly distinct from brain regions activated by empathy or theory of mind. Also, our between groups cross-sectional study found that a simple gratitude writing intervention was associated with significantly greater and lasting neural sensitivity to gratitude - subjects who participated in gratitude letter writing showed both behavioral increases in gratitude and significantly greater neural modulation by gratitude in the medial prefrontal cortex three months later. Copyright © 2015 Elsevier Inc. All rights reserved.

  5. Autonomic neural functions in space.

    Science.gov (United States)

    Mano, T

    2005-08-01

    microgravity indicate that sympathetic neural control is lowered when exposed to short-term microgravity but becomes enhanced after exposure to long-term microgravity. A lack of enhanced sympathetic neural response to orthostatic stress may induce orthostatic intolerance. Based on these findings effective countermeasures should be developed to prevent autonomic dysfunctions induced by exposure to microgravity. These include development of prescription and devices of physical exercise, electrical and magnetic nerve stimulations, body vibration, elastic bandage and stocking, lower body negative pressure, artificial gravity, medical drugs, and combinations of them. These countermeasures will be beneficial to prevent autonomic dysfunctions related to gravitational stress such encountered in bedridden subjects as orthostatic hypotension, atrophy of antigravity muscles and so on. This is particularly important in the present aged-society with many bedridden elderly people. The knowledge accumulated from studies on autonomic neural functions in space should be very useful to establish effective countermeasures and preventive methods for gravity-dependent autonomic dysfunctions.

  6. Translocation of latex beads after laser ablation of the avian neural crest.

    Science.gov (United States)

    Coulombe, J N; Bronner-Fraser, M

    1984-11-01

    Previous studies from this laboratory (M.E. Bronner-Fraser, 1982, Dev. Biol. 91, 50-63) have demonstrated that latex beads translocate ventrally after injection into avian embryos during the phase of neural crest migration, to settle in the vicinity of neural-crest-derived structures. In order to examine the role of host neural crest cells in the ventral translocation of implanted beads, latex beads have been injected into regions of embryos from which the neural crest cells have been ablated using a laser microbeam. Prior to their migratory phase, neural crest cells reside in the dorsal portion of the neural tube. Laser irradiation of the dorsal neural tube was used to reproducibly achieve either partial or complete ablation of neural crest cells from the irradiated regions. The effectiveness of the ablation was assessed by the degree of reduction in dorsal root ganglia, a neural crest derivative. Because of the rapidity and precision of this technique, it was possible to selectively remove neural crest cells without significantly altering other embryonic structures. The results indicate that, after injection of latex beads into the somites of embryos whose neural crest cells were removed by laser irradiation, the beads translocate ventrally in the absence of the endogenous neural crest.

  7. Neural network for constrained nonsmooth optimization using Tikhonov regularization.

    Science.gov (United States)

    Qin, Sitian; Fan, Dejun; Wu, Guangxi; Zhao, Lijun

    2015-03-01

    This paper presents a one-layer neural network to solve nonsmooth convex optimization problems based on the Tikhonov regularization method. Firstly, it is shown that the optimal solution of the original problem can be approximated by the optimal solution of a strongly convex optimization problems. Then, it is proved that for any initial point, the state of the proposed neural network enters the equality feasible region in finite time, and is globally convergent to the unique optimal solution of the related strongly convex optimization problems. Compared with the existing neural networks, the proposed neural network has lower model complexity and does not need penalty parameters. In the end, some numerical examples and application are given to illustrate the effectiveness and improvement of the proposed neural network. Copyright © 2014 Elsevier Ltd. All rights reserved.

  8. Role of SDF1/CXCR4 Interaction in Experimental Hemiplegic Models with Neural Cell Transplantation

    Directory of Open Access Journals (Sweden)

    Noboru Suzuki

    2012-02-01

    Full Text Available Much attention has been focused on neural cell transplantation because of its promising clinical applications. We have reported that embryonic stem (ES cell derived neural stem/progenitor cell transplantation significantly improved motor functions in a hemiplegic mouse model. It is important to understand the molecular mechanisms governing neural regeneration of the damaged motor cortex after the transplantation. Recent investigations disclosed that chemokines participated in the regulation of migration and maturation of neural cell grafts. In this review, we summarize the involvement of inflammatory chemokines including stromal cell derived factor 1 (SDF1 in neural regeneration after ES cell derived neural stem/progenitor cell transplantation in mouse stroke models.

  9. Cardiovascular Development and the Colonizing Cardiac Neural Crest Lineage

    Directory of Open Access Journals (Sweden)

    Paige Snider

    2007-01-01

    Full Text Available Although it is well established that transgenic manipulation of mammalian neural crest-related gene expression and microsurgical removal of premigratory chicken and Xenopus embryonic cardiac neural crest progenitors results in a wide spectrum of both structural and functional congenital heart defects, the actual functional mechanism of the cardiac neural crest cells within the heart is poorly understood. Neural crest cell migration and appropriate colonization of the pharyngeal arches and outflow tract septum is thought to be highly dependent on genes that regulate cell-autonomous polarized movement (i.e., gap junctions, cadherins, and noncanonical Wnt1 pathway regulators. Once the migratory cardiac neural crest subpopulation finally reaches the heart, they have traditionally been thought to participate in septation of the common outflow tract into separate aortic and pulmonary arteries. However, several studies have suggested these colonizing neural crest cells may also play additional unexpected roles during cardiovascular development and may even contribute to a crest-derived stem cell population. Studies in both mice and chick suggest they can also enter the heart from the venous inflow as well as the usual arterial outflow region, and may contribute to the adult semilunar and atrioventricular valves as well as part of the cardiac conduction system. Furthermore, although they are not usually thought to give rise to the cardiomyocyte lineage, neural crest cells in the zebrafish (Danio rerio can contribute to the myocardium and may have different functions in a species-dependent context. Intriguingly, both ablation of chick and Xenopus premigratory neural crest cells, and a transgenic deletion of mouse neural crest cell migration or disruption of the normal mammalian neural crest gene expression profiles, disrupts ventral myocardial function and/or cardiomyocyte proliferation. Combined, this suggests that either the cardiac neural crest

  10. Neural classifiers using one-time updating.

    Science.gov (United States)

    Diamantaras, K I; Strintzis, M G

    1998-01-01

    The linear threshold element (LTE), or perceptron, is a linear classifier with limited capabilities due to the problems arising when the input pattern set is linearly nonseparable. Assuming that the patterns are presented in a sequential fashion, we derive a theory for the detection of linear nonseparability as soon as it appears in the pattern set. This theory is based on the precise determination of the solution region in the weight space with the help of a special set of vectors. For this region, called the solution cone, we present a recursive computation procedure which allows immediate detection of nonseparability. The separability-violating patterns may be skipped so that, at the end, we derive a totally separable subset of the original pattern set along with its solution cone. The intriguing aspect of this algorithm is that it can be directly cast into a simple neural-network implementation. In this model the synaptic weights are committed (they are updated only once, and the only change that may happen after that is their destruction). This bears resemblance to the behavior of biological neural networks, and it is a feature unlike those of most other artificial neural techniques. Finally, by combining many such neural models we develop a learning procedure capable of separating convex classes.

  11. The Effects of GABAergic Polarity Changes on Episodic Neural Network Activity in Developing Neural Systems

    Directory of Open Access Journals (Sweden)

    Wilfredo Blanco

    2017-09-01

    Full Text Available Early in development, neural systems have primarily excitatory coupling, where even GABAergic synapses are excitatory. Many of these systems exhibit spontaneous episodes of activity that have been characterized through both experimental and computational studies. As development progress the neural system goes through many changes, including synaptic remodeling, intrinsic plasticity in the ion channel expression, and a transformation of GABAergic synapses from excitatory to inhibitory. What effect each of these, and other, changes have on the network behavior is hard to know from experimental studies since they all happen in parallel. One advantage of a computational approach is that one has the ability to study developmental changes in isolation. Here, we examine the effects of GABAergic synapse polarity change on the spontaneous activity of both a mean field and a neural network model that has both glutamatergic and GABAergic coupling, representative of a developing neural network. We find some intuitive behavioral changes as the GABAergic neurons go from excitatory to inhibitory, shared by both models, such as a decrease in the duration of episodes. We also find some paradoxical changes in the activity that are only present in the neural network model. In particular, we find that during early development the inter-episode durations become longer on average, while later in development they become shorter. In addressing this unexpected finding, we uncover a priming effect that is particularly important for a small subset of neurons, called the “intermediate neurons.” We characterize these neurons and demonstrate why they are crucial to episode initiation, and why the paradoxical behavioral change result from priming of these neurons. The study illustrates how even arguably the simplest of developmental changes that occurs in neural systems can present non-intuitive behaviors. It also makes predictions about neural network behavioral changes

  12. The LILARTI neural network system

    Energy Technology Data Exchange (ETDEWEB)

    Allen, J.D. Jr.; Schell, F.M.; Dodd, C.V.

    1992-10-01

    The material of this Technical Memorandum is intended to provide the reader with conceptual and technical background information on the LILARTI neural network system of detail sufficient to confer an understanding of the LILARTI method as it is presently allied and to facilitate application of the method to problems beyond the scope of this document. Of particular importance in this regard are the descriptive sections and the Appendices which include operating instructions, partial listings of program output and data files, and network construction information.

  13. Optical implementation of neural networks

    Science.gov (United States)

    Yu, Francis T. S.; Guo, Ruyan

    2002-12-01

    An adaptive optical neuro-computing (ONC) using inexpensive pocket size liquid crystal televisions (LCTVs) had been developed by the graduate students in the Electro-Optics Laboratory at The Pennsylvania State University. Although this neuro-computing has only 8×8=64 neurons, it can be easily extended to 16×20=320 neurons. The major advantages of this LCTV architecture as compared with other reported ONCs, are low cost and the flexibility to operate. To test the performance, several neural net models are used. These models are Interpattern Association, Hetero-association and unsupervised learning algorithms. The system design considerations and experimental demonstrations are also included.

  14. Pansharpening by Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Giuseppe Masi

    2016-07-01

    Full Text Available A new pansharpening method is proposed, based on convolutional neural networks. We adapt a simple and effective three-layer architecture recently proposed for super-resolution to the pansharpening problem. Moreover, to improve performance without increasing complexity, we augment the input by including several maps of nonlinear radiometric indices typical of remote sensing. Experiments on three representative datasets show the proposed method to provide very promising results, largely competitive with the current state of the art in terms of both full-reference and no-reference metrics, and also at a visual inspection.

  15. Neural bases of congenital amusia in tonal language speakers.

    Science.gov (United States)

    Zhang, Caicai; Peng, Gang; Shao, Jing; Wang, William S-Y

    2017-03-01

    Congenital amusia is a lifelong neurodevelopmental disorder of fine-grained pitch processing. In this fMRI study, we examined the neural bases of congenial amusia in speakers of a tonal language - Cantonese. Previous studies on non-tonal language speakers suggest that the neural deficits of congenital amusia lie in the music-selective neural circuitry in the right inferior frontal gyrus (IFG). However, it is unclear whether this finding can generalize to congenital amusics in tonal languages. Tonal language experience has been reported to shape the neural processing of pitch, which raises the question of how tonal language experience affects the neural bases of congenital amusia. To investigate this question, we examined the neural circuitries sub-serving the processing of relative pitch interval in pitch-matched Cantonese level tone and musical stimuli in 11 Cantonese-speaking amusics and 11 musically intact controls. Cantonese-speaking amusics exhibited abnormal brain activities in a widely distributed neural network during the processing of lexical tone and musical stimuli. Whereas the controls exhibited significant activation in the right superior temporal gyrus (STG) in the lexical tone condition and in the cerebellum regardless of the lexical tone and music conditions, no activation was found in the amusics in those regions, which likely reflects a dysfunctional neural mechanism of relative pitch processing in the amusics. Furthermore, the amusics showed abnormally strong activation of the right middle frontal gyrus and precuneus when the pitch stimuli were repeated, which presumably reflect deficits of attending to repeated pitch stimuli or encoding them into working memory. No significant group difference was found in the right IFG in either the whole-brain analysis or region-of-interest analysis. These findings imply that the neural deficits in tonal language speakers might differ from those in non-tonal language speakers, and overlap partly with the

  16. Maximum entropy methods for extracting the learned features of deep neural networks.

    Science.gov (United States)

    Finnegan, Alex; Song, Jun S

    2017-10-01

    New architectures of multilayer artificial neural networks and new methods for training them are rapidly revolutionizing the application of machine learning in diverse fields, including business, social science, physical sciences, and biology. Interpreting deep neural networks, however, currently remains elusive, and a critical challenge lies in understanding which meaningful features a network is actually learning. We present a general method for interpreting deep neural networks and extracting network-learned features from input data. We describe our algorithm in the context of biological sequence analysis. Our approach, based on ideas from statistical physics, samples from the maximum entropy distribution over possible sequences, anchored at an input sequence and subject to constraints implied by the empirical function learned by a network. Using our framework, we demonstrate that local transcription factor binding motifs can be identified from a network trained on ChIP-seq data and that nucleosome positioning signals are indeed learned by a network trained on chemical cleavage nucleosome maps. Imposing a further constraint on the maximum entropy distribution also allows us to probe whether a network is learning global sequence features, such as the high GC content in nucleosome-rich regions. This work thus provides valuable mathematical tools for interpreting and extracting learned features from feed-forward neural networks.

  17. Multiple cranial organ defects after conditionally knocking out Fgf10 in the neural crest

    Directory of Open Access Journals (Sweden)

    Tathyane H.N. Teshima

    2016-10-01

    Full Text Available Fgf10 is necessary for the development of a number of organs that fail to develop or are reduced in size in the null mutant. Here we have knocked out Fgf10 specifically in the neural crest driven by Wnt1cre. The Wnt1creFgf10fl/fl mouse phenocopies many of the null mutant defects, including cleft palate, loss of salivary glands and ocular glands, highlighting the neural crest origin of the Fgf10 expressing mesenchyme surrounding these organs. In contrast tissues such as the limbs and lungs, where Fgf10 is expressed by the surrounding mesoderm, were unaffected, as was the pituitary gland where Fgf10 is expressed by the neuroepithelium. The circumvallate papilla of the tongue formed but was hypoplastic in the conditional and Fgf10 null embryos, suggesting that other sources of FGF can compensate in development of this structure. The tracheal cartilage rings showed normal patterning in the conditional knockout, indicating that the source of Fgf10 for this tissue is mesodermal, which was confirmed using Wnt1cre-dtTom to lineage trace the boundary of the neural crest in this region. The thyroid, thymus and parathyroid glands surrounding the trachea were present but hypoplastic in the conditional mutant, indicating that a neighbouring source of mesodermal Fgf10 might be able to partially compensate for loss of neural crest derived Fgf10.

  18. Multivariate Cross-Classification: Applying machine learning techniques to characterize abstraction in neural representations

    Directory of Open Access Journals (Sweden)

    Jonas eKaplan

    2015-03-01

    Full Text Available Here we highlight an emerging trend in the use of machine learning classifiers to test for abstraction across patterns of neural activity. When a classifier algorithm is trained on data from one cognitive context, and tested on data from another, conclusions can be drawn about the role of a given brain region in representing information that abstracts across those cognitive contexts. We call this kind of analysis Multivariate Cross-Classification (MVCC, and review several domains where it has recently made an impact. MVCC has been important in establishing correspondences among neural patterns across cognitive domains, including motor-perception matching and cross-sensory matching. It has been used to test for similarity between neural patterns evoked by perception and those generated from memory. Other work has used MVCC to investigate the similarity of representations for semantic categories across different kinds of stimulus presentation, and in the presence of different cognitive demands. We use these examples to demonstrate the power of MVCC as a tool for investigating neural abstraction and discuss some important methodological issues related to its application.

  19. Dissociable neural mechanisms underlying the modulation of pain and anxiety? An FMRI pilot study.

    Directory of Open Access Journals (Sweden)

    Katja Wiech

    Full Text Available The down-regulation of pain through beliefs is commonly discussed as a form of emotion regulation. In line with this interpretation, the analgesic effect has been shown to co-occur with reduced anxiety and increased activity in the ventrolateral prefrontal cortex (VLPFC, which is a key region of emotion regulation. This link between pain and anxiety modulation raises the question whether the two effects are rooted in the same neural mechanism. In this pilot fMRI study, we compared the neural basis of the analgesic and anxiolytic effect of two types of threat modulation: a "behavioral control" paradigm, which involves the ability to terminate a noxious stimulus, and a "safety signaling" paradigm, which involves visual cues that signal the threat (or absence of threat that a subsequent noxious stimulus might be of unusually high intensity. Analgesia was paralleled by VLPFC activity during behavioral control. Safety signaling engaged elements of the descending pain control system, including the rostral anterior cingulate cortex that showed increased functional connectivity with the periaqueductal gray and VLPFC. Anxiety reduction, in contrast, scaled with dorsolateral prefrontal cortex activation during behavioral control but had no distinct neural signature during safety signaling. Our pilot data therefore suggest that analgesic and anxiolytic effects are instantiated in distinguishable neural mechanisms and differ between distinct stress- and pain-modulatory approaches, supporting the recent notion of multiple pathways subserving top-down modulation of the pain experience. Additional studies in larger cohorts are needed to follow up on these preliminary findings.

  20. Neural networks supporting autobiographical memory retrieval in post-traumatic stress disorder

    Science.gov (United States)

    Jacques, Peggy L.; Kragel, Philip A.; Rubin, David C.

    2013-01-01

    Post-traumatic stress disorder (PTSD) affects the functional recruitment and connectivity between neural regions during autobiographical memory (AM) retrieval that overlap with default and control networks. Whether such univariate changes relate to potential differences in the contribution of large-scale neural networks supporting cognition in PTSD is unknown. In the current functional MRI (fMRI) study we employ independent component analysis to examine the influence the engagement of neural networks during the recall of personal memories in PTSD (15 participants) compared to non-trauma exposed, healthy controls (14 participants). We found that the PTSD group recruited similar neural networks when compared to controls during AM recall, including default network subsystems and control networks, but there were group differences in the spatial and temporal characteristics of these networks. First, there were spatial differences in the contribution of the anterior and posterior midline across the networks, and with the amygdala in particular for the medial temporal subsystem of the default network. Second, there were temporal differences in the relationship of the medial prefrontal subsystem of the default network, with less temporal coupling of this network during AM retrieval in PTSD relative to controls. These findings suggest that spatial and temporal characteristics of the default and control networks potentially differ in PTSD versus healthy controls, and contribute to altered recall of personal memory. PMID:23483523

  1. Neural interactions mediating conflict control and its training-induced plasticity.

    Science.gov (United States)

    Hu, Min; Wang, Xiangpeng; Zhang, Wenwen; Hu, Xueping; Chen, Antao

    2017-12-01

    Cognitive control is of great plasticity. Training programs targeted on improving it have been suggested to yield neural changes in the brain. However, until recently, the relationship between training-induced brain changes and improvements in cognitive control is still an open issue. Besides, although the literature has attributed the operation of cognitive control to interactions between large-scale networks, the neural pathways directly associated with it remain unclear. The current study aimed to examine these issues by focusing on conflict processing. In particular, we employed a training program with a randomized controlled design. The main findings were as follows: 1) In behavior, the training group showed reduced conflict effect after training, relative to the control group; 2) In the pretest stage, the behavioral conflict effect was negatively correlated with a number of neural pathways, including the connectivity from the cingulo-opercular network (CON) to the cerebellum and to sub-regions of the dorsal visual network; 3) increase in the connectivity strength of several network interactions, such as the connectivity from the CON to the cerebellum and to the primary visual network, was associated with behavioral gains; 4) there were also nonlinear correlations between behavioral and neural changes. These findings highlighted a critical role of the modulation of CON on other networks in mediating conflict processing and its plasticity, and raised the significance of investigating nonlinear relationship in the field of cognitive training. Copyright © 2017 Elsevier Inc. All rights reserved.

  2. Reorganization of the injured brain: Implications for studies of the neural substrate of cognition

    Directory of Open Access Journals (Sweden)

    Jesper eMogensen

    2011-01-01

    Full Text Available In the search for a neural substrate of cognitive processes, a frequently utilized method is the scrutiny of posttraumatic symptoms exhibited by individuals suffering focal injury to the brain. For instance, the presence or absence of conscious awareness within a particular domain may, combined with knowledge of which regions of the brain have been injured, provide important data in the search for neural correlates of consciousness. Like all studies addressing the consequences of brain injury, however, such research has to face the fact that in most cases, posttraumatic impairments are accompanied by a functional recovery during which symptoms are reduced or eliminated. The apparent contradiction between localization and recovery, respectively, of functions constitutes a problem to almost all aspects of cognitive neuroscience. Several lines of investigation indicate that although the brain remains highly plastic throughout life, the posttraumatic plasticity does not recreate a copy of the neural mechanisms lost to injury. Instead, the uninjured parts of the brain are functionally reorganized in a manner which – in spite of not recreating the basic information processing lost to injury – is able to allow a more or less complete return of the surface phenomena (including manifestations of consciousness originally impaired by the trauma. A novel model (the REF-model of these processes is presented – and some of its implications discussed relative to studies of the neural substrates of cognition and consciousness.

  3. A Neural Marker for Social Bias Toward In-group Accents.

    Science.gov (United States)

    Bestelmeyer, Patricia E G; Belin, Pascal; Ladd, D Robert

    2015-10-01

    Accents provide information about the speaker's geographical, socio-economic, and ethnic background. Research in applied psychology and sociolinguistics suggests that we generally prefer our own accent to other varieties of our native language and attribute more positive traits to it. Despite the widespread influence of accents on social interactions, educational and work settings the neural underpinnings of this social bias toward our own accent and, what may drive this bias, are unexplored. We measured brain activity while participants from two different geographical backgrounds listened passively to 3 English accent types embedded in an adaptation design. Cerebral activity in several regions, including bilateral amygdalae, revealed a significant interaction between the participants' own accent and the accent they listened to: while repetition of own accents elicited an enhanced neural response, repetition of the other group's accent resulted in reduced responses classically associated with adaptation. Our findings suggest that increased social relevance of, or greater emotional sensitivity to in-group accents, may underlie the own-accent bias. Our results provide a neural marker for the bias associated with accents, and show, for the first time, that the neural response to speech is partly shaped by the geographical background of the listener. © The Author 2014. Published by Oxford University Press.

  4. Theory of mind in schizophrenia: exploring neural mechanisms of belief attribution.

    Science.gov (United States)

    Lee, Junghee; Quintana, Javier; Nori, Poorang; Green, Michael F

    2011-01-01

    Although previous behavioral studies have shown that schizophrenia patients have impaired theory of mind (ToM), the neural mechanisms associated with this impairment are poorly understood. This study aimed to identify the neural mechanisms of ToM in schizophrenia, using functional magnetic resonance imaging (fMRI) with a belief attribution task. In the scanner, 12 schizophrenia patients and 13 healthy control subjects performed the belief attribution task with three conditions: a false belief condition, a false photograph condition, and a simple reading condition. For the false belief versus simple reading conditions, schizophrenia patients showed reduced neural activation in areas including the temporoparietal junction (TPJ) and medial prefrontal cortex (MPFC) compared with controls. Further, during the false belief versus false photograph conditions, we observed increased activations in the TPJ and the MPFC in healthy controls, but not in schizophrenia patients. For the false photograph versus simple reading condition, both groups showed comparable neural activations. Schizophrenia patients showed reduced task-related activation in the TPJ and the MPFC during the false belief condition compared with controls, but not for the false photograph condition. This pattern suggests that reduced activation in these regions is associated with, and specific to, impaired ToM in schizophrenia.

  5. A One-Layer Recurrent Neural Network for Constrained Complex-Variable Convex Optimization.

    Science.gov (United States)

    Qin, Sitian; Feng, Jiqiang; Song, Jiahui; Wen, Xingnan; Xu, Chen

    2016-12-22

    In this paper, based on CR calculus and penalty method, a one-layer recurrent neural network is proposed for solving constrained complex-variable convex optimization. It is proved that for any initial point from a given domain, the state of the proposed neural network reaches the feasible region in finite time and converges to an optimal solution of the constrained complex-variable convex optimization finally. In contrast to existing neural networks for complex-variable convex optimization, the proposed neural network has a lower model complexity and better convergence. Some numerical examples and application are presented to substantiate the effectiveness of the proposed neural network.

  6. Neural correlates of social perception on response bias.

    Science.gov (United States)

    Shin, Yeon Soon; Kim, Hye-Young; Han, Sanghoon

    2014-07-01

    Accurate person perception is crucial in social decision-making. One of the central elements in successful social perception is the ability to understand another's response bias; this is because the same behavior can represent different inner states depending on whether other people are yea-sayers or naysayers. In the present study, we have tried to investigate how the internal biases of others are perceived. Using a multi-trial learning paradigm, perceivers made predictions about a target's responses to various suggested activities and then received feedback for each prediction trial-by-trial. Our hypotheses were that (1) the internal decision criterion of the targets would be realized through repeated experiences, and (2) due to positive-negative asymmetry, yea-sayers would be recognized more gradually than naysayers through the probabilistic integration of repeated experiences. To find neural evidence that tracks probabilistic integration when forming person knowledge on response biases, we employed a model-based fMRI with a State-Space Model. We discovered that person knowledge about yea-sayers modulated several brain regions, including caudate nucleus, DLPFC, hippocampus, etc. Moreover, when person knowledge was updated with incorrect performance feedback, brain regions including the caudate nucleus, DLPFC, dmPFC, and TPJ were also involved. There were overlapping regions for both processes, caudate nucleus and DLPFC, suggesting that these regions take crucial roles in forming person knowledge with repeated feedback, while reflecting acquired information up to the current prediction. Copyright © 2014 Elsevier Inc. All rights reserved.

  7. Neural correlates of proactive and reactive aggression in adolescent twins.

    Science.gov (United States)

    Yang, Yaling; Joshi, Shantanu H; Jahanshad, Neda; Thompson, Paul M; Baker, Laura A

    2017-05-01

    Verbal and physical aggression begin early in life and steadily decline thereafter in normal development. As a result, elevated aggressive behavior in adolescence may signal atypical development and greater vulnerability for negative mental and health outcomes. Converging evidence suggests that brain disturbances in regions involved in impulse control, emotional regulation, and sensation seeking may contribute to heightened aggression. However, little is known regarding the neural mechanisms underlying subtypes of aggression (i.e., proactive and reactive aggression) and whether they differ between males and females. Using a sample of 106 14-year-old adolescent twins, this study found that striatal enlargement was associated with both proactive and reactive aggression. We also found that volumetric alterations in several frontal regions including smaller middle frontal and larger orbitofrontal cortex were correlated with higher levels of aggression in adolescent twins. In addition, cortical thickness analysis showed that thickness alterations in many overlapping regions including middle frontal, superior frontal, and anterior cingulate cortex and temporal regions were associated with aggression in adolescent twins. Results support the involvement of fronto-limbic-striatal circuit in the etiology of aggression during adolescence. Aggr. Behav. 43:230-240, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  8. Neural mechanisms of rhythm perception: current findings and future perspectives.

    Science.gov (United States)

    Grahn, Jessica A

    2012-10-01

    Perception of temporal patterns is fundamental to normal hearing, speech, motor control, and music. Certain types of pattern understanding are unique to humans, such as musical rhythm. Although human responses to musical rhythm are universal, there is much we do not understand about how rhythm is processed in the brain. Here, I consider findings from research into basic timing mechanisms and models through to the neuroscience of rhythm and meter. A network of neural areas, including motor regions, is regularly implicated in basic timing as well as processing of musical rhythm. However, fractionating the specific roles of individual areas in this network has remained a challenge. Distinctions in activity patterns appear between "automatic" and "cognitively controlled" timing processes, but the perception of musical rhythm requires features of both automatic and controlled processes. In addition, many experimental manipulations rely on participants directing their attention toward or away from certain stimulus features, and measuring corresponding differences in neural activity. Many temporal features, however, are implicitly processed whether attended to or not, making it difficult to create controlled baseline conditions for experimental comparisons. The variety of stimuli, paradigms, and definitions can further complicate comparisons across domains or methodologies. Despite these challenges, the high level of interest and multitude of methodological approaches from different cognitive domains (including music, language, and motor learning) have yielded new insights and hold promise for future progress. Copyright © 2012 Cognitive Science Society, Inc.

  9. Application of a neural network for reflectance spectrum classification

    Science.gov (United States)

    Yang, Gefei; Gartley, Michael

    2017-05-01

    Traditional reflectance spectrum classification algorithms are based on comparing spectrum across the electromagnetic spectrum anywhere from the ultra-violet to the thermal infrared regions. These methods analyze reflectance on a pixel by pixel basis. Inspired by high performance that Convolution Neural Networks (CNN) have demonstrated in image classification, we applied a neural network to analyze directional reflectance pattern images. By using the bidirectional reflectance distribution function (BRDF) data, we can reformulate the 4-dimensional into 2 dimensions, namely incident direction × reflected direction × channels. Meanwhile, RIT's micro-DIRSIG model is utilized to simulate additional training samples for improving the robustness of the neural networks training. Unlike traditional classification by using hand-designed feature extraction with a trainable classifier, neural networks create several layers to learn a feature hierarchy from pixels to classifier and all layers are trained jointly. Hence, the our approach of utilizing the angular features are different to traditional methods utilizing spatial features. Although training processing typically has a large computational cost, simple classifiers work well when subsequently using neural network generated features. Currently, most popular neural networks such as VGG, GoogLeNet and AlexNet are trained based on RGB spatial image data. Our approach aims to build a directional reflectance spectrum based neural network to help us to understand from another perspective. At the end of this paper, we compare the difference among several classifiers and analyze the trade-off among neural networks parameters.

  10. The neural correlates of speech motor sequence learning.

    Science.gov (United States)

    Segawa, Jennifer A; Tourville, Jason A; Beal, Deryk S; Guenther, Frank H

    2015-04-01

    Speech is perhaps the most sophisticated example of a species-wide movement capability in the animal kingdom, requiring split-second sequencing of approximately 100 muscles in the respiratory, laryngeal, and oral movement systems. Despite the unique role speech plays in human interaction and the debilitating impact of its disruption, little is known about the neural mechanisms underlying speech motor learning. Here, we studied the behavioral and neural correlates of learning new speech motor sequences. Participants repeatedly produced novel, meaningless syllables comprising illegal consonant clusters (e.g., GVAZF) over 2 days of practice. Following practice, participants produced the sequences with fewer errors and shorter durations, indicative of motor learning. Using fMRI, we compared brain activity during production of the learned illegal sequences and novel illegal sequences. Greater activity was noted during production of novel sequences in brain regions linked to non-speech motor sequence learning, including the BG and pre-SMA. Activity during novel sequence production was also greater in brain regions associated with learning and maintaining speech motor programs, including lateral premotor cortex, frontal operculum, and posterior superior temporal cortex. Measures of learning success correlated positively with activity in left frontal operculum and white matter integrity under left posterior superior temporal sulcus. These findings indicate speech motor sequence learning relies not only on brain areas involved generally in motor sequencing learning but also those associated with feedback-based speech motor learning. Furthermore, learning success is modulated by the integrity of structural connectivity between these motor and sensory brain regions.

  11. Hyperbolic Hopfield neural networks.

    Science.gov (United States)

    Kobayashi, M

    2013-02-01

    In recent years, several neural networks using Clifford algebra have been studied. Clifford algebra is also called geometric algebra. Complex-valued Hopfield neural networks (CHNNs) are the most popular neural networks using Clifford algebra. The aim of this brief is to construct hyperbolic HNNs (HHNNs) as an analog of CHNNs. Hyperbolic algebra is a Clifford algebra based on Lorentzian geometry. In this brief, a hyperbolic neuron is defined in a manner analogous to a phasor neuron, which is a typical complex-valued neuron model. HHNNs share common concepts with CHNNs, such as the angle and energy. However, HHNNs and CHNNs are different in several aspects. The states of hyperbolic neurons do not form a circle, and, therefore, the start and end states are not identical. In the quantized version, unlike complex-valued neurons, hyperbolic neurons have an infinite number of states.

  12. Neural Semantic Encoders.

    Science.gov (United States)

    Munkhdalai, Tsendsuren; Yu, Hong

    2017-04-01

    We present a memory augmented neural network for natural language understanding: Neural Semantic Encoders. NSE is equipped with a novel memory update rule and has a variable sized encoding memory that evolves over time and maintains the understanding of input sequences through read, compose and write operations. NSE can also access multiple and shared memories. In this paper, we demonstrated the effectiveness and the flexibility of NSE on five different natural language tasks: natural language inference, question answering, sentence classification, document sentiment analysis and machine translation where NSE achieved state-of-the-art performance when evaluated on publically available benchmarks. For example, our shared-memory model showed an encouraging result on neural machine translation, improving an attention-based baseline by approximately 1.0 BLEU.

  13. Instrumentation for Scientific Computing in Neural Networks, Information Science, Artificial Intelligence, and Applied Mathematics.

    Science.gov (United States)

    1987-10-01

    include Security Classification) Instrumentation for scientific computing in neural networks, information science, artificial intelligence, and...instrumentation grant to purchase equipment for support of research in neural networks, information science, artificail intellignece , and applied mathematics...in Neural Networks, Information Science, Artificial Intelligence, and Applied Mathematics Contract AFOSR 86-0282 Principal Investigator: Stephen

  14. A Topological Perspective of Neural Network Structure

    Science.gov (United States)

    Sizemore, Ann; Giusti, Chad; Cieslak, Matthew; Grafton, Scott; Bassett, Danielle

    The wiring patterns of white matter tracts between brain regions inform functional capabilities of the neural network. Indeed, densely connected and cyclically arranged cognitive systems may communicate and thus perform distinctly. However, previously employed graph theoretical statistics are local in nature and thus insensitive to such global structure. Here we present an investigation of the structural neural network in eight healthy individuals using persistent homology. An extension of homology to weighted networks, persistent homology records both circuits and cliques (all-to-all connected subgraphs) through a repetitive thresholding process, thus perceiving structural motifs. We report structural features found across patients and discuss brain regions responsible for these patterns, finally considering the implications of such motifs in relation to cognitive function.

  15. The neural crest and neural crest cells: discovery and significance ...

    Indian Academy of Sciences (India)

    In this paper I provide a brief overview of the major phases of investigation into the neural crest and the major players involved, discuss how the origin of the neural crest relates to the origin of the nervous system in vertebrate embryos, discuss the impact on the germ-layer theory of the discovery of the neural crest and of ...

  16. Distinct Neural Substrates for Maintaining Locations and Spatial Relations in Working Memory

    Directory of Open Access Journals (Sweden)

    Kara J Blacker

    2016-11-01

    Full Text Available Previous work has demonstrated a distinction between maintenance of two types of spatial information in working memory (WM: spatial locations and spatial relations. While a body of work has investigated the neural mechanisms of sensory-based information like spatial locations, little is known about how spatial relations are maintained in WM. In two experiments, we used fMRI to investigate the involvement of early visual cortex in the maintenance of spatial relations in WM. In both experiments, we found less quadrant-specific BOLD activity in visual cortex when a single spatial relation, compared to a single spatial location, was held in WM. Also across both experiments, we found a consistent set of brain regions that were differentially activated during maintenance of locations versus relations. Maintaining a location, compared to a relation, was associated with greater activity in typical spatial WM regions like posterior parietal cortex and prefrontal regions. Whereas maintaining a relation, compared to a location, was associated with greater activity in the parahippocampal gyrus and precuneus/retrosplenial cortex. Further, in Experiment 2 we manipulated WM load and included trials where participants had to maintain three spatial locations or relations. Under this high load condition, the regions sensitive to locations versus relations were somewhat different than under low load. We also identified regions that were sensitive to load specifically for location or relation maintenance, as well as overlapping regions sensitive to load more generally. These results suggest that the neural substrates underlying WM maintenance of spatial locations and relations are distinct from one another and that the neural representations of these distinct types of spatial information change with load.

  17. Young adult smokers' neural response to graphic cigarette warning labels

    Directory of Open Access Journals (Sweden)

    Adam E. Green

    2016-06-01

    Conclusions: In this sample of young adult smokers, GWLs promoted neural activation in brain regions involved in cognitive and affective decision-making and memory formation and the effects of GWLs did not differ on branded or plain cigarette packaging. These findings complement other recent neuroimaging GWL studies conducted with older adult smokers and with adolescents by demonstrating similar patterns of neural activation in response to GWLs among young adult smokers.

  18. Introduction to Artificial Neural Networks

    DEFF Research Database (Denmark)

    Larsen, Jan

    1999-01-01

    The note addresses introduction to signal analysis and classification based on artificial feed-forward neural networks.......The note addresses introduction to signal analysis and classification based on artificial feed-forward neural networks....

  19. Deconvolution using a neural network

    Energy Technology Data Exchange (ETDEWEB)

    Lehman, S.K.

    1990-11-15

    Viewing one dimensional deconvolution as a matrix inversion problem, we compare a neural network backpropagation matrix inverse with LMS, and pseudo-inverse. This is a largely an exercise in understanding how our neural network code works. 1 ref.

  20. Language evolution: neural homologies and neuroinformatics.

    Science.gov (United States)

    Arbib, Michael; Bota, Mihail

    2003-11-01

    This paper contributes to neurolinguistics by grounding an evolutionary account of the readiness of the human brain for language in the search for homologies between different cortical areas in macaque and human. We consider two hypotheses for this grounding, that of Aboitiz and Garci;a [Brain Res. Rev. 25 (1997) 381] and the Mirror System Hypothesis of Rizzolatti and Arbib [Trends Neurosci. 21 (1998) 188] and note the promise of computational modeling of neural circuitry of the macaque and its linkage to analysis of human brain imaging data. In addition to the functional differences between the two hypotheses, problems arise because they are grounded in different cortical maps of the macaque brain. In order to address these divergences, we have developed several neuroinformatics tools included in an on-line knowledge management system, the NeuroHomology Database, which is equipped with inference engines both to relate and translate information across equivalent cortical maps and to evaluate degrees of homology for brain regions of interest in different species.

  1. Neural Networks for Mindfulness and Emotion Suppression.

    Directory of Open Access Journals (Sweden)

    Hiroki Murakami

    Full Text Available Mindfulness, an attentive non-judgmental focus on "here and now" experiences, has been incorporated into various cognitive behavioral therapy approaches and beneficial effects have been demonstrated. Recently, mindfulness has also been identified as a potentially effective emotion regulation strategy. On the other hand, emotion suppression, which refers to trying to avoid or escape from experiencing and being aware of one's own emotions, has been identified as a potentially maladaptive strategy. Previous studies suggest that both strategies can decrease affective responses to emotional stimuli. They would, however, be expected to provide regulation through different top-down modulation systems. The present study was aimed at elucidating the different neural systems underlying emotion regulation via mindfulness and emotion suppression approaches. Twenty-one healthy participants used the two types of strategy in response to emotional visual stimuli while functional magnetic resonance imaging was conducted. Both strategies attenuated amygdala responses to emotional triggers, but the pathways to regulation differed across the two. A mindful approach appears to regulate amygdala functioning via functional connectivity from the medial prefrontal cortex, while suppression uses connectivity with other regions, including the dorsolateral prefrontal cortex. Thus, the two types of emotion regulation recruit different top-down modulation processes localized at prefrontal areas. These different pathways are discussed.

  2. Enhancement of Neural Salty Preference in Obesity

    Directory of Open Access Journals (Sweden)

    Qiang Li

    2017-10-01

    Full Text Available Background/Aims: Obesity and high salt intake are major risk factors for hypertension and cardiometabolic diseases. Obese individuals often consume more dietary salt. We aim to examine the neurophysiologic effects underlying obesity-related high salt intake. Methods: A multi-center, random-order, double-blind taste study, SATIETY-1, was conducted in the communities of four cities in China; and an interventional study was also performed in the local community of Chongqing, using brain positron emission tomography/computed tomography (PET/CT scanning. Results: We showed that overweight/obese individuals were prone to consume a higher daily salt intake (2.0 g/day higher compared with normal weight individuals after multivariable adjustment, 95% CI, 1.2-2.8 g/day, P < 0.001, furthermore they exhibited reduced salt sensitivity and a higher salt preference. The altered salty taste and salty preference in the overweight/obese individuals was related to increased activity in brain regions that included the orbitofrontal cortex (OFC, r = 0.44, P= 0.01, insula (r = 0.38, P= 0.03, and parahippocampus (r = 0.37, P= 0.04. Conclusion: Increased salt intake among overweight/obese individuals is associated with altered salt sensitivity and preference that related to the abnormal activity of gustatory cortex. This study provides insights for reducing salt intake by modifying neural processing of salty preference in obesity.

  3. A neural basis for general intelligence

    OpenAIRE

    Duncan, J.; Seitz, R.J.; Kolodny, J.; Bor, D.; Herzog, H; Ahmed, A.; Newell, F. N.; Emslie, H

    2000-01-01

    Universal positive correlations between different cognitive tests motivate the concept of "general intelligence" or Spearman's g. Here the neural basis for g is investigated by means of positron emission tomography. Spatial, verbal, and perceptuo-motor tasks with high-g involvement are compared with matched Low-g control tasks. In contrast to the common view that g reflects a broad sample of major cognitive functions, high-g tasks do not show diffuse recruitment of multiple brain regions. Ins...

  4. Neural correlates of verb argument structure processing.

    Science.gov (United States)

    Thompson, Cynthia K; Bonakdarpour, Borna; Fix, Stephen C; Blumenfeld, Henrike K; Parrish, Todd B; Gitelman, Darren R; Mesulam, M-Marsel

    2007-11-01

    Neuroimaging and lesion studies suggest that processing of word classes, such as verbs and nouns, is associated with distinct neural mechanisms. Such studies also suggest that subcategories within these broad word class categories are differentially processed in the brain. Within the class of verbs, argument structure provides one linguistic dimension that distinguishes among verb exemplars, with some requiring more complex argument structure entries than others. This study examined the neural instantiation of verbs by argument structure complexity: one-, two-, and three-argument verbs. Stimuli of each type, along with nouns and pseudowords, were presented for lexical decision using an event-related functional magnetic resonance imaging design. Results for 14 young normal participants indicated largely overlapping activation maps for verbs and nouns, with no areas of significant activation for verbs compared to nouns, or vice versa. Pseudowords also engaged neural tissue overlapping with that for both word classes, with more widespread activation noted in visual, motor, and peri-sylvian regions. Examination of verbs by argument structure revealed activation of the supramarginal and angular gyri, limited to the left hemisphere only when verbs with two obligatory arguments were compared to verbs with a single argument. However, bilateral activation was noted when both two- and three-argument verbs were compared to one-argument verbs. These findings suggest that posterior peri-sylvian regions are engaged for processing argument structure information associated with verbs, with increasing neural tissue in the inferior parietal region associated with increasing argument structure complexity. These findings are consistent with processing accounts, which suggest that these regions are crucial for semantic integration.

  5. Automatic identification of species with neural networks.

    Science.gov (United States)

    Hernández-Serna, Andrés; Jiménez-Segura, Luz Fernanda

    2014-01-01

    A new automatic identification system using photographic images has been designed to recognize fish, plant, and butterfly species from Europe and South America. The automatic classification system integrates multiple image processing tools to extract the geometry, morphology, and texture of the images. Artificial neural networks (ANNs) were used as the pattern recognition method. We tested a data set that included 740 species and 11,198 individuals. Our results show that the system performed with high accuracy, reaching 91.65% of true positive fish identifications, 92.87% of plants and 93.25% of butterflies. Our results highlight how the neural networks are complementary to species identification.

  6. Automatic identification of species with neural networks

    Directory of Open Access Journals (Sweden)

    Andrés Hernández-Serna

    2014-11-01

    Full Text Available A new automatic identification system using photographic images has been designed to recognize fish, plant, and butterfly species from Europe and South America. The automatic classification system integrates multiple image processing tools to extract the geometry, morphology, and texture of the images. Artificial neural networks (ANNs were used as the pattern recognition method. We tested a data set that included 740 species and 11,198 individuals. Our results show that the system performed with high accuracy, reaching 91.65% of true positive fish identifications, 92.87% of plants and 93.25% of butterflies. Our results highlight how the neural networks are complementary to species identification.

  7. Cell delamination in the mesencephalic neural fold and its implication for the origin of ectomesenchyme

    Science.gov (United States)

    Lee, Raymond Teck Ho; Nagai, Hiroki; Nakaya, Yukiko; Sheng, Guojun; Trainor, Paul A.; Weston, James A.; Thiery, Jean Paul

    2013-01-01

    The neural crest is a transient structure unique to vertebrate embryos that gives rise to multiple lineages along the rostrocaudal axis. In cranial regions, neural crest cells are thought to differentiate into chondrocytes, osteocytes, pericytes and stromal cells, which are collectively termed ectomesenchyme derivatives, as well as pigment and neuronal derivatives. There is still no consensus as to whether the neural crest can be classified as a homogenous multipotent population of cells. This unresolved controversy has important implications for the formation of ectomesenchyme and for confirmation of whether the neural fold is compartmentalized into distinct domains, each with a different repertoire of derivatives. Here we report in mouse and chicken that cells in the neural fold delaminate over an extended period from different regions of the cranial neural fold to give rise to cells with distinct fates. Importantly, cells that give rise to ectomesenchyme undergo epithelial-mesenchymal transition from a lateral neural fold domain that does not express definitive neural markers, such as Sox1 and N-cadherin. Additionally, the inference that cells originating from the cranial neural ectoderm have a common origin and cell fate with trunk neural crest cells prompted us to revisit the issue of what defines the neural crest and the origin of the ectomesenchyme. PMID:24198279

  8. Scientific progress regarding neural regeneration in the Web of Science: A 10-year bibliometric analysis.

    Science.gov (United States)

    Pan, Yuntao; Zhang, Yuhua; Gao, Xiaopei; Jia, Jia; Gao, Jiping; Ma, Zheng

    2013-12-25

    Neural regeneration following nerve injury is an emerging field that attracts extending interests all over the world. To use bibliometric indexes to track studies focusing on neural regeneration, and to investigate the relationships among geographic origin, countries and institutes, keywords in the published articles, and especially focus on the region distribution, institution distribution, as well as collaborations in Chinese papers indexed in the Web of Science. A list of neural regeneration studies was generated by searching the database of the Web of Science-Expanded using the term "Neural Regenera*". Inclusive criteria: (1) articles in the field of neural regeneration; (2) fundamental research on animals, clinical trials and case reports; (3) article types: article, review, proceedings paper, note, letter, editorial material, discussion, book chapter; (4) year of publication: 2003-2012; and (5) citation database: Science Citation Index-Expanded. Exclusive criteria: (1) articles requiring manual searching or with access only by telephone; (2) unpublished articles; and (3) corrections. A total of 4 893 papers were retrieved from the Web of Science published between 2003 and 2012. The papers covered 65 countries or regions, of which the United States ranked first with 1 691 papers. The most relevant papers were in the neurosciences and cell biology, and the keyword "stem cell" was the most frequent. In recent years, China showed a great increase in the number of papers. Over the entire 10 years, there were 922 Chinese papers, with Jilin University ranking first with 58 articles. Chinese papers were published in connection with many countries, including the United States, Japan, and the United Kingdom. Among the connections, the papers published by the Chinese and the American are 107, with the highest rate. With regard to funding, 689 articles were funded from various projects, occupying 74.72% of the total amount. In these projects, National Foundation and

  9. Weather forecasting based on hybrid neural model

    Science.gov (United States)

    Saba, Tanzila; Rehman, Amjad; AlGhamdi, Jarallah S.

    2017-02-01

    Making deductions and expectations about climate has been a challenge all through mankind's history. Challenges with exact meteorological directions assist to foresee and handle problems well in time. Different strategies have been investigated using various machine learning techniques in reported forecasting systems. Current research investigates climate as a major challenge for machine information mining and deduction. Accordingly, this paper presents a hybrid neural model (MLP and RBF) to enhance the accuracy of weather forecasting. Proposed hybrid model ensure precise forecasting due to the specialty of climate anticipating frameworks. The study concentrates on the data representing Saudi Arabia weather forecasting. The main input features employed to train individual and hybrid neural networks that include average dew point, minimum temperature, maximum temperature, mean temperature, average relative moistness, precipitation, normal wind speed, high wind speed and average cloudiness. The output layer composed of two neurons to represent rainy and dry weathers. Moreover, trial and error approach is adopted to select an appropriate number of inputs to the hybrid neural network. Correlation coefficient, RMSE and scatter index are the standard yard sticks adopted for forecast accuracy measurement. On individual standing MLP forecasting results are better than RBF, however, the proposed simplified hybrid neural model comes out with better forecasting accuracy as compared to both individual networks. Additionally, results are better than reported in the state of art, using a simple neural structure that reduces training time and complexity.

  10. Weather forecasting based on hybrid neural model

    Science.gov (United States)

    Saba, Tanzila; Rehman, Amjad; AlGhamdi, Jarallah S.

    2017-11-01

    Making deductions and expectations about climate has been a challenge all through mankind's history. Challenges with exact meteorological directions assist to foresee and handle problems well in time. Different strategies have been investigated using various machine learning techniques in reported forecasting systems. Current research investigates climate as a major challenge for machine information mining and deduction. Accordingly, this paper presents a hybrid neural model (MLP and RBF) to enhance the accuracy of weather forecasting. Proposed hybrid model ensure precise forecasting due to the specialty of climate anticipating frameworks. The study concentrates on the data representing Saudi Arabia weather forecasting. The main input features employed to train individual and hybrid neural networks that include average dew point, minimum temperature, maximum temperature, mean temperature, average relative moistness, precipitation, normal wind speed, high wind speed and average cloudiness. The output layer composed of two neurons to represent rainy and dry weathers. Moreover, trial and error approach is adopted to select an appropriate number of inputs to the hybrid neural network. Correlation coefficient, RMSE and scatter index are the standard yard sticks adopted for forecast accuracy measurement. On individual standing MLP forecasting results are better than RBF, however, the proposed simplified hybrid neural model comes out with better forecasting accuracy as compared to both individual networks. Additionally, results are better than reported in the state of art, using a simple neural structure that reduces training time and complexity.

  11. Abnormally increased effective connectivity between parahippocampal gyrus and ventromedial prefrontal regions during emotion labeling in bipolar disorder

    Science.gov (United States)

    Almeida, Jorge R. C.; Mechelli, Andrea; Hassel, Stefanie; Versace, Amelia; Kupfer, David J.; Phillips, Mary L.

    2009-01-01

    Emotional liability and mood dysregulation characterize bipolar disorder (BD), yet no study has examined effective connectivity between parahippocampal gyrus and prefrontal cortical regions in ventromedial and dorsal/lateral neural systems subserving mood regulation in BD. Forty-six individuals (age range: 18–56 years); 21 with a DSM-IV diagnosis of BD, type I currently remitted; 25 age- and gender-matched healthy controls (HC). Participants performed an event-related paradigm, viewing mild and intense happy and neutral faces. We employed dynamic causal modeling (DCM) to identify significant alterations in effective connectivity between BD and HC. Bayes model selection was used to determine the best model. The right parahippocampal gyrus (PHG) and right subgenual cingulate gyrus (sgCG) were included as representative regions of the ventromedial neural system. The right dorsolateral prefrontal cortex (DLPFC) region was included as representative of the dorsal/lateral neural system. Right PHG-sgCG effective connectivity was significantly greater in BD than HC, reflecting more rapid, forward PHG-sgCG signaling in BD than HC. There was no between-group difference in sgCG-DLPFC effective connectivity. In BD, abnormally increased right PHG-sgCG effective connectivity and reduced right PHG activity to emotional stimuli suggest a dysfunctional ventromedial neural system implicated in early stimulus appraisal, encoding and automatic regulation of emotion, that may represent a pathophysiological functional neural mechanism for mood dysregulation in BD. PMID:19910166

  12. Decreased neural precursor cell pool in NADPH oxidase 2-deficiency: From mouse brain to neural differentiation of patient derived iPSC

    Directory of Open Access Journals (Sweden)

    Zeynab Nayernia

    2017-10-01

    Full Text Available There is emerging evidence for the involvement of reactive oxygen species (ROS in the regulation of stem cells and cellular differentiation. Absence of the ROS-generating NADPH oxidase NOX2 in chronic granulomatous disease (CGD patients, predominantly manifests as immune deficiency, but has also been associated with decreased cognition. Here, we investigate the role of NOX enzymes in neuronal homeostasis in adult mouse brain and in neural cells derived from human induced pluripotent stem cells (iPSC. High levels of NOX2 were found in mouse adult neurogenic regions. In NOX2-deficient mice, neurogenic regions showed diminished redox modifications, as well as decrease in neuroprecursor numbers and in expression of genes involved in neural differentiation including NES, BDNF and OTX2. iPSC from healthy subjects and patients with CGD were used to study the role of NOX2 in human in vitro neuronal development. Expression of NOX2 was low in undifferentiated iPSC, upregulated upon neural induction, and disappeared during neuronal differentiation. In human neurospheres, NOX2 protein and ROS generation were polarized within the inner cell layer of rosette structures. NOX2 deficiency in CGD-iPSCs resulted in an abnormal neural induction in vitro, as revealed by a reduced expression of neuroprogenitor markers (NES, BDNF, OTX2, NRSF/REST, and a decreased generation of mature neurons. Vector-mediated NOX2 expression in NOX2-deficient iPSCs rescued neurogenesis. Taken together, our study provides novel evidence for a regulatory role of NOX2 during early stages of neurogenesis in mouse and human.

  13. Pulmonary Nodule Classification with Deep Convolutional Neural Networks on Computed Tomography Images

    Directory of Open Access Journals (Sweden)

    Wei Li

    2016-01-01

    Full Text Available Computer aided detection (CAD systems can assist radiologists by offering a second opinion on early diagnosis of lung cancer. Classification and feature representation play critical roles in false-positive reduction (FPR in lung nodule CAD. We design a deep convolutional neural networks method for nodule classification, which has an advantage of autolearning representation and strong generalization ability. A specified network structure for nodule images is proposed to solve the recognition of three types of nodules, that is, solid, semisolid, and ground glass opacity (GGO. Deep convolutional neural networks are trained by 62,492 regions-of-interest (ROIs samples including 40,772 nodules and 21,720 nonnodules from the Lung Image Database Consortium (LIDC database. Experimental results demonstrate the effectiveness of the proposed method in terms of sensitivity and overall accuracy and that it consistently outperforms the competing methods.

  14. Adolescents' Reward-related Neural Activation: Links to Thoughts of Nonsuicidal Self-Injury.

    Science.gov (United States)

    Poon, Jennifer A; Thompson, James C; Forbes, Erika E; Chaplin, Tara M

    2018-01-19

    Adolescence is a critical developmental period marked by an increase in risk behaviors, including nonsuicidal self-injury (NSSI). Heightened reward-related brain activation and relatively limited recruitment of prefrontal regions contribute to the initiation of risky behaviors in adolescence. However, neural reward processing has not been examined among adolescents who are at risk for future engagement for NSSI specifically, but who have yet to actually engage in this behavior. In the current fMRI study (N = 71), we hypothesized that altered reward processing would be associated with adolescents' thoughts of NSSI. Results showed that NSSI youth exhibited heightened activation in the bilateral putamen in response to a monetary reward. This pattern of findings suggests that heightened neural sensitivity to reward is associated with thoughts of NSSI in early adolescence. Implications for prevention are discussed. © 2018 The American Association of Suicidology.

  15. Led into temptation? Rewarding brand logos bias the neural encoding of incidental economic decisions.

    Directory of Open Access Journals (Sweden)

    Carsten Murawski

    Full Text Available Human decision-making is driven by subjective values assigned to alternative choice options. These valuations are based on reward cues. It is unknown, however, whether complex reward cues, such as brand logos, may bias the neural encoding of subjective value in unrelated decisions. In this functional magnetic resonance imaging (fMRI study, we subliminally presented brand logos preceding intertemporal choices. We demonstrated that priming biased participants' preferences towards more immediate rewards in the subsequent temporal discounting task. This was associated with modulations of the neural encoding of subjective values of choice options in a network of brain regions, including but not restricted to medial prefrontal cortex. Our findings demonstrate the general susceptibility of the human decision making system to apparently incidental contextual information. We conclude that the brain incorporates seemingly unrelated value information that modifies decision making outside the decision-maker's awareness.

  16. Led into temptation? Rewarding brand logos bias the neural encoding of incidental economic decisions.

    Science.gov (United States)

    Murawski, Carsten; Harris, Philip G; Bode, Stefan; Domínguez D, Juan F; Egan, Gary F

    2012-01-01

    Human decision-making is driven by subjective values assigned to alternative choice options. These valuations are based on reward cues. It is unknown, however, whether complex reward cues, such as brand logos, may bias the neural encoding of subjective value in unrelated decisions. In this functional magnetic resonance imaging (fMRI) study, we subliminally presented brand logos preceding intertemporal choices. We demonstrated that priming biased participants' preferences towards more immediate rewards in the subsequent temporal discounting task. This was associated with modulations of the neural encoding of subjective values of choice options in a network of brain regions, including but not restricted to medial prefrontal cortex. Our findings demonstrate the general susceptibility of the human decision making system to apparently incidental contextual information. We conclude that the brain incorporates seemingly unrelated value information that modifies decision making outside the decision-maker's awareness.

  17. Pulmonary Nodule Classification with Deep Convolutional Neural Networks on Computed Tomography Images

    Science.gov (United States)

    Li, Wei; Zhao, Dazhe; Wang, Junbo

    2016-01-01

    Computer aided detection (CAD) systems can assist radiologists by offering a second opinion on early diagnosis of lung cancer. Classification and feature representation play critical roles in false-positive reduction (FPR) in lung nodule CAD. We design a deep convolutional neural networks method for nodule classification, which has an advantage of autolearning representation and strong generalization ability. A specified network structure for nodule images is proposed to solve the recognition of three types of nodules, that is, solid, semisolid, and ground glass opacity (GGO). Deep convolutional neural networks are trained by 62,492 regions-of-interest (ROIs) samples including 40,772 nodules and 21,720 nonnodules from the Lung Image Database Consortium (LIDC) database. Experimental results demonstrate the effectiveness of the proposed method in terms of sensitivity and overall accuracy and that it consistently outperforms the competing methods. PMID:28070212

  18. Modeling and computing of stock index forecasting based on neural network and Markov chain.

    Science.gov (United States)

    Dai, Yonghui; Han, Dongmei; Dai, Weihui

    2014-01-01

    The stock index reflects the fluctuation of the stock market. For a long time, there have been a lot of researches on the forecast of stock index. However, the traditional method is limited to achieving an ideal precision in the dynamic market due to the influences of many factors such as the economic situation, policy changes, and emergency events. Therefore, the approach based on adaptive modeling and conditional probability transfer causes the new attention of researchers. This paper presents a new forecast method by the combination of improved back-propagation (BP) neural network and Markov chain, as well as its modeling and computing technology. This method includes initial forecasting by improved BP neural network, division of Markov state region, computing of the state transition probability matrix, and the prediction adjustment. Results of the empirical study show that this method can achieve high accuracy in the stock index prediction, and it could provide a good reference for the investment in stock market.

  19. Neural and Neural Gray-Box Modeling for Entry Temperature Prediction in a Hot Strip Mill

    Science.gov (United States)

    Barrios, José Angel; Torres-Alvarado, Miguel; Cavazos, Alberto; Leduc, Luis

    2011-10-01

    In hot strip mills, initial controller set points have to be calculated before the steel bar enters the mill. Calculations rely on the good knowledge of rolling variables. Measurements are available only after the bar has entered the mill, and therefore they have to be estimated. Estimation of process variables, particularly that of temperature, is of crucial importance for the bar front section to fulfill quality requirements, and the same must be performed in the shortest possible time to preserve heat. Currently, temperature estimation is performed by physical modeling; however, it is highly affected by measurement uncertainties, variations in the incoming bar conditions, and final product changes. In order to overcome these problems, artificial intelligence techniques such as artificial neural networks and fuzzy logic have been proposed. In this article, neural network-based systems, including neural-based Gray-Box models, are applied to estimate scale breaker entry temperature, given its importance, and their performance is compared to that of the physical model used in plant. Several neural systems and several neural-based Gray-Box models are designed and tested with real data. Taking advantage of the flexibility of neural networks for input incorporation, several factors which are believed to have influence on the process are also tested. The systems proposed in this study were proven to have better performance indexes and hence better prediction capabilities than the physical models currently used in plant.

  20. Dissociable neural imprints of perception and grammar in auditory functional imaging.

    Science.gov (United States)

    Herrmann, Björn; Obleser, Jonas; Kalberlah, Christian; Haynes, John-Dylan; Friederici, Angela D

    2012-03-01

    In language processing, the relative contribution of early sensory and higher cognitive brain areas is still an open issue. A recent controversial hypothesis proposes that sensory cortices show sensitivity to syntactic processes, whereas other studies suggest a wider neural network outside sensory regions. The goal of the current event-related fMRI study is to clarify the contribution of sensory cortices in auditory syntactic processing in a 2 × 2 design. Two-word utterances were presented auditorily and varied both in perceptual markedness (presence or absence of an overt word category marking "-t"), and in grammaticality (syntactically correct or incorrect). A multivariate pattern classification approach was applied to the data, flanked by conventional cognitive subtraction analyses. The combination of methods and the 2 × 2 design revealed a clear picture: The cognitive subtraction analysis found initial syntactic processing signatures in a neural network including the left IFG, the left aSTG, the left superior temporal sulcus (STS), as well as the right STS/STG. Classification of local multivariate patterns indicated the left-hemispheric regions in IFG, aSTG, and STS to be more syntax-specific than the right-hemispheric regions. Importantly, auditory sensory cortices were only sensitive to the overt perceptual marking, but not to the grammaticality, speaking against syntax-inflicted sensory cortex modulations. Instead, our data provide clear evidence for a distinction between regions involved in pure perceptual processes and regions involved in initial syntactic processes. Copyright © 2011 Wiley Periodicals, Inc.

  1. Neural Network Ensembles

    DEFF Research Database (Denmark)

    Hansen, Lars Kai; Salamon, Peter

    1990-01-01

    We propose several means for improving the performance an training of neural networks for classification. We use crossvalidation as a tool for optimizing network parameters and architecture. We show further that the remaining generalization error can be reduced by invoking ensembles of similar...... networks....

  2. Neural systems for control

    National Research Council Canada - National Science Library

    Omidvar, Omid; Elliott, David L

    1997-01-01

    ... is reprinted with permission from A. Barto, "Reinforcement Learning," Handbook of Brain Theory and Neural Networks, M.A. Arbib, ed.. The MIT Press, Cambridge, MA, pp. 804-809, 1995. Chapter 4, Figures 4-5 and 7-9 and Tables 2-5, are reprinted with permission, from S. Cho, "Map Formation in Proprioceptive Cortex," International Jour...

  3. Neural Tube Defects

    Science.gov (United States)

    ... pregnancies each year in the United States. A baby’s neural tube normally develops into the brain and spinal cord. ... fluid in the brain. This is called hydrocephalus. Babies with this condition are treated with surgery to insert a tube (called a shunt) into the brain. The shunt ...

  4. Tumor Diagnosis Using Backpropagation Neural Network Method

    Science.gov (United States)

    Ma, Lixing; Looney, Carl; Sukuta, Sydney; Bruch, Reinhard; Afanasyeva, Natalia

    1998-05-01

    For characterization of skin cancer, an artificial neural network (ANN) method has been developed to diagnose normal tissue, benign tumor and melanoma. The pattern recognition is based on a three-layer neural network fuzzy learning system. In this study, the input neuron data set is the Fourier Transform infrared (FT-IR)spectrum obtained by a new Fiberoptic Evanescent Wave Fourier Transform Infrared (FEW-FTIR) spectroscopy method in the range of 1480 to 1850 cm-1. Ten input features are extracted from the absorbency values in this region. A single hidden layer of neural nodes with sigmoids activation functions clusters the feature space into small subclasses and the output nodes are separated in different nonconvex classes to permit nonlinear discrimination of disease states. The output is classified as three classes: normal tissue, benign tumor and melanoma. The results obtained from the neural network pattern recognition are shown to be consistent with traditional medical diagnosis. Input features have also been extracted from the absorbency spectra using chemical factor analysis. These abstract features or factors are also used in the classification.

  5. Neural mechanisms underlying melodic perception and memory for pitch.

    Science.gov (United States)

    Zatorre, R J; Evans, A C; Meyer, E

    1994-04-01

    The neural correlates of music perception were studied by measuring cerebral blood flow (CBF) changes with positron emission tomography (PET). Twelve volunteers were scanned using the bolus water method under four separate conditions: (1) listening to a sequence of noise bursts, (2) listening to unfamiliar tonal melodies, (3) comparing the pitch of the first two notes of the same set of melodies, and (4) comparing the pitch of the first and last notes of the melodies. The latter two conditions were designed to investigate short-term pitch retention under low or high memory load, respectively. Subtraction of the obtained PET images, superimposed on matched MRI scans, provides anatomical localization of CBF changes associated with specific cognitive functions. Listening to melodies, relative to acoustically matched noise sequences, resulted in CBF increases in the right superior temporal and right occipital cortices. Pitch judgments of the first two notes of each melody, relative to passive listening to the same stimuli, resulted in right frontal-lobe activation. Analysis of the high memory load condition relative to passive listening revealed the participation of a number of cortical and subcortical regions, notably in the right frontal and right temporal lobes, as well as in parietal and insular cortex. Both pitch judgment conditions also revealed CBF decreases within the left primary auditory cortex. We conclude that specialized neural systems in the right superior temporal cortex participate in perceptual analysis of melodies; pitch comparisons are effected via a neural network that includes right prefrontal cortex, but active retention of pitch involves the interaction of right temporal and frontal cortices.

  6. Child Maltreatment and Neural Systems Underlying Emotion Regulation.

    Science.gov (United States)

    McLaughlin, Katie A; Peverill, Matthew; Gold, Andrea L; Alves, Sonia; Sheridan, Margaret A

    2015-09-01

    The strong associations between child maltreatment and psychopathology have generated interest in identifying neurodevelopmental processes that are disrupted following maltreatment. Previous research has focused largely on neural response to negative facial emotion. We determined whether child maltreatment was associated with neural responses during passive viewing of negative and positive emotional stimuli and effortful attempts to regulate emotional responses. A total of 42 adolescents aged 13 to 19 years, half with exposure to physical and/or sexual abuse, participated. Blood oxygen level-dependent (BOLD) response was measured during passive viewing of negative and positive emotional stimuli and attempts to modulate emotional responses using cognitive reappraisal. Maltreated adolescents exhibited heightened response in multiple nodes of the salience network, including amygdala, putamen, and anterior insula, to negative relative to neutral stimuli. During attempts to decrease responses to negative stimuli relative to passive viewing, maltreatment was associated with greater recruitment of superior frontal gyrus, dorsal anterior cingulate cortex, and frontal pole; adolescents with and without maltreatment down-regulated amygdala response to a similar degree. No associations were observed between maltreatment and neural response to positive emotional stimuli during passive viewing or effortful regulation. Child maltreatment heightens the salience of negative emotional stimuli. Although maltreated adolescents modulate amygdala responses to negative cues to a degree similar to that of non-maltreated youths, they use regions involved in effortful control to a greater degree to do so, potentially because greater effort is required to modulate heightened amygdala responses. These findings are promising, given the centrality of cognitive restructuring in trauma-focused treatments for children. Copyright © 2015 American Academy of Child and Adolescent Psychiatry

  7. Optimization of neural network algorithm of the land market description

    Directory of Open Access Journals (Sweden)

    M. A. Karpovich

    2016-01-01

    Full Text Available The advantages of neural network technology is shown in comparison of traditional descriptions of dynamically changing systems, which include a modern land market. The basic difficulty arising in the practical implementation of neural network models of the land market and construction products is revealed It is the formation of a representative set of training and test examples. The requirements which are necessary for the correct description of the current economic situation has been determined, it consists in the fact that Train-paid-set in the feature space should not has the ranges with a low density of observations. The methods of optimization of empirical array, which allow to avoid the long-range extrapolation of data from range of concentration of the set of examples are formulated. It is shown that a radical method of optimization a set of training and test examples enclosing to collect supplemantary information, is associated with significant costs time and resources for the economic problems and the ratio of cost / efficiency is less efficient than an algorithm optimization neural network models the earth market fixed set of empirical data. Algorithm of optimization based on the transformation of arrays of information which represents the expansion of the ranges of concentration of the set of examples and compression the ranges of low density of observations is analyzed in details. The significant reduction in the relative error of land price description is demonstrated on the specific example of Voronezh region market of lands which intend for road construction, it makes the using of radical method of empirical optimization of the array costeffective with accounting the significant absolute value of the land. The high economic efficiency of the proposed algorithms is demonstrated.

  8. Neural correlates of cognitive improvements following cognitive remediation in schizophrenia: a systematic review of randomized trials

    Directory of Open Access Journals (Sweden)

    Clémence Isaac

    2016-03-01

    Full Text Available Background: Cognitive impairments are a core feature in schizophrenia and are linked to poor social functioning. Numerous studies have shown that cognitive remediation can enhance cognitive and functional abilities in patients with this pathology. The underlying mechanism of these behavioral improvements seems to be related to structural and functional changes in the brain. However, studies on neural correlates of such enhancement remain scarce. Objectives: We explored the neural correlates of cognitive enhancement following cognitive remediation interventions in schizophrenia and the differential effect between cognitive training and other therapeutic interventions or patients’ usual care. Method: We searched MEDLINE, PsycInfo, and ScienceDirect databases for studies on cognitive remediation therapy in schizophrenia that used neuroimaging techniques and a randomized design. Search terms included randomized controlled trial, cognitive remediation, cognitive training, rehabilitation, magnetic resonance imaging, positron emission tomography, electroencephalography, magnetoencephalography, near infrared spectroscopy, and diffusion tensor imaging. We selected randomized controlled trials that proposed multiple sessions of cognitive training to adult patients with a schizophrenia spectrum disorder and assessed its efficacy with imaging techniques. Results: In total, 15 reports involving 19 studies were included in the systematic review. They involved a total of 455 adult patients, 271 of whom received cognitive remediation. Cognitive remediation therapy seems to provide a neurobiological enhancing effect in schizophrenia. After therapy, increased activations are observed in various brain regions mainly in frontal – especially prefrontal – and also in occipital and anterior cingulate regions during working memory and executive tasks. Several studies provide evidence of an improved functional connectivity after cognitive training, suggesting a

  9. Improving protein disorder prediction by deep bidirectional long short-term memory recurrent neural networks.

    Science.gov (United States)

    Hanson, Jack; Yang, Yuedong; Paliwal, Kuldip; Zhou, Yaoqi

    2017-03-01

    Capturing long-range interactions between structural but not sequence neighbors of proteins is a long-standing challenging problem in bioinformatics. Recently, long short-term memory (LSTM) networks have significantly improved the accuracy of speech and image classification problems by remembering useful past information in long sequential events. Here, we have implemented deep bidirectional LSTM recurrent neural networks in the problem of protein intrinsic disorder prediction. The new method, named SPOT-Disorder, has steadily improved over a similar method using a traditional, window-based neural network (SPINE-D) in all datasets tested without separate training on short and long disordered regions. Independent tests on four other datasets including the datasets from critical assessment of structure prediction (CASP) techniques and >10 000 annotated proteins from MobiDB, confirmed SPOT-Disorder as one of the best methods in disorder prediction. Moreover, initial studies indicate that the method is more accurate in predicting functional sites in disordered regions. These results highlight the usefulness combining LSTM with deep bidirectional recurrent neural networks in capturing non-local, long-range interactions for bioinformatics applications. SPOT-disorder is available as a web server and as a standalone program at: http://sparks-lab.org/server/SPOT-disorder/index.php . j.hanson@griffith.edu.au or yuedong.yang@griffith.edu.au or yaoqi.zhou@griffith.edu.au. Supplementary data is available at Bioinformatics online.

  10. Distinct representations of subtraction and multiplication in the neural systems for numerosity and language

    Science.gov (United States)

    Prado, Jérôme; Mutreja, Rachna; Zhang, Hongchuan; Mehta, Rucha; Desroches, Amy S.; Minas, Jennifer E.; Booth, James R.

    2010-01-01

    It has been proposed that recent cultural inventions such as symbolic arithmetic recycle evolutionary older neural mechanisms. A central assumption of this hypothesis is that the degree to which a pre-existing mechanism is recycled depends upon the degree of similarity between its initial function and the novel task. To test this assumption, we investigated whether the brain region involved in magnitude comparison in the intraparietal sulcus (IPS), localized by a numerosity comparison task, is recruited to a greater degree by arithmetic problems that involve number comparison (single-digit subtractions) than by problems that involve retrieving facts from memory (single-digit multiplications). Our results confirmed that subtractions are associated with greater activity in the IPS than multiplications, whereas multiplications elicit greater activity than subtractions in regions involved in verbal processing including the middle temporal gyrus and inferior frontal gyrus that were localized by a phonological processing task. Pattern analyses further indicated that the neural mechanisms more active for subtraction than multiplication in the IPS overlap with those involved in numerosity comparison, and that the strength of this overlap predicts inter-individual performance in the subtraction task. These findings provide novel evidence that elementary arithmetic relies on the co-option of evolutionary older neural circuits. PMID:21246667

  11. WITHDRAWN: The Neural Cell Adhesion Molecule NCAM2/OCAM/RNCAM, a Close Relative to NCAM.

    Science.gov (United States)

    Kulahin, Nikolaj; Walmod, Peter S

    2008-03-27

    Cell adhesion molecules (CAMs) constitute a large class of plasma membrane-anchored proteins that mediate attachment between neighboring cells and between cells and the surrounding extracellular matrix (ECM). However, CAMs are more than simple mediators of cell adhesion. The neural cell adhesion molecule (NCAM) is a well characterized, ubiquitously expressed CAM that is highly expressed in the nervous system. In addition to mediating cell adhesion, NCAM participates in a multitude of cellular events, including survival, migration, and differentiation of cells, outgrowth of neurites, and formation and plasticity of synapses. NCAM shares an overall sequence identity of approximately 44% with the neural cell adhesion molecule 2 (NCAM2), a protein also known as olfactory cell adhesion molecule (OCAM) and Rb-8 neural cell adhesion molecule (RNCAM), and the region-for-region sequence homology between the two proteins suggests that they are transcribed from paralogous genes. However, very little is known about the function of NCAM2, although it originally was described more than 20 years ago. In this review we summarize the known properties and functions of NCAM2 and describe some of the differences and similarities between NCAM and NCAM2.

  12. Neural bases of goal-directed implicit learning.

    Science.gov (United States)

    Rostami, Maryam; Hosseini, S M Hadi; Takahashi, Makoto; Sugiura, Motoaki; Kawashima, Ryuta

    2009-10-15

    Several neuropsychological and neuroimaging studies have been performed to clarify the neural bases of implicit learning, but the question of which brain regions are involved in different forms of implicit learning, including goal-directed learning and habit learning, has not yet been resolved. The present study sought to clarify the mechanisms of goal-directed implicit learning by examining the sugar production factory (SPF) task in conjunction with functional magnetic resonance imaging (fMRI). Several brain regions were identified that contribute to learning in the SPF task. Significant learning-related decreases in brain activity were found in the right inferior parietal lobule (IPL), left superior frontal gyrus, right medial frontal gyrus, cerebellar vermis, and left inferior frontal gyrus, while significant learning-related increases in activity were observed in the right inferior frontal gyrus, left precenteral gyrus and, left precuneus. Among these regions, we speculate that the IPL and medial frontal gyrus may specifically be involved in the early stage of goal-directed implicit learning. We also attempted to investigate the role of the striatum, which has a significant role in habit learning, during learning of the SPF task. The results of ROI analysis showed no learning-related change in the activity of the striatum. Although some of the observed learning-related activations in this study have also been previously reported in neuroimaging studies of habit learning, the possibility that specific brain regions involved in goal-direct implicit learning cannot be excluded.

  13. Autonomous Navigation Apparatus With Neural Network for a Mobile Vehicle

    Science.gov (United States)

    Quraishi, Naveed (Inventor)

    1996-01-01

    An autonomous navigation system for a mobile vehicle arranged to move within an environment includes a plurality of sensors arranged on the vehicle and at least one neural network including an input layer coupled to the sensors, a hidden layer coupled to the input layer, and an output layer coupled to the hidden layer. The neural network produces output signals representing respective positions of the vehicle, such as the X coordinate, the Y coordinate, and the angular orientation of the vehicle. A plurality of patch locations within the environment are used to train the neural networks to produce the correct outputs in response to the distances sensed.

  14. Spinal neural tube defects in Lagos University Teaching Hospital, Nigeria.

    Science.gov (United States)

    Bankole, O B; Arigbabu, S O; Kanu, O O

    2012-01-01

    The incidence of neural tube defects is known to vary among regions. Very little has been reported about the incidence in Sub-Saharan Africa except for the general impression that the prevalent rates are low. To determine the profile of patients presenting with neural tube defects in Lagos, Nigeria We studied all patients with congenital midline back swellings presenting to one of two neurosurgical services in the state over a 5-year period to establish the incidence of spina bifida and develop demographic data. Data collected included the age at presentation, maternal age, education and parity, presence of co-existing anomalies and the social status of the parents. One hundred and eight patients with congenital midline swellings of the back were studied. Meningomyelocele accounted for 96% of the cases seen. Half the patients presented within the first two weeks of life and although fifty percent of mothers had ultrasound scans done during pregnancy none of the patients were diagnosed prenatally. Seventy-three percent of mothers of affected children were from a low socio-economic class. The commonest co-existing congenital anomaly was lower limb deformity (Talipes equino-varus). Spina bifida is the commonest indication for neurosurgical clinic referral with the exception of trauma in our environment. The prevalence is higher among women in the lower socio-economic groups. Improved perinatal care is required to ensure that children with such birth defects get prompt medical attention and thereby prevent worsening of an already complex problem.

  15. Deep Neural Network Based Demand Side Short Term Load Forecasting

    Directory of Open Access Journals (Sweden)

    Seunghyoung Ryu

    2016-12-01

    Full Text Available In the smart grid, one of the most important research areas is load forecasting; it spans from traditional time series analyses to recent machine learning approaches and mostly focuses on forecasting aggregated electricity consumption. However, the importance of demand side energy management, including individual load forecasting, is becoming critical. In this paper, we propose deep neural network (DNN-based load forecasting models and apply them to a demand side empirical load database. DNNs are trained in two different ways: a pre-training restricted Boltzmann machine and using the rectified linear unit without pre-training. DNN forecasting models are trained by individual customer’s electricity consumption data and regional meteorological elements. To verify the performance of DNNs, forecasting results are compared with a shallow neural network (SNN, a double seasonal Holt–Winters (DSHW model and the autoregressive integrated moving average (ARIMA. The mean absolute percentage error (MAPE and relative root mean square error (RRMSE are used for verification. Our results show that DNNs exhibit accurate and robust predictions compared to other forecasting models, e.g., MAPE and RRMSE are reduced by up to 17% and 22% compared to SNN and 9% and 29% compared to DSHW.

  16. Lifelong bilingualism maintains neural efficiency for cognitive control in aging.

    Science.gov (United States)

    Gold, Brian T; Kim, Chobok; Johnson, Nathan F; Kryscio, Richard J; Smith, Charles D

    2013-01-09

    Recent behavioral data have shown that lifelong bilingualism can maintain youthful cognitive control abilities in aging. Here, we provide the first direct evidence of a neural basis for the bilingual cognitive control boost in aging. Two experiments were conducted, using a perceptual task-switching paradigm, including a total of 110 participants. In Experiment 1, older adult bilinguals showed better perceptual switching performance than their monolingual peers. In Experiment 2, younger and older adult monolinguals and bilinguals completed the same perceptual task-switching experiment while functional magnetic resonance imaging (fMRI) was performed. Typical age-related performance reductions and fMRI activation increases were observed. However, like younger adults, bilingual older adults outperformed their monolingual peers while displaying decreased activation in left lateral frontal cortex and cingulate cortex. Critically, this attenuation of age-related over-recruitment associated with bilingualism was directly correlated with better task-switching performance. In addition, the lower blood oxygenation level-dependent response in frontal regions accounted for 82% of the variance in the bilingual task-switching reaction time advantage. These results suggest that lifelong bilingualism offsets age-related declines in the neural efficiency for cognitive control processes.

  17. Multifunctional nanowire scaffolds for neural tissue engineering applications

    Science.gov (United States)

    Bechara, Samuel Leo

    Unlike other regions of the body, the nervous system is extremely vulnerable to damage and injury because it has a limited ability to self-repair. Over 250,000 people in the United States have spinal cord injuries and due to the complicated pathophysiology of such injuries, there are few options available for functional regeneration of the spinal column. Furthermore, peripheral nerve damage is troublingly common in the United States, with an estimated 200,000 patients treated surgically each year. The current gold standard in treatment for peripheral nerve damage is a nerve autograft. This technique was pioneered over 45 years ago, but suffers from a major drawback. By transecting a nerve from another part of the body, function is regained at the expense of destroying a nerve connection elsewhere. Because of these issues, the investigation of different materials for regenerating nervous tissue is necessary. This work examines multi-functional nanowire scaffolds to provide physical and chemical guidance cues to neural stem cells to enhance cellular activity from a biomedical engineering perspective. These multi-functional scaffolds include a unique nanowire nano-topography to provide physical cues to guide cellular adhesion. The nanowires were then coated with an electrically conductive polymer to further enhance cellular activity. Finally, nerve growth factor was conjugated to the surface of the scaffolds to provide chemical cues for the neural stem cells. The results in this work suggest that these multifunctional nanowire scaffolds could be used in vivo to repair nervous system tissue.

  18. Mammographic Image Analysis of Breast Using Neural Network

    Directory of Open Access Journals (Sweden)

    Lesa MAMBWE

    2015-07-01

    Full Text Available This paper discusses the various stages of detecting tumours of the breast mammogram images. A Neural Network algorithm is applied for obtaining the complete classification of the tumour into normal or abnormal. The most important procedure or technique for obtaining the classification is the feature extraction, by extracting a few of discriminative features, first-order statistical intensities and gradients. The Image Pre-processing technique is essential prior to Image Segmentation in order to obtain accurate segmentation. Thus mass detection can be carried out. The processes involved in achieving the three techniques mentioned above include global equalization transformation, denoising, binarization, breast orientation determination and the pectoral muscle suppression. The presented feature difference matrices could be created by five features extracted from a suspicious region of interest (ROI. Grey Level Co-occurrence Matrix (GLCM aids the obtaining of statistical features such as correlation, energy, entropy and homogeneity. The other statistical to features to obtain are area, moment, variance, entropy, standard deviation and moment. The Neural network technique yields results of abnormal mammograms.

  19. Neural control of daily and seasonal timing of songbird migration.

    Science.gov (United States)

    Stevenson, Tyler J; Kumar, Vinod

    2017-07-01

    Bird migration is one of most salient annual events in nature. It involves predictable seasonal movements between breeding and non-breeding habitats. Both circadian and circannual clocks are entrained by photoperiodic cues and time daily and seasonal changes in migratory physiology and behavior. This mini-review provides an update on daily and seasonal rhythms of migratory behavior, and examines the neuroendocrine and molecular pathways involved in the timing of migration in songbirds. Recent findings have identified key neural substrates, and suggest the involvement of multiple neuroendocrine regulatory systems in controlling seasonal states in migrants. We propose that four distinct neural substrates are involved in the timing of migration and include (1) pineal gland and suprachiasmatic nucleus (mSCN); (2) a cluster of hypothalamic nuclei, the mediobasal hypothalamus (MBH); (3) dorsomedial hypothalamic nucleus (DMH); and (4) tanycytes along ependymal layer of the 3rd ventricle (3V). Cluster N, a nucleus in the telencephalon involved in the integration of geomagnetic cues, likely maintains functional connectivity with brain regions involved in timing songbird migration. These nuclei form an interconnected network that coordinates daily timing (pineal gland/mSCN), annual photoperiodic response (MBH, 3V), energetic state (MBH, DMH, 3V), and magnetic compass information (i.e., cluster N) for migration in songbirds.

  20. Alterations in neural connectivity in preterm children at school age.

    Science.gov (United States)

    Gozzo, Yeisid; Vohr, Betty; Lacadie, Cheryl; Hampson, Michelle; Katz, Karol H; Maller-Kesselman, Jill; Schneider, Karen C; Peterson, Bradley S; Rajeevan, Nallakkandi; Makuch, Robert W; Constable, R Todd; Ment, Laura R

    2009-11-01

    Converging data suggest recovery from injury in the preterm brain. We used functional magnetic resonance imaging (fMRI) to test the hypothesis that cerebral connectivity involving Wernicke's area and other important cortical language regions would differ between preterm (PT) and term (T) control school age children during performance of an auditory language task. Fifty-four PT children (600-1250 g birth weight) and 24 T controls were evaluated using an fMRI passive language task and neurodevelopmental assessments including: the Wechsler Intelligence Scale for Children - III (WISC-III), the Peabody Individual Achievement Test - Revised (PIAT-R) and the Peabody Picture Vocabulary Test - Revised (PPVT-R) at 8 years of age. Neural activity was assessed for language processing and the data were evaluated for connectivity and correlations to cognitive outcomes. We found that PT subjects scored significantly lower on all components of the WISC-III (planguage function at school age differently than T controls; these alterations may represent a delay in maturation of neural networks or the engagement of alternate circuits for language processing.

  1. Automatic Detection of Welding Defects using Deep Neural Network

    Science.gov (United States)

    Hou, Wenhui; Wei, Ye; Guo, Jie; Jin, Yi; Zhu, Chang’an

    2018-01-01

    In this paper, we propose an automatic detection schema including three stages for weld defects in x-ray images. Firstly, the preprocessing procedure for the image is implemented to locate the weld region; Then a classification model which is trained and tested by the patches cropped from x-ray images is constructed based on deep neural network. And this model can learn the intrinsic feature of images without extra calculation; Finally, the sliding-window approach is utilized to detect the whole images based on the trained model. In order to evaluate the performance of the model, we carry out several experiments. The results demonstrate that the classification model we proposed is effective in the detection of welded joints quality.

  2. Separate neural systems value immediate and delayed monetary rewards.

    Science.gov (United States)

    McClure, Samuel M; Laibson, David I; Loewenstein, George; Cohen, Jonathan D

    2004-10-15

    When humans are offered the choice between rewards available at different points in time, the relative values of the options are discounted according to their expected delays until delivery. Using functional magnetic resonance imaging, we examined the neural correlates of time discounting while subjects made a series of choices between monetary reward options that varied by delay to delivery. We demonstrate that two separate systems are involved in such decisions. Parts of the limbic system associated with the midbrain dopamine system, including paralimbic cortex, are preferentially activated by decisions involving immediately available rewards. In contrast, regions of the lateral prefrontal cortex and posterior parietal cortex are engaged uniformly by intertemporal choices irrespective of delay. Furthermore, the relative engagement of the two systems is directly associated with subjects' choices, with greater relative fronto-parietal activity when subjects choose longer term options.

  3. Architecture Analysis of an FPGA-Based Hopfield Neural Network

    Directory of Open Access Journals (Sweden)

    Miguel Angelo de Abreu de Sousa

    2014-01-01

    Full Text Available Interconnections between electronic circuits and neural computation have been a strongly researched topic in the machine learning field in order to approach several practical requirements, including decreasing training and operation times in high performance applications and reducing cost, size, and energy consumption for autonomous or embedded developments. Field programmable gate array (FPGA hardware shows some inherent features typically associated with neural networks, such as, parallel processing, modular executions, and dynamic adaptation, and works on different types of FPGA-based neural networks were presented in recent years. This paper aims to address different aspects of architectural characteristics analysis on a Hopfield Neural Network implemented in FPGA, such as maximum operating frequency and chip-area occupancy according to the network capacity. Also, the FPGA implementation methodology, which does not employ multipliers in the architecture developed for the Hopfield neural model, is presented, in detail.

  4. 23rd Workshop of the Italian Neural Networks Society (SIREN)

    CERN Document Server

    Esposito, Anna; Morabito, Francesco

    2014-01-01

    This volume collects a selection of contributions which has been presented at the 23rd Italian Workshop on Neural Networks, the yearly meeting of the Italian Society for Neural Networks (SIREN). The conference was held in Vietri sul Mare, Salerno, Italy during May 23-24, 2013. The annual meeting of SIREN is sponsored by International Neural Network Society (INNS), European Neural Network Society (ENNS) and IEEE Computational Intelligence Society (CIS). The book – as well as the workshop-  is organized in two main components, a special session and a group of regular sessions featuring different aspects and point of views of artificial neural networks, artificial and natural intelligence, as well as psychological and cognitive theories for modeling human behaviors and human machine interactions, including Information Communication applications of compelling interest.  .

  5. Neural crest specification: tissues, signals, and transcription factors.

    Science.gov (United States)

    Rogers, C D; Jayasena, C S; Nie, S; Bronner, M E

    2012-01-01

    The neural crest is a transient population of multipotent and migratory cells unique to vertebrate embryos. Initially derived from the borders of the neural plate, these cells undergo an epithelial to mesenchymal transition to leave the central nervous system, migrate extensively in the periphery, and differentiate into numerous diverse derivatives. These include but are not limited to craniofacial cartilage, pigment cells, and peripheral neurons and glia. Attractive for their similarities to stem cells and metastatic cancer cells, neural crest cells are a popular model system for studying cell/tissue interactions and signaling factors that influence cell fate decisions and lineage transitions. In this review, we discuss the mechanisms required for neural crest formation in various vertebrate species, focusing on the importance of signaling factors from adjacent tissues and conserved gene regulatory interactions, which are required for induction and specification of the ectodermal tissue that will become neural crest. Copyright © 2011 Wiley Periodicals, Inc.

  6. Bioprinting for Neural Tissue Engineering.

    Science.gov (United States)

    Knowlton, Stephanie; Anand, Shivesh; Shah, Twisha; Tasoglu, Savas

    2018-01-01

    Bioprinting is a method by which a cell-encapsulating bioink is patterned to create complex tissue architectures. Given the potential impact of this technology on neural research, we review the current state-of-the-art approaches for bioprinting neural tissues. While 2D neural cultures are ubiquitous for studying neural cells, 3D cultures can more accurately replicate the microenvironment of neural tissues. By bioprinting neuronal constructs, one can precisely control the microenvironment by specifically formulating the bioink for neural tissues, and by spatially patterning cell types and scaffold properties in three dimensions. We review a range of bioprinted neural tissue models and discuss how they can be used to observe how neurons behave, understand disease processes, develop new therapies and, ultimately, design replacement tissues. Copyright © 2017 Elsevier Ltd. All rights reserved.

  7. Systematic review of the neural basis of social cognition in patients with mood disorders

    Science.gov (United States)

    Cusi, Andrée M.; Nazarov, Anthony; Holshausen, Katherine; MacQueen, Glenda M.; McKinnon, Margaret C.

    2012-01-01

    Background This review integrates neuroimaging studies of 2 domains of social cognition — emotion comprehension and theory of mind (ToM) — in patients with major depressive disorder and bipolar disorder. The influence of key clinical and method variables on patterns of neural activation during social cognitive processing is also examined. Methods Studies were identified using PsycINFO and PubMed (January 1967 to May 2011). The search terms were “fMRI,” “emotion comprehension,” “emotion perception,” “affect comprehension,” “affect perception,” “facial expression,” “prosody,” “theory of mind,” “mentalizing” and “empathy” in combination with “major depressive disorder,” “bipolar disorder,” “major depression,” “unipolar depression,” “clinical depression” and “mania.” Results Taken together, neuroimaging studies of social cognition in patients with mood disorders reveal enhanced activation in limbic and emotion-related structures and attenuated activity within frontal regions associated with emotion regulation and higher cognitive functions. These results reveal an overall lack of inhibition by higher-order cognitive structures on limbic and emotion-related structures during social cognitive processing in patients with mood disorders. Critically, key variables, including illness burden, symptom severity, comorbidity, medication status and cognitive load may moderate this pattern of neural activation. Limitations Studies that did not include control tasks or a comparator group were included in this review. Conclusion Further work is needed to examine the contribution of key moderator variables and to further elucidate the neural networks underlying altered social cognition in patients with mood disorders. The neural networks underlying higher-order social cognitive processes, including empathy, remain unexplored in patients with mood disorders. PMID:22297065

  8. Systematic review of the neural basis of social cognition in patients with mood disorders.

    Science.gov (United States)

    Cusi, Andrée M; Nazarov, Anthony; Holshausen, Katherine; Macqueen, Glenda M; McKinnon, Margaret C

    2012-05-01

    This review integrates neuroimaging studies of 2 domains of social cognition--emotion comprehension and theory of mind (ToM)--in patients with major depressive disorder and bipolar disorder. The influence of key clinical and method variables on patterns of neural activation during social cognitive processing is also examined. Studies were identified using PsycINFO and PubMed (January 1967 to May 2011). The search terms were "fMRI," "emotion comprehension," "emotion perception," "affect comprehension," "affect perception," "facial expression," "prosody," "theory of mind," "mentalizing" and "empathy" in combination with "major depressive disorder," "bipolar disorder," "major depression," "unipolar depression," "clinical depression" and "mania." Taken together, neuroimaging studies of social cognition in patients with mood disorders reveal enhanced activation in limbic and emotion-related structures and attenuated activity within frontal regions associated with emotion regulation and higher cognitive functions. These results reveal an overall lack of inhibition by higher-order cognitive structures on limbic and emotion-related structures during social cognitive processing in patients with mood disorders. Critically, key variables, including illness burden, symptom severity, comorbidity, medication status and cognitive load may moderate this pattern of neural activation. Studies that did not include control tasks or a comparator group were included in this review. Further work is needed to examine the contribution of key moderator variables and to further elucidate the neural networks underlying altered social cognition in patients with mood disorders. The neural networks under lying higher-order social cognitive processes, including empathy, remain unexplored in patients with mood disorders.

  9. Learning to read words in a new language shapes the neural organization of the prior languages.

    Science.gov (United States)

    Mei, Leilei; Xue, Gui; Lu, Zhong-Lin; Chen, Chuansheng; Zhang, Mingxia; He, Qinghua; Wei, Miao; Dong, Qi

    2014-12-01

    Learning a new language entails interactions with one׳s prior language(s). Much research has shown how native language affects the cognitive and neural mechanisms of a new language, but little is known about whether and how learning a new language shapes the neural mechanisms of prior language(s). In two experiments in the current study, we used an artificial language training paradigm in combination with an fMRI to examine (1) the effects of different linguistic components (phonology and semantics) of a new language on the neural process of prior languages (i.e., native and second languages), and (2) whether such effects were modulated by the proficiency level in the new language. Results of Experiment 1 showed that when the training in a new language involved semantics (as opposed to only visual forms and phonology), neural activity during word reading in the native language (Chinese) was reduced in several reading-related regions, including the left pars opercularis, pars triangularis, bilateral inferior temporal gyrus, fusiform gyrus, and inferior occipital gyrus. Results of Experiment 2 replicated the results of Experiment 1 and further found that semantic training also affected neural activity during word reading in the subjects׳ second language (English). Furthermore, we found that the effects of the new language were modulated by the subjects׳ proficiency level in the new language. These results provide critical imaging evidence for the influence of learning to read words in a new language on word reading in native and second languages. Copyright © 2014 Elsevier Ltd. All rights reserved.

  10. Enhanced expression of FNDC5 in human embryonic stem cell-derived neural cells along with relevant embryonic neural tissues.

    Science.gov (United States)

    Ghahrizjani, Fatemeh Ahmadi; Ghaedi, Kamran; Salamian, Ahmad; Tanhaei, Somayeh; Nejati, Alireza Shoaraye; Salehi, Hossein; Nabiuni, Mohammad; Baharvand, Hossein; Nasr-Esfahani, Mohammad Hossein

    2015-02-25

    Availability of human embryonic stem cells (hESCs) has enhanced the capability of basic and clinical research in the context of human neural differentiation. Derivation of neural progenitor (NP) cells from hESCs facilitates the process of human embryonic development through the generation of neuronal subtypes. We have recently indicated that fibronectin type III domain containing 5 protein (FNDC5) expression is required for appropriate neural differentiation of mouse embryonic stem cells (mESCs). Bioinformatics analyses have shown the presence of three isoforms for human FNDC5 mRNA. To differentiate which isoform of FNDC5 is involved in the process of human neural differentiation, we have used hESCs as an in vitro model for neural differentiation by retinoic acid (RA) induction. The hESC line, Royan H5, was differentiated into a neural lineage in defined adherent culture treated by RA and basic fibroblast growth factor (bFGF). We collected all cell types that included hESCs, rosette structures, and neural cells in an attempt to assess the expression of FNDC5 isoforms. There was a contiguous increase in all three FNDC5 isoforms during the neural differentiation process. Furthermore, the highest level of expression of the isoforms was significantly observed in neural cells compared to hESCs and the rosette structures known as neural precursor cells (NPCs). High expression levels of FNDC5 in human fetal brain and spinal cord tissues have suggested the involvement of this gene in neural tube development. Additional research is necessary to determine the major function of FDNC5 in this process. Copyright © 2014 Elsevier B.V. All rights reserved.

  11. Neural network approaches for noisy language modeling.

    Science.gov (United States)

    Li, Jun; Ouazzane, Karim; Kazemian, Hassan B; Afzal, Muhammad Sajid

    2013-11-01

    Text entry from people is not only grammatical and distinct, but also noisy. For example, a user's typing stream contains all the information about the user's interaction with computer using a QWERTY keyboard, which may include the user's typing mistakes as well as specific vocabulary, typing habit, and typing performance. In particular, these features are obvious in disabled users' typing streams. This paper proposes a new concept called noisy language modeling by further developing information theory and applies neural networks to one of its specific application-typing stream. This paper experimentally uses a neural network approach to analyze the disabled users' typing streams both in general and specific ways to identify their typing behaviors and subsequently, to make typing predictions and typing corrections. In this paper, a focused time-delay neural network (FTDNN) language model, a time gap model, a prediction model based on time gap, and a probabilistic neural network model (PNN) are developed. A 38% first hitting rate (HR) and a 53% first three HR in symbol prediction are obtained based on the analysis of a user's typing history through the FTDNN language modeling, while the modeling results using the time gap prediction model and the PNN model demonstrate that the correction rates lie predominantly in between 65% and 90% with the current testing samples, and 70% of all test scores above basic correction rates, respectively. The modeling process demonstrates that a neural network is a suitable and robust language modeling tool to analyze the noisy language stream. The research also paves the way for practical application development in areas such as informational analysis, text prediction, and error correction by providing a theoretical basis of neural network approaches for noisy language modeling.

  12. Foetal ECG recovery using dynamic neural networks.

    Science.gov (United States)

    Camps-Valls, Gustavo; Martínez-Sober, Marcelino; Soria-Olivas, Emilio; Magdalena-Benedito, Rafael; Calpe-Maravilla, Javier; Guerrero-Martínez, Juan

    2004-07-01

    Non-invasive electrocardiography has proven to be a very interesting method for obtaining information about the foetus state and thus to assure its well-being during pregnancy. One of the main applications in this field is foetal electrocardiogram (ECG) recovery by means of automatic methods. Evident problems found in the literature are the limited number of available registers, the lack of performance indicators, and the limited use of non-linear adaptive methods. In order to circumvent these problems, we first introduce the generation of synthetic registers and discuss the influence of different kinds of noise to the modelling. Second, a method which is based on numerical (correlation coefficient) and statistical (analysis of variance, ANOVA) measures allows us to select the best recovery model. Finally, finite impulse response (FIR) and gamma neural networks are included in the adaptive noise cancellation (ANC) scheme in order to provide highly non-linear, dynamic capabilities to the recovery model. Neural networks are benchmarked with classical adaptive methods such as the least mean squares (LMS) and the normalized LMS (NLMS) algorithms in simulated and real registers and some conclusions are drawn. For synthetic registers, the most determinant factor in the identification of the models is the foetal-maternal signal-to-noise ratio (SNR). In addition, as the electromyogram contribution becomes more relevant, neural networks clearly outperform the LMS-based algorithm. From the ANOVA test, we found statistical differences between LMS-based models and neural models when complex situations (high foetal-maternal and foetal-noise SNRs) were present. These conclusions were confirmed after doing robustness tests on synthetic registers, visual inspection of the recovered signals and calculation of the recognition rates of foetal R-peaks for real situations. Finally, the best compromise between model complexity and outcomes was provided by the FIR neural network. Both

  13. The neural subjective frame: from bodily signals to perceptual consciousness

    Science.gov (United States)

    Park, Hyeong-Dong; Tallon-Baudry, Catherine

    2014-01-01

    The report ‘I saw the stimulus’ operationally defines visual consciousness, but where does the ‘I’ come from? To account for the subjective dimension of perceptual experience, we introduce the concept of the neural subjective frame. The neural subjective frame would be based on the constantly updated neural maps of the internal state of the body and constitute a neural referential from which first person experience can be created. We propose to root the neural subjective frame in the neural representation of visceral information which is transmitted through multiple anatomical pathways to a number of target sites, including posterior insula, ventral anterior cingulate cortex, amygdala and somatosensory cortex. We review existing experimental evidence showing that the processing of external stimuli can interact with visceral function. The neural subjective frame is a low-level building block of subjective experience which is not explicitly experienced by itself which is necessary but not sufficient for perceptual experience. It could also underlie other types of subjective experiences such as self-consciousness and emotional feelings. Because the neural subjective frame is tightly linked to homeostatic regulations involved in vigilance, it could also make a link between state and content consciousness. PMID:24639580

  14. Fate map of the chicken neural plate at stage 4.

    Science.gov (United States)

    Fernández-Garre, Pedro; Rodríguez-Gallardo, Lucia; Gallego-Díaz, Victoria; Alvarez, Ignacio S; Puelles, Luis

    2002-06-01

    A detailed fate map was obtained for the early chick neural plate (stages 3d/4). Numerous overlapping plug grafts were performed upon New-cultured chick embryos, using fixable carboxyfluorescein diacetate succinimidyl ester to label donor chick tissue. The specimens were harvested 24 hours after grafting and reached in most cases stages 9-11 (early neural tube). The label was detected immunocytochemically in wholemounts, and cross-sections were later obtained. The positions of the graft-derived cells were classified first into sets of purely neural, purely non-neural and mixed grafts. Comparisons between these sets established the neural plate boundary at stages 3d/4. Further analysis categorized graft contributions to anteroposterior and dorsoventral subdivisions of the early neural tube, including data on the floor plate and the eye field. The rostral boundary of the neural plate was contained within the earliest expression domain of the Ganf gene, and the overall shape of the neural plate was contrasted and discussed with regard to the expression patterns of the genes Plato, Sox2, Otx2 and Dlx5 (and others reported in the literature) at stages 3d/4.

  15. Dynamic behaviors of the non-neural ectoderm during mammalian cranial neural tube closure.

    Science.gov (United States)

    Ray, Heather J; Niswander, Lee A

    2016-08-15

    The embryonic brain and spinal cord initially form through the process of neural tube closure (NTC). NTC is thought to be highly similar between rodents and humans, and studies of mouse genetic mutants have greatly increased our understanding of the molecular basis of NTC with relevance for human neural tube defects. In addition, studies using amphibian and chick embryos have shed light into the cellular and tissue dynamics underlying NTC. However, the dynamics of mammalian NTC has been difficult to study due to in utero development until recently when advances in mouse embryo ex vivo culture techniques along with confocal microscopy have allowed for imaging of mouse NTC in real time. Here, we have performed live imaging of mouse embryos with a particular focus on the non-neural ectoderm (NNE). Previous studies in multiple model systems have found that the NNE is important for proper NTC, but little is known about the behavior of these cells during mammalian NTC. Here we utilized a NNE-specific genetic labeling system to assess NNE dynamics during murine NTC and identified different NNE cell behaviors as the cranial region undergoes NTC. These results bring valuable new insight into regional differences in cellular behavior during NTC that may be driven by different molecular regulators and which may underlie the various positional disruptions of NTC observed in humans with neural tube defects. Copyright © 2016 Elsevier Inc. All rights reserved.

  16. The neural basis of learning to spell again: An fMRI study of spelling training in acquired dysgraphia.

    Directory of Open Access Journals (Sweden)

    Jeremy Purcell

    2015-05-01

    1 For all participants we identified brain areas associated with a normalized response for the TRAINING words at the post-training time point. 2 For all participants we identified an up-regulation of the TRAINING response (i.e., the TRAINING neural response was initially low and then increased post-training; whereas in only one participant did we also observe a down-regulation of the training response (i.e., the TRAINING neural response was initially high, but then decreased post-training. 3 Although the areas associated with the normalized TRAINING response were different in each individual, they all include areas typically associated with the spelling system (Purcell et al. 2011, including the right homologues of typically left hemisphere spelling regions. Across the participants, the following areas of normalization were observed: bilateral superior temporal gyrus, inferior frontal gyrus, and the bilateral inferior temporal/fusiform gyrus. Discussion: We found that the predominant BOLD response to training involved an up-regulation of the neural response to spelling the TRAINING items. In addition, we found individual differences in the neurotopography of the normalization response patterns although all were with within brain areas that form a part of the spelling network(Purcell et al. 2011. This work provides evidence regarding one aspect of the multiplicity of neural responses associated with recovery of spelling in individuals with acquired dysgraphia.

  17. Neural predictive control for active buffet alleviation

    Science.gov (United States)

    Pado, Lawrence E.; Lichtenwalner, Peter F.; Liguore, Salvatore L.; Drouin, Donald

    1998-06-01

    The adaptive neural control of aeroelastic response (ANCAR) and the affordable loads and dynamics independent research and development (IRAD) programs at the Boeing Company jointly examined using neural network based active control technology for alleviating undesirable vibration and aeroelastic response in a scale model aircraft vertical tail. The potential benefits of adaptive control includes reducing aeroelastic response associated with buffet and atmospheric turbulence, increasing flutter margins, and reducing response associated with nonlinear phenomenon like limit cycle oscillations. By reducing vibration levels and thus loads, aircraft structures can have lower acquisition cost, reduced maintenance, and extended lifetimes. Wind tunnel tests were undertaken on a rigid 15% scale aircraft in Boeing's mini-speed wind tunnel, which is used for testing at very low air speeds up to 80 mph. The model included a dynamically scaled flexible fail consisting of an aluminum spar with balsa wood cross sections with a hydraulically powered rudder. Neural predictive control was used to actuate the vertical tail rudder in response to strain gauge feedback to alleviate buffeting effects. First mode RMS strain reduction of 50% was achieved. The neural predictive control system was developed and implemented by the Boeing Company to provide an intelligent, adaptive control architecture for smart structures applications with automated synthesis, self-optimization, real-time adaptation, nonlinear control, and fault tolerance capabilities. It is designed to solve complex control problems though a process of automated synthesis, eliminating costly control design and surpassing it in many instances by accounting for real world non-linearities.

  18. Localization of neural efficiency of the mathematically gifted brain through a feature subset selection method

    OpenAIRE

    Zhang, Li; Gan, John Q.; Wang, Haixian

    2015-01-01

    Based on the neural efficiency hypothesis and task-induced EEG gamma-band response (GBR), this study investigated the brain regions where neural resource could be most efficiently recruited by the math-gifted adolescents in response to varying cognitive demands. In this experiment, various GBR-based mental states were generated with three factors (level of mathematical ability, task complexity, and short-term learning) modulating the level of neural activation. A feature subset selection meth...

  19. Subgradient-based neural networks for nonsmooth nonconvex optimization problems.

    Science.gov (United States)

    Bian, Wei; Xue, Xiaoping

    2009-06-01

    This paper presents a subgradient-based neural network to solve a nonsmooth nonconvex optimization problem with a nonsmooth nonconvex objective function, a class of affine equality constraints, and a class of nonsmooth convex inequality constraints. The proposed neural network is modeled with a differential inclusion. Under a suitable assumption on the constraint set and a proper assumption on the objective function, it is proved that for a sufficiently large penalty parameter, there exists a unique global solution to the neural network and the trajectory of the network can reach the feasible region in finite time and stay there thereafter. It is proved that the trajectory of the neural network converges to the set which consists of the equilibrium points of the neural network, and coincides with the set which consists of the critical points of the objective function in the feasible region. A condition is given to ensure the convergence to the equilibrium point set in finite time. Moreover, under suitable assumptions, the coincidence between the solution to the differential inclusion and the "slow solution" of it is also proved. Furthermore, three typical examples are given to present the effectiveness of the theoretic results obtained in this paper and the good performance of the proposed neural network.

  20. Genetics and development of neural tube defects

    Science.gov (United States)

    Copp, Andrew J.; Greene, Nicholas D. E.

    2014-01-01

    Congenital defects of neural tube closure (neural tube defects; NTDs) are among the commonest and most severe disorders of the fetus and newborn. Disturbance of any of the sequential events of embryonic neurulation produce NTDs, with the phenotype (e.g. anencephaly, spina bifida) varying depending on the region of neural tube that remains open. While mutation of more than 200 genes is known to cause NTDs in mice, the pattern of occurrence in humans suggests a multifactorial polygenic or oligogenic aetiology. This emphasises the importance of gene-gene and gene-environment interactions in the origin of these defects. A number of cell biological functions are essential for neural tube closure, with defects of the cytoskeleton, cell cycle and molecular regulation of cell viability prominent among the mouse NTD mutants. Many transcriptional regulators and proteins that affect chromatin structure are also required for neural tube closure, although the downstream molecular pathways regulated by these proteins is unknown. Some key signalling pathways for NTDs have been identified: over-activation of sonic hedgehog signalling and loss of function in the planar cell polarity (non-canonical Wnt) pathway are potent causes of NTD, with requirements also for retinoid and inositol signalling. Folic acid supplementation is an effective method for primary prevention of a proportion of NTDs, in both humans and mice, although the embryonic mechanism of folate action remains unclear. Folic acid-resistant cases can be prevented by inositol supplementation in mice, raising the possibility that this could lead to an additional preventive strategy for human NTDs in future. PMID:19918803

  1. Neural basis of multisensory looming signals.

    Science.gov (United States)

    Tyll, Sascha; Bonath, Björn; Schoenfeld, Mircea Ariel; Heinze, Hans-Jochen; Ohl, Frank W; Noesselt, Tömme

    2013-01-15

    Approaching or looming signals are often related to extremely relevant environmental events (e.g. threats or collisions) making these signals critical for survival. However, the neural network underlying multisensory looming processing is not yet fully understood. Using functional magnetic resonance imaging (fMRI) we identified the neural correlates of audiovisual looming processing in humans: audiovisual looming (vs. receding) signals enhance fMRI-responses in low-level visual and auditory areas plus multisensory cortex (superior temporal sulcus; plus parietal and frontal structures). When characterizing the fMRI-response profiles for multisensory looming stimuli, we found significant enhancements relative to the mean and maximum of unisensory responses in looming-sensitive visual and auditory cortex plus STS. Superadditive enhancements were observed in visual cortex. Subject-specific region-of-interest analyses further revealed superadditive response profiles within all sensory-specific looming-sensitive structures plus bilateral STS for audiovisual looming vs. summed unisensory looming conditions. Finally, we observed enhanced connectivity of bilateral STS with low-level visual areas in the context of looming processing. This enhanced coupling of STS with unisensory regions might potentially serve to enhance the salience of unisensory stimulus features and is accompanied by superadditive fMRI-responses. We suggest that this preference in neural signaling for looming stimuli effectively informs animals to avoid potential threats or collisions. Copyright © 2012 Elsevier Inc. All rights reserved.

  2. Neural network technologies

    Science.gov (United States)

    Villarreal, James A.

    1991-01-01

    A whole new arena of computer technologies is now beginning to form. Still in its infancy, neural network technology is a biologically inspired methodology which draws on nature's own cognitive processes. The Software Technology Branch has provided a software tool, Neural Execution and Training System (NETS), to industry, government, and academia to facilitate and expedite the use of this technology. NETS is written in the C programming language and can be executed on a variety of machines. Once a network has been debugged, NETS can produce a C source code which implements the network. This code can then be incorporated into other software systems. Described here are various software projects currently under development with NETS and the anticipated future enhancements to NETS and the technology.

  3. Analysis of neural data

    CERN Document Server

    Kass, Robert E; Brown, Emery N

    2014-01-01

    Continual improvements in data collection and processing have had a huge impact on brain research, producing data sets that are often large and complicated. By emphasizing a few fundamental principles, and a handful of ubiquitous techniques, Analysis of Neural Data provides a unified treatment of analytical methods that have become essential for contemporary researchers. Throughout the book ideas are illustrated with more than 100 examples drawn from the literature, ranging from electrophysiology, to neuroimaging, to behavior. By demonstrating the commonality among various statistical approaches the authors provide the crucial tools for gaining knowledge from diverse types of data. Aimed at experimentalists with only high-school level mathematics, as well as computationally-oriented neuroscientists who have limited familiarity with statistics, Analysis of Neural Data serves as both a self-contained introduction and a reference work.

  4. Neural tube defects

    Directory of Open Access Journals (Sweden)

    M.E. Marshall

    1981-09-01

    Full Text Available Neural tube defects refer to any defect in the morphogenesis of the neural tube, the most common types being spina bifida and anencephaly. Spina bifida has been recognised in skeletons found in north-eastern Morocco and estimated to have an age of almost 12 000 years. It was also known to the ancient Greek and Arabian physicians who thought that the bony defect was due to the tumour. The term spina bifida was first used by Professor Nicolai Tulp of Amsterdam in 1652. Many other terms have been used to describe this defect, but spina bifida remains the most useful general term, as it describes the separation of the vertebral elements in the midline.

  5. In vitro neurogenesis from neural progenitor cells isolated from the hippocampus region of the brain of adult rats exposed to ethanol during early development through their alcohol-drinking mothers.

    Science.gov (United States)

    Singh, Ashok K; Gupta, Shveta; Jiang, Yin; Younus, Mohammed; Ramzan, Mohammed

    2009-01-01

    This study was aimed to determine whether ethanol exposure during early development altered neurogenesis in the brain of adult rats. Pregnant rats were given either ethanol-mixed or mannose-mixed (for control) rodent liquid diet ad libitum. Ethanol drinking continued during pregnancy and nursing. After weaning, the pups (AC(o): pups from control mothers, AE(o): pups from ethanol exposed mothers) received normal diet and water ad libitum for 11 weeks. Then the rats were anesthetized, their brains were collected and the hippocampal samples were processed for isolation of neural progenitor cells (NPCs). AC(o) NPCs and AE(o) NPCs were sequentially grown in media containing different growth factors that induced proliferation and differentiation. Neuronal maturation was significantly delayed in ethanol-exposed rats. AC(o) NPCs, up to day 7 of culture, exhibited high beta-catenin-probe binding, an increase in Ca(2+) when exposed to gamma-amino butyric acid (GABA) and lack of response to glutamate (Glu) exposure. beta-Catenin-probe binding and the stimulatory effects of GABA declined thereafter. AC(o) NPCs, at culture day 29, exhibited high beta-catenin-probe binding, lack of response to GABA and elevated Glu-induced increase in Ca(2+i). Cultures of AE(o) NPCs showed an amplified stimulatory effects of GABA, attenuated stimulatory effects of Glu and attenuated the delayed (culture day 29) increase in the expression of Wnt proteins and beta-catenin-probe binding. This suggests a significant alteration in neurogenesis and synapse formation in adult rats exposed to ethanol at early development through their alcohol-drinking mothers.

  6. Neural networks for triggering

    Energy Technology Data Exchange (ETDEWEB)

    Denby, B. (Fermi National Accelerator Lab., Batavia, IL (USA)); Campbell, M. (Michigan Univ., Ann Arbor, MI (USA)); Bedeschi, F. (Istituto Nazionale di Fisica Nucleare, Pisa (Italy)); Chriss, N.; Bowers, C. (Chicago Univ., IL (USA)); Nesti, F. (Scuola Normale Superiore, Pisa (Italy))

    1990-01-01

    Two types of neural network beauty trigger architectures, based on identification of electrons in jets and recognition of secondary vertices, have been simulated in the environment of the Fermilab CDF experiment. The efficiencies for B's and rejection of background obtained are encouraging. If hardware tests are successful, the electron identification architecture will be tested in the 1991 run of CDF. 10 refs., 5 figs., 1 tab.

  7. Artificial neural network modelling

    CERN Document Server

    Samarasinghe, Sandhya

    2016-01-01

    This book covers theoretical aspects as well as recent innovative applications of Artificial Neural networks (ANNs) in natural, environmental, biological, social, industrial and automated systems. It presents recent results of ANNs in modelling small, large and complex systems under three categories, namely, 1) Networks, Structure Optimisation, Robustness and Stochasticity 2) Advances in Modelling Biological and Environmental Systems and 3) Advances in Modelling Social and Economic Systems. The book aims at serving undergraduates, postgraduates and researchers in ANN computational modelling. .

  8. Neurally-mediated sincope.

    Science.gov (United States)

    Can, I; Cytron, J; Jhanjee, R; Nguyen, J; Benditt, D G

    2009-08-01

    Syncope is a syndrome characterized by a relatively sudden, temporary and self-terminating loss of consciousness; the causes may vary, but they have in common a temporary inadequacy of cerebral nutrient flow, usually due to a fall in systemic arterial pressure. However, while syncope is a common problem, it is only one explanation for episodic transient loss of consciousness (TLOC). Consequently, diagnostic evaluation should start with a broad consideration of real or seemingly real TLOC. Among those patients in whom TLOC is deemed to be due to ''true syncope'', the focus may then reasonably turn to assessing the various possible causes; in this regard, the neurally-mediated syncope syndromes are among the most frequently encountered. There are three common variations: vasovagal syncope (often termed the ''common'' faint), carotid sinus syndrome, and the so-called ''situational faints''. Defining whether the cause is due to a neurally-mediated reflex relies heavily on careful history taking and selected testing (e.g., tilt-test, carotid massage). These steps are important. Despite the fact that neurally-mediated faints are usually relatively benign from a mortality perspective, they are nevertheless only infrequently an isolated event; neurally-mediated syncope tends to recur, and physical injury resulting from falls or accidents, diminished quality-of-life, and possible restriction from employment or avocation are real concerns. Consequently, defining the specific form and developing an effective treatment strategy are crucial. In every case the goal should be to determine the cause of syncope with sufficient confidence to provide patients and family members with a reliable assessment of prognosis, recurrence risk, and treatment options.

  9. The Neural Noisy Channel

    OpenAIRE

    Yu, Lei; Blunsom, Phil; Dyer, Chris; Grefenstette, Edward; Kocisky, Tomas

    2016-01-01

    We formulate sequence to sequence transduction as a noisy channel decoding problem and use recurrent neural networks to parameterise the source and channel models. Unlike direct models which can suffer from explaining-away effects during training, noisy channel models must produce outputs that explain their inputs, and their component models can be trained with not only paired training samples but also unpaired samples from the marginal output distribution. Using a latent variable to control ...

  10. Neural basis of nonanalytical reasoning expertise during clinical evaluation.

    Science.gov (United States)

    Durning, Steven J; Costanzo, Michelle E; Artino, Anthony R; Graner, John; van der Vleuten, Cees; Beckman, Thomas J; Wittich, Christopher M; Roy, Michael J; Holmboe, Eric S; Schuwirth, Lambert

    2015-03-01

    Understanding clinical reasoning is essential for patient care and medical education. Dual-processing theory suggests that nonanalytic reasoning is an essential aspect of expertise; however, assessing nonanalytic reasoning is challenging because it is believed to occur on the subconscious level. This assumption makes concurrent verbal protocols less reliable assessment tools. Functional magnetic resonance imaging was used to explore the neural basis of nonanalytic reasoning in internal medicine interns (novices) and board-certified staff internists (experts) while completing United States Medical Licensing Examination and American Board of Internal Medicine multiple-choice questions. The results demonstrated that novices and experts share a common neural network in addition to nonoverlapping neural resources. However, experts manifested greater neural processing efficiency in regions such as the prefrontal cortex during nonanalytical reasoning. These findings reveal a multinetwork system that supports the dual-process mode of expert clinical reasoning during medical evaluation.

  11. A Hybrid Fuzzy Neural Networks for the Detection of Tumors in Medical Images

    OpenAIRE

    N. Benamrane; A. Freville; R. Nekkache

    2005-01-01

    In this study, we propose an approach to detect suspect zones or tumors in medical images. The idea is to define with precision the existence of different kinds of lesions using a hybrid system, which combines Fuzzy Neural Networks and Expert System. After applying a method of image segmentation to extract regions (by region growing algorithm or by mathematical morphology algorithm), the fuzzy neural networks detect the suspect regions, which are validated by an expert system to determine the...

  12. Neurobiology of pair bonding in fishes; convergence of neural mechanisms across distant vertebrate lineages

    KAUST Repository

    Nowicki, Jessica

    2017-11-14

    Pair bonding has independently evolved numerous times among vertebrates. The governing neural mechanisms of pair bonding have only been studied in depth in the mammalian model species, the prairie vole, Microtus ochrogaster. In this species, oxytocin (OT), arginine vasopressin (AVP), dopamine (DA), and opioid (OP) systems play key roles in signaling in the formation and maintenance of pair bonding by targeting specific social and reward-mediating brain regions. By contrast, the neural basis of pair bonding is poorly studied in other vertebrates, and especially those of early origins, limiting our understanding of the evolutionary history of pair bonding regulatory mechanisms. We compared receptor gene expression between pair bonded and solitary individuals across eight socio-functional brain regions. We found that in females, ITR and V1aR receptor expression varied in the lateral septum-like region (the Vv/Vl), while in both sexes D1R, D2R, and MOR expression varied within the mesolimbic reward system, including a striatum-like region (the Vc); mirroring sites of action in M. ochrogaster. This study provides novel insights into the neurobiology of teleost pair bonding. It also reveals high convergence in the neurochemical mechanisms governing pair bonding across actinopterygians and sarcopterygians, by repeatedly co-opting and similarly assembling deep neurochemical and neuroanatomical homologies that originated in ancestral osteithes.

  13. Neural correlates of rumination in adolescents with remitted major depressive disorder and healthy controls.

    Science.gov (United States)

    Burkhouse, Katie L; Jacobs, Rachel H; Peters, Amy T; Ajilore, Olu; Watkins, Edward R; Langenecker, Scott A

    2017-04-01

    The aim of the present study was to use fMRI to examine the neural correlates of engaging in rumination among a sample of remitted depressed adolescents, a population at high risk for future depressive relapse. A rumination induction task was used to assess differences in the patterns of neural activation during rumination versus a distraction condition among 26 adolescents in remission from major depressive disorder (rMDD) and in 15 healthy control adolescents. Self-report depression and rumination, as well as clinician-rated depression, were also assessed among all participants. All of the participants recruited regions in the default mode network (DMN), including the posterior cingulate cortex, medial prefrontal cortex, inferior parietal lobe, and medial temporal gyrus, during rumination. Increased activation in these regions during rumination was correlated with increased self-report rumination and symptoms of depression across all participants. Adolescents with rMDD also exhibited greater activation in regions involved in visual, somatosensory, and emotion processing than did healthy peers. The present findings suggest that during ruminative thought, adolescents with rMDD are characterized by increased recruitment of regions within the DMN and in areas involved in visual, somatosensory, and emotion processing.

  14. Learning Topologies with the Growing Neural Forest.

    Science.gov (United States)

    Palomo, Esteban José; López-Rubio, Ezequiel

    2016-06-01

    In this work, a novel self-organizing model called growing neural forest (GNF) is presented. It is based on the growing neural gas (GNG), which learns a general graph with no special provisions for datasets with separated clusters. On the contrary, the proposed GNF learns a set of trees so that each tree represents a connected cluster of data. High dimensional datasets often contain large empty regions among clusters, so this proposal is better suited to them than other self-organizing models because it represents these separated clusters as connected components made of neurons. Experimental results are reported which show the self-organization capabilities of the model. Moreover, its suitability for unsupervised clustering and foreground detection applications is demonstrated. In particular, the GNF is shown to correctly discover the connected component structure of some datasets. Moreover, it outperforms some well-known foreground detectors both in quantitative and qualitative terms.

  15. The neural bases for valuing social equality.

    Science.gov (United States)

    Aoki, Ryuta; Yomogida, Yukihito; Matsumoto, Kenji

    2015-01-01

    The neural basis of how humans value and pursue social equality has become a major topic in social neuroscience research. Although recent studies have identified a set of brain regions and possible mechanisms that are involved in the neural processing of equality of outcome between individuals, how the human brain processes equality of opportunity remains unknown. In this review article, first we describe the importance of the distinction between equality of outcome and equality of opportunity, which has been emphasized in philosophy and economics. Next, we discuss possible approaches for empirical characterization of human valuation of equality of opportunity vs. equality of outcome. Understanding how these two concepts are distinct and interact with each other may provide a better explanation of complex human behaviors concerning fairness and social equality. Copyright © 2014 Elsevier Ireland Ltd and the Japan Neuroscience Society. All rights reserved.

  16. Automatic thoracic body region localization

    Science.gov (United States)

    Bai, PeiRui; Udupa, Jayaram K.; Tong, YuBing; Xie, ShiPeng; Torigian, Drew A.

    2017-03-01

    Radiological imaging and image interpretation for clinical decision making are mostly specific to each body region such as head & neck, thorax, abdomen, pelvis, and extremities. For automating image analysis and consistency of results, standardizing definitions of body regions and the various anatomic objects, tissue regions, and zones in them becomes essential. Assuming that a standardized definition of body regions is available, a fundamental early step needed in automated image and object analytics is to automatically trim the given image stack into image volumes exactly satisfying the body region definition. This paper presents a solution to this problem based on the concept of virtual landmarks and evaluates it on whole-body positron emission tomography/computed tomography (PET/CT) scans. The method first selects a (set of) reference object(s), segments it (them) roughly, and identifies virtual landmarks for the object(s). The geometric relationship between these landmarks and the boundary locations of body regions in the craniocaudal direction is then learned through a neural network regressor, and the locations are predicted. Based on low-dose unenhanced CT images of 180 near whole-body PET/CT scans (which includes 34 whole-body PET/CT scans), the mean localization error for the boundaries of superior of thorax (TS) and inferior of thorax (TI), expressed as number of slices (slice spacing ≍ 4mm)), and using either the skeleton or the pleural spaces as reference objects, is found to be 3,2 (using skeleton) and 3, 5 (using pleural spaces) respectively, or in mm 13, 10 mm (using skeleton) and 10.5, 20 mm (using pleural spaces), respectively. Improvements of this performance via optimal selection of objects and virtual landmarks and other object analytics applications are currently being pursued. and the skeleton and pleural spaces used as a reference objects

  17. Neural networks: Application to medical imaging

    Science.gov (United States)

    Clarke, Laurence P.

    1994-01-01

    The research mission is the development of computer assisted diagnostic (CAD) methods for improved diagnosis of medical images including digital x-ray sensors and tomographic imaging modalities. The CAD algorithms include advanced methods for adaptive nonlinear filters for image noise suppression, hybrid wavelet methods for feature segmentation and enhancement, and high convergence neural networks for feature detection and VLSI implementation of neural networks for real time analysis. Other missions include (1) implementation of CAD methods on hospital based picture archiving computer systems (PACS) and information networks for central and remote diagnosis and (2) collaboration with defense and medical industry, NASA, and federal laboratories in the area of dual use technology conversion from defense or aerospace to medicine.

  18. Fuzzy logic and neural network technologies

    Science.gov (United States)

    Villarreal, James A.; Lea, Robert N.; Savely, Robert T.

    1992-01-01

    Applications of fuzzy logic technologies in NASA projects are reviewed to examine their advantages in the development of neural networks for aerospace and commercial expert systems and control. Examples of fuzzy-logic applications include a 6-DOF spacecraft controller, collision-avoidance systems, and reinforcement-learning techniques. The commercial applications examined include a fuzzy autofocusing system, an air conditioning system, and an automobile transmission application. The practical use of fuzzy logic is set in the theoretical context of artificial neural systems (ANSs) to give the background for an overview of ANS research programs at NASA. The research and application programs include the Network Execution and Training Simulator and faster training algorithms such as the Difference Optimized Training Scheme. The networks are well suited for pattern-recognition applications such as predicting sunspots, controlling posture maintenance, and conducting adaptive diagnoses.

  19. CHD7, the gene mutated in CHARGE syndrome, regulates genes involved in neural crest cell guidance.

    Science.gov (United States)

    Schulz, Yvonne; Wehner, Peter; Opitz, Lennart; Salinas-Riester, Gabriela; Bongers, Ernie M H F; van Ravenswaaij-Arts, Conny M A; Wincent, Josephine; Schoumans, Jacqueline; Kohlhase, Jürgen; Borchers, Annette; Pauli, Silke

    2014-08-01

    Heterozygous loss of function mutations in CHD7 (chromodomain helicase DNA-binding protein 7) lead to CHARGE syndrome, a complex developmental disorder affecting craniofacial structures, cranial nerves and several organ systems. Recently, it was demonstrated that CHD7 is essential for the formation of multipotent migratory neural crest cells, which migrate from the neural tube to many regions of the embryo, where they differentiate into various tissues including craniofacial and heart structures. So far, only few CHD7 target genes involved in neural crest cell development have been identified and the role of CHD7 in neural crest cell guidance and the regulation of mesenchymal-epithelial transition are unknown. Therefore, we undertook a genome-wide microarray expression analysis on wild-type and CHD7 deficient (Chd7 (Whi/+) and Chd7 (Whi/Whi)) mouse embryos at day 9.5, a time point of neural crest cell migration. We identified 98 differentially expressed genes between wild-type and Chd7 (Whi/Whi) embryos. Interestingly, many misregulated genes are involved in neural crest cell and axon guidance such as semaphorins and ephrin receptors. By performing knockdown experiments for Chd7 in Xenopus laevis embryos, we found abnormalities in the expression pattern of Sema3a, a protein involved in the pathogenesis of Kallmann syndrome, in vivo. In addition, we detected non-synonymous SEMA3A variations in 3 out of 45 CHD7-negative CHARGE patients. In summary, we discovered for the first time that Chd7 regulates genes involved in neural crest cell guidance, demonstrating a new aspect in the pathogenesis of CHARGE syndrome. Furthermore, we showed for Sema3a a conserved regulatory mechanism across different species, highlighting its significance during development. Although we postulated that the non-synonymous SEMA3A variants which we found in CHD7-negative CHARGE patients alone are not sufficient to produce the phenotype, we suggest an important modifier role for SEMA3A in the

  20. Neural Predictors of Visuomotor Adaptation Rate and Multi-Day Savings

    Science.gov (United States)

    Cassady, Kaitlin; Ruitenberg, Marit; Koppelmans, Vincent; Reuter-Lorenz, Patricia; De Dios, Yiri; Gadd, Nichole; Wood, Scott; Riascos Castenada, Roy; Kofman, Igor; Bloomberg, Jacob; hide

    2017-01-01

    Recent studies of sensorimotor adaptation have found that individual differences in task-based functional brain activation are associated with the rate of adaptation and savings at subsequent sessions. However, few studies to date have investigated offline neural predictors of adaptation and multi-day savings. In the present study, we explore whether individual differences in the rate of visuomotor adaptation and multi-day savings are associated with differences in resting state functional connectivity and gray matter volume. Thirty-four participants performed a manual adaptation task during two separate test sessions, on average 9 days apart. We found that resting state functional connectivity strength between sensorimotor, anterior cingulate, and temporoparietal areas of the brain was a significant predictor of adaptation rate during the early, cognitive phase of practice. In contrast, default mode network functional connectivity strength was found to predict late adaptation rate and savings on day two, which suggests that these behaviors may rely on overlapping processes. We also found that gray matter volume in temporoparietal and occipital regions was a significant predictor of early learning, whereas gray matter volume in superior posterior regions of the cerebellum was a significant predictor of late adaptation. The results from this study suggest that offline neural predictors of early adaptation facilitate the cognitive mechanisms of sensorimotor adaptation, with support from by the involvement of temporoparietal and cingulate networks. In contrast, the neural predictors of late adaptation and savings, including the default mode network and the cerebellum, likely support the storage and modification of newly acquired sensorimotor representations. These findings provide novel insights into the neural processes associated with individual differences in sensorimotor adaptation.

  1. Supramodal neural processing of abstract information conveyed by speech and gesture

    Directory of Open Access Journals (Sweden)

    Benjamin eStraube

    2013-09-01

    Full Text Available Abstractness and modality of interpersonal communication have a considerable impact on comprehension. They are relevant for determining thoughts and constituting internal models of the environment. Whereas concrete object-related information can be represented in mind irrespective of language, abstract concepts require a representation in speech. Consequently, modality-independent processing of abstract information can be expected. Here we investigated the neural correlates of abstractness (abstract vs. concrete and modality (speech vs. gestures, to identify an abstractness-specific supramodal neural network.During fMRI data acquisition 20 participants were presented with videos of an actor either speaking sentences with an abstract-social [AS] or concrete-object-related content [CS], or performing meaningful abstract-social emblematic [AG] or concrete-object-related tool-use gestures [CG]. Gestures were accompanied by a foreign language to increase the comparability between conditions and to frame the communication context of the gesture videos. Participants performed a content judgment task referring to the person vs. object-relatedness of the utterances.The behavioral data suggest a comparable comprehension of contents communicated by speech or gesture. Furthermore, we found common neural processing for abstract information independent of modality (AS>CS ∩ AG>CG in a left hemispheric network including the left inferior frontal gyrus, temporal pole and medial frontal cortex. Modality specific activations were found in bilateral occipital, parietal and temporal as well as right inferior frontal brain regions for gesture (G>S and in left anterior temporal regions and the left angular gyrus for the processing of speech semantics (S>G.These data support the idea that abstract concepts are represented in a supramodal manner. Consequently, gestures referring to abstract concepts are processed in a predominantly left hemispheric language related

  2. Neural responses to expression and gaze in the posterior superior temporal sulcus interact with facial identity.

    Science.gov (United States)

    Baseler, Heidi A; Harris, Richard J; Young, Andrew W; Andrews, Timothy J

    2014-03-01

    Neural models of human face perception propose parallel pathways. One pathway (including posterior superior temporal sulcus, pSTS) is responsible for processing changeable aspects of faces such as gaze and expression, and the other pathway (including the fusiform face area, FFA) is responsible for relatively invariant aspects such as identity. However, to be socially meaningful, changes in expression and gaze must be tracked across an individual face. Our aim was to investigate how this is achieved. Using functional magnetic resonance imaging, we found a region in pSTS that responded more to sequences of faces varying in gaze and expression in which the identity was constant compared with sequences in which the identity varied. To determine whether this preferential response to same identity faces was due to the processing of identity in the pSTS or was a result of interactions between pSTS and other regions thought to code face identity, we measured the functional connectivity between face-selective regions. We found increased functional connectivity between the pSTS and FFA when participants viewed same identity faces compared with different identity faces. Together, these results suggest that distinct neural pathways involved in expression and identity interact to process the changeable features of the face in a socially meaningful way.

  3. Compensatory Neural Activity in Response to Cognitive Fatigue.

    Science.gov (United States)

    Wang, Chao; Trongnetrpunya, Amy; Samuel, Immanuel Babu Henry; Ding, Mingzhou; Kluger, Benzi M

    2016-04-06

    Prolonged continuous performance of a cognitively demanding task induces cognitive fatigue and is associated with a time-related deterioration of objective performance, the degree of which is referred to cognitive fatigability. Although the neural underpinnings of cognitive fatigue are poorly understood, prior studies report changes in neural activity consistent with deterioration of task-related networks over time. While compensatory brain activity is reported to maintain motor task performance in the face of motor fatigue and cognitive performance in the face of other stressors (e.g., aging) and structural changes, there are no studies to date demonstrating compensatory activity for cognitive fatigue. High-density electroencephalography was recorded from human subjects during a 160 min continuous performance of a cognitive control task. While most time-varying neural activity showed a linear decline over time, we identified an evoked potential over the anterior frontal region which demonstrated an inverted U-shaped time-on-task profile. This evoked brain activity peaked between 60 and 100 min into the task and was positively associated with better behavioral performance only during this interval. Following the peak and during subsequent decline of this anterior frontal activity, the rate of performance decline also accelerated. These findings demonstrate that this anterior frontal brain activity, which is not part of the primary task-related activity at baseline, is recruited to compensate for fatigue-induced impairments in the primary task-related network, and that this compensation terminates as cognitive fatigue further progresses. These findings may be relevant to understanding individual differences in cognitive fatigability and developing interventions for clinical conditions afflicted by fatigue. Fatigue refers to changes in objective performance and subjective effort induced by continuous task performance. We examined the neural underpinnings of cognitive

  4. Neural correlates of viewing paintings

    DEFF Research Database (Denmark)

    Vartanian, Oshin; Skov, Martin

    2014-01-01

    Many studies involving functional magnetic resonance imaging (fMRI) have exposed participants to paintings under varying task demands. To isolate neural systems that are activated reliably across fMRI studies in response to viewing paintings regardless of variation in task demands, a quantitative...... meta-analysis of fifteen experiments using the activation likelihood estimation (ALE) method was conducted. As predicted, viewing paintings was correlated with activation in a distributed system including the occipital lobes, temporal lobe structures in the ventral stream involved in object (fusiform...... gyrus) and scene (parahippocampal gyrus) perception, and the anterior insula-a key structure in experience of emotion. In addition, we also observed activation in the posterior cingulate cortex bilaterally-part of the brain's default network. These results suggest that viewing paintings engages not only...

  5. Optical neural network system for pose determination of spinning satellites

    Science.gov (United States)

    Lee, Andrew; Casasent, David

    1990-01-01

    An optical neural network architecture and algorithm based on a Hopfield optimization network are presented for multitarget tracking. This tracker utilizes a neuron for every possible target track, and a quadratic energy function of neural activities which is minimized using gradient descent neural evolution. The neural net tracker is demonstrated as part of a system for determining position and orientation (pose) of spinning satellites with respect to a robotic spacecraft. The input to the system is time sequence video from a single camera. Novelty detection and filtering are utilized to locate and segment novel regions from the input images. The neural net multitarget tracker determines the correspondences (or tracks) of the novel regions as a function of time, and hence the paths of object (satellite) parts. The path traced out by a given part or region is approximately elliptical in image space, and the position, shape and orientation of the ellipse are functions of the satellite geometry and its pose. Having a geometric model of the satellite, and the elliptical path of a part in image space, the three-dimensional pose of the satellite is determined. Digital simulation results using this algorithm are presented for various satellite poses and lighting conditions.

  6. Neural Network Analysis and Evaluation of the Fetal Heart Rate

    Directory of Open Access Journals (Sweden)

    Yasuaki Noguchi

    2009-01-01

    Full Text Available The aim of the present study is to obtain a highly objective automatic fetal heart rate (FHR diagnosis. The neural network software was composed of three layers with the back propagation, to which 8 FHR data, including sinusoidal FHR, were input and the system was educated by the data of 20 cases with a known outcome. The output was the probability of a normal, intermediate, or pathologic outcome. The neural index studied prolonged monitoring. The neonatal states and the FHR score strongly correlated with the outcome probability. The neural index diagnosis was correct. The completed software was transferred to other computers, where the system function was correct.

  7. Neural Mobilization: Treating Nerve-Related Musculoskeletal Conditions.

    Science.gov (United States)

    2017-09-01

    Physical therapists often assess and treat patients whose pain and disability stem from impaired mobility of the peripheral nervous system. Neural mobilization is a movement-based therapy, applied manually or via exercise. The nerve is mobilized relative to adjacent structures, with the aim of reducing symptoms through mechanisms that may be mechanical or neurophysiologic. A new systematic review published in the September 2017 issue of JOSPT includes 40 studies of neural mobilization in various neuromusculoskeletal conditions. The available evidence suggests that neural mobilization can be considered when treating certain nerve-related musculoskeletal conditions. J Orthop Sports Phys Ther 2017;47(9):616. doi:10.2519/jospt.2017.0509.

  8. Computational capabilities of graph neural networks.

    Science.gov (United States)

    Scarselli, Franco; Gori, Marco; Tsoi, Ah Chung; Hagenbuchner, Markus; Monfardini, Gabriele

    2009-01-01

    In this paper, we will consider the approximation properties of a recently introduced neural network model called graph neural network (GNN), which can be used to process-structured data inputs, e.g., acyclic graphs, cyclic graphs, and directed or undirected graphs. This class of neural networks implements a function tau(G,n) is an element of IR(m) that maps a graph G and one of its nodes n onto an m-dimensional Euclidean space. We characterize the functions that can be approximated by GNNs, in probability, up to any prescribed degree of precision. This set contains the maps that satisfy a property called preservation of the unfolding equivalence, and includes most of the practically useful functions on graphs; the only known exception is when the input graph contains particular patterns of symmetries when unfolding equivalence may not be preserved. The result can be considered an extension of the universal approximation property established for the classic feedforward neural networks (FNNs). Some experimental examples are used to show the computational capabilities of the proposed model.

  9. Neutron spectrometry with artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Vega C, H.R.; Hernandez D, V.M.; Manzanares A, E.; Rodriguez, J.M.; Mercado S, G.A. [Universidad Autonoma de Zacatecas, A.P. 336, 98000 Zacatecas (Mexico); Iniguez de la Torre Bayo, M.P. [Universidad de Valladolid, Valladolid (Spain); Barquero, R. [Hospital Universitario Rio Hortega, Valladolid (Spain); Arteaga A, T. [Envases de Zacatecas, S.A. de C.V., Zacatecas (Mexico)]. e-mail: rvega@cantera.reduaz.mx

    2005-07-01

    An artificial neural network has been designed to obtain the neutron spectra from the Bonner spheres spectrometer's count rates. The neural network was trained using 129 neutron spectra. These include isotopic neutron sources; reference and operational spectra from accelerators and nuclear reactors, spectra from mathematical functions as well as few energy groups and monoenergetic spectra. The spectra were transformed from lethargy to energy distribution and were re-bin ned to 31 energy groups using the MCNP 4C code. Re-binned spectra and UTA4 response matrix were used to calculate the expected count rates in Bonner spheres spectrometer. These count rates were used as input and the respective spectrum was used as output during neural network training. After training the network was tested with the Bonner spheres count rates produced by a set of neutron spectra. This set contains data used during network training as well as data not used. Training and testing was carried out in the Mat lab program. To verify the network unfolding performance the original and unfolded spectra were compared using the {chi}{sup 2}-test and the total fluence ratios. The use of Artificial Neural Networks to unfold neutron spectra in neutron spectrometry is an alternative procedure that overcomes the drawbacks associated in this ill-conditioned problem. (Author)

  10. Neutron spectrometry using artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Vega-Carrillo, Hector Rene [Unidad Academica de Estudios Nucleares, Universidad Autonoma de Zacatecas, Apdo. Postal 336, 98000 Zacatecas, Zac. (Mexico)]|[Unidad Academica de Ing. Electrica, Universidad Autonoma de Zacatecas, Apdo. Postal 336, 98000 Zacatecas, Zac. (Mexico)]|[Unidad Academica de Matematicas, Universidad Autonoma de Zacatecas, Apdo. Postal 336, 98000 Zacatecas, Zac. (Mexico)]. E-mail: fermineutron@yahoo.com; Martin Hernandez-Davila, Victor [Unidad Academica de Estudios Nucleares, Universidad Autonoma de Zacatecas, Apdo. Postal 336, 98000 Zacatecas, Zac. (Mexico)]|[Unidad Academica de Ing. Electrica, Universidad Autonoma de Zacatecas, Apdo. Postal 336, 98000 Zacatecas, Zac. (Mexico); Manzanares-Acuna, Eduardo [Unidad Academica de Estudios Nucleares, Universidad Autonoma de Zacatecas, Apdo. Postal 336, 98000 Zacatecas, Zac. (Mexico); Mercado Sanchez, Gema A. [Unidad Academica de Matematicas, Universidad Autonoma de Zacatecas, Apdo. Postal 336, 98000 Zacatecas, Zac. (Mexico); Pilar Iniguez de la Torre, Maria [Depto. Fisica Teorica, Molecular y Nuclear, Universidad de Valladolid, Valladolid (Spain); Barquero, Raquel [Hospital Universitario Rio Hortega, Valladolid (Spain); Palacios, Francisco; Mendez Villafane, Roberto [Depto. Fisica Teorica, Molecular y Nuclear, Universidad de Valladolid, Valladolid (Spain)]|[Universidad Europea Miguel de Cervantes, C. Padre Julio Chevalier No. 2, 47012 Valladolid (Spain); Arteaga Arteaga, Tarcicio [Unidad Academica de Estudios Nucleares, Universidad Autonoma de Zacatecas, Apdo. Postal 336, 98000 Zacatecas, Zac. (Mexico)]|[Envases de Zacatecas, SA de CV, Parque Industrial de Calera de Victor Rosales, Zac. (Mexico); Manuel Ortiz Rodriguez, Jose [Unidad Academica de Estudios Nucleares, Universidad Autonoma de Zacatecas, Apdo. Postal 336, 98000 Zacatecas, Zac. (Mexico)]|[Unidad Academica de Ing. Electrica, Universidad Autonoma de Zacatecas, Apdo. Postal 336, 98000 Zacatecas, Zac. (Mexico)

    2006-04-15

    An artificial neural network has been designed to obtain neutron spectra from Bonner spheres spectrometer count rates. The neural network was trained using 129 neutron spectra. These include spectra from isotopic neutron sources; reference and operational spectra from accelerators and nuclear reactors, spectra based on mathematical functions as well as few energy groups and monoenergetic spectra. The spectra were transformed from lethargy to energy distribution and were re-binned to 31 energy groups using the MCNP 4C code. The re-binned spectra and the UTA4 response matrix were used to calculate the expected count rates in Bonner spheres spectrometer. These count rates were used as input and their respective spectra were used as output during the neural network training. After training, the network was tested with the Bonner spheres count rates produced by folding a set of neutron spectra with the response matrix. This set contains data used during network training as well as data not used. Training and testing was carried out using the Matlab{sup (R)} program. To verify the network unfolding performance, the original and unfolded spectra were compared using the root mean square error. The use of artificial neural networks to unfold neutron spectra in neutron spectrometry is an alternative procedure that overcomes the drawbacks associated with this ill-conditioned problem.

  11. Artificial astrocytes improve neural network performance.

    Science.gov (United States)

    Porto-Pazos, Ana B; Veiguela, Noha; Mesejo, Pablo; Navarrete, Marta; Alvarellos, Alberto; Ibáñez, Oscar; Pazos, Alejandro; Araque, Alfonso

    2011-04-19

    Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. However, the actual impact of astrocytes in neural network function is largely unknown and its application in artificial intelligence remains untested. We have investigated the consequences of including artificial astrocytes, which present the biologically defined properties involved in astrocyte-neuron communication, on artificial neural network performance. Using connectionist systems and evolutionary algorithms, we have compared the performance of artificial neural networks (NN) and artificial neuron-glia networks (NGN) to solve classification problems. We show that the degree of success of NGN is superior to NN. Analysis of performances of NN with different number of neurons or different architectures indicate that the effects of NGN cannot be accounted for an increased number of network elements, but rather they are specifically due to astrocytes. Furthermore, the relative efficacy of NGN vs. NN increases as the complexity of the network increases. These results indicate that artificial astrocytes improve neural network performance, and established the concept of Artificial Neuron-Glia Networks, which represents a novel concept in Artificial Intelligence with implications in computational science as well as in the understanding of brain function.

  12. Artificial neural networks in neutron dosimetry

    Energy Technology Data Exchange (ETDEWEB)

    Vega C, H.R.; Hernandez D, V.M.; Manzanares A, E.; Mercado, G.A.; Perales M, W.A.; Robles R, J.A. [Unidades Academicas de Estudios Nucleares, UAZ, A.P. 336, 98000 Zacatecas (Mexico); Gallego, E.; Lorente, A. [Depto. de Ingenieria Nuclear, Universidad Politecnica de Madrid, (Spain)

    2005-07-01

    An artificial neural network has been designed to obtain the neutron doses using only the Bonner spheres spectrometer's count rates. Ambient, personal and effective neutron doses were included. 187 neutron spectra were utilized to calculate the Bonner count rates and the neutron doses. The spectra were transformed from lethargy to energy distribution and were re-binned to 31 energy groups using the MCNP 4C code. Re-binned spectra, UTA4 response matrix and fluence-to-dose coefficients were used to calculate the count rates in Bonner spheres spectrometer and the doses. Count rates were used as input and the respective doses were used as output during neural network training. Training and testing was carried out in Mat lab environment. The artificial neural network performance was evaluated using the {chi}{sup 2}- test, where the original and calculated doses were compared. The use of Artificial Neural Networks in neutron dosimetry is an alternative procedure that overcomes the drawbacks associated in this ill-conditioned problem. (Author)

  13. Neural networks for sign language translation

    Science.gov (United States)

    Wilson, Beth J.; Anspach, Gretel

    1993-09-01

    A neural network is used to extract relevant features of sign language from video images of a person communicating in American Sign Language or Signed English. The key features are hand motion, hand location with respect to the body, and handshape. A modular hybrid design is under way to apply various techniques, including neural networks, in the development of a translation system that will facilitate communication between deaf and hearing people. One of the neural networks described here is used to classify video images of handshapes into their linguistic counterpart in American Sign Language. The video image is preprocessed to yield Fourier descriptors that encode the shape of the hand silhouette. These descriptors are then used as inputs to a neural network that classifies their shapes. The network is trained with various examples from different signers and is tested with new images from new signers. The results have shown that for coarse handshape classes, the network is invariant to the type of camera used to film the various signers and to the segmentation technique.

  14. Genome-wide copy number profiling to detect gene amplifications in neural progenitor cells

    Directory of Open Access Journals (Sweden)

    U. Fischer

    2014-12-01

    Full Text Available DNA sequence amplification occurs at defined stages during normal development in amphibians and flies and seems to be restricted in humans to drug-resistant and tumor cells only. We used array-CGH to discover copy number changes including gene amplifications and deletions during differentiation of human neural progenitor cells. Here, we describe cell culture features, DNA extraction, and comparative genomic hybridization (CGH analysis tailored towards the identification of genomic copy number changes. Further detailed analysis of amplified chromosome regions associated with this experiment, was published by Fischer and colleagues in PLOS One in 2012 (Fischer et al., 2012. We provide detailed information on deleted chromosome regions during differentiation and give an overview on copy number changes during differentiation induction for two representative chromosome regions.

  15. Neural Correlates of Stimulus Reportability

    OpenAIRE

    Hulme, Oliver J.; Friston, Karl F.; Zeki, Semir

    2009-01-01

    Most experiments on the “neural correlates of consciousness” employ stimulus reportability as an operational definition of what is consciously perceived. The interpretation of such experiments therefore depends critically on understanding the neural basis of stimulus reportability. Using a high volume of fMRI data, we investigated the neural correlates of stimulus reportability using a partial report object detection paradigm. Subjects were presented with a random array of circularly arranged...

  16. Symbolic processing in neural networks

    OpenAIRE

    Neto, João Pedro; Hava T Siegelmann; Costa,J.Félix

    2003-01-01

    In this paper we show that programming languages can be translated into recurrent (analog, rational weighted) neural nets. Implementation of programming languages in neural nets turns to be not only theoretical exciting, but has also some practical implications in the recent efforts to merge symbolic and sub symbolic computation. To be of some use, it should be carried in a context of bounded resources. Herein, we show how to use resource bounds to speed up computations over neural nets, thro...

  17. The neural basis of theory of mind and its relationship to social functioning and social anhedonia in individuals with schizophrenia

    Directory of Open Access Journals (Sweden)

    David Dodell-Feder

    2014-01-01

    Full Text Available Theory of mind (ToM, the ability to attribute and reason about the mental states of others, is a strong determinant of social functioning among individuals with schizophrenia. Identifying the neural bases of ToM and their relationship to social functioning may elucidate functionally relevant neurobiological targets for intervention. ToM ability may additionally account for other social phenomena that affect social functioning, such as social anhedonia (SocAnh. Given recent research in schizophrenia demonstrating improved neural functioning in response to increased use of cognitive skills, it is possible that SocAnh, which decreases one's opportunity to engage in ToM, could compromise social functioning through its deleterious effect on ToM-related neural circuitry. Here, twenty individuals with schizophrenia and 18 healthy controls underwent fMRI while performing the False-Belief Task. Aspects of social functioning were assessed using multiple methods including self-report (Interpersonal Reactivity Index, Social Adjustment Scale, clinician-ratings (Global Functioning Social Scale, and performance-based tasks (MSCEIT—Managing Emotions. SocAnh was measured with the Revised Social Anhedonia Scale. Region-of-interest and whole-brain analyses revealed reduced recruitment of medial prefrontal cortex (MPFC for ToM in individuals with schizophrenia. Across all participants, activity in this region correlated with most social variables. Mediation analysis revealed that neural activity for ToM in MPFC accounted for the relationship between SocAnh and social functioning. These findings demonstrate that reduced recruitment of MPFC for ToM is an important neurobiological determinant of social functioning. Furthermore, SocAhn may affect social functioning through its impact on ToM-related neural circuitry. Together, these findings suggest ToM ability as an important locus for intervention.

  18. At the interface: convergence of neural regeneration and neural prostheses for restoration of function.

    Science.gov (United States)

    Grill, W M; McDonald, J W; Peckham, P H; Heetderks, W; Kocsis, J; Weinrich, M

    2001-01-01

    The rapid pace of recent advances in development and application of electrical stimulation of the nervous system and in neural regeneration has created opportunities to combine these two approaches to restoration of function. This paper relates the discussion on this topic from a workshop at the International Functional Electrical Stimulation Society. The goals of this workshop were to discuss the current state of interaction between the fields of neural regeneration and neural prostheses and to identify potential areas of future research that would have the greatest impact on achieving the common goal of restoring function after neurological damage. Identified areas include enhancement of axonal regeneration with applied electric fields, development of hybrid neural interfaces combining synthetic silicon and biologically derived elements, and investigation of the role of patterned neural activity in regulating various neuronal processes and neurorehabilitation. Increased communication and cooperation between the two communities and recognition by each field that the other has something to contribute to their efforts are needed to take advantage of these opportunities. In addition, creative grants combining the two approaches and more flexible funding mechanisms to support the convergence of their perspectives are necessary to achieve common objectives.

  19. Neural remodeling in retinal degeneration.

    Science.gov (United States)

    Marc, Robert E; Jones, Bryan W; Watt, Carl B; Strettoi, Enrica

    2003-09-01

    Mammalian retinal degenerations initiated by gene defects in rods, cones or the retinal pigmented epithelium (RPE) often trigger loss of the sensory retina, effectively leaving the neural retina deafferented. The neural retina responds to this challenge by remodeling, first by subtle changes in neuronal structure and later by large-scale reorganization. Retinal degenerations in the mammalian retina generally progress through three phases. Phase 1 initiates with expression of a primary insult, followed by phase 2 photoreceptor death that ablates the sensory retina via initial photoreceptor stress, phenotype deconstruction, irreversible stress and cell death, including bystander effects or loss of trophic support. The loss of cones heralds phase 3: a protracted period of global remodeling of the remnant neural retina. Remodeling resembles the responses of many CNS assemblies to deafferentation or trauma, and includes neuronal cell death, neuronal and glial migration, elaboration of new neurites and synapses, rewiring of retinal circuits, glial hypertrophy and the evolution of a fibrotic glial seal that isolates the remnant neural retina from the surviving RPE and choroid. In early phase 2, stressed photoreceptors sprout anomalous neurites that often reach the inner plexiform and ganglion cell layers. As death of rods and cones progresses, bipolar and horizontal cells are deafferented and retract most of their dendrites. Horizontal cells develop anomalous axonal processes and dendritic stalks that enter the inner plexiform layer. Dendrite truncation in rod bipolar cells is accompanied by revision of their macromolecular phenotype, including the loss of functioning mGluR6 transduction. After ablation of the sensory retina, Müller cells increase intermediate filament synthesis, forming a dense fibrotic layer in the remnant subretinal space. This layer invests the remnant retina and seals it from access via the choroidal route. Evidence of bipolar cell death begins in

  20. A systematic review of the neural correlates of positive emotions

    Directory of Open Access Journals (Sweden)

    Leonardo Machado

    Full Text Available Objective: To conduct a systematic literature review of human studies reporting neural correlates of positive emotions. Methods: The PubMed and Web of Science databases were searched in January 2016 for scientific papers written in English. No restrictions were placed on year of publication. Results: Twenty-two articles were identified and 12 met the established criteria. Five had been published during the last 4 years. Formation and regulation of positive emotions, including happiness, are associated with significant reductions in activity in the right prefrontal cortex and bilaterally in the temporoparietal cortex, as well as with increased activity in the left prefrontal regions. They are also associated with increased activity in the cingulate gyrus, inferior and middle temporal gyri, amygdalae, and ventral striatum. Conclusion: It is too early to claim that there is an established understanding of the neuroscience of positive emotions and happiness. However, despite overlap in the brain regions involved in the formation and regulation of positive and negative emotions, we can conclude that positive emotions such as happiness activate specific brain regions.

  1. Dissecting the anticipation of aversion reveals dissociable neural networks.

    Science.gov (United States)

    Grupe, Daniel W; Oathes, Desmond J; Nitschke, Jack B

    2013-08-01

    The anticipation of future adversity confers adaptive benefits by engaging a suite of preparatory mechanisms, but this process can also be deleterious when carried out in excess. Neuroscientific investigations have largely treated anticipation as a unitary process, but we show here using functional magnetic resonance imaging that distinct stages of aversive anticipation are supported by dissociable neural mechanisms. Immediate anticipatory responses were observed in regions associated with threat detection and early processing of predictive cues, including the orbitofrontal cortex and pregenual anterior cingulate cortex, as well as the amygdala for individuals with elevated anxiety symptoms. Sustained anticipatory activity was observed in the forebrain/bed nucleus of the stria terminalis, anterior insula, anterior mid-cingulate cortex (aMCC), and midbrain/periaqueductal gray, regions associated with anxiety, interoception, and defensive behavior. The aMCC showed increased functional coupling with the midbrain during sustained anticipation of aversion, highlighting a circuit critical for the expression of preparatory fear responses. These data implicate distinct sets of regions that are active during different temporal stages of anticipation, and provide insight into how the human brain faces the future both adaptively and maladaptively.

  2. [Artificial neural networks in Neurosciences].

    Science.gov (United States)

    Porras Chavarino, Carmen; Salinas Martínez de Lecea, José María

    2011-11-01

    This article shows that artificial neural networks are used for confirming the relationships between physiological and cognitive changes. Specifically, we explore the influence of a decrease of neurotransmitters on the behaviour of old people in recognition tasks. This artificial neural network recognizes learned patterns. When we change the threshold of activation in some units, the artificial neural network simulates the experimental results of old people in recognition tasks. However, the main contributions of this paper are the design of an artificial neural network and its operation inspired by the nervous system and the way the inputs are coded and the process of orthogonalization of patterns.

  3. Neural Correlates of Face Detection

    National Research Council Canada - National Science Library

    Xu, Xiaokun; Biederman, Irving

    2014-01-01

    Although face detection likely played an essential adaptive role in our evolutionary past and in contemporary social interactions, there have been few rigorous studies investigating its neural correlates...

  4. Neural Networks in R Using the Stuttgart Neural Network Simulator: RSNNS

    Directory of Open Access Journals (Sweden)

    Christopher Bergmeir

    2012-01-01

    Full Text Available Neural networks are important standard machine learning procedures for classification and regression. We describe the R package RSNNS that provides a convenient interface to the popular Stuttgart Neural Network Simulator SNNS. The main features are (a encapsulation of the relevant SNNS parts in a C++ class, for sequential and parallel usage of different networks, (b accessibility of all of the SNNSalgorithmic functionality from R using a low-level interface, and (c a high-level interface for convenient, R-style usage of many standard neural network procedures. The package also includes functions for visualization and analysis of the models and the training procedures, as well as functions for data input/output from/to the original SNNSfile formats.

  5. Exploring the spatio-temporal neural basis of face learning.

    Science.gov (United States)

    Yang, Ying; Xu, Yang; Jew, Carol A; Pyles, John A; Kass, Robert E; Tarr, Michael J

    2017-06-01

    Humans are experts at face individuation. Although previous work has identified a network of face-sensitive regions and some of the temporal signatures of face processing, as yet, we do not have a clear understanding of how such face-sensitive regions support learning at different time points. To study the joint spatio-temporal neural basis of face learning, we trained subjects to categorize two groups of novel faces and recorded their neural responses using magnetoencephalography (MEG) throughout learning. A regression analysis of neural responses in face-sensitive regions against behavioral learning curves revealed significant correlations with learning in the majority of the face-sensitive regions in the face network, mostly between 150-250 ms, but also after 300 ms. However, the effect was smaller in nonventral regions (within the superior temporal areas and prefrontal cortex) than that in the ventral regions (within the inferior occipital gyri (IOG), midfusiform gyri (mFUS) and anterior temporal lobes). A multivariate discriminant analysis also revealed that IOG and mFUS, which showed strong correlation effects with learning, exhibited significant discriminability between the two face categories at different time points both between 150-250 ms and after 300 ms. In contrast, the nonventral face-sensitive regions, where correlation effects with learning were smaller, did exhibit some significant discriminability, but mainly after 300 ms. In sum, our findings indicate that early and recurring temporal components arising from ventral face-sensitive regions are critically involved in learning new faces.

  6. Modulation of Neural Activity in the Temporoparietal Junction with Transcranial Direct Current Stimulation Changes the Role of Beliefs in Moral Judgment

    OpenAIRE

    Ye, Hang; Chen, Shu; Huang, Daqiang; Zheng, Haoli; Jia, Yongmin; Luo, Jun

    2015-01-01

    Judgments about whether an action is morally right or wrong typically depend on our capacity to infer the actor’s beliefs and the outcomes of the action. Prior neuroimaging studies have found that mental state (e.g., beliefs, intentions) attribution for moral judgment involves a complex neural network that includes the temporoparietal junction (TPJ). However, neuroimaging studies cannot demonstrate a direct causal relationship between the activity of this brain region and mental state attribu...

  7. Modulation of neural activity in the temporoparietal junction with transcranial direct current stimulation changes the role of beliefs in moral judgment

    OpenAIRE

    Hang eYe; Shu eChen; Daqiang eHuang; Haoli eZheng; Yongmin eJia; Jun eLuo

    2015-01-01

    Judgments about whether an action is morally right or wrong typically depend on our capacity to infer the actor’s beliefs and the outcomes of the action. Prior neuroimaging studies have found that mental state (e.g., beliefs, intentions) attribution for moral judgment involves a complex neural network that includes the temporoparietal junction (TPJ). However, neuroimaging studies cannot demonstrate a direct causal relationship between the activity of this brain region and mental state attribu...

  8. Differences in neural responses to reward and punishment processing between anorexia nervosa subtypes: An fMRI study.

    Science.gov (United States)

    Murao, Ema; Sugihara, Genichi; Isobe, Masanori; Noda, Tomomi; Kawabata, Michiko; Matsukawa, Noriko; Takahashi, Hidehiko; Murai, Toshiya; Noma, Shun'ichi

    2017-09-01

    Anorexia nervosa (AN) includes the restricting (AN-r) and binge-eating/purging (AN-bp) subtypes, which have been reported to differ regarding their underlying pathophysiologies as well as their behavioral patterns. However, the differences in neural mechanisms of reward systems between AN subtypes remain unclear. The aim of the present study was to explore differences in the neural processing of reward and punishment between AN subtypes. Twenty-three female patients with AN (11 AN-r and 12 AN-bp) and 20 healthy women underwent functional magnetic resonance imaging while performing a monetary incentive delay task. Whole-brain one-way analysis of variance was conducted to test between-group differences. There were significant group differences in brain activation in the rostral anterior cingulate cortex and right posterior insula during loss anticipation, with increased brain activation in the AN-bp group relative to the AN-r and healthy women groups. No significant differences were found during gain anticipation. AN-bp patients showed altered neural responses to punishment in brain regions implicated in emotional arousal. Our findings suggest that individuals with AN-bp are more sensitive to potential punishment than individuals with AN-r and healthy individuals at the neural level. The present study provides preliminary evidence that there are neurobiological differences between AN subtypes with regard to the reward system, especially punishment processing. © 2017 The Authors. Psychiatry and Clinical Neurosciences © 2017 Japanese Society of Psychiatry and Neurology.

  9. Discrimination Analysis of Earthquakes and Man-Made Events Using ARMA Coefficients Determination by Artificial Neural Networks

    Energy Technology Data Exchange (ETDEWEB)

    AllamehZadeh, Mostafa, E-mail: dibaparima@yahoo.com [International Institute of Earthquake Engineering and Seismology (Iran, Islamic Republic of)

    2011-12-15

    A Quadratic Neural Networks (QNNs) model has been developed for identifying seismic source classification problem at regional distances using ARMA coefficients determination by Artificial Neural Networks (ANNs). We have devised a supervised neural system to discriminate between earthquakes and chemical explosions with filter coefficients obtained by windowed P-wave phase spectra (15 s). First, we preprocess the recording's signals to cancel out instrumental and attenuation site effects and obtain a compact representation of seismic records. Second, we use a QNNs system to obtain ARMA coefficients for feature extraction in the discrimination problem. The derived coefficients are then applied to the neural system to train and classification. In this study, we explore the possibility of using single station three-component (3C) covariance matrix traces from a priori-known explosion sites (learning) for automatically recognizing subsequent explosions from the same site. The results have shown that this feature extraction gives the best classifier for seismic signals and performs significantly better than other classification methods. The events have been tested, which include 36 chemical explosions at the Semipalatinsk test site in Kazakhstan and 61 earthquakes (mb = 5.0-6.5) recorded by the Iranian National Seismic Network (INSN). The 100% correct decisions were obtained between site explosions and some of non-site events. The above approach to event discrimination is very flexible as we can combine several 3C stations.

  10. Dissociating neural subsystems for grammar by contrasting word order and inflection.

    Science.gov (United States)

    Newman, Aaron J; Supalla, Ted; Hauser, Peter; Newport, Elissa L; Bavelier, Daphne

    2010-04-20

    An important question in understanding language processing is whether there are distinct neural mechanisms for processing specific types of grammatical structure, such as syntax versus morphology, and, if so, what the basis of the specialization might be. However, this question is difficult to study: A given language typically conveys its grammatical information in one way (e.g., English marks "who did what to whom" using word order, and German uses inflectional morphology). American Sign Language permits either device, enabling a direct within-language comparison. During functional (f)MRI, native signers viewed sentences that used only word order and sentences that included inflectional morphology. The two sentence types activated an overlapping network of brain regions, but with differential patterns. Word order sentences activated left-lateralized areas involved in working memory and lexical access, including the dorsolateral prefrontal cortex, the inferior frontal gyrus, the inferior parietal lobe, and the middle temporal gyrus. In contrast, inflectional morphology sentences activated areas involved in building and analyzing combinatorial structure, including bilateral inferior frontal and anterior temporal regions as well as the basal ganglia and medial temporal/limbic areas. These findings suggest that for a given linguistic function, neural recruitment may depend upon on the cognitive resources required to process specific types of linguistic cues.

  11. A NEURAL NETWORK BASED IRIS RECOGNITION SYSTEM FOR PERSONAL IDENTIFICATION

    Directory of Open Access Journals (Sweden)

    Usham Dias

    2010-10-01

    Full Text Available This paper presents biometric personal identification based on iris recognition using artificial neural networks. Personal identification system consists of localization of the iris region, normalization, enhancement and then iris pattern recognition using neural network. In this paper, through results obtained, we have shown that a person’s left and right eye are unique. In this paper, we also show that the network is sensitive to the initial weights and that over-training gives bad results. We also propose a fast algorithm for the localization of the inner and outer boundaries of the iris region. Results of simulations illustrate the effectiveness of the neural system in personal identification. Finally a hardware iris recognition model is proposed and implementation aspects are discussed.

  12. Statistical modelling of neural networks in {gamma}-spectrometry applications

    Energy Technology Data Exchange (ETDEWEB)

    Vigneron, V.; Martinez, J.M. [CEA Centre d`Etudes de Saclay, 91 - Gif-sur-Yvette (France). Dept. de Mecanique et de Technologie; Morel, J.; Lepy, M.C. [CEA Centre d`Etudes de Saclay, 91 - Gif-sur-Yvette (France). Dept. des Applications et de la Metrologie des Rayonnements Ionisants

    1995-12-31

    Layered Neural Networks, which are a class of models based on neural computation, are applied to the measurement of uranium enrichment, i.e. the isotope ratio {sup 235} U/({sup 235} U + {sup 236} U + {sup 238} U). The usual method consider a limited number of {Gamma}-ray and X-ray peaks, and require previously calibrated instrumentation for each sample. But, in practice, the source-detector ensemble geometry conditions are critically different, thus a means of improving the above convention methods is to reduce the region of interest: this is possible by focusing on the K{sub {alpha}} X region where the three elementary components are present. Real data are used to study the performance of neural networks. Training is done with a Maximum Likelihood method to measure uranium {sup 235} U and {sup 238} U quantities in infinitely thick samples. (authors). 18 refs., 6 figs., 3 tabs.

  13. Optics in neural computation

    Science.gov (United States)

    Levene, Michael John

    In all attempts to emulate the considerable powers of the brain, one is struck by both its immense size, parallelism, and complexity. While the fields of neural networks, artificial intelligence, and neuromorphic engineering have all attempted oversimplifications on the considerable complexity, all three can benefit from the inherent scalability and parallelism of optics. This thesis looks at specific aspects of three modes in which optics, and particularly volume holography, can play a part in neural computation. First, holography serves as the basis of highly-parallel correlators, which are the foundation of optical neural networks. The huge input capability of optical neural networks make them most useful for image processing and image recognition and tracking. These tasks benefit from the shift invariance of optical correlators. In this thesis, I analyze the capacity of correlators, and then present several techniques for controlling the amount of shift invariance. Of particular interest is the Fresnel correlator, in which the hologram is displaced from the Fourier plane. In this case, the amount of shift invariance is limited not just by the thickness of the hologram, but by the distance of the hologram from the Fourier plane. Second, volume holography can provide the huge storage capacity and high speed, parallel read-out necessary to support large artificial intelligence systems. However, previous methods for storing data in volume holograms have relied on awkward beam-steering or on as-yet non- existent cheap, wide-bandwidth, tunable laser sources. This thesis presents a new technique, shift multiplexing, which is capable of very high densities, but which has the advantage of a very simple implementation. In shift multiplexing, the reference wave consists of a focused spot a few millimeters in front of the hologram. Multiplexing is achieved by simply translating the hologram a few tens of microns or less. This thesis describes the theory for how shift

  14. Neural basis of individual differences in the response to mental stress: a magnetoencephalography study.

    Science.gov (United States)

    Yamano, Emi; Ishii, Akira; Tanaka, Masaaki; Nomura, Shusaku; Watanabe, Yasuyoshi

    2016-12-01

    Stress is a risk factor for the onset of mental disorders. Although stress response varies across individuals, the mechanism of individual differences remains unclear. Here, we investigated the neural basis of individual differences in response to mental stress using magnetoencephalography (MEG). Twenty healthy male volunteers completed the Temperament and Character Inventory (TCI). The experiment included two types of tasks: a non-stress-inducing task and a stress-inducing task. During these tasks, participants passively viewed non-stress-inducing images and stress-inducing images, respectively, and MEG was recorded. Before and after each task, MEG and electrocardiography were recorded and subjective ratings were obtained. We grouped participants according to Novelty seeking (NS) - tendency to be exploratory, and Harm avoidance (HA) - tendency to be cautious. Participants with high NS and low HA (n = 10) assessed by TCI had a different neural response to stress than those with low NS and high HA (n = 10). Event-related desynchronization (ERD) in the beta frequency band was observed only in participants with high NS and low HA in the brain region extending from Brodmann's area 31 (including the posterior cingulate cortex and precuneus) from 200 to 350 ms after the onset of picture presentation in the stress-inducing task. Individual variation in personality traits (NS and HA) was associated with the neural response to mental stress. These findings increase our understanding of the psychological and neural basis of individual differences in the stress response, and will contribute to development of the psychotherapeutic approaches to stress-related disorders.

  15. Neural activity during emotion recognition after combined cognitive plus social cognitive training in schizophrenia.

    Science.gov (United States)

    Hooker, Christine I; Bruce, Lori; Fisher, Melissa; Verosky, Sara C; Miyakawa, Asako; Vinogradov, Sophia

    2012-08-01

    Cognitive remediation training has been shown to improve both cognitive and social cognitive deficits in people with schizophrenia, but the mechanisms that support this behavioral improvement are largely unknown. One hypothesis is that intensive behavioral training in cognition and/or social cognition restores the underlying neural mechanisms that support targeted skills. However, there is little research on the neural effects of cognitive remediation training. This study investigated whether a 50 h (10-week) remediation intervention which included both cognitive and social cognitive training would influence neural function in regions that support social cognition. Twenty-two stable, outpatient schizophrenia participants were randomized to a treatment condition consisting of auditory-based cognitive training (AT) [Brain Fitness Program/auditory module ~60 min/day] plus social cognition training (SCT) which was focused on emotion recognition [~5-15 min per day] or a placebo condition of non-specific computer games (CG) for an equal amount of time. Pre and post intervention assessments included an fMRI task of positive and negative facial emotion recognition, and standard behavioral assessments of cognition, emotion processing, and functional outcome. There were no significant intervention-related improvements in general cognition or functional outcome. fMRI results showed the predicted group-by-time interaction. Specifically, in comparison to CG, AT+SCT participants had a greater pre-to-post intervention increase in postcentral gyrus activity during emotion recognition of both positive and negative emotions. Furthermore, among all participants, the increase in postcentral gyrus activity predicted behavioral improvement on a standardized test of emotion processing (MSCEIT: Perceiving Emotions). Results indicate that combined cognition and social cognition training impacts neural mechanisms that support social cognition skills. Copyright © 2012 Elsevier B.V. All

  16. System and method for determining stability of a neural system

    Science.gov (United States)

    Curtis, Steven A. (Inventor)

    2011-01-01

    Disclosed are methods, systems, and computer-readable media for determining stability of a neural system. The method includes tracking a function world line of an N element neural system within at least one behavioral space, determining whether the tracking function world line is approaching a psychological stability surface, and implementing a quantitative solution that corrects instability if the tracked function world line is approaching the psychological stability surface.

  17. Optical production systems using neural networks and symbolic substitution

    Science.gov (United States)

    Botha, Elizabeth; Casasent, David; Barnard, Etienne

    1988-01-01

    Two optical implementations of production systems are advanced. The production systems operate on a knowledge base where facts and rules are encoded as formulas in propositional calculus. The first implementation is a binary neural network. An analog neural network is used to include reasoning with uncertainties. The second implementation uses a new optical symbolic substitution correlator. This implementation is useful when a set of similar situations has to be handled in parallel on one processor.

  18. Recurrent neural networks training with stable bounding ellipsoid algorithm.

    Science.gov (United States)

    Yu, Wen; de Jesús Rubio, José

    2009-06-01

    Bounding ellipsoid (BE) algorithms offer an attractive alternative to traditional training algorithms for neural networks, for example, backpropagation and least squares methods. The benefits include high computational efficiency and fast convergence speed. In this paper, we propose an ellipsoid propagation algorithm to train the weights of recurrent neural networks for nonlinear systems identification. Both hidden layers and output layers can be updated. The stability of the BE algorithm is proven.

  19. Classification-based Financial Markets Prediction using Deep Neural Networks

    OpenAIRE

    Dixon, Matthew; Klabjan, Diego; Bang, Jin Hoon

    2016-01-01

    Deep neural networks (DNNs) are powerful types of artificial neural networks (ANNs) that use several hidden layers. They have recently gained considerable attention in the speech transcription and image recognition community (Krizhevsky et al., 2012) for their superior predictive properties including robustness to overfitting. However their application to algorithmic trading has not been previously researched, partly because of their computational complexity. This paper describes the applicat...

  20. Neural correlates of fear: insights from neuroimaging

    Directory of Open Access Journals (Sweden)

    Garfinkel SN

    2014-12-01

    Full Text Available Sarah N Garfinkel,1,2 Hugo D Critchley1,2 1Sackler Centre for Consciousness Science, 2Department of Psychiatry, Brighton and Sussex Medical School, University of Sussex, Brighton, UK Abstract: Fear anticipates a challenge to one's well-being and is a reaction to the risk of harm. The expression of fear in the individual is a constellation of physiological, behavioral, cognitive, and experiential responses. Fear indicates risk and will guide adaptive behavior, yet fear is also fundamental to the symptomatology of most psychiatric disorders. Neuroimaging studies of normal and abnormal fear in humans extend knowledge gained from animal experiments. Neuroimaging permits the empirical evaluation of theory (emotions as response tendencies, mental states, and valence and arousal dimensions, and improves our understanding of the mechanisms of how fear is controlled by both cognitive processes and bodily states. Within the human brain, fear engages a set of regions that include insula and anterior cingulate cortices, the amygdala, and dorsal brain-stem centers, such as periaqueductal gray matter. This same fear matrix is also implicated in attentional orienting, mental planning, interoceptive mapping, bodily feelings, novelty and motivational learning, behavioral prioritization, and the control of autonomic arousal. The stereotyped expression of fear can thus be viewed as a special construction from combinations of these processes. An important motivator for understanding neural fear mechanisms is the debilitating clinical expression of anxiety. Neuroimaging studies of anxiety patients highlight the role of learning and memory in pathological fear. Posttraumatic stress disorder is further distinguished by impairment in cognitive control and contextual memory. These processes ultimately need to be targeted for symptomatic recovery. Neuroscientific knowledge of fear has broader relevance to understanding human and societal behavior. As yet, only some of

  1. The neural basis of human tool use

    Directory of Open Access Journals (Sweden)

    Guy A Orban

    2014-04-01

    Full Text Available In this review, we propose that the neural basis for the spontaneous, diversified human tool use is an area devoted to the execution and observation of tool actions, located in the left anterior supramarginal gyrus (aSMG. The aSMG activation elicited by observing tool use is typical of human subjects, as macaques show no similar activation, even after an extensive training to use tools. The execution of tool actions, as well as their observation, requires the convergence upon aSMG of inputs from different parts of the dorsal and ventral visual streams. Non semantic features of the target object may be provided by the posterior parietal cortex (PPC for tool-object interaction, paralleling the well-known PPC input to AIP for hand-object interaction. Semantic information regarding tool identity, and knowledge of the typical manner of handling the tool, could be provided by inferior and middle regions of the temporal lobe. Somatosensory feedback and technical reasoning, as well as motor and intentional constraints also play roles during the planning of tool actions and consequently their signals likewise converge upon aSMG.We further propose that aSMG may have arisen though duplication of monkey AIP and invasion of the duplicate area by afferents from PPC providing distinct signals depending on the kinematics of the manipulative action. This duplication may have occurred when Homo Habilis or Homo Erectus emerged, generating the Oldowan or Acheulean Industrial complexes respectively. Hence tool use may have emerged during hominid evolution between bipedalism and language.We conclude that humans have two parietal systems involved in tool behavior: a biological circuit for grasping objects, including tools, and an artifactual system devoted specifically to tool use. Only the latter allows humans to understand the causal relationship between tool use and obtaining the goal, and is likely to be the basis of all technological developments.

  2. A renaissance of neural networks in drug discovery.

    Science.gov (United States)

    Baskin, Igor I; Winkler, David; Tetko, Igor V

    2016-08-01

    Neural networks are becoming a very popular method for solving machine learning and artificial intelligence problems. The variety of neural network types and their application to drug discovery requires expert knowledge to choose the most appropriate approach. In this review, the authors discuss traditional and newly emerging neural network approaches to drug discovery. Their focus is on backpropagation neural networks and their variants, self-organizing maps and associated methods, and a relatively new technique, deep learning. The most important technical issues are discussed including overfitting and its prevention through regularization, ensemble and multitask modeling, model interpretation, and estimation of applicability domain. Different aspects of using neural networks in drug discovery are considered: building structure-activity models with respect to various targets; predicting drug selectivity, toxicity profiles, ADMET and physicochemical properties; characteristics of drug-delivery systems and virtual screening. Neural networks continue to grow in importance for drug discovery. Recent developments in deep learning suggests further improvements may be gained in the analysis of large chemical data sets. It's anticipated that neural networks will be more widely used in drug discovery in the future, and applied in non-traditional areas such as drug delivery systems, biologically compatible materials, and regenerative medicine.

  3. Neural reuse: a fundamental organizational principle of the brain.

    Science.gov (United States)

    Anderson, Michael L

    2010-08-01

    An emerging class of theories concerning the functional structure of the brain takes the reuse of neural circuitry for various cognitive purposes to be a central organizational principle. According to these theories, it is quite common for neural circuits established for one purpose to be exapted (exploited, recycled, redeployed) during evolution or normal development, and be put to different uses, often without losing their original functions. Neural reuse theories thus differ from the usual understanding of the role of neural plasticity (which is, after all, a kind of reuse) in brain organization along the following lines: According to neural reuse, circuits can continue to acquire new uses after an initial or original function is established; the acquisition of new uses need not involve unusual circumstances such as injury or loss of established function; and the acquisition of a new use need not involve (much) local change to circuit structure (e.g., it might involve only the establishment of functional connections to new neural partners). Thus, neural reuse theories offer a distinct perspective on several topics of general interest, such as: the evolution and development of the brain, including (for instance) the evolutionary-developmental pathway supporting primate tool use and human language; the degree of modularity in brain organization; the degree of localization of cognitive function; and the cortical parcellation problem and the prospects (and proper methods to employ) for function to structure mapping. The idea also has some practical implications in the areas of rehabilitative medicine and machine interface design.

  4. Flexible neural interfaces with integrated stiffening shank

    Energy Technology Data Exchange (ETDEWEB)

    Tooker, Angela C.; Felix, Sarah H.; Pannu, Satinderpall S.; Shah, Kedar G.; Sheth, Heeral; Tolosa, Vanessa

    2017-10-17

    A neural interface includes a first dielectric material having at least one first opening for a first electrical conducting material, a first electrical conducting material in the first opening, and at least one first interconnection trace electrical conducting material connected to the first electrical conducting material. A stiffening shank material is located adjacent the first dielectric material, the first electrical conducting material, and the first interconnection trace electrical conducting material.

  5. Deep Learning in Neural Networks: An Overview

    OpenAIRE

    Schmidhuber, Juergen

    2014-01-01

    In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarises relevant work, much of it from the previous millennium. Shallow and deep learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpr...

  6. Neural networks of human nature and nurture

    Directory of Open Access Journals (Sweden)

    Daniel S. Levine

    2009-11-01

    Full Text Available Neural network methods have facilitated the unification of several unfortunate splits in psychology, including nature versus nurture. We review the contributions of this methodology and then discuss tentative network theories of caring behavior, of uncaring behavior, and of how the frontal lobes are involved in the choices between them. The implications of our theory are optimistic about the prospects of society to encourage the human potential for caring.

  7. Neural network optimization, components, and design selection

    Science.gov (United States)

    Weller, Scott W.

    1990-07-01

    Neural Networks are part of a revived technology which has received a lot of hype in recent years. As is apt to happen in any hyped technology, jargon and predictions make its assimilation and application difficult. Nevertheless, Neural Networks have found use in a number of areas, working on non-trivial and noncontrived problems. For example, one net has been trained to "read", translating English text into phoneme sequences. Other applications of Neural Networks include data base manipulation and the solving of muting and classification types of optimization problems. Neural Networks are constructed from neurons, which in electronics or software attempt to model but are not constrained by the real thing, i.e., neurons in our gray matter. Neurons are simple processing units connected to many other neurons over pathways which modify the incoming signals. A single synthetic neuron typically sums its weighted inputs, runs this sum through a non-linear function, and produces an output. In the brain, neurons are connected in a complex topology: in hardware/software the topology is typically much simpler, with neurons lying side by side, forming layers of neurons which connect to the layer of neurons which receive their outputs. This simplistic model is much easier to construct than the real thing, and yet can solve real problems. The information in a network, or its "memory", is completely contained in the weights on the connections from one neuron to another. Establishing these weights is called "training" the network. Some networks are trained by design -- once constructed no further learning takes place. Other types of networks require iterative training once wired up, but are not trainable once taught Still other types of networks can continue to learn after initial construction. The main benefit to using Neural Networks is their ability to work with conflicting or incomplete ("fuzzy") data sets. This ability and its usefulness will become evident in the following

  8. Implementing Signature Neural Networks with Spiking Neurons.

    Science.gov (United States)

    Carrillo-Medina, José Luis; Latorre, Roberto

    2016-01-01

    Spiking Neural Networks constitute the most promising approach to develop realistic Artificial Neural Networks (ANNs). Unlike traditional firing rate-based paradigms, information coding in spiking models is based on the precise timing of individual spikes. It has been demonstrated that spiking ANNs can be successfully and efficiently applied to multiple realistic problems solvable with traditional strategies (e.g., data classification or pattern recognition). In recent years, major breakthroughs in neuroscience research have discovered new relevant computational principles in different living neural systems. Could ANNs benefit from some of these recent findings providing novel elements of inspiration? This is an intriguing question for the research community and the development of spiking ANNs including novel bio-inspired information coding and processing strategies is gaining attention. From this perspective, in this work, we adapt the core concepts of the recently proposed Signature Neural Network paradigm-i.e., neural signatures to identify each unit in the network, local information contextualization during the processing, and multicoding strategies for information propagation regarding the origin and the content of the data-to be employed in a spiking neural network. To the best of our knowledge, none of these mechanisms have been used yet in the context of ANNs of spiking neurons. This paper provides a proof-of-concept for their applicability in such networks. Computer simulations show that a simple network model like the discussed here exhibits complex self-organizing properties. The combination of multiple simultaneous encoding schemes allows the network to generate coexisting spatio-temporal patterns of activity encoding information in different spatio-temporal spaces. As a function of the network and/or intra-unit parameters shaping the corresponding encoding modality, different forms of competition among the evoked patterns can emerge even in the absence