WorldWideScience

Sample records for brain language network

  1. Big words, halved brains and small worlds: complex brain networks of figurative language comprehension.

    Science.gov (United States)

    Arzouan, Yossi; Solomon, Sorin; Faust, Miriam; Goldstein, Abraham

    2011-04-27

    Language comprehension is a complex task that involves a wide network of brain regions. We used topological measures to qualify and quantify the functional connectivity of the networks used under various comprehension conditions. To that aim we developed a technique to represent functional networks based on EEG recordings, taking advantage of their excellent time resolution in order to capture the fast processes that occur during language comprehension. Networks were created by searching for a specific causal relation between areas, the negative feedback loop, which is ubiquitous in many systems. This method is a simple way to construct directed graphs using event-related activity, which can then be analyzed topologically. Brain activity was recorded while subjects read expressions of various types and indicated whether they found them meaningful. Slightly different functional networks were obtained for event-related activity evoked by each expression type. The differences reflect the special contribution of specific regions in each condition and the balance of hemispheric activity involved in comprehending different types of expressions and are consistent with the literature in the field. Our results indicate that representing event-related brain activity as a network using a simple temporal relation, such as the negative feedback loop, to indicate directional connectivity is a viable option for investigation which also derives new information about aspects not reflected in the classical methods for investigating brain activity.

  2. Neurological impressions on the organization of language networks in the human brain.

    Science.gov (United States)

    Oliveira, Fabricio Ferreira de; Marin, Sheilla de Medeiros Correia; Bertolucci, Paulo Henrique Ferreira

    2017-01-01

    More than 95% of right-handed individuals, as well as almost 80% of left-handed individuals, have left hemisphere dominance for language. The perisylvian networks of the dominant hemisphere tend to be the most important language systems in human brains, usually connected by bidirectional fibres originated from the superior longitudinal fascicle/arcuate fascicle system and potentially modifiable by learning. Neuroplasticity mechanisms take place to preserve neural functions after brain injuries. Language is dependent on a hierarchical interlinkage of serial and parallel processing areas in distinct brain regions considered to be elementary processing units. Whereas aphasic syndromes typically result from injuries to the dominant hemisphere, the extent of the distribution of language functions seems to be variable for each individual. Review of the literature Results: Several theories try to explain the organization of language networks in the human brain from a point of view that involves either modular or distributed processing or sometimes both. The most important evidence for each approach is discussed under the light of modern theories of organization of neural networks. Understanding the connectivity patterns of language networks may provide deeper insights into language functions, supporting evidence-based rehabilitation strategies that focus on the enhancement of language organization for patients with aphasic syndromes.

  3. Not single brain areas but a network is involved in language: Applications in presurgical planning.

    Science.gov (United States)

    Alemi, Razieh; Batouli, Seyed Amir Hossein; Behzad, Ebrahim; Ebrahimpoor, Mitra; Oghabian, Mohammad Ali

    2018-02-01

    Language is an important human function, and is a determinant of the quality of life. In conditions such as brain lesions, disruption of the language function may occur, and lesion resection is a solution for that. Presurgical planning to determine the language-related brain areas would enhance the chances of language preservation after the operation; however, availability of a normative language template is essential. In this study, using data from 60 young individuals who were meticulously checked for mental and physical health, and using fMRI and robust imaging and data analysis methods, functional brain maps for the language production, perception and semantic were produced. The obtained templates showed that the language function should be considered as the product of the collaboration of a network of brain regions, instead of considering only few brain areas to be involved in that. This study has important clinical applications, and extends our knowledge on the neuroanatomy of the language function. Copyright © 2018 Elsevier B.V. All rights reserved.

  4. Large-scale brain networks underlying language acquisition in early infancy

    Directory of Open Access Journals (Sweden)

    Fumitaka eHomae

    2011-05-01

    Full Text Available A critical issue in human development is that of whether the language-related areas in the left frontal and temporal regions work as a functional network in preverbal infants. Here, we used 94-channel near-infrared spectroscopy (NIRS to reveal the functional networks in the brains of sleeping 3-month-old infants with and without presenting speech sounds. During the first 3 min, we measured spontaneous brain activation (period 1. After period 1, we provided stimuli by playing Japanese sentences for 3 min (period 2. Finally, we measured brain activation for 3 min without providing the stimulus (period 3, as in period 1. We found that not only the bilateral temporal and temporoparietal regions but also the prefrontal and occipital regions showed oxygenated hemoglobin (oxy-Hb signal increases and deoxygenated hemoglobin (deoxy-Hb signal decreases when speech sounds were presented to infants. By calculating time-lagged cross-correlations and coherences of oxy-Hb signals between channels, we tested the functional connectivity for the 3 periods. The oxy-Hb signals in neighboring channels, as well as their homologous channels in the contralateral hemisphere, showed high correlation coefficients in period 1. Similar correlations were observed in period 2; however, the number of channels showing high correlations was higher in the ipsilateral hemisphere, especially in the anterior-posterior direction. The functional connectivity in period 3 showed a close relationship between the frontal and temporal regions, which was less prominent in period 1, indicating that these regions form the functional networks and work as a hysteresis system that has memory of the previous inputs. We propose a hypothesis that the spatiotemporally large-scale brain networks, including the frontal and temporal regions, underlie speech processing in infants and they might play important roles in language acquisition during infancy.

  5. Stimulating the Brain's Language Network: Syntactic Ambiguity Resolution after TMS to the Inferior Frontal Gyrus and Middle Temporal Gyrus

    NARCIS (Netherlands)

    Acheson, D.J.; Hagoort, P.

    2013-01-01

    The posterior middle temporal gyrus (MTG) and inferior frontal gyrus (IFG) are two critical nodes of the brain's language network. Previous neuroimaging evidence has supported a dissociation in language comprehension in which parts of the MTG are involved in the retrieval of lexical syntactic

  6. Whole-brain functional connectivity during acquisition of novel grammar: Distinct functional networks depend on language learning abilities.

    Science.gov (United States)

    Kepinska, Olga; de Rover, Mischa; Caspers, Johanneke; Schiller, Niels O

    2017-03-01

    In an effort to advance the understanding of brain function and organisation accompanying second language learning, we investigate the neural substrates of novel grammar learning in a group of healthy adults, consisting of participants with high and average language analytical abilities (LAA). By means of an Independent Components Analysis, a data-driven approach to functional connectivity of the brain, the fMRI data collected during a grammar-learning task were decomposed into maps representing separate cognitive processes. These included the default mode, task-positive, working memory, visual, cerebellar and emotional networks. We further tested for differences within the components, representing individual differences between the High and Average LAA learners. We found high analytical abilities to be coupled with stronger contributions to the task-positive network from areas adjacent to bilateral Broca's region, stronger connectivity within the working memory network and within the emotional network. Average LAA participants displayed stronger engagement within the task-positive network from areas adjacent to the right-hemisphere homologue of Broca's region and typical to lower level processing (visual word recognition), and increased connectivity within the default mode network. The significance of each of the identified networks for the grammar learning process is presented next to a discussion on the established markers of inter-individual learners' differences. We conclude that in terms of functional connectivity, the engagement of brain's networks during grammar acquisition is coupled with one's language learning abilities. Copyright © 2016 Elsevier B.V. All rights reserved.

  7. Functional mapping of language networks in the normal brain using a word-association task

    International Nuclear Information System (INIS)

    Ghosh, Shantanu; Basu, Amrita; Kumaran, Senthil S; Khushu, Subash

    2010-01-01

    Language functions are known to be affected in diverse neurological conditions, including ischemic stroke, traumatic brain injury, and brain tumors. Because language networks are extensive, interpretation of functional data depends on the task completed during evaluation. The aim was to map the hemodynamic consequences of word association using functional magnetic resonance imaging (fMRI) in normal human subjects. Ten healthy subjects underwent fMRI scanning with a postlexical access semantic association task vs lexical processing task. The fMRI protocol involved a T2*-weighted gradient-echo echo-planar imaging (GE-EPI) sequence (TR 4523 ms, TE 64 ms, flip angle 90°) with alternate baseline and activation blocks. A total of 78 scans were taken (interscan interval = 3 s) with a total imaging time of 587 s. Functional data were processed in Statistical Parametric Mapping software (SPM2) with 8-mm Gaussian kernel by convolving the blood oxygenation level-dependent (BOLD) signal with an hemodynamic response function estimated by general linear method to generate SPM{t} and SPM{F} maps. Single subject analysis of the functional data (FWE-corrected, P≤0.001) revealed extensive activation in the frontal lobes, with overlaps among middle frontal gyrus (MFG), superior, and inferior frontal gyri. BOLD activity was also found in the medial frontal gyrus, middle occipital gyrus (MOG), anterior fusiform gyrus, superior and inferior parietal lobules, and to a smaller extent, the thalamus and right anterior cerebellum. Group analysis (FWE-corrected, P≤0.001) revealed neural recruitment of bilateral lingual gyri, left MFG, bilateral MOG, left superior occipital gyrus, left fusiform gyrus, bilateral thalami, and right cerebellar areas. Group data analysis revealed a cerebellar–occipital–fusiform–thalamic network centered around bilateral lingual gyri for word association, thereby indicating how these areas facilitate language comprehension by activating a semantic

  8. Functional mapping of language networks in the normal brain using a word-association task

    Directory of Open Access Journals (Sweden)

    Ghosh Shantanu

    2010-01-01

    Full Text Available Background: Language functions are known to be affected in diverse neurological conditions, including ischemic stroke, traumatic brain injury, and brain tumors. Because language networks are extensive, interpretation of functional data depends on the task completed during evaluation. Aim: The aim was to map the hemodynamic consequences of word association using functional magnetic resonance imaging (fMRI in normal human subjects. Materials and Methods: Ten healthy subjects underwent fMRI scanning with a postlexical access semantic association task vs lexical processing task. The fMRI protocol involved a T2FNx01-weighted gradient-echo echo-planar imaging (GE-EPI sequence (TR 4523 ms, TE 64 ms, flip angle 90º with alternate baseline and activation blocks. A total of 78 scans were taken (interscan interval = 3 s with a total imaging time of 587 s. Functional data were processed in Statistical Parametric Mapping software (SPM2 with 8-mm Gaussian kernel by convolving the blood oxygenation level-dependent (BOLD signal with an hemodynamic response function estimated by general linear method to generate SPM{t} and SPM{F} maps. Results: Single subject analysis of the functional data (FWE-corrected, P≤0.001 revealed extensive activation in the frontal lobes, with overlaps among middle frontal gyrus (MFG, superior, and inferior frontal gyri. BOLD activity was also found in the medial frontal gyrus, middle occipital gyrus (MOG, anterior fusiform gyrus, superior and inferior parietal lobules, and to a smaller extent, the thalamus and right anterior cerebellum. Group analysis (FWE-corrected, P≤0.001 revealed neural recruitment of bilateral lingual gyri, left MFG, bilateral MOG, left superior occipital gyrus, left fusiform gyrus, bilateral thalami, and right cerebellar areas. Conclusions: Group data analysis revealed a cerebellar-occipital-fusiform-thalamic network centered around bilateral lingual gyri for word association, thereby indicating how these

  9. Aberrant Topologies and Reconfiguration Pattern of Functional Brain Network in Children with Second Language Reading Impairment

    Science.gov (United States)

    Liu, Lanfang; Li, Hehui; Zhang, Manli; Wang, Zhengke; Wei, Na; Liu, Li; Meng, Xiangzhi; Ding, Guosheng

    2016-01-01

    Prior work has extensively studied neural deficits in children with reading impairment (RI) in their native language but has rarely examined those of RI children in their second language (L2). A recent study revealed that the function of the local brain regions was disrupted in children with RI in L2, but it is not clear whether the disruption…

  10. Language differences in the brain network for reading in naturalistic story reading and lexical decision.

    Directory of Open Access Journals (Sweden)

    Xiaojuan Wang

    Full Text Available Differences in how writing systems represent language raise important questions about whether there could be a universal functional architecture for reading across languages. In order to study potential language differences in the neural networks that support reading skill, we collected fMRI data from readers of alphabetic (English and morpho-syllabic (Chinese writing systems during two reading tasks. In one, participants read short stories under conditions that approximate natural reading, and in the other, participants decided whether individual stimuli were real words or not. Prior work comparing these two writing systems has overwhelmingly used meta-linguistic tasks, generally supporting the conclusion that the reading system is organized differently for skilled readers of Chinese and English. We observed that language differences in the reading network were greatly dependent on task. In lexical decision, a pattern consistent with prior research was observed in which the Middle Frontal Gyrus (MFG and right Fusiform Gyrus (rFFG were more active for Chinese than for English, whereas the posterior temporal sulcus was more active for English than for Chinese. We found a very different pattern of language effects in a naturalistic reading paradigm, during which significant differences were only observed in visual regions not typically considered specific to the reading network, and the middle temporal gyrus, which is thought to be important for direct mapping of orthography to semantics. Indeed, in areas that are often discussed as supporting distinct cognitive or linguistic functions between the two languages, we observed interaction. Specifically, language differences were most pronounced in MFG and rFFG during the lexical decision task, whereas no language differences were observed in these areas during silent reading of text for comprehension.

  11. Language differences in the brain network for reading in naturalistic story reading and lexical decision.

    Science.gov (United States)

    Wang, Xiaojuan; Yang, Jianfeng; Yang, Jie; Mencl, W Einar; Shu, Hua; Zevin, Jason David

    2015-01-01

    Differences in how writing systems represent language raise important questions about whether there could be a universal functional architecture for reading across languages. In order to study potential language differences in the neural networks that support reading skill, we collected fMRI data from readers of alphabetic (English) and morpho-syllabic (Chinese) writing systems during two reading tasks. In one, participants read short stories under conditions that approximate natural reading, and in the other, participants decided whether individual stimuli were real words or not. Prior work comparing these two writing systems has overwhelmingly used meta-linguistic tasks, generally supporting the conclusion that the reading system is organized differently for skilled readers of Chinese and English. We observed that language differences in the reading network were greatly dependent on task. In lexical decision, a pattern consistent with prior research was observed in which the Middle Frontal Gyrus (MFG) and right Fusiform Gyrus (rFFG) were more active for Chinese than for English, whereas the posterior temporal sulcus was more active for English than for Chinese. We found a very different pattern of language effects in a naturalistic reading paradigm, during which significant differences were only observed in visual regions not typically considered specific to the reading network, and the middle temporal gyrus, which is thought to be important for direct mapping of orthography to semantics. Indeed, in areas that are often discussed as supporting distinct cognitive or linguistic functions between the two languages, we observed interaction. Specifically, language differences were most pronounced in MFG and rFFG during the lexical decision task, whereas no language differences were observed in these areas during silent reading of text for comprehension.

  12. Interaction of language, auditory and memory brain networks in auditory verbal hallucinations

    NARCIS (Netherlands)

    Curcic-Blake, Branislava; Ford, Judith M.; Hubl, Daniela; Orlov, Natasza D.; Sommer, Iris E.; Waters, Flavie; Allen, Paul; Jardri, Renaud; Woodruff, Peter W.; David, Olivier; Mulert, Christoph; Woodward, Todd S.; Aleman, Andre

    Auditory verbal hallucinations (AVH) occur in psychotic disorders, but also as a symptom of other conditions and even in healthy people. Several current theories on the origin of AVH converge, with neuroimaging studies suggesting that the language, auditory and memory/limbic networks are of

  13. Interaction of language, auditory and memory brain networks in auditory verbal hallucinations.

    Science.gov (United States)

    Ćurčić-Blake, Branislava; Ford, Judith M; Hubl, Daniela; Orlov, Natasza D; Sommer, Iris E; Waters, Flavie; Allen, Paul; Jardri, Renaud; Woodruff, Peter W; David, Olivier; Mulert, Christoph; Woodward, Todd S; Aleman, André

    2017-01-01

    Auditory verbal hallucinations (AVH) occur in psychotic disorders, but also as a symptom of other conditions and even in healthy people. Several current theories on the origin of AVH converge, with neuroimaging studies suggesting that the language, auditory and memory/limbic networks are of particular relevance. However, reconciliation of these theories with experimental evidence is missing. We review 50 studies investigating functional (EEG and fMRI) and anatomic (diffusion tensor imaging) connectivity in these networks, and explore the evidence supporting abnormal connectivity in these networks associated with AVH. We distinguish between functional connectivity during an actual hallucination experience (symptom capture) and functional connectivity during either the resting state or a task comparing individuals who hallucinate with those who do not (symptom association studies). Symptom capture studies clearly reveal a pattern of increased coupling among the auditory, language and striatal regions. Anatomical and symptom association functional studies suggest that the interhemispheric connectivity between posterior auditory regions may depend on the phase of illness, with increases in non-psychotic individuals and first episode patients and decreases in chronic patients. Leading hypotheses involving concepts as unstable memories, source monitoring, top-down attention, and hybrid models of hallucinations are supported in part by the published connectivity data, although several caveats and inconsistencies remain. Specifically, possible changes in fronto-temporal connectivity are still under debate. Precise hypotheses concerning the directionality of connections deduced from current theoretical approaches should be tested using experimental approaches that allow for discrimination of competing hypotheses. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

  14. Presurgical brain mapping of the language network in patients with brain tumors using resting-state fMRI: Comparison with task fMRI.

    Science.gov (United States)

    Sair, Haris I; Yahyavi-Firouz-Abadi, Noushin; Calhoun, Vince D; Airan, Raag D; Agarwal, Shruti; Intrapiromkul, Jarunee; Choe, Ann S; Gujar, Sachin K; Caffo, Brian; Lindquist, Martin A; Pillai, Jay J

    2016-03-01

    To compare language networks derived from resting-state fMRI (rs-fMRI) with task-fMRI in patients with brain tumors and investigate variables that affect rs-fMRI vs task-fMRI concordance. Independent component analysis (ICA) of rs-fMRI was performed with 20, 30, 40, and 50 target components (ICA20 to ICA50) and language networks identified for patients presenting for presurgical fMRI mapping between 1/1/2009 and 7/1/2015. 49 patients were analyzed fulfilling criteria for presence of brain tumors, no prior brain surgery, and adequate task-fMRI performance. Rs-vs-task-fMRI concordance was measured using Dice coefficients across varying fMRI thresholds before and after noise removal. Multi-thresholded Dice coefficient volume under the surface (DiceVUS) and maximum Dice coefficient (MaxDice) were calculated. One-way Analysis of Variance (ANOVA) was performed to determine significance of DiceVUS and MaxDice between the four ICA order groups. Age, Sex, Handedness, Tumor Side, Tumor Size, WHO Grade, number of scrubbed volumes, image intensity root mean square (iRMS), and mean framewise displacement (FD) were used as predictors for VUS in a linear regression. Artificial elevation of rs-fMRI vs task-fMRI concordance is seen at low thresholds due to noise. Noise-removed group-mean DiceVUS and MaxDice improved as ICA order increased, however ANOVA demonstrated no statistically significant difference between the four groups. Linear regression demonstrated an association between iRMS and DiceVUS for ICA30-50, and iRMS and MaxDice for ICA50. Overall there is moderate group level rs-vs-task fMRI language network concordance, however substantial subject-level variability exists; iRMS may be used to determine reliability of rs-fMRI derived language networks. © 2015 Wiley Periodicals, Inc.

  15. How does language distance between L1 and L2 affect the L2 brain network? An fMRI study of Korean-Chinese-English trilinguals.

    Science.gov (United States)

    Kim, Say Young; Qi, Ting; Feng, Xiaoxia; Ding, Guosheng; Liu, Li; Cao, Fan

    2016-04-01

    The present study tested the hypothesis that language distance between first language (L1) and second language (L2) influences the assimilation and accommodation pattern in Korean-Chinese-English trilinguals. The distance between English and Korean is smaller than that between Chinese and Korean in terms of orthographic transparency, because both English and Korean are alphabetic, whereas Chinese is logographic. During fMRI, Korean trilingual participants performed a visual rhyming judgment task in three languages (Korean: KK, Chinese: KC, English: KE). Two L1 control groups were native Chinese and English speakers performing the task in their native languages (CC and EE, respectively). The general pattern of brain activation of KC was more similar to that of CC than KK, suggesting accommodation. Higher accuracy in KC was associated with decreased activation in regions of the KK network, suggesting reduced assimilation. In contrast, the brain activation of KE was more similar to that of KK than EE, suggesting assimilation. Higher accuracy in KE was associated with decreased activation in regions of the EE network, suggesting reduced accommodation. Finally, an ROI analysis on the left middle frontal gyrus revealed greater activation for KC than for KE, suggesting its selective involvement in the L2 with more arbitrary mapping between orthography and phonology (i.e., Chinese). Taken together, the brain network involved in L2 reading is similar to the L1 network when L2 and L1 are similar in orthographic transparency, while significant accommodation is expected when L2 is more opaque than L1. Copyright © 2015. Published by Elsevier Inc.

  16. Bilingual experience and resting-state brain connectivity: Impacts of L2 age of acquisition and social diversity of language use on control networks.

    Science.gov (United States)

    Gullifer, Jason W; Chai, Xiaoqian J; Whitford, Veronica; Pivneva, Irina; Baum, Shari; Klein, Denise; Titone, Debra

    2018-05-01

    We investigated the independent contributions of second language (L2) age of acquisition (AoA) and social diversity of language use on intrinsic brain organization using seed-based resting-state functional connectivity among highly proficient French-English bilinguals. There were two key findings. First, earlier L2 AoA related to greater interhemispheric functional connectivity between homologous frontal brain regions, and to decreased reliance on proactive executive control in an AX-Continuous Performance Task completed outside the scanner. Second, greater diversity in social language use in daily life related to greater connectivity between the anterior cingulate cortex and the putamen bilaterally, and to increased reliance on proactive control in the same task. These findings suggest that early vs. late L2 AoA links to a specialized neural framework for processing two languages that may engage a specific type of executive control (e.g., reactive control). In contrast, higher vs. lower degrees of diversity in social language use link to a broadly distributed set of brain networks implicated in proactive control and context monitoring. Copyright © 2018 Elsevier Ltd. All rights reserved.

  17. Brain Network Modelling

    DEFF Research Database (Denmark)

    Andersen, Kasper Winther

    Three main topics are presented in this thesis. The first and largest topic concerns network modelling of functional Magnetic Resonance Imaging (fMRI) and Diffusion Weighted Imaging (DWI). In particular nonparametric Bayesian methods are used to model brain networks derived from resting state f...... for their ability to reproduce node clustering and predict unseen data. Comparing the models on whole brain networks, BCD and IRM showed better reproducibility and predictability than IDM, suggesting that resting state networks exhibit community structure. This also points to the importance of using models, which...... allow for complex interactions between all pairs of clusters. In addition, it is demonstrated how the IRM can be used for segmenting brain structures into functionally coherent clusters. A new nonparametric Bayesian network model is presented. The model builds upon the IRM and can be used to infer...

  18. Mapping Language Problems in the Brain

    Science.gov (United States)

    ... issue Health Capsule Mapping Language Problems in the Brain En español Send us your comments We often ... more about how language is organized in the brain, an NIH-funded research team studied people with ...

  19. Multilayer Brain Networks

    Science.gov (United States)

    Vaiana, Michael; Muldoon, Sarah Feldt

    2018-01-01

    The field of neuroscience is facing an unprecedented expanse in the volume and diversity of available data. Traditionally, network models have provided key insights into the structure and function of the brain. With the advent of big data in neuroscience, both more sophisticated models capable of characterizing the increasing complexity of the data and novel methods of quantitative analysis are needed. Recently, multilayer networks, a mathematical extension of traditional networks, have gained increasing popularity in neuroscience due to their ability to capture the full information of multi-model, multi-scale, spatiotemporal data sets. Here, we review multilayer networks and their applications in neuroscience, showing how incorporating the multilayer framework into network neuroscience analysis has uncovered previously hidden features of brain networks. We specifically highlight the use of multilayer networks to model disease, structure-function relationships, network evolution, and link multi-scale data. Finally, we close with a discussion of promising new directions of multilayer network neuroscience research and propose a modified definition of multilayer networks designed to unite and clarify the use of the multilayer formalism in describing real-world systems.

  20. History of aphasia: From brain to language

    NARCIS (Netherlands)

    Eling, P.A.T.M.; Whitaker, H.A.; Finger, S.; Boller, F.; Tyler, K.L.

    2009-01-01

    An historical overview is presented that focuses on the changes both in approach and topics with respect to language disturbances due to brain lesions. Early cases of language disorders were described without any theorizing about language or its relation to the brain. Also, three forms of speech

  1. Reorganization of the Cerebro-Cerebellar Network of Language Production in Patients with Congenital Left-Hemispheric Brain Lesions

    Science.gov (United States)

    Lidzba, K.; Wilke, M.; Staudt, M.; Krageloh-Mann, I.; Grodd, W.

    2008-01-01

    Patients with congenital lesions of the left cerebral hemisphere may reorganize language functions into the right hemisphere. In these patients, language production is represented homotopically to the left-hemispheric language areas. We studied cerebellar activation in five patients with congenital lesions of the left cerebral hemisphere to assess…

  2. FROM BRAIN DRAIN TO BRAIN NETWORKING

    Directory of Open Access Journals (Sweden)

    Irina BONCEA

    2015-06-01

    Full Text Available Scientific networking is the most accessible way a country can turn the brain drain into brain gain. Diaspora’s members offer valuable information, advice or financial support from the destination country, without being necessary to return. This article aims to investigate Romania’s potential of turning brain drain into brain networking, using evidence from the medical sector. The main factors influencing the collaboration with the country of origin are investigated. The conclusions suggest that Romania could benefit from the diaspora option, through an active implication at institutional level and the implementation of a strategy in this area.

  3. Brain correlates of constituent structure in sign language comprehension.

    Science.gov (United States)

    Moreno, Antonio; Limousin, Fanny; Dehaene, Stanislas; Pallier, Christophe

    2018-02-15

    During sentence processing, areas of the left superior temporal sulcus, inferior frontal gyrus and left basal ganglia exhibit a systematic increase in brain activity as a function of constituent size, suggesting their involvement in the computation of syntactic and semantic structures. Here, we asked whether these areas play a universal role in language and therefore contribute to the processing of non-spoken sign language. Congenitally deaf adults who acquired French sign language as a first language and written French as a second language were scanned while watching sequences of signs in which the size of syntactic constituents was manipulated. An effect of constituent size was found in the basal ganglia, including the head of the caudate and the putamen. A smaller effect was also detected in temporal and frontal regions previously shown to be sensitive to constituent size in written language in hearing French subjects (Pallier et al., 2011). When the deaf participants read sentences versus word lists, the same network of language areas was observed. While reading and sign language processing yielded identical effects of linguistic structure in the basal ganglia, the effect of structure was stronger in all cortical language areas for written language relative to sign language. Furthermore, cortical activity was partially modulated by age of acquisition and reading proficiency. Our results stress the important role of the basal ganglia, within the language network, in the representation of the constituent structure of language, regardless of the input modality. Copyright © 2017 Elsevier Inc. All rights reserved.

  4. Language Networks as Complex Systems

    Science.gov (United States)

    Lee, Max Kueiming; Ou, Sheue-Jen

    2008-01-01

    Starting in the late eighties, with a growing discontent with analytical methods in science and the growing power of computers, researchers began to study complex systems such as living organisms, evolution of genes, biological systems, brain neural networks, epidemics, ecology, economy, social networks, etc. In the early nineties, the research…

  5. Language Choice & Global Learning Networks

    Directory of Open Access Journals (Sweden)

    Dennis Sayers

    1995-05-01

    Full Text Available How can other languages be used in conjunction with English to further intercultural and multilingual learning when teachers and students participate in computer-based global learning networks? Two portraits are presented of multilingual activities in the Orillas and I*EARN learning networks, and are discussed as examples of the principal modalities of communication employed in networking projects between distant classes. Next, an important historical precedent --the social controversy which accompanied the introduction of telephone technology at the end of the last century-- is examined in terms of its implications for language choice in contemporary classroom telecomputing projects. Finally, recommendations are offered to guide decision making concerning the role of language choice in promoting collaborative critical inquiry.

  6. Language or Music, Mother or Mozart? Structural and Environmental Influences on Infants' Language Networks

    Science.gov (United States)

    Dehaene-Lambertz, G.; Montavont, A.; Jobert, A.; Allirol, L.; Dubois, J.; Hertz-Pannier, L.; Dehaene, S.

    2010-01-01

    Understanding how language emerged in our species calls for a detailed investigation of the initial specialization of the human brain for speech processing. Our earlier research demonstrated that an adult-like left-lateralized network of perisylvian areas is already active when infants listen to sentences in their native language, but did not…

  7. Language and Tools for Networkers

    NARCIS (Netherlands)

    Wielinga, E.; Vrolijk, M.

    2009-01-01

    The network society has a major impact on knowledge systems, and in agricultural and rural development. It has changed relationships between actors such as farmers, extension workers, researchers, policy-makers, businessmen and consumers. These changes require different language, concepts and tools

  8. Brain readiness and the nature of language.

    Science.gov (United States)

    Bouchard, Denis

    2015-01-01

    To identify the neural components that make a brain ready for language, it is important to have well defined linguistic phenotypes, to know precisely what language is. There are two central features to language: the capacity to form signs (words), and the capacity to combine them into complex structures. We must determine how the human brain enables these capacities. A sign is a link between a perceptual form and a conceptual meaning. Acoustic elements and content elements, are already brain-internal in non-human animals, but as categorical systems linked with brain-external elements. Being indexically tied to objects of the world, they cannot freely link to form signs. A crucial property of a language-ready brain is the capacity to process perceptual forms and contents offline, detached from any brain-external phenomena, so their "representations" may be linked into signs. These brain systems appear to have pleiotropic effects on a variety of phenotypic traits and not to be specifically designed for language. Syntax combines signs, so the combination of two signs operates simultaneously on their meaning and form. The operation combining the meanings long antedates its function in language: the primitive mode of predication operative in representing some information about an object. The combination of the forms is enabled by the capacity of the brain to segment vocal and visual information into discrete elements. Discrete temporal units have order and juxtaposition, and vocal units have intonation, length, and stress. These are primitive combinatorial processes. So the prior properties of the physical and conceptual elements of the sign introduce combinatoriality into the linguistic system, and from these primitive combinatorial systems derive concatenation in phonology and combination in morphosyntax. Given the nature of language, a key feature to our understanding of the language-ready brain is to be found in the mechanisms in human brains that enable the unique

  9. Brain readiness and the nature of language

    Directory of Open Access Journals (Sweden)

    Denis eBouchard

    2015-09-01

    Full Text Available To identify the neural components that make a brain ready for language, it is important to have well defined linguistic phenotypes, to know precisely what language is. There are two central features to language: the capacity to form signs (words, and the capacity to combine them into complex structures. We must determine how the human brain enables these capacities.A sign is a link between a perceptual form and a conceptual meaning. Acoustic elements and content elements, are already brain-internal in non-human animals, but as categorical systems linked with brain-external elements. Being indexically tied to objects of the world, they cannot freely link to form signs. A crucial property of a language-ready brain is the capacity to process perceptual forms and contents offline, detached from any brain-external phenomena, so their representations may be linked into signs. These brain systems appear to have pleiotropic effects on a variety of phenotypic traits and not to be specifically designed for language.Syntax combines signs, so the combination of two signs operates simultaneously on their meaning and form. The operation combining the meanings long antedates its function in language: the primitive mode of predication operative in representing some information about an object. The combination of the forms is enabled by the capacity of the brain to segment vocal and visual information into discrete elements. Discrete temporal units have order and juxtaposition, and vocal units have intonation, length, and stress. These are primitive combinatorial processes. So the prior properties of the physical and conceptual elements of the sign introduce combinatoriality into the linguistic system, and from these primitive combinatorial systems derive concatenation in phonology and combination in morphosyntax.Given the nature of language, a key feature to our understanding of the language-ready brain is to be found in the mechanisms in human brains that

  10. Language Networks as Models of Cognition: Understanding Cognition through Language

    Science.gov (United States)

    Beckage, Nicole M.; Colunga, Eliana

    Language is inherently cognitive and distinctly human. Separating the object of language from the human mind that processes and creates language fails to capture the full language system. Linguistics traditionally has focused on the study of language as a static representation, removed from the human mind. Network analysis has traditionally been focused on the properties and structure that emerge from network representations. Both disciplines could gain from looking at language as a cognitive process. In contrast, psycholinguistic research has focused on the process of language without committing to a representation. However, by considering language networks as approximations of the cognitive system we can take the strength of each of these approaches to study human performance and cognition as related to language. This paper reviews research showcasing the contributions of network science to the study of language. Specifically, we focus on the interplay of cognition and language as captured by a network representation. To this end, we review different types of language network representations before considering the influence of global level network features. We continue by considering human performance in relation to network structure and conclude with theoretical network models that offer potential and testable explanations of cognitive and linguistic phenomena.

  11. The Language Faculty - mind or brain?

    DEFF Research Database (Denmark)

    Thrane, Torben

    2009-01-01

    I. Dretske. Apart from a brief introduction and a conclusion, the paper contains 5 main sections: Three levels of Chomskyan linguistics, Representational theories of mind, Representational systems, Representational architecture, and finally The language faculty in brain studies.......The paper subjects Chomsky's compound creation - the 'mind/brain' - to scrutiny. It argues that it creates a slipway for talk about the human language faculty,  such that what should properly be discussed in functional terms - what the brain does when processing language - is instead talked about...

  12. Brain anatomical network and intelligence.

    Directory of Open Access Journals (Sweden)

    Yonghui Li

    2009-05-01

    Full Text Available Intuitively, higher intelligence might be assumed to correspond to more efficient information transfer in the brain, but no direct evidence has been reported from the perspective of brain networks. In this study, we performed extensive analyses to test the hypothesis that individual differences in intelligence are associated with brain structural organization, and in particular that higher scores on intelligence tests are related to greater global efficiency of the brain anatomical network. We constructed binary and weighted brain anatomical networks in each of 79 healthy young adults utilizing diffusion tensor tractography and calculated topological properties of the networks using a graph theoretical method. Based on their IQ test scores, all subjects were divided into general and high intelligence groups and significantly higher global efficiencies were found in the networks of the latter group. Moreover, we showed significant correlations between IQ scores and network properties across all subjects while controlling for age and gender. Specifically, higher intelligence scores corresponded to a shorter characteristic path length and a higher global efficiency of the networks, indicating a more efficient parallel information transfer in the brain. The results were consistently observed not only in the binary but also in the weighted networks, which together provide convergent evidence for our hypothesis. Our findings suggest that the efficiency of brain structural organization may be an important biological basis for intelligence.

  13. Understanding emotion with brain networks.

    Science.gov (United States)

    Pessoa, Luiz

    2018-02-01

    Emotional processing appears to be interlocked with perception, cognition, motivation, and action. These interactions are supported by the brain's large-scale non-modular anatomical and functional architectures. An important component of this organization involves characterizing the brain in terms of networks. Two aspects of brain networks are discussed: brain networks should be considered as inherently overlapping (not disjoint) and dynamic (not static). Recent work on multivariate pattern analysis shows that affective dimensions can be detected in the activity of distributed neural systems that span cortical and subcortical regions. More broadly, the paper considers how we should think of causation in complex systems like the brain, so as to inform the relationship between emotion and other mental aspects, such as cognition.

  14. Evolution, brain, and the nature of language.

    Science.gov (United States)

    Berwick, Robert C; Friederici, Angela D; Chomsky, Noam; Bolhuis, Johan J

    2013-02-01

    Language serves as a cornerstone for human cognition, yet much about its evolution remains puzzling. Recent research on this question parallels Darwin's attempt to explain both the unity of all species and their diversity. What has emerged from this research is that the unified nature of human language arises from a shared, species-specific computational ability. This ability has identifiable correlates in the brain and has remained fixed since the origin of language approximately 100 thousand years ago. Although songbirds share with humans a vocal imitation learning ability, with a similar underlying neural organization, language is uniquely human. Copyright © 2012 Elsevier Ltd. All rights reserved.

  15. Shapley ratings in brain networks

    Directory of Open Access Journals (Sweden)

    Rolf Kötter

    2007-11-01

    Full Text Available Recent applications of network theory to brain networks as well as the expanding empirical databases of brain architecture spawn an interest in novel techniques for analyzing connectivity patterns in the brain. Treating individual brain structures as nodes in a directed graph model permits the application of graph theoretical concepts to the analysis of these structures within their large-scale connectivity networks. In this paper, we explore the application of concepts from graph and game theory toward this end. Specifically, we utilize the Shapley value principle, which assigns a rank to players in a coalition based upon their individual contributions to the collective profit of that coalition, to assess the contributions of individual brain structures to the graph derived from the global connectivity network. We report Shapley values for variations of a prefrontal network, as well as for a visual cortical network, which had both been extensively investigated previously. This analysis highlights particular nodes as strong or weak contributors to global connectivity. To understand the nature of their contribution, we compare the Shapley values obtained from these networks and appropriate controls to other previously described nodal measures of structural connectivity. We find a strong correlation between Shapley values and both betweenness centrality and connection density. Moreover, a stepwise multiple linear regression analysis indicates that approximately 79% of the variance in Shapley values obtained from random networks can be explained by betweenness centrality alone. Finally, we investigate the effects of local lesions on the Shapley ratings, showing that the present networks have an immense structural resistance to degradation. We discuss our results highlighting the use of such measures for characterizing the organization and functional role of brain networks.

  16. Attention shifts the language network reflecting paradigm presentation

    Directory of Open Access Journals (Sweden)

    Kathrin eKollndorfer

    2013-11-01

    Full Text Available Objectives: Functional magnetic resonance imaging (fMRI is a reliable and non-invasive method with which to localize language function in pre-surgical planning. In clinical practice, visual stimulus presentation is often difficult or impossible, due to the patient’s restricted language or attention abilities. Therefore, our aim was to investigate modality-specific differences in visual and auditory stimulus presentation.Methods: Ten healthy subjects participated in an fMRI study comprising two experiments with visual and auditory stimulus presentation. In both experiments, two language paradigms (one for language comprehension and one for language production used in clinical practice were investigated. In addition to standard data analysis by the means of the general linear model (GLM, independent component analysis (ICA was performed to achieve more detailed information on language processing networks.Results: GLM analysis revealed modality-specific brain activation for both language paradigms for the contrast visual > auditory in the area of the intraparietal sulcus and the hippocampus, two areas related to attention and working memory. Using group ICA, a language network was detected for both paradigms independent of stimulus presentation modality. The investigation of language lateralization revealed no significant variations. Visually presented stimuli further activated an attention-shift network, which could not be identified for the auditory presented language.Conclusion: The results of this study indicate that the visually presented language stimuli additionally activate an attention-shift network. These findings will provide important information for pre-surgical planning in order to preserve reading abilities after brain surgery, significantly improving surgical outcomes. Our findings suggest that the presentation modality for language paradigms should be adapted on behalf of individual indication.

  17. How Localized are Language Brain Areas? A Review of Brodmann Areas Involvement in Oral Language.

    Science.gov (United States)

    Ardila, Alfredo; Bernal, Byron; Rosselli, Monica

    2016-02-01

    The interest in understanding how language is "localized" in the brain has existed for centuries. Departing from seven meta-analytic studies of functional magnetic resonance imaging activity during the performance of different language activities, it is proposed here that there are two different language networks in the brain: first, a language reception/understanding system, including a "core Wernicke's area" involved in word recognition (BA21, BA22, BA41, and BA42), and a fringe or peripheral area ("extended Wernicke's area:" BA20, BA37, BA38, BA39, and BA40) involved in language associations (associating words with other information); second, a language production system ("Broca's complex:" BA44, BA45, and also BA46, BA47, partially BA6-mainly its mesial supplementary motor area-and extending toward the basal ganglia and the thalamus). This paper additionally proposes that the insula (BA13) plays a certain coordinating role in interconnecting these two brain language systems. © The Author 2015. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  18. Neuroanatomical prerequisites for language functions in the maturing brain.

    Science.gov (United States)

    Brauer, Jens; Anwander, Alfred; Friederici, Angela D

    2011-02-01

    The 2 major language-relevant cortical regions in the human brain, Broca's area and Wernicke's area, are connected via the fibers of the arcuate fasciculus/superior longitudinal fasciculus (AF/SLF). Here, we compared this pathway in adults and children and its relation to language processing during development. Comparison of fiber properties demonstrated lower anisotropy in children's AF/SLF, arguing for an immature status of this particular pathway with conceivably a lower degree of myelination. Combined diffusion tensor imaging (DTI) data and functional magnetic resonance imaging (fMRI) data indicated that in adults the termination of the AF/SLF fiber projection is compatible with functional activation in Broca's area, that is pars opercularis. In children, activation in Broca's area extended from the pars opercularis into the pars triangularis revealing an alternative connection to the temporal lobe (Wernicke's area) via the ventrally projecting extreme capsule fiber system. fMRI and DTI data converge to indicate that adults make use of a more confined language network than children based on ongoing maturation of the structural network. Our data suggest relations between language development and brain maturation and, moreover, indicate the brain's plasticity to adjust its function to available structural prerequisites.

  19. Scholastic performance and functional connectivity of brain networks in children.

    Directory of Open Access Journals (Sweden)

    Laura Chaddock-Heyman

    Full Text Available One of the keys to understanding scholastic success is to determine the neural processes involved in school performance. The present study is the first to use a whole-brain connectivity approach to explore whether functional connectivity of resting state brain networks is associated with scholastic performance in seventy-four 7- to 9-year-old children. We demonstrate that children with higher scholastic performance across reading, math and language have more integrated and interconnected resting state networks, specifically the default mode network, salience network, and frontoparietal network. To add specificity, core regions of the dorsal attention and visual networks did not relate to scholastic performance. The results extend the cognitive role of brain networks in children as well as suggest the importance of network connectivity in scholastic success.

  20. Early bilingualism, language attainment, and brain development.

    Science.gov (United States)

    Berken, Jonathan A; Gracco, Vincent L; Klein, Denise

    2017-04-01

    The brain demonstrates a remarkable capacity to undergo structural and functional change in response to experience throughout the lifespan. Evidence suggests that, in many domains of skill acquisition, the manifestation of this neuroplasticity depends on the age at which learning begins. The fact that most skills are acquired late in childhood or in adulthood has proven to be a limitation in studies aimed at determining the relationship between age of acquisition and brain plasticity. Bilingualism, however, provides an optimal model for discerning differences in how the brain wires when a skill is acquired from birth, when the brain circuitry for language is being constructed, versus later in life, when the pathways subserving the first language are already well developed. This review examines some of the existing knowledge about optimal periods in language development, with particular attention to the attainment of native-like phonology. It focuses on the differences in brain structure and function between simultaneous and sequential bilinguals and the compensatory mechanisms employed when bilingualism is achieved later in life, based on evidence from studies using a variety of neuroimaging modalities, including positron emission tomography (PET), task-based and resting-state functional magnetic resonance imaging (fMRI), and structural MRI. The discussion concludes with the presentation of recent neuroimaging studies that explore the concept of nested optimal periods in language development and the different neural paths to language proficiency taken by simultaneous and sequential bilinguals, with extrapolation to general notions of the relationship between age of acquisition and ultimate skill performance. Copyright © 2016 Elsevier Ltd. All rights reserved.

  1. Pediatric traumatic brain injury: Language outcomes and their relationship to the arcuate fasciculus

    OpenAIRE

    Liégeois, Frédérique J.; Mahony, Kate; Connelly, Alan; Pigdon, Lauren; Tournier, Jacques-Donald; Morgan, Angela T.

    2013-01-01

    Pediatric traumatic brain injury (TBI) may result in long-lasting language impairments alongside dysarthria, a motor-speech disorder. Whether this co-morbidity is due to the functional links between speech and language networks, or to widespread damage affecting both motor and language tracts, remains unknown. Here we investigated language function and diffusion metrics (using diffusion-weighted tractography) within the arcuate fasciculus, the uncinate fasciculus, and the corpus callosum in 3...

  2. Pediatric traumatic brain injury: Language outcomes and their relationship to the arcuate fasciculus

    OpenAIRE

    Liégeois, F. J.; Mahony, K.; Connelly, A.; Tournier, J-D; Pigdon, L.; Morgan, A. T.

    2013-01-01

    Pediatric traumatic brain injury (TBI) may result in long-lasting language impairments alongside dysarthria, a motor-speech disorder. Whether this co-morbidity is due to the functional links between speech and language networks, or to widespread damage affecting both motor and language tracts, remains unknown.Here we investigated language function and diffusion metrics (using diffusion-weighted tractography) within the arcuate fasciculus, the uncinate fasciculus, and the corpus callosum in 32...

  3. Altered functional connectivity of the language network in ASD: Role of classical language areas and cerebellum☆

    Science.gov (United States)

    Verly, Marjolein; Verhoeven, Judith; Zink, Inge; Mantini, Dante; Peeters, Ronald; Deprez, Sabine; Emsell, Louise; Boets, Bart; Noens, Ilse; Steyaert, Jean; Lagae, Lieven; De Cock, Paul; Rommel, Nathalie; Sunaert, Stefan

    2014-01-01

    The development of language, social interaction and communicative skills is remarkably different in the child with autism spectrum disorder (ASD). Atypical brain connectivity has frequently been reported in this patient population. However, the neural correlates underlying their disrupted language development and functioning are still poorly understood. Using resting state fMRI, we investigated the functional connectivity properties of the language network in a group of ASD patients with clear comorbid language impairment (ASD-LI; N = 19) and compared them to the language related connectivity properties of 23 age-matched typically developing children. A verb generation task was used to determine language components commonly active in both groups. Eight joint language components were identified and subsequently used as seeds in a resting state analysis. Interestingly, both the interregional and the seed-based whole brain connectivity analysis showed preserved connectivity between the classical intrahemispheric language centers, Wernicke's and Broca's areas. In contrast however, a marked loss of functional connectivity was found between the right cerebellar region and the supratentorial regulatory language areas. Also, the connectivity between the interhemispheric Broca regions and modulatory control dorsolateral prefrontal region was found to be decreased. This disruption of normal modulatory control and automation function by the cerebellum may underlie the abnormal language function in children with ASD-LI. PMID:24567909

  4. Altered functional connectivity of the language network in ASD: Role of classical language areas and cerebellum

    Directory of Open Access Journals (Sweden)

    Marjolein Verly

    2014-01-01

    Full Text Available The development of language, social interaction and communicative skills is remarkably different in the child with autism spectrum disorder (ASD. Atypical brain connectivity has frequently been reported in this patient population. However, the neural correlates underlying their disrupted language development and functioning are still poorly understood. Using resting state fMRI, we investigated the functional connectivity properties of the language network in a group of ASD patients with clear comorbid language impairment (ASD-LI; N = 19 and compared them to the language related connectivity properties of 23 age-matched typically developing children. A verb generation task was used to determine language components commonly active in both groups. Eight joint language components were identified and subsequently used as seeds in a resting state analysis. Interestingly, both the interregional and the seed-based whole brain connectivity analysis showed preserved connectivity between the classical intrahemispheric language centers, Wernicke's and Broca's areas. In contrast however, a marked loss of functional connectivity was found between the right cerebellar region and the supratentorial regulatory language areas. Also, the connectivity between the interhemispheric Broca regions and modulatory control dorsolateral prefrontal region was found to be decreased. This disruption of normal modulatory control and automation function by the cerebellum may underlie the abnormal language function in children with ASD-LI.

  5. Smart Cities and Languages: The Language Network

    Directory of Open Access Journals (Sweden)

    Andrea Gobbi

    2013-05-01

    Full Text Available This paper intends to analyze the potential of smart cities from a linguistic perspective, with particular attention towards aspects such as second language acquisition (SLA, social inclusion and innovation, but also positive influences on sectors such as tourism and commerce. After an introduction of the theoretical foundations, the possible developing scenarios will be taken into consideration and analyzed more in detail.

  6. Early Language Learning and the Social Brain.

    Science.gov (United States)

    Kuhl, Patricia K

    2014-01-01

    Explaining how every typically developing child acquires language is one of the grand challenges of cognitive neuroscience. Historically, language learning provoked classic debates about the contributions of innately specialized as opposed to general learning mechanisms. Now, new data are being brought to bear from studies that employ magnetoencephalograph (MEG), electroencephalograph (EEG), magnetic resonance imaging (MRI), and diffusion tensor imaging (DTI) studies on young children. These studies examine the patterns of association between brain and behavioral measures. The resulting data offer both expected results and surprises that are altering theory. As we uncover what it means to be human through the lens of young children, and their ability to speak, what we learn will not only inform theories of human development, but also lead to the discovery of neural biomarkers, early in life, that indicate risk for language impairment and allow early intervention for children with developmental disabilities involving language. Copyright © 2014 Cold Spring Harbor Laboratory Press; all rights reserved.

  7. Cerebro, lenguaje y comunicacion (Brain, Language, and Communication).

    Science.gov (United States)

    Strejilevich, Leonardo

    1978-01-01

    Discusses the relationship between the brain, language, and communication in the following sections: (1) combining words, (2) language as a system, (3) language as a function of the brain, (4) the science of communication, and (5) language as a social institution. (NCR)

  8. Approaching human language with complex networks

    Science.gov (United States)

    Cong, Jin; Liu, Haitao

    2014-12-01

    The interest in modeling and analyzing human language with complex networks is on the rise in recent years and a considerable body of research in this area has already been accumulated. We survey three major lines of linguistic research from the complex network approach: 1) characterization of human language as a multi-level system with complex network analysis; 2) linguistic typological research with the application of linguistic networks and their quantitative measures; and 3) relationships between the system-level complexity of human language (determined by the topology of linguistic networks) and microscopic linguistic (e.g., syntactic) features (as the traditional concern of linguistics). We show that the models and quantitative tools of complex networks, when exploited properly, can constitute an operational methodology for linguistic inquiry, which contributes to the understanding of human language and the development of linguistics. We conclude our review with suggestions for future linguistic research from the complex network approach: 1) relationships between the system-level complexity of human language and microscopic linguistic features; 2) expansion of research scope from the global properties to other levels of granularity of linguistic networks; and 3) combination of linguistic network analysis with other quantitative studies of language (such as quantitative linguistics).

  9. Intra-cranial recordings of brain activity during language production

    Directory of Open Access Journals (Sweden)

    Anais eLlorens

    2011-12-01

    Full Text Available Recent findings in the neurophysiology of language production have provided a detailed description of the brain network underlying this behavior, as well as some indications about the timing of operations. Despite their invaluable utility, these data generally suffer from limitations either in terms of temporal resolution, or in terms of spatial localization. In addition, studying the neural basis of speech is complicated by the presence of articulation artifacts such as electro-myographic activity that interferes with the neural signal. These difficulties are virtually absent in a powerful albeit much less frequent methodology, namely the recording of intra-cranial brain activity (iEEG. Such recordings are only possible under very specific clinical circumstances requiring functional mapping before brain surgery, most notably patients that suffer for pharmaco-resistant epilepsy. Here we review the research conducted with this methodology in the field of language production, with explicit consideration of its advantages and drawbacks. The available evidence is shown to be diverse, both in terms of the tasks and cognitive processes tested and in terms of the brain localizations being studied. Still, the review provides valuable information for characterizing the dynamics of the neural events occurring in the language production network. Following modality specific activities (in auditory or visual cortices, there is a convergence of activity in superior temporal sulcus, which is a plausible neural correlate of phonological encoding processes. Later, between 500 and 800 ms, inferior frontal gyrus (around Broca's area is involved. Peri-rolandic areas are recruited in the two modalities relatively early (200-500 ms window, suggesting a very early involvement of (pre- motor processes. We discuss how some of these findings may be at odds with conclusions drawn from available meta-analysis of language production.

  10. Origin of hyperbolicity in brain-to-brain coordination networks

    Science.gov (United States)

    Tadić, Bosiljka; Andjelković, Miroslav; Šuvakov, Milovan

    2018-02-01

    Hyperbolicity or negative curvature of complex networks is the intrinsic geometric proximity of nodes in the graph metric space, which implies an improved network function. Here, we investigate hidden combinatorial geometries in brain-to-brain coordination networks arising through social communications. The networks originate from correlations among EEG signals previously recorded during spoken communications comprising of 14 individuals with 24 speaker-listener pairs. We find that the corresponding networks are delta-hyperbolic with delta_max=1 and the graph diameter D=3 in each brain. While the emergent hyperbolicity in the two-brain networks satisfies delta_max/D/2 neuronal correlation patterns ranging from weak coordination to super-brain structure. These topology features are in qualitative agreement with the listener’s self-reported ratings of own experience and quality of the speaker, suggesting that studies of the cross-brain connector networks can reveal new insight into the neural mechanisms underlying human social behavior.

  11. Aging and functional brain networks

    International Nuclear Information System (INIS)

    Tomasi D.; Volkow, N.D.

    2012-01-01

    Aging is associated with changes in human brain anatomy and function and cognitive decline. Recent studies suggest the aging decline of major functional connectivity hubs in the 'default-mode' network (DMN). Aging effects on other networks, however, are largely unknown. We hypothesized that aging would be associated with a decline of short- and long-range functional connectivity density (FCD) hubs in the DMN. To test this hypothesis, we evaluated resting-state data sets corresponding to 913 healthy subjects from a public magnetic resonance imaging database using functional connectivity density mapping (FCDM), a voxelwise and data-driven approach, together with parallel computing. Aging was associated with pronounced long-range FCD decreases in DMN and dorsal attention network (DAN) and with increases in somatosensory and subcortical networks. Aging effects in these networks were stronger for long-range than for short-range FCD and were also detected at the level of the main functional hubs. Females had higher short- and long-range FCD in DMN and lower FCD in the somatosensory network than males, but the gender by age interaction effects were not significant for any of the networks or hubs. These findings suggest that long-range connections may be more vulnerable to aging effects than short-range connections and that, in addition to the DMN, the DAN is also sensitive to aging effects, which could underlie the deterioration of attention processes that occurs with aging.

  12. Language and the Brain. Cambridge Approaches to Linguistics.

    Science.gov (United States)

    Obler, Loraine K.; Gjerlow, Kris

    This book examines how the brain enables people to speak creatively and build up an understanding of language. The discussion looks at the linguistic and neuro-anatomical underpinnings of language and considers how language skills can systematically break down in individuals with different types of brain damage. By studying children with language…

  13. Brain networks of social comparison.

    Science.gov (United States)

    Kedia, Gayannée; Lindner, Michael; Mussweiler, Thomas; Ihssen, Niklas; Linden, David E J

    2013-03-27

    Social comparison, that is, the process of comparing oneself to other people, is a ubiquitous social cognitive mechanism; however, so far its neural correlates have remained unknown. The present study tested the hypothesis that social comparisons are supported by partly dissociated networks, depending on whether the dimension under comparison concerns a physical or a psychological attribute. We measured brain activity with functional MRI, whereas participants were comparing their own height or intelligence to that of individuals they personally know. Height comparisons were associated with higher activity in a frontoparietal network involved in spatial and numerical cognition. Conversely, intelligence comparisons recruited a network of midline areas that have been previously implicated in the attribution of mental states to oneself and others (Theory of mind). These findings suggest that social comparisons rely on diverse domain-specific mechanisms rather than on one unitary process.

  14. Social Networking Sites and Language Learning

    Science.gov (United States)

    Brick, Billy

    2011-01-01

    This article examines a study of seven learners who logged their experiences on the language leaning social networking site Livemocha over a period of three months. The features of the site are described and the likelihood of their future success is considered. The learners were introduced to the Social Networking Site (SNS) and asked to learn a…

  15. Hemispheric lateralization of topological organization in structural brain networks.

    Science.gov (United States)

    Caeyenberghs, Karen; Leemans, Alexander

    2014-09-01

    The study on structural brain asymmetries in healthy individuals plays an important role in our understanding of the factors that modulate cognitive specialization in the brain. Here, we used fiber tractography to reconstruct the left and right hemispheric networks of a large cohort of 346 healthy participants (20-86 years) and performed a graph theoretical analysis to investigate this brain laterality from a network perspective. Findings revealed that the left hemisphere is significantly more "efficient" than the right hemisphere, whereas the right hemisphere showed higher values of "betweenness centrality" and "small-worldness." In particular, left-hemispheric networks displayed increased nodal efficiency in brain regions related to language and motor actions, whereas the right hemisphere showed an increase in nodal efficiency in brain regions involved in memory and visuospatial attention. In addition, we found that hemispheric networks decrease in efficiency with age. Finally, we observed significant gender differences in measures of global connectivity. By analyzing the structural hemispheric brain networks, we have provided new insights into understanding the neuroanatomical basis of lateralized brain functions. Copyright © 2014 Wiley Periodicals, Inc.

  16. Lesion characteristics driving right-hemispheric language reorganization in congenital left-hemispheric brain damage.

    Science.gov (United States)

    Lidzba, Karen; de Haan, Bianca; Wilke, Marko; Krägeloh-Mann, Ingeborg; Staudt, Martin

    2017-10-01

    Pre- or perinatally acquired ("congenital") left-hemispheric brain lesions can be compensated for by reorganizing language into homotopic brain regions in the right hemisphere. Language comprehension may be hemispherically dissociated from language production. We investigated the lesion characteristics driving inter-hemispheric reorganization of language comprehension and language production in 19 patients (7-32years; eight females) with congenital left-hemispheric brain lesions (periventricular lesions [n=11] and middle cerebral artery infarctions [n=8]) by fMRI. 16/17 patients demonstrated reorganized language production, while 7/19 patients had reorganized language comprehension. Lesions to the insular cortex and the temporo-parietal junction (predominantly supramarginal gyrus) were significantly more common in patients in whom both, language production and comprehension were reorganized. These areas belong to the dorsal stream of the language network, participating in the auditory-motor integration of language. Our data suggest that the integrity of this stream might be crucial for a normal left-lateralized language development. Copyright © 2017. Published by Elsevier Inc.

  17. Brain anatomical networks in early human brain development.

    Science.gov (United States)

    Fan, Yong; Shi, Feng; Smith, Jeffrey Keith; Lin, Weili; Gilmore, John H; Shen, Dinggang

    2011-02-01

    Recent neuroimaging studies have demonstrated that human brain networks have economic small-world topology and modular organization, enabling efficient information transfer among brain regions. However, it remains largely unknown how the small-world topology and modular organization of human brain networks emerge and develop. Using longitudinal MRI data of 28 healthy pediatric subjects, collected at their ages of 1 month, 1 year, and 2 years, we analyzed development patterns of brain anatomical networks derived from morphological correlations of brain regional volumes. The results show that the brain network of 1-month-olds has the characteristically economic small-world topology and nonrandom modular organization. The network's cost efficiency increases with the brain development to 1 year and 2 years, so does the modularity, providing supportive evidence for the hypothesis that the small-world topology and the modular organization of brain networks are established during early brain development to support rapid synchronization and information transfer with minimal rewiring cost, as well as to balance between local processing and global integration of information. Copyright © 2010. Published by Elsevier Inc.

  18. Monitoring Different Phonological Parameters of Sign Language Engages the Same Cortical Language Network but Distinctive Perceptual Ones.

    Science.gov (United States)

    Cardin, Velia; Orfanidou, Eleni; Kästner, Lena; Rönnberg, Jerker; Woll, Bencie; Capek, Cheryl M; Rudner, Mary

    2016-01-01

    The study of signed languages allows the dissociation of sensorimotor and cognitive neural components of the language signal. Here we investigated the neurocognitive processes underlying the monitoring of two phonological parameters of sign languages: handshape and location. Our goal was to determine if brain regions processing sensorimotor characteristics of different phonological parameters of sign languages were also involved in phonological processing, with their activity being modulated by the linguistic content of manual actions. We conducted an fMRI experiment using manual actions varying in phonological structure and semantics: (1) signs of a familiar sign language (British Sign Language), (2) signs of an unfamiliar sign language (Swedish Sign Language), and (3) invented nonsigns that violate the phonological rules of British Sign Language and Swedish Sign Language or consist of nonoccurring combinations of phonological parameters. Three groups of participants were tested: deaf native signers, deaf nonsigners, and hearing nonsigners. Results show that the linguistic processing of different phonological parameters of sign language is independent of the sensorimotor characteristics of the language signal. Handshape and location were processed by different perceptual and task-related brain networks but recruited the same language areas. The semantic content of the stimuli did not influence this process, but phonological structure did, with nonsigns being associated with longer RTs and stronger activations in an action observation network in all participants and in the supramarginal gyrus exclusively in deaf signers. These results suggest higher processing demands for stimuli that contravene the phonological rules of a signed language, independently of previous knowledge of signed languages. We suggest that the phonological characteristics of a language may arise as a consequence of more efficient neural processing for its perception and production.

  19. Brain network clustering with information flow motifs

    NARCIS (Netherlands)

    Märtens, M.; Meier, J.M.; Hillebrand, Arjan; Tewarie, Prejaas; Van Mieghem, P.F.A.

    2017-01-01

    Recent work has revealed frequency-dependent global patterns of information flow by a network analysis of magnetoencephalography data of the human brain. However, it is unknown which properties on a small subgraph-scale of those functional brain networks are dominant at different frequencies bands.

  20. Study of functional brain imaging for bilingual language cognition

    International Nuclear Information System (INIS)

    Sun Da

    2008-01-01

    Bilingual and multilingual brain studies of language recognition is an interdisciplinary subject which needs to identify different levels involved in the neural representation of languages, such as neuroanatomical, neurofunctional, biochemical, psychological and linguistic levels. Furthermore, specific factor's such as age, manner of acquisition and environmental factors seem to affect the neural representation. Functional brain imaging, such as PET, SPECT and functional MRI can explore the neurolinguistics representation of bilingualism in the brain in subjects, and elucidate the neuronal mechanisms of bilingual language processing. Functional imaging methods show differences in the pattern of cerebral activation associated with a second language compared with the subject's native language. It shows that verbal memory processing in two unrelated languages is mediated by a common neural system with some distinct cortical areas. The different patterns of activation differ according to the language used. It also could be ascribed either to age of acquisition or to proficiency level. And attained proficiency is more important than age of acquisition as a determinant of the cortical representation of the second language. The study used PET and SPECT shows that sign and spoken language seem to be localized in the same brain areas, and elicit similar regional cerebral blood flow patterns. But for sign language perception, the functional anatomy overlaps that of language processing contain both auditory and visual components. And the sign language is dependent on spatial information too. (authors)

  1. Temporal reliability and lateralization of the resting-state language network.

    Science.gov (United States)

    Zhu, Linlin; Fan, Yang; Zou, Qihong; Wang, Jue; Gao, Jia-Hong; Niu, Zhendong

    2014-01-01

    The neural processing loop of language is complex but highly associated with Broca's and Wernicke's areas. The left dominance of these two areas was the earliest observation of brain asymmetry. It was demonstrated that the language network and its functional asymmetry during resting state were reproducible across institutions. However, the temporal reliability of resting-state language network and its functional asymmetry are still short of knowledge. In this study, we established a seed-based resting-state functional connectivity analysis of language network with seed regions located at Broca's and Wernicke's areas, and investigated temporal reliability of language network and its functional asymmetry. The language network was found to be temporally reliable in both short- and long-term. In the aspect of functional asymmetry, the Broca's area was found to be left lateralized, while the Wernicke's area is mainly right lateralized. Functional asymmetry of these two areas revealed high short- and long-term reliability as well. In addition, the impact of global signal regression (GSR) on reliability of the resting-state language network was investigated, and our results demonstrated that GSR had negligible effect on the temporal reliability of the resting-state language network. Our study provided methodology basis for future cross-culture and clinical researches of resting-state language network and suggested priority of adopting seed-based functional connectivity for its high reliability.

  2. Temporal reliability and lateralization of the resting-state language network.

    Directory of Open Access Journals (Sweden)

    Linlin Zhu

    Full Text Available The neural processing loop of language is complex but highly associated with Broca's and Wernicke's areas. The left dominance of these two areas was the earliest observation of brain asymmetry. It was demonstrated that the language network and its functional asymmetry during resting state were reproducible across institutions. However, the temporal reliability of resting-state language network and its functional asymmetry are still short of knowledge. In this study, we established a seed-based resting-state functional connectivity analysis of language network with seed regions located at Broca's and Wernicke's areas, and investigated temporal reliability of language network and its functional asymmetry. The language network was found to be temporally reliable in both short- and long-term. In the aspect of functional asymmetry, the Broca's area was found to be left lateralized, while the Wernicke's area is mainly right lateralized. Functional asymmetry of these two areas revealed high short- and long-term reliability as well. In addition, the impact of global signal regression (GSR on reliability of the resting-state language network was investigated, and our results demonstrated that GSR had negligible effect on the temporal reliability of the resting-state language network. Our study provided methodology basis for future cross-culture and clinical researches of resting-state language network and suggested priority of adopting seed-based functional connectivity for its high reliability.

  3. Temporal Reliability and Lateralization of the Resting-State Language Network

    Science.gov (United States)

    Zou, Qihong; Wang, Jue; Gao, Jia-Hong; Niu, Zhendong

    2014-01-01

    The neural processing loop of language is complex but highly associated with Broca's and Wernicke's areas. The left dominance of these two areas was the earliest observation of brain asymmetry. It was demonstrated that the language network and its functional asymmetry during resting state were reproducible across institutions. However, the temporal reliability of resting-state language network and its functional asymmetry are still short of knowledge. In this study, we established a seed-based resting-state functional connectivity analysis of language network with seed regions located at Broca's and Wernicke's areas, and investigated temporal reliability of language network and its functional asymmetry. The language network was found to be temporally reliable in both short- and long-term. In the aspect of functional asymmetry, the Broca's area was found to be left lateralized, while the Wernicke's area is mainly right lateralized. Functional asymmetry of these two areas revealed high short- and long-term reliability as well. In addition, the impact of global signal regression (GSR) on reliability of the resting-state language network was investigated, and our results demonstrated that GSR had negligible effect on the temporal reliability of the resting-state language network. Our study provided methodology basis for future cross-culture and clinical researches of resting-state language network and suggested priority of adopting seed-based functional connectivity for its high reliability. PMID:24475058

  4. A comparison of brain activity associated with language production in brain tumor patients with left and right sided language laterality

    NARCIS (Netherlands)

    Jansma, J. M.; Ramsey, N.; Rutten, G.J.M.

    2015-01-01

    Aim. Language dominance is an important factor for clinical decision making in brain tumor surgery. Functional MM can provide detailed information about the organization of language in the brain. One often used measure derived from fMRI data is the laterality index (LI). The LI is typically based on

  5. The social brain network and autism.

    Science.gov (United States)

    Misra, Vivek

    2014-04-01

    Available research data in Autism suggests the role of a network of brain areas, often known as the 'social brain'. Recent studies highlight the role of genetic mutations as underlying patho-mechanism in Autism. This mini review, discusses the basic concepts behind social brain networks, theory of mind and genetic factors associated with Autism. It critically evaluates and explores the relationship between the behavioral outcomes and genetic factors providing a conceptual framework for understanding of autism.

  6. Language and Brain Volumes in Children with Epilepsy

    Science.gov (United States)

    Caplan, Rochelle; Levitt, Jennifer; Siddarth, Prabha; Wu, Keng Nei; Gurbani, Suresh; Shields, W. Donald; Sankar, Raman

    2010-01-01

    This study compared the relationship of language skill with fronto-temporal volumes in 69 medically treated epilepsy subjects and 34 healthy children, aged 6.1-16.6 years. It also determined if the patients with linguistic deficits had abnormal volumes and atypical associations between volumes and language skills in these brain regions. The children underwent language testing and magnetic resonance imaging scans at 1.5 Tesla. Brain tissue was segmented and fronto-temporal volumes were computed. Higher mean language scores were significantly associated with larger inferior frontal gyrus, temporal lobe, and posterior superior temporal gyrus gray matter volumes in the epilepsy group and in the children with epilepsy with average language scores. Increased total brain and dorsolateral prefrontal gray and white matter volumes, however, were associated with higher language scores in the healthy controls. Within the epilepsy group, linguistic deficits were related to smaller anterior superior temporal gyrus gray matter volumes and a negative association between language scores and dorsolateral prefrontal gray matter volumes. These findings demonstrate abnormal development of language related brain regions, and imply differential reorganization of brain regions subserving language in children with epilepsy with normal linguistic skills and in those with impaired language. PMID:20149755

  7. Executive and Language Control in the Multilingual Brain

    Directory of Open Access Journals (Sweden)

    Anthony Pak-Hin Kong

    2014-01-01

    Full Text Available Neuroimaging studies suggest that the neural network involved in language control may not be specific to bi-/multilingualism but is part of a domain-general executive control system. We report a trilingual case of a Cantonese (L1, English (L2, and Mandarin (L3 speaker, Dr. T, who sustained a brain injury at the age of 77 causing lesions in the left frontal lobe and in the left temporo-parietal areas resulting in fluent aphasia. Dr. T’s executive functions were impaired according to a modified version of the Stroop color-word test and the Wisconsin Card Sorting Test performance was characterized by frequent perseveration errors. Dr. T demonstrated pathological language switching and mixing across her three languages. Code switching in Cantonese was more prominent in discourse production than confrontation naming. Our case suggests that voluntary control of spoken word production in trilingual speakers shares neural substrata in the frontobasal ganglia system with domain-general executive control mechanisms. One prediction is that lesions to such a system would give rise to both pathological switching and impairments of executive functions in trilingual speakers.

  8. Executive and language control in the multilingual brain.

    Science.gov (United States)

    Kong, Anthony Pak-Hin; Abutalebi, Jubin; Lam, Karen Sze-Yan; Weekes, Brendan

    2014-01-01

    Neuroimaging studies suggest that the neural network involved in language control may not be specific to bi-/multilingualism but is part of a domain-general executive control system. We report a trilingual case of a Cantonese (L1), English (L2), and Mandarin (L3) speaker, Dr. T, who sustained a brain injury at the age of 77 causing lesions in the left frontal lobe and in the left temporo-parietal areas resulting in fluent aphasia. Dr. T's executive functions were impaired according to a modified version of the Stroop color-word test and the Wisconsin Card Sorting Test performance was characterized by frequent perseveration errors. Dr. T demonstrated pathological language switching and mixing across her three languages. Code switching in Cantonese was more prominent in discourse production than confrontation naming. Our case suggests that voluntary control of spoken word production in trilingual speakers shares neural substrata in the frontobasal ganglia system with domain-general executive control mechanisms. One prediction is that lesions to such a system would give rise to both pathological switching and impairments of executive functions in trilingual speakers.

  9. Defining nodes in complex brain networks

    Directory of Open Access Journals (Sweden)

    Matthew Lawrence Stanley

    2013-11-01

    Full Text Available Network science holds great promise for expanding our understanding of the human brain in health, disease, development, and aging. Network analyses are quickly becoming the method of choice for analyzing functional MRI data. However, many technical issues have yet to be confronted in order to optimize results. One particular issue that remains controversial in functional brain network analyses is the definition of a network node. In functional brain networks a node represents some predefined collection of brain tissue, and an edge measures the functional connectivity between pairs of nodes. The characteristics of a node, chosen by the researcher, vary considerably in the literature. This manuscript reviews the current state of the art based on published manuscripts and highlights the strengths and weaknesses of three main methods for defining nodes. Voxel-wise networks are constructed by assigning a node to each, equally sized brain area (voxel. The fMRI time-series recorded from each voxel is then used to create the functional network. Anatomical methods utilize atlases to define the nodes based on brain structure. The fMRI time-series from all voxels within the anatomical area are averaged and subsequently used to generate the network. Functional activation methods rely on data from traditional fMRI activation studies, often from databases, to identify network nodes. Such methods identify the peaks or centers of mass from activation maps to determine the location of the nodes. Small (~10-20 millimeter diameter spheres located at the coordinates of the activation foci are then applied to the data being used in the network analysis. The fMRI time-series from all voxels in the sphere are then averaged, and the resultant time series is used to generate the network. We attempt to clarify the discussion and move the study of complex brain networks forward. While the correct method to be used remains an open, possibly unsolvable question that

  10. Pain: a distributed brain information network?

    Directory of Open Access Journals (Sweden)

    Hiroaki Mano

    2015-01-01

    Full Text Available Understanding how pain is processed in the brain has been an enduring puzzle, because there doesn't appear to be a single "pain cortex" that directly codes the subjective perception of pain. An emerging concept is that, instead, pain might emerge from the coordinated activity of an integrated brain network. In support of this view, Woo and colleagues present evidence that distinct brain networks support the subjective changes in pain that result from nociceptive input and self-directed cognitive modulation. This evidence for the sensitivity of distinct neural subsystems to different aspects of pain opens up the way to more formal computational network theories of pain.

  11. Language Views on Social Networking Sites for Language Learning: The Case of Busuu

    Science.gov (United States)

    Álvarez Valencia, José Aldemar

    2016-01-01

    Social networking has compelled the area of computer-assisted language learning (CALL) to expand its research palette and account for new virtual ecologies that afford language learning and socialization. This study focuses on Busuu, a social networking site for language learning (SNSLL), and analyzes the views of language that are enacted through…

  12. Interaction between lexical and grammatical language systems in the brain

    Science.gov (United States)

    Ardila, Alfredo

    2012-06-01

    This review concentrates on two different language dimensions: lexical/semantic and grammatical. This distinction between a lexical/semantic system and a grammatical system is well known in linguistics, but in cognitive neurosciences it has been obscured by the assumption that there are several forms of language disturbances associated with focal brain damage and hence language includes a diversity of functions (phoneme discrimination, lexical memory, grammar, repetition, language initiation ability, etc.), each one associated with the activity of a specific brain area. The clinical observation of patients with cerebral pathology shows that there are indeed only two different forms of language disturbances (disturbances in the lexical/semantic system and disturbances in the grammatical system); these two language dimensions are supported by different brain areas (temporal and frontal) in the left hemisphere. Furthermore, these two aspects of the language are developed at different ages during child's language acquisition, and they probably appeared at different historical moments during human evolution. Mechanisms of learning are different for both language systems: whereas the lexical/semantic knowledge is based in a declarative memory, grammatical knowledge corresponds to a procedural type of memory. Recognizing these two language dimensions can be crucial in understanding language evolution and human cognition.

  13. Predicting language diversity with complex networks

    Science.gov (United States)

    Gubiec, Tomasz

    2018-01-01

    We analyze the model of social interactions with coevolution of the topology and states of the nodes. This model can be interpreted as a model of language change. We propose different rewiring mechanisms and perform numerical simulations for each. Obtained results are compared with the empirical data gathered from two online databases and anthropological study of Solomon Islands. We study the behavior of the number of languages for different system sizes and we find that only local rewiring, i.e. triadic closure, is capable of reproducing results for the empirical data in a qualitative manner. Furthermore, we cancel the contradiction between previous models and the Solomon Islands case. Our results demonstrate the importance of the topology of the network, and the rewiring mechanism in the process of language change. PMID:29702699

  14. Report on Networking and Programming Languages 2017

    KAUST Repository

    Bjorner, Nikolaj

    2017-10-26

    The third workshop on Networking and Programming Lan-guages, NetPL 2017, was held in conjunction with SIG-COMM 2017. The workshop series attracts invited speakers from academia and industry and a selection of contributed abstracts for short presentations. NetPL brings together re-searchers from the networking community and researchers from the programming languages and verification communities. The workshop series is a timely forum for exciting trends, technological and scientific advances in the intersection of these communities. We describe some of the high-lights from the invited talks through the lens of three trends: Advances in network machine architectures, network programming abstractions, and network verification. NetPL included five invited speakers, four from academia, and one from industry. The program contained six contributed talks out of eight submitted for presentation. The workshop organizers reviewed the abstracts for quality and scope. A total of 42 registrations were received and the attendance occupied the lecture room to the brink. Slides and abstracts from all talks are available from the workshop home page.1 Videos of the presentations are available in the NetPL YouTube channel.2.

  15. Report on Networking and Programming Languages 2017

    KAUST Repository

    Bjorner, Nikolaj; Canini, Marco; Sultana, Nik

    2017-01-01

    The third workshop on Networking and Programming Lan-guages, NetPL 2017, was held in conjunction with SIG-COMM 2017. The workshop series attracts invited speakers from academia and industry and a selection of contributed abstracts for short presentations. NetPL brings together re-searchers from the networking community and researchers from the programming languages and verification communities. The workshop series is a timely forum for exciting trends, technological and scientific advances in the intersection of these communities. We describe some of the high-lights from the invited talks through the lens of three trends: Advances in network machine architectures, network programming abstractions, and network verification. NetPL included five invited speakers, four from academia, and one from industry. The program contained six contributed talks out of eight submitted for presentation. The workshop organizers reviewed the abstracts for quality and scope. A total of 42 registrations were received and the attendance occupied the lecture room to the brink. Slides and abstracts from all talks are available from the workshop home page.1 Videos of the presentations are available in the NetPL YouTube channel.2.

  16. Nicotine increases brain functional network efficiency.

    Science.gov (United States)

    Wylie, Korey P; Rojas, Donald C; Tanabe, Jody; Martin, Laura F; Tregellas, Jason R

    2012-10-15

    Despite the use of cholinergic therapies in Alzheimer's disease and the development of cholinergic strategies for schizophrenia, relatively little is known about how the system modulates the connectivity and structure of large-scale brain networks. To better understand how nicotinic cholinergic systems alter these networks, this study examined the effects of nicotine on measures of whole-brain network communication efficiency. Resting state fMRI was acquired from fifteen healthy subjects before and after the application of nicotine or placebo transdermal patches in a single blind, crossover design. Data, which were previously examined for default network activity, were analyzed with network topology techniques to measure changes in the communication efficiency of whole-brain networks. Nicotine significantly increased local efficiency, a parameter that estimates the network's tolerance to local errors in communication. Nicotine also significantly enhanced the regional efficiency of limbic and paralimbic areas of the brain, areas which are especially altered in diseases such as Alzheimer's disease and schizophrenia. These changes in network topology may be one mechanism by which cholinergic therapies improve brain function. Published by Elsevier Inc.

  17. Language networks associated with computerized semantic indices.

    Science.gov (United States)

    Pakhomov, Serguei V S; Jones, David T; Knopman, David S

    2015-01-01

    Tests of generative semantic verbal fluency are widely used to study organization and representation of concepts in the human brain. Previous studies demonstrated that clustering and switching behavior during verbal fluency tasks is supported by multiple brain mechanisms associated with semantic memory and executive control. Previous work relied on manual assessments of semantic relatedness between words and grouping of words into semantic clusters. We investigated a computational linguistic approach to measuring the strength of semantic relatedness between words based on latent semantic analysis of word co-occurrences in a subset of a large online encyclopedia. We computed semantic clustering indices and compared them to brain network connectivity measures obtained with task-free fMRI in a sample consisting of healthy participants and those differentially affected by cognitive impairment. We found that semantic clustering indices were associated with brain network connectivity in distinct areas including fronto-temporal, fronto-parietal and fusiform gyrus regions. This study shows that computerized semantic indices complement traditional assessments of verbal fluency to provide a more complete account of the relationship between brain and verbal behavior involved organization and retrieval of lexical information from memory. Copyright © 2014 Elsevier Inc. All rights reserved.

  18. Identifying modular relations in complex brain networks

    DEFF Research Database (Denmark)

    Andersen, Kasper Winther; Mørup, Morten; Siebner, Hartwig

    2012-01-01

    We evaluate the infinite relational model (IRM) against two simpler alternative nonparametric Bayesian models for identifying structures in multi subject brain networks. The models are evaluated for their ability to predict new data and infer reproducible structures. Prediction and reproducibility...... and obtains comparable reproducibility and predictability. For resting state functional magnetic resonance imaging data from 30 healthy controls the IRM model is also superior to the two simpler alternatives, suggesting that brain networks indeed exhibit universal complex relational structure...

  19. Exploring Mechanisms Underlying Impaired Brain Function in Gulf War Illness through Advanced Network Analysis

    Science.gov (United States)

    2017-10-01

    networks of the brain responsible for visual processing, mood regulation, motor coordination, sensory processing, and language command, but increased...4    For each subject, the rsFMRI voxel time-series were temporally shifted to account for differences in slice acquisition times...responsible for visual processing, mood regulation, motor coordination, sensory processing, and language command, but increased connectivity in

  20. Broca's area network in language function.Broca's area network in language function: A pooling-data connectivity study

    Directory of Open Access Journals (Sweden)

    Byron eBernal

    2015-05-01

    Full Text Available Background and Objective. Modern neuroimaging developments have demonstrated that cognitive functions correlate with brain networks rather than specific areas. The purpose of this paper was to analyze the connectivity of Broca's area based on language tasks. Methods. A connectivity modeling study was performed by pooling data of Broca's activation in language tasks. Fifty-seven papers that included 883 subjects in 84 experiments were analyzed. Analysis of Likelihood Estimates of pooled data was utilized to generate the map; thresholds at p < 0.01 were corrected for multiple comparisons and false discovery rate. Resulting images were co-registered into MNI standard space. Results. A network consisting of 16 clusters of activation was obtained. Main clusters were located in the frontal operculum, left posterior temporal region, supplementary motor area, and the parietal lobe. Less common clusters were seen in the sub-cortical structures including the left thalamus, left putamen, secondary visual areas and the right cerebellum. Conclusions. BA44-related networks involved in language processing were demonstrated utilizing a pooling-data connectivity study. Significance, interpretation and limitations of the results are discussed.

  1. The development of brain network architecture

    NARCIS (Netherlands)

    Wierenga, Lara M.; van den Heuvel, Martijn P.; van Dijk, Sarai; Rijks, Yvonne; de Reus, Marcel A.; Durston, Sarah

    2016-01-01

    Brain connectivity shows protracted development throughout childhood and adolescence, and, as such, the topology of brain networks changes during this period. The complexity of these changes with development is reflected by regional differences in maturation. This study explored age-related changes

  2. Body language in the brain: constructing meaning from expressive movement

    Directory of Open Access Journals (Sweden)

    Christine Marie Tipper

    2015-08-01

    Full Text Available This fMRI study investigated neural systems that interpret body language - the meaningful emotive expressions conveyed by body movement. Participants watched videos of performers engaged in modern dance or pantomime that conveyed specific themes such as hope, agony, lust, or exhaustion. We tested whether the meaning of an affectively laden performance was decoded in localized brain substrates as a distinct property of action separable from other superficial features, such as choreography, kinematics, performer, and low-level visual stimuli. A repetition suppression (RS procedure was used to identify brain regions that decoded the meaningful affective state of a performer, as evidenced by decreased activity when emotive themes were repeated in successive performances. Because the theme was the only feature repeated across video clips that were otherwise entirely different, the occurrence of RS identified brain substrates that differentially coded the specific meaning of expressive performances. RS was observed bilaterally, extending anteriorly along middle and superior temporal gyri into temporal pole, medially into insula, rostrally into inferior orbitofrontal cortex, and caudally into hippocampus and amygdala. Behavioral data on a separate task indicated that interpreting themes from modern dance was more difficult than interpreting pantomime; a result that was also reflected in the fMRI data. There was greater RS in left hemisphere, suggesting that the more abstract metaphors used to express themes in dance compared to pantomime posed a greater challenge to brain substrates directly involved in decoding those themes. We propose that the meaning-sensitive temporal-orbitofrontal regions observed here comprise a superordinate functional module of a known hierarchical action observation network, which is critical to the construction of meaning from expressive movement. The findings are discussed with respect to a predictive coding model of action

  3. Topological isomorphisms of human brain and financial market networks

    Directory of Open Access Journals (Sweden)

    Petra E Vértes

    2011-09-01

    Full Text Available Although metaphorical and conceptual connections between the human brain and the financial markets have often been drawn, rigorous physical or mathematical underpinnings of this analogy remain largely unexplored. Here, we apply a statistical and graph theoretic approach to the study of two datasets - the timeseries of 90 stocks from the New York Stock Exchange over a three-year period, and the fMRI-derived timeseries acquired from 90 brain regions over the course of a 10 min-long functional MRI scan of resting brain function in healthy volunteers. Despite the many obvious substantive differences between these two datasets, graphical analysis demonstrated striking commonalities in terms of global network topological properties. Both the human brain and the market networks were non-random, small-world, modular, hierarchical systems with fat-tailed degree distributions indicating the presence of highly connected hubs. These properties could not be trivially explained by the univariate time series statistics of stock price returns. This degree of topological isomorphism suggests that brains and markets can be regarded broadly as members of the same family of networks. The two systems, however, were not topologically identical. The financial market was more efficient and more modular - more highly optimised for information processing - than the brain networks; but also less robust to systemic disintegration as a result of hub deletion. We conclude that the conceptual connections between brains and markets are not merely metaphorical; rather these two information processing systems can be rigorously compared in the same mathematical language and turn out often to share important topological properties in common to some degree. There will be interesting scientific arbitrage opportunities in further work at the graph theoretically-mediated interface between systems neuroscience and the statistical physics of financial markets.

  4. Topological isomorphisms of human brain and financial market networks.

    Science.gov (United States)

    Vértes, Petra E; Nicol, Ruth M; Chapman, Sandra C; Watkins, Nicholas W; Robertson, Duncan A; Bullmore, Edward T

    2011-01-01

    Although metaphorical and conceptual connections between the human brain and the financial markets have often been drawn, rigorous physical or mathematical underpinnings of this analogy remain largely unexplored. Here, we apply a statistical and graph theoretic approach to the study of two datasets - the time series of 90 stocks from the New York stock exchange over a 3-year period, and the fMRI-derived time series acquired from 90 brain regions over the course of a 10-min-long functional MRI scan of resting brain function in healthy volunteers. Despite the many obvious substantive differences between these two datasets, graphical analysis demonstrated striking commonalities in terms of global network topological properties. Both the human brain and the market networks were non-random, small-world, modular, hierarchical systems with fat-tailed degree distributions indicating the presence of highly connected hubs. These properties could not be trivially explained by the univariate time series statistics of stock price returns. This degree of topological isomorphism suggests that brains and markets can be regarded broadly as members of the same family of networks. The two systems, however, were not topologically identical. The financial market was more efficient and more modular - more highly optimized for information processing - than the brain networks; but also less robust to systemic disintegration as a result of hub deletion. We conclude that the conceptual connections between brains and markets are not merely metaphorical; rather these two information processing systems can be rigorously compared in the same mathematical language and turn out often to share important topological properties in common to some degree. There will be interesting scientific arbitrage opportunities in further work at the graph-theoretically mediated interface between systems neuroscience and the statistical physics of financial markets.

  5. English and Chinese languages as weighted complex networks

    Science.gov (United States)

    Sheng, Long; Li, Chunguang

    2009-06-01

    In this paper, we analyze statistical properties of English and Chinese written human language within the framework of weighted complex networks. The two language networks are based on an English novel and a Chinese biography, respectively, and both of the networks are constructed in the same way. By comparing the intensity and density of connections between the two networks, we find that high weight connections in Chinese language networks prevail more than those in English language networks. Furthermore, some of the topological and weighted quantities are compared. The results display some differences in the structural organizations between the two language networks. These observations indicate that the two languages may have different linguistic mechanisms and different combinatorial natures.

  6. Whole Brain Thinking : An Educational Alternative for Language Instructors

    OpenAIRE

    Ogawa,Ruby Toshimi

    2008-01-01

    Whole brain thinking offers new potentials in providing an educational alternative in teaching English as a Second Language (ESL). Prevailing research has shown that the right and the left sides of the brain function and process information differently according to Nobel Prize Winner Roger Sperry in his split-brain research on epileptics. While acknowledging these physical neurological differences, current research suggesting that in view of traditional teaching methods that rely on left-brai...

  7. The overlapping community structure of structural brain network in young healthy individuals.

    Directory of Open Access Journals (Sweden)

    Kai Wu

    2011-05-01

    Full Text Available Community structure is a universal and significant feature of many complex networks in biology, society, and economics. Community structure has also been revealed in human brain structural and functional networks in previous studies. However, communities overlap and share many edges and nodes. Uncovering the overlapping community structure of complex networks remains largely unknown in human brain networks. Here, using regional gray matter volume, we investigated the structural brain network among 90 brain regions (according to a predefined anatomical atlas in 462 young, healthy individuals. Overlapped nodes between communities were defined by assuming that nodes (brain regions can belong to more than one community. We demonstrated that 90 brain regions were organized into 5 overlapping communities associated with several well-known brain systems, such as the auditory/language, visuospatial, emotion, decision-making, social, control of action, memory/learning, and visual systems. The overlapped nodes were mostly involved in an inferior-posterior pattern and were primarily related to auditory and visual perception. The overlapped nodes were mainly attributed to brain regions with higher node degrees and nodal efficiency and played a pivotal role in the flow of information through the structural brain network. Our results revealed fuzzy boundaries between communities by identifying overlapped nodes and provided new insights into the understanding of the relationship between the structure and function of the human brain. This study provides the first report of the overlapping community structure of the structural network of the human brain.

  8. BrainNetCNN: Convolutional neural networks for brain networks; towards predicting neurodevelopment.

    Science.gov (United States)

    Kawahara, Jeremy; Brown, Colin J; Miller, Steven P; Booth, Brian G; Chau, Vann; Grunau, Ruth E; Zwicker, Jill G; Hamarneh, Ghassan

    2017-02-01

    We propose BrainNetCNN, a convolutional neural network (CNN) framework to predict clinical neurodevelopmental outcomes from brain networks. In contrast to the spatially local convolutions done in traditional image-based CNNs, our BrainNetCNN is composed of novel edge-to-edge, edge-to-node and node-to-graph convolutional filters that leverage the topological locality of structural brain networks. We apply the BrainNetCNN framework to predict cognitive and motor developmental outcome scores from structural brain networks of infants born preterm. Diffusion tensor images (DTI) of preterm infants, acquired between 27 and 46 weeks gestational age, were used to construct a dataset of structural brain connectivity networks. We first demonstrate the predictive capabilities of BrainNetCNN on synthetic phantom networks with simulated injury patterns and added noise. BrainNetCNN outperforms a fully connected neural-network with the same number of model parameters on both phantoms with focal and diffuse injury patterns. We then apply our method to the task of joint prediction of Bayley-III cognitive and motor scores, assessed at 18 months of age, adjusted for prematurity. We show that our BrainNetCNN framework outperforms a variety of other methods on the same data. Furthermore, BrainNetCNN is able to identify an infant's postmenstrual age to within about 2 weeks. Finally, we explore the high-level features learned by BrainNetCNN by visualizing the importance of each connection in the brain with respect to predicting the outcome scores. These findings are then discussed in the context of the anatomy and function of the developing preterm infant brain. Copyright © 2016 Elsevier Inc. All rights reserved.

  9. Sign Language Recognition using Neural Networks

    Directory of Open Access Journals (Sweden)

    Sabaheta Djogic

    2014-11-01

    Full Text Available – Sign language plays a great role as communication media for people with hearing difficulties.In developed countries, systems are made for overcoming a problem in communication with deaf people. This encouraged us to develop a system for the Bosnian sign language since there is a need for such system. The work is done with the use of digital image processing methods providing a system that teaches a multilayer neural network using a back propagation algorithm. Images are processed by feature extraction methods, and by masking method the data set has been created. Training is done using cross validation method for better performance thus; an accuracy of 84% is achieved.

  10. Phonological memory in sign language relies on the visuomotor neural system outside the left hemisphere language network.

    Science.gov (United States)

    Kanazawa, Yuji; Nakamura, Kimihiro; Ishii, Toru; Aso, Toshihiko; Yamazaki, Hiroshi; Omori, Koichi

    2017-01-01

    Sign language is an essential medium for everyday social interaction for deaf people and plays a critical role in verbal learning. In particular, language development in those people should heavily rely on the verbal short-term memory (STM) via sign language. Most previous studies compared neural activations during signed language processing in deaf signers and those during spoken language processing in hearing speakers. For sign language users, it thus remains unclear how visuospatial inputs are converted into the verbal STM operating in the left-hemisphere language network. Using functional magnetic resonance imaging, the present study investigated neural activation while bilinguals of spoken and signed language were engaged in a sequence memory span task. On each trial, participants viewed a nonsense syllable sequence presented either as written letters or as fingerspelling (4-7 syllables in length) and then held the syllable sequence for 12 s. Behavioral analysis revealed that participants relied on phonological memory while holding verbal information regardless of the type of input modality. At the neural level, this maintenance stage broadly activated the left-hemisphere language network, including the inferior frontal gyrus, supplementary motor area, superior temporal gyrus and inferior parietal lobule, for both letter and fingerspelling conditions. Interestingly, while most participants reported that they relied on phonological memory during maintenance, direct comparisons between letters and fingers revealed strikingly different patterns of neural activation during the same period. Namely, the effortful maintenance of fingerspelling inputs relative to letter inputs activated the left superior parietal lobule and dorsal premotor area, i.e., brain regions known to play a role in visuomotor analysis of hand/arm movements. These findings suggest that the dorsal visuomotor neural system subserves verbal learning via sign language by relaying gestural inputs to

  11. Development of the brain's functional network architecture.

    Science.gov (United States)

    Vogel, Alecia C; Power, Jonathan D; Petersen, Steven E; Schlaggar, Bradley L

    2010-12-01

    A full understanding of the development of the brain's functional network architecture requires not only an understanding of developmental changes in neural processing in individual brain regions but also an understanding of changes in inter-regional interactions. Resting state functional connectivity MRI (rs-fcMRI) is increasingly being used to study functional interactions between brain regions in both adults and children. We briefly review methods used to study functional interactions and networks with rs-fcMRI and how these methods have been used to define developmental changes in network functional connectivity. The developmental rs-fcMRI studies to date have found two general properties. First, regional interactions change from being predominately anatomically local in children to interactions spanning longer cortical distances in young adults. Second, this developmental change in functional connectivity occurs, in general, via mechanisms of segregation of local regions and integration of distant regions into disparate subnetworks.

  12. From "rest" to language task: Task activation selects and prunes from broader resting-state network.

    Science.gov (United States)

    Doucet, Gaelle E; He, Xiaosong; Sperling, Michael R; Sharan, Ashwini; Tracy, Joseph I

    2017-05-01

    Resting-state networks (RSNs) show spatial patterns generally consistent with networks revealed during cognitive tasks. However, the exact degree of overlap between these networks has not been clearly quantified. Such an investigation shows promise for decoding altered functional connectivity (FC) related to abnormal language functioning in clinical populations such as temporal lobe epilepsy (TLE). In this context, we investigated the network configurations during a language task and during resting state using FC. Twenty-four healthy controls, 24 right and 24 left TLE patients completed a verb generation (VG) task and a resting-state fMRI scan. We compared the language network revealed by the VG task with three FC-based networks (seeding the left inferior frontal cortex (IFC)/Broca): two from the task (ON, OFF blocks) and one from the resting state. We found that, for both left TLE patients and controls, the RSN recruited regions bilaterally, whereas both VG-on and VG-off conditions produced more left-lateralized FC networks, matching more closely with the activated language network. TLE brings with it variability in both task-dependent and task-independent networks, reflective of atypical language organization. Overall, our findings suggest that our RSN captured bilateral activity, reflecting a set of prepotent language regions. We propose that this relationship can be best understood by the notion of pruning or winnowing down of the larger language-ready RSN to carry out specific task demands. Our data suggest that multiple types of network analyses may be needed to decode the association between language deficits and the underlying functional mechanisms altered by disease. Hum Brain Mapp 38:2540-2552, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  13. Brain metabolite levels and language abilities in preschool children.

    Science.gov (United States)

    Lebel, Catherine; MacMaster, Frank P; Dewey, Deborah

    2016-10-01

    Language acquisition occurs rapidly during early childhood and lays the foundation for future reading success. However, little is known about the brain-language relationships in young children. The goal of this study was to investigate relationships between brain metabolites and prereading language abilities in healthy preschool-aged children. Participants were 67 healthy children aged 3.0-5.4 years scanned on a 3T GE MR750w MRI scanner using short echo proton spectroscopy with a voxel placed in the anterior cingulate gyrus ( n  = 56) and/or near the left angular gyrus ( n  = 45). Children completed the NEPSY-II Phonological Processing and Speeded Naming subtests at the same time as their MRI scan. We calculated glutamate, glutamine, creatine/phosphocreatine, choline, inositol, and NAA concentrations, and correlated these with language skills. In the anterior cingulate, Phonological Processing Scaled Scores were significantly correlated with glutamate, creatine, and inositol concentrations. In the left angular gyrus, Speeded Naming Combined Scaled Scores showed trend correlations with choline and glutamine concentrations. For the first time, we demonstrate relationships between brain metabolites and prereading language abilities in young children. Our results show relationships between language and inositol and glutamate that may reflect glial differences underlying language function, and a relationship of language with creatine. The trend between Speeded Naming and choline is consistent with previous research in older children and adults; however, larger sample sizes are needed to confirm whether this relationship is indeed significant in young children. These findings help understand the brain basis of language, and may ultimately lead to earlier and more effective interventions for reading disabilities.

  14. The Structural Connectivity Underpinning Language Aptitude, Working Memory, and IQ in the Perisylvian Language Network

    Science.gov (United States)

    Xiang, Huadong; Dediu, Dan; Roberts, Leah; van Oort, Erik; Norris, David G.; Hagoort, Peter

    2012-01-01

    In this article, we report the results of a study on the relationship between individual differences in language learning aptitude and the structural connectivity of language pathways in the adult brain, the first of its kind. We measured four components of language aptitude ("vocabulary learning"; "sound recognition"; "sound-symbol…

  15. The Structural Connectivity Underpinning Language Aptitude, Working Memory, and IQ in the Perisylvian Language Network

    NARCIS (Netherlands)

    Xiang, H.; Dediu, D.; Roberts, M.J.; Oort, E.S.B. van; Norris, D.; Hagoort, P.

    2012-01-01

    In this article, we report the results of a study on the relationship between individual differences in language learning aptitude and the structural connectivity of language pathways in the adult brain, the first of its kind. We measured four components of language aptitude (vocabulary learning;

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

    Science.gov (United States)

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

    2016-01-20

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

  17. Social in, social out: How the brain responds to social language with more social language.

    Science.gov (United States)

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

    Social connection is a fundamental human need. As such, people's brains are sensitized to social cues, such as those carried by language, and to promoting social communication. The neural mechanisms of certain key building blocks in this process, such as receptivity to and reproduction of social language, however, are not known. We combined quantitative linguistic analysis and neuroimaging to connect neural activity in brain regions used to simulate the mental states of others with exposure to, and re-transmission of, social language. Our results link findings on successful idea transmission from communication science, sociolinguistics and cognitive neuroscience to prospectively predict the degree of social language that participants utilize when re-transmitting ideas as a function of 1) initial language inputs and 2) neural activity during idea exposure.

  18. Structural covariance networks in the mouse brain.

    Science.gov (United States)

    Pagani, Marco; Bifone, Angelo; Gozzi, Alessandro

    2016-04-01

    The presence of networks of correlation between regional gray matter volume as measured across subjects in a group of individuals has been consistently described in several human studies, an approach termed structural covariance MRI (scMRI). Complementary to prevalent brain mapping modalities like functional and diffusion-weighted imaging, the approach can provide precious insights into the mutual influence of trophic and plastic processes in health and pathological states. To investigate whether analogous scMRI networks are present in lower mammal species amenable to genetic and experimental manipulation such as the laboratory mouse, we employed high resolution morphoanatomical MRI in a large cohort of genetically-homogeneous wild-type mice (C57Bl6/J) and mapped scMRI networks using a seed-based approach. We show that the mouse brain exhibits robust homotopic scMRI networks in both primary and associative cortices, a finding corroborated by independent component analyses of cortical volumes. Subcortical structures also showed highly symmetric inter-hemispheric correlations, with evidence of distributed antero-posterior networks in diencephalic regions of the thalamus and hypothalamus. Hierarchical cluster analysis revealed six identifiable clusters of cortical and sub-cortical regions corresponding to previously described neuroanatomical systems. Our work documents the presence of homotopic cortical and subcortical scMRI networks in the mouse brain, thus supporting the use of this species to investigate the elusive biological and neuroanatomical underpinnings of scMRI network development and its derangement in neuropathological states. The identification of scMRI networks in genetically homogeneous inbred mice is consistent with the emerging view of a key role of environmental factors in shaping these correlational networks. Copyright © 2016 Elsevier Inc. All rights reserved.

  19. An eccentric perspective on brain networks

    NARCIS (Netherlands)

    Reus, M.A. de

    2015-01-01

    The adult human brain comprises an estimated number of 80-100 billion neurons. These neurons do not operate independently, but are interconnected to each other through circa 100-500 trillion neuronal connections, together forming a network of incredible complexity. Although this vast system of

  20. Chapter 36: history of aphasia: from brain to language.

    Science.gov (United States)

    Eling, Paul; Whitaker, Harry

    2010-01-01

    An historical overview is presented that focuses on the changes both in approach and topics with respect to language disturbances due to brain lesions. Early cases of language disorders were described without any theorizing about language or its relation to the brain. Also, three forms of speech disorder were distinguished: traulotes, psellotes and ischophonia, which are only marginally related to aphasia. In the 18th century some authors, in particular Gesner and Crichton, attempted to explain language disorders in terms of mental processes. The great debate on both the anatomical (Broca, Wernicke) and functional (Wernicke, Lichtheim) aspects of aphasia dominated late 19th century discussion of localization of function, leading to the development of what we now call the cognitive neurosciences. In this period, language processing was described in terms of a simple functional model of word recognition and production; linguistic principles played no role. At the beginning of the 20th century the discussion on language disorders waned due to a decrease of interest in the issue of localization; aphasia became primarily a clinical issue of how best to classify patients. In the second half of the 20th century, the field of aphasia developed rapidly due to studies performed at the Boston Aphasia Unit and, more importantly, to a change of orientation to linguistic notions of language structure, as introduced by Chomsky.

  1. Functional Language Shift to the Right Hemisphere in Patients with Language-Eloquent Brain Tumors

    Science.gov (United States)

    Krieg, Sandro M.; Sollmann, Nico; Hauck, Theresa; Ille, Sebastian; Foerschler, Annette; Meyer, Bernhard; Ringel, Florian

    2013-01-01

    Objectives Language function is mainly located within the left hemisphere of the brain, especially in right-handed subjects. However, functional MRI (fMRI) has demonstrated changes of language organization in patients with left-sided perisylvian lesions to the right hemisphere. Because intracerebral lesions can impair fMRI, this study was designed to investigate human language plasticity with a virtual lesion model using repetitive navigated transcranial magnetic stimulation (rTMS). Experimental design Fifteen patients with lesions of left-sided language-eloquent brain areas and 50 healthy and purely right-handed participants underwent bilateral rTMS language mapping via an object-naming task. All patients were proven to have left-sided language function during awake surgery. The rTMS-induced language errors were categorized into 6 different error types. The error ratio (induced errors/number of stimulations) was determined for each brain region on both hemispheres. A hemispheric dominance ratio was then defined for each region as the quotient of the error ratio (left/right) of the corresponding area of both hemispheres (ratio >1  =  left dominant; ratio dominant). Results Patients with language-eloquent lesions showed a statistically significantly lower ratio than healthy participants concerning “all errors” and “all errors without hesitations”, which indicates a higher participation of the right hemisphere in language function. Yet, there was no cortical region with pronounced difference in language dominance compared to the whole hemisphere. Conclusions This is the first study that shows by means of an anatomically accurate virtual lesion model that a shift of language function to the non-dominant hemisphere can occur. PMID:24069410

  2. Functional language shift to the right hemisphere in patients with language-eloquent brain tumors.

    Science.gov (United States)

    Krieg, Sandro M; Sollmann, Nico; Hauck, Theresa; Ille, Sebastian; Foerschler, Annette; Meyer, Bernhard; Ringel, Florian

    2013-01-01

    Language function is mainly located within the left hemisphere of the brain, especially in right-handed subjects. However, functional MRI (fMRI) has demonstrated changes of language organization in patients with left-sided perisylvian lesions to the right hemisphere. Because intracerebral lesions can impair fMRI, this study was designed to investigate human language plasticity with a virtual lesion model using repetitive navigated transcranial magnetic stimulation (rTMS). Fifteen patients with lesions of left-sided language-eloquent brain areas and 50 healthy and purely right-handed participants underwent bilateral rTMS language mapping via an object-naming task. All patients were proven to have left-sided language function during awake surgery. The rTMS-induced language errors were categorized into 6 different error types. The error ratio (induced errors/number of stimulations) was determined for each brain region on both hemispheres. A hemispheric dominance ratio was then defined for each region as the quotient of the error ratio (left/right) of the corresponding area of both hemispheres (ratio >1 = left dominant; ratio right dominant). Patients with language-eloquent lesions showed a statistically significantly lower ratio than healthy participants concerning "all errors" and "all errors without hesitations", which indicates a higher participation of the right hemisphere in language function. Yet, there was no cortical region with pronounced difference in language dominance compared to the whole hemisphere. This is the first study that shows by means of an anatomically accurate virtual lesion model that a shift of language function to the non-dominant hemisphere can occur.

  3. Assessing Group Interaction with Social Language Network Analysis

    Science.gov (United States)

    Scholand, Andrew J.; Tausczik, Yla R.; Pennebaker, James W.

    In this paper we discuss a new methodology, social language network analysis (SLNA), that combines tools from social language processing and network analysis to assess socially situated working relationships within a group. Specifically, SLNA aims to identify and characterize the nature of working relationships by processing artifacts generated with computer-mediated communication systems, such as instant message texts or emails. Because social language processing is able to identify psychological, social, and emotional processes that individuals are not able to fully mask, social language network analysis can clarify and highlight complex interdependencies between group members, even when these relationships are latent or unrecognized.

  4. The Influence of Japanese Anime Language to Chinese Network Buzzwords

    OpenAIRE

    Cai Jin Chang

    2016-01-01

    In this paper, the influence of Japanese anime language to Chinese network buzzwords was studied, and the maturity and rigor were quoted. The Japanese anime language refined, creative can be so popular in the factors of psychologies also was analyzed. According to these studies, some suggestions were put forward that how to standardize the network buzzwords and how to raise its taste.

  5. Complex brain networks: From topological communities to clustered

    Indian Academy of Sciences (India)

    Complex brain networks: From topological communities to clustered dynamics ... Recent research has revealed a rich and complicated network topology in the cortical connectivity of mammalian brains. ... Pramana – Journal of Physics | News.

  6. Complex brain networks: From topological communities to clustered ...

    Indian Academy of Sciences (India)

    functional connectivity of the human brain has shown that both types of brain networks share .... the areas and also of the whole network, the Pearson correlation coefficient r and ..... Several areas important for intercommunity communication.

  7. Generative adversarial networks for brain lesion detection

    Science.gov (United States)

    Alex, Varghese; Safwan, K. P. Mohammed; Chennamsetty, Sai Saketh; Krishnamurthi, Ganapathy

    2017-02-01

    Manual segmentation of brain lesions from Magnetic Resonance Images (MRI) is cumbersome and introduces errors due to inter-rater variability. This paper introduces a semi-supervised technique for detection of brain lesion from MRI using Generative Adversarial Networks (GANs). GANs comprises of a Generator network and a Discriminator network which are trained simultaneously with the objective of one bettering the other. The networks were trained using non lesion patches (n=13,000) from 4 different MR sequences. The network was trained on BraTS dataset and patches were extracted from regions excluding tumor region. The Generator network generates data by modeling the underlying probability distribution of the training data, (PData). The Discriminator learns the posterior probability P (Label Data) by classifying training data and generated data as "Real" or "Fake" respectively. The Generator upon learning the joint distribution, produces images/patches such that the performance of the Discriminator on them are random, i.e. P (Label Data = GeneratedData) = 0.5. During testing, the Discriminator assigns posterior probability values close to 0.5 for patches from non lesion regions, while patches centered on lesion arise from a different distribution (PLesion) and hence are assigned lower posterior probability value by the Discriminator. On the test set (n=14), the proposed technique achieves whole tumor dice score of 0.69, sensitivity of 91% and specificity of 59%. Additionally the generator network was capable of generating non lesion patches from various MR sequences.

  8. Developmental process emerges from extended brain-body-behavior networks

    Science.gov (United States)

    Byrge, Lisa; Sporns, Olaf; Smith, Linda B.

    2014-01-01

    Studies of brain connectivity have focused on two modes of networks: structural networks describing neuroanatomy and the intrinsic and evoked dependencies of functional networks at rest and during tasks. Each mode constrains and shapes the other across multiple time scales, and each also shows age-related changes. Here we argue that understanding how brains change across development requires understanding the interplay between behavior and brain networks: changing bodies and activities modify the statistics of inputs to the brain; these changing inputs mold brain networks; these networks, in turn, promote further change in behavior and input. PMID:24862251

  9. Narrative Language in Traumatic Brain Injury

    Science.gov (United States)

    Marini, Andrea; Galetto, Valentina; Zampieri, Elisa; Vorano, Lorenza; Zettin, Marina; Carlomagno, Sergio

    2011-01-01

    Persons with traumatic brain injury (TBI) often show impaired linguistic and/or narrative abilities. The present study aimed to document the features of narrative discourse impairment in a group of adults with TBI. 14 severe TBI non-aphasic speakers (GCS less than 8) in the phase of neurological stability and 14 neurologically intact participants…

  10. Network Theory and Effects of Transcranial Brain Stimulation Methods on the Brain Networks

    Directory of Open Access Journals (Sweden)

    Sema Demirci

    2014-12-01

    Full Text Available In recent years, there has been a shift from classic localizational approaches to new approaches where the brain is considered as a complex system. Therefore, there has been an increase in the number of studies involving collaborations with other areas of neurology in order to develop methods to understand the complex systems. One of the new approaches is graphic theory that has principles based on mathematics and physics. According to this theory, the functional-anatomical connections of the brain are defined as a network. Moreover, transcranial brain stimulation techniques are amongst the recent research and treatment methods that have been commonly used in recent years. Changes that occur as a result of applying brain stimulation techniques on physiological and pathological networks help better understand the normal and abnormal functions of the brain, especially when combined with techniques such as neuroimaging and electroencephalography. This review aims to provide an overview of the applications of graphic theory and related parameters, studies conducted on brain functions in neurology and neuroscience, and applications of brain stimulation systems in the changing treatment of brain network models and treatment of pathological networks defined on the basis of this theory.

  11. Bayesian Recurrent Neural Network for Language Modeling.

    Science.gov (United States)

    Chien, Jen-Tzung; Ku, Yuan-Chu

    2016-02-01

    A language model (LM) is calculated as the probability of a word sequence that provides the solution to word prediction for a variety of information systems. A recurrent neural network (RNN) is powerful to learn the large-span dynamics of a word sequence in the continuous space. However, the training of the RNN-LM is an ill-posed problem because of too many parameters from a large dictionary size and a high-dimensional hidden layer. This paper presents a Bayesian approach to regularize the RNN-LM and apply it for continuous speech recognition. We aim to penalize the too complicated RNN-LM by compensating for the uncertainty of the estimated model parameters, which is represented by a Gaussian prior. The objective function in a Bayesian classification network is formed as the regularized cross-entropy error function. The regularized model is constructed not only by calculating the regularized parameters according to the maximum a posteriori criterion but also by estimating the Gaussian hyperparameter by maximizing the marginal likelihood. A rapid approximation to a Hessian matrix is developed to implement the Bayesian RNN-LM (BRNN-LM) by selecting a small set of salient outer-products. The proposed BRNN-LM achieves a sparser model than the RNN-LM. Experiments on different corpora show the robustness of system performance by applying the rapid BRNN-LM under different conditions.

  12. The hierarchical brain network for face recognition.

    Science.gov (United States)

    Zhen, Zonglei; Fang, Huizhen; Liu, Jia

    2013-01-01

    Numerous functional magnetic resonance imaging (fMRI) studies have identified multiple cortical regions that are involved in face processing in the human brain. However, few studies have characterized the face-processing network as a functioning whole. In this study, we used fMRI to identify face-selective regions in the entire brain and then explore the hierarchical structure of the face-processing network by analyzing functional connectivity among these regions. We identified twenty-five regions mainly in the occipital, temporal and frontal cortex that showed a reliable response selective to faces (versus objects) across participants and across scan sessions. Furthermore, these regions were clustered into three relatively independent sub-networks in a face-recognition task on the basis of the strength of functional connectivity among them. The functionality of the sub-networks likely corresponds to the recognition of individual identity, retrieval of semantic knowledge and representation of emotional information. Interestingly, when the task was switched to object recognition from face recognition, the functional connectivity between the inferior occipital gyrus and the rest of the face-selective regions were significantly reduced, suggesting that this region may serve as an entry node in the face-processing network. In sum, our study provides empirical evidence for cognitive and neural models of face recognition and helps elucidate the neural mechanisms underlying face recognition at the network level.

  13. Hierarchical modularity in human brain functional networks

    Directory of Open Access Journals (Sweden)

    David Meunier

    2009-10-01

    Full Text Available The idea that complex systems have a hierarchical modular organization originates in the early 1960s and has recently attracted fresh support from quantitative studies of large scale, real-life networks. Here we investigate the hierarchical modular (or “modules-within-modules” decomposition of human brain functional networks, measured using functional magnetic resonance imaging (fMRI in 18 healthy volunteers under no-task or resting conditions. We used a customized template to extract networks with more than 1800 regional nodes, and we applied a fast algorithm to identify nested modular structure at several hierarchical levels. We used mutual information, 0 < I < 1, to estimate the similarity of community structure of networks in different subjects, and to identify the individual network that is most representative of the group. Results show that human brain functional networks have a hierarchical modular organization with a fair degree of similarity between subjects, I=0.63. The largest 5 modules at the highest level of the hierarchy were medial occipital, lateral occipital, central, parieto-frontal and fronto-temporal systems; occipital modules demonstrated less sub-modular organization than modules comprising regions of multimodal association cortex. Connector nodes and hubs, with a key role in inter-modular connectivity, were also concentrated in association cortical areas. We conclude that methods are available for hierarchical modular decomposition of large numbers of high resolution brain functional networks using computationally expedient algorithms. This could enable future investigations of Simon's original hypothesis that hierarchy or near-decomposability of physical symbol systems is a critical design feature for their fast adaptivity to changing environmental conditions.

  14. Functional brain networks in schizophrenia: a review

    Directory of Open Access Journals (Sweden)

    Vince D Calhoun

    2009-08-01

    Full Text Available Functional magnetic resonance imaging (fMRI has become a major technique for studying cognitive function and its disruption in mental illness, including schizophrenia. The major proportion of imaging studies focused primarily upon identifying regions which hemodynamic response amplitudes covary with particular stimuli and differentiate between patient and control groups. In addition to such amplitude based comparisons, one can estimate temporal correlations and compute maps of functional connectivity between regions which include the variance associated with event related responses as well as intrinsic fluctuations of hemodynamic activity. Functional connectivity maps can be computed by correlating all voxels with a seed region when a spatial prior is available. An alternative are multivariate decompositions such as independent component analysis (ICA which extract multiple components, each of which is a spatially distinct map of voxels with a common time course. Recent work has shown that these networks are pervasive in relaxed resting and during task performance and hence provide robust measures of intact and disturbed brain activity. This in turn bears the prospect of yielding biomarkers for schizophrenia, which can be described both in terms of disrupted local processing as well as altered global connectivity between large scale networks. In this review we will summarize functional connectivity measures with a focus upon work with ICA and discuss the meaning of intrinsic fluctuations. In addition, examples of how brain networks have been used for classification of disease will be shown. We present work with functional network connectivity, an approach that enables the evaluation of the interplay between multiple networks and how they are affected in disease. We conclude by discussing new variants of ICA for extracting maximally group discriminative networks from data. In summary, it is clear that identification of brain networks and their

  15. Disrupting the brain to validate hypotheses on the neurobiology of language

    Directory of Open Access Journals (Sweden)

    Liuba ePapeo

    2013-04-01

    Full Text Available Comprehension of words is an important part of the language faculty, involving the joint activity of frontal and temporo-parietal brain regions. Transcranial Magnetic Stimulation (TMS enables the controlled perturbation of brain activity, and thus offers a unique tool to test specific predictions about the causal relationship between brain regions and language understanding. This potential has been exploited to better define the role of regions that are classically accepted as part of the language-semantic network. For instance, TMS has contributed to establish the semantic relevance of the left anterior temporal lobe, or to solve the ambiguity between the semantic versus phonological function assigned to the left inferior frontal gyrus. We consider, more closely, the results from studies where the same technique, similar paradigms (lexical-semantic tasks and materials (words have been used to assess the relevance of regions outside the classically-defined language-semantic network – i.e., precentral motor regions – for the semantic analysis of words. This research shows that different aspects of the left precentral gyrus (primary motor and premotor sites are sensitive to the action-non action distinction of words’ meanings. However, the behavioral changes due to TMS over these sites are incongruent with what is expected after perturbation of a task-relevant brain region. Thus, the relationship between motor activity and language-semantic behavior remains far from clear. A better understanding of this issue could be guaranteed by investigating functional interactions between motor sites and semantically-relevant regions.

  16. BRAIN NETWORKS. Correlated gene expression supports synchronous activity in brain networks.

    Science.gov (United States)

    Richiardi, Jonas; Altmann, Andre; Milazzo, Anna-Clare; Chang, Catie; Chakravarty, M Mallar; Banaschewski, Tobias; Barker, Gareth J; Bokde, Arun L W; Bromberg, Uli; Büchel, Christian; Conrod, Patricia; Fauth-Bühler, Mira; Flor, Herta; Frouin, Vincent; Gallinat, Jürgen; Garavan, Hugh; Gowland, Penny; Heinz, Andreas; Lemaître, Hervé; Mann, Karl F; Martinot, Jean-Luc; Nees, Frauke; Paus, Tomáš; Pausova, Zdenka; Rietschel, Marcella; Robbins, Trevor W; Smolka, Michael N; Spanagel, Rainer; Ströhle, Andreas; Schumann, Gunter; Hawrylycz, Mike; Poline, Jean-Baptiste; Greicius, Michael D

    2015-06-12

    During rest, brain activity is synchronized between different regions widely distributed throughout the brain, forming functional networks. However, the molecular mechanisms supporting functional connectivity remain undefined. We show that functional brain networks defined with resting-state functional magnetic resonance imaging can be recapitulated by using measures of correlated gene expression in a post mortem brain tissue data set. The set of 136 genes we identify is significantly enriched for ion channels. Polymorphisms in this set of genes significantly affect resting-state functional connectivity in a large sample of healthy adolescents. Expression levels of these genes are also significantly associated with axonal connectivity in the mouse. The results provide convergent, multimodal evidence that resting-state functional networks correlate with the orchestrated activity of dozens of genes linked to ion channel activity and synaptic function. Copyright © 2015, American Association for the Advancement of Science.

  17. Flexible brain network reconfiguration supporting inhibitory control.

    Science.gov (United States)

    Spielberg, Jeffrey M; Miller, Gregory A; Heller, Wendy; Banich, Marie T

    2015-08-11

    The ability to inhibit distracting stimuli from interfering with goal-directed behavior is crucial for success in most spheres of life. Despite an abundance of studies examining regional brain activation, knowledge of the brain networks involved in inhibitory control remains quite limited. To address this critical gap, we applied graph theory tools to functional magnetic resonance imaging data collected while a large sample of adults (n = 101) performed a color-word Stroop task. Higher demand for inhibitory control was associated with restructuring of the global network into a configuration that was more optimized for specialized processing (functional segregation), more efficient at communicating the output of such processing across the network (functional integration), and more resilient to potential interruption (resilience). In addition, there were regional changes with right inferior frontal sulcus and right anterior insula occupying more central positions as network hubs, and dorsal anterior cingulate cortex becoming more tightly coupled with its regional subnetwork. Given the crucial role of inhibitory control in goal-directed behavior, present findings identifying functional network organization supporting inhibitory control have the potential to provide additional insights into how inhibitory control may break down in a wide variety of individuals with neurological or psychiatric difficulties.

  18. Brain. Conscious and Unconscious Mechanisms of Cognition, Emotions, and Language

    Directory of Open Access Journals (Sweden)

    Roman Ilin

    2012-12-01

    Full Text Available Conscious and unconscious brain mechanisms, including cognition, emotions and language are considered in this review. The fundamental mechanisms of cognition include interactions between bottom-up and top-down signals. The modeling of these interactions since the 1960s is briefly reviewed, analyzing the ubiquitous difficulty: incomputable combinatorial complexity (CC. Fundamental reasons for CC are related to the Gödel’s difficulties of logic, a most fundamental mathematical result of the 20th century. Many scientists still “believed” in logic because, as the review discusses, logic is related to consciousness; non-logical processes in the brain are unconscious. CC difficulty is overcome in the brain by processes “from vague-unconscious to crisp-conscious” (representations, plans, models, concepts. These processes are modeled by dynamic logic, evolving from vague and unconscious representations toward crisp and conscious thoughts. We discuss experimental proofs and relate dynamic logic to simulators of the perceptual symbol system. “From vague to crisp” explains interactions between cognition and language. Language is mostly conscious, whereas cognition is only rarely so; this clarifies much about the mind that might seem mysterious. All of the above involve emotions of a special kind, aesthetic emotions related to knowledge and to cognitive dissonances. Cognition-language-emotional mechanisms operate throughout the hierarchy of the mind and create all higher mental abilities. The review discusses cognitive functions of the beautiful, sublime, music.

  19. Approach of Complex Networks for the Determination of Brain Death

    Institute of Scientific and Technical Information of China (English)

    SUN Wei-Gang; CAO Jian-Ting; WANG Ru-Bin

    2011-01-01

    In clinical practice, brain death is the irreversible end of all brain activity. Compared to current statistical methods for the determination of brain death, we focus on the approach of complex networks for real-world electroencephalography in its determination. Brain functional networks constructed by correlation analysis are derived, and statistical network quantities used for distinguishing the patients in coma or brain death state, such as average strength, clustering coefficient and average path length, are calculated. Numerical results show that the values of network quantities of patients in coma state are larger than those of patients in brain death state. Our Sndings might provide valuable insights on the determination of brain death.%@@ In clinical practice, brain death is the irreversible end of all brain activity.Compared to current statistical methods for the determination of brain death, we focus on the approach of complex networks for real-world electroencephalography in its determination.Brain functional networks constructed by correlation analysis axe derived, and statistical network quantities used for distinguishing the patients in coma or brain death state, such as average strength, clustering coefficient and average path length, are calculated.Numerical results show that the values of network quantities of patients in coma state are larger than those of patients in brain death state.Our findings might provide valuable insights on the determination of brain death.

  20. Mutated Genes in Schizophrenia Map to Brain Networks

    Science.gov (United States)

    ... Matters NIH Research Matters August 12, 2013 Mutated Genes in Schizophrenia Map to Brain Networks Schizophrenia networks ... have a high number of spontaneous mutations in genes that form a network in the front region ...

  1. Abnormal functional lateralization and activity of language brain areas in typical specific language impairment (developmental dysphasia)

    Science.gov (United States)

    De Guibert, Clément; Maumet, Camille; Jannin, Pierre; Ferré, Jean-Christophe; Tréguier, Catherine; Barillot, Christian; Le Rumeur, Elisabeth; Allaire, Catherine; Biraben, Arnaud

    2011-01-01

    Atypical functional lateralization and specialization for language have been proposed to account for developmental language disorders, yet results from functional neuroimaging studies are sparse and inconsistent. This functional magnetic resonance imaging study compared children with a specific subtype of specific language impairment affecting structural language (n=21), to a matched group of typically-developing children using a panel of four language tasks neither requiring reading nor metalinguistic skills, including two auditory lexico-semantic tasks (category fluency and responsive naming) and two visual phonological tasks based on picture naming. Data processing involved normalizing the data with respect to a matched pairs pediatric template, groups and between-groups analysis, and laterality indexes assessment within regions of interest using single and combined task analysis. Children with specific language impairment exhibited a significant lack of left lateralization in all core language regions (inferior frontal gyrus-opercularis, inferior frontal gyrus-triangularis, supramarginal gyrus, superior temporal gyrus), across single or combined task analysis, but no difference of lateralization for the rest of the brain. Between-group comparisons revealed a left hypoactivation of Wernicke’s area at the posterior superior temporal/supramarginal junction during the responsive naming task, and a right hyperactivation encompassing the anterior insula with adjacent inferior frontal gyrus and the head of the caudate nucleus during the first phonological task. This study thus provides evidence that this specific subtype of specific language impairment is associated with atypical lateralization and functioning of core language areas. PMID:21719430

  2. The Language Faculty – Mind or Brain?

    Directory of Open Access Journals (Sweden)

    Torben Thrane

    2009-05-01

    Full Text Available Artiklens sigte er at underkaste Chomskys nyskabende nominale sammensætning – "the mind/brain" – en nøjere undersøgelse. Hovedargumentet er at den skaber en glidebane, således at det der burde være en funktionel diskussion – hvad er det hjernen gør når den behandler sprog? – i stedet bliver en systemdiskussion – hvad er den interne struktur af den menneskelige sprogevne? Argumentet søges ført igennem under streng iagttagelse af de kognitive videnskabers opfattelse af information og repræsentation, specielt som forstået af den amerikanske filosof Frederick I. Dretske.

  3. Broca Pars Triangularis Constitutes a “Hub” of the Language-Control Network during Simultaneous Language Translation

    Directory of Open Access Journals (Sweden)

    Stefan Elmer

    2016-09-01

    Full Text Available Until now, several branches of research have fundamentally contributed to a better understanding of the ramifications of bilingualism, multilingualism, and language expertise on psycholinguistic-, cognitive-, and neural implications. In this context, it is noteworthy to mention that from a cognitive perspective, there is a strong convergence of data pointing to an influence of multilingual speech competence on a variety of cognitive functions, including attention, short-term- and working memory, set shifting, switching, and inhibition. In addition, complementary neuroimaging findings have highlighted a specific set of cortical and subcortical brain regions which fundamentally contribute to administrate cognitive control in the multilingual brain, namely Broca’s area, the middle-anterior cingulate cortex, the inferior parietal lobe, and the basal ganglia. However, a disadvantage of focusing on group analyses is that this procedure only enables an approximation of the neural networks shared within a population while at the same time smoothing inter-individual differences. In order to address both commonalities (i.e., within group analyses and inter-individual variability (i.e., single-subject analyses in language control mechanisms, here I measured five professional simultaneous interpreters while the participants overtly translated or repeated sentences with a simple subject-verb-object structure. Results demonstrated that pars triangularis was commonly activated across participants during backward translation (i.e., from L2 to L1, whereas the other brain regions of the control network showed a strong inter-individual variability during both backward and forward (i.e., from L1 to L2 translation. Thus, I propose that pars triangularis plays a crucial role within the language-control network and behaves as a fundamental processing entity supporting simultaneous language translation.

  4. Language comprehension and brain function in individuals with an optimal outcome from autism.

    Science.gov (United States)

    Eigsti, Inge-Marie; Stevens, Michael C; Schultz, Robert T; Barton, Marianne; Kelley, Elizabeth; Naigles, Letitia; Orinstein, Alyssa; Troyb, Eva; Fein, Deborah A

    2016-01-01

    Although Autism Spectrum Disorder (ASD) is generally a lifelong disability, a minority of individuals with ASD overcome their symptoms to such a degree that they are generally indistinguishable from their typically-developing peers. That is, they have achieved an Optimal Outcome (OO). The question addressed by the current study is whether this normalized behavior reflects normalized brain functioning, or alternatively, the action of compensatory systems. Either possibility is plausible, as most participants with OO received years of intensive therapy that could alter brain networks to align with typical function or work around ASD-related neural dysfunction. Individuals ages 8 to 21 years with high-functioning ASD (n = 23), OO (n = 16), or typical development (TD; n = 20) completed a functional MRI scan while performing a sentence comprehension task. Results indicated similar activations in frontal and temporal regions (left middle frontal, left supramarginal, and right superior temporal gyri) and posterior cingulate in OO and ASD groups, where both differed from the TD group. Furthermore, the OO group showed heightened "compensatory" activation in numerous left- and right-lateralized regions (left precentral/postcentral gyri, right precentral gyrus, left inferior parietal lobule, right supramarginal gyrus, left superior temporal/parahippocampal gyrus, left middle occipital gyrus) and cerebellum, relative to both ASD and TD groups. Behaviorally normalized language abilities in OO individuals appear to utilize atypical brain networks, with increased recruitment of language-specific as well as right homologue and other systems. Early intensive learning and experience may normalize behavioral language performance in OO, but some brain regions involved in language processing may continue to display characteristics that are more similar to ASD than typical development, while others show characteristics not like ASD or typical development.

  5. Brain function differences in language processing in children and adults with autism.

    Science.gov (United States)

    Williams, Diane L; Cherkassky, Vladimir L; Mason, Robert A; Keller, Timothy A; Minshew, Nancy J; Just, Marcel Adam

    2013-08-01

    Comparison of brain function between children and adults with autism provides an understanding of the effects of the disorder and associated maturational differences on language processing. Functional imaging (functional magnetic resonance imaging) was used to examine brain activation and cortical synchronization during the processing of literal and ironic texts in 15 children with autism, 14 children with typical development, 13 adults with autism, and 12 adult controls. Both the children and adults with autism had lower functional connectivity (synchronization of brain activity among activated areas) than their age and ability comparison group in the left hemisphere language network during irony processing, and neither autism group had an increase in functional connectivity in response to increased task demands. Activation differences for the literal and irony conditions occurred in key language-processing regions (left middle temporal, left pars triangularis, left pars opercularis, left medial frontal, and right middle temporal). The children and adults with autism differed from each other in the use of some brain regions during the irony task, with the adults with autism having activation levels similar to those of the control groups. Overall, the children and adults with autism differed from the adult and child controls in (a) the degree of network coordination, (b) the distribution of the workload among member nodes, and (3) the dynamic recruitment of regions in response to text content. Moreover, the differences between the two autism age groups may be indicative of positive changes in the neural function related to language processing associated with maturation and/or educational experience. © 2013 International Society for Autism Research, Wiley Periodicals, Inc.

  6. Language comprehension and brain function in individuals with an optimal outcome from autism

    Directory of Open Access Journals (Sweden)

    Inge-Marie Eigsti

    2016-01-01

    Full Text Available Although Autism Spectrum Disorder (ASD is generally a lifelong disability, a minority of individuals with ASD overcome their symptoms to such a degree that they are generally indistinguishable from their typically-developing peers. That is, they have achieved an Optimal Outcome (OO. The question addressed by the current study is whether this normalized behavior reflects normalized brain functioning, or alternatively, the action of compensatory systems. Either possibility is plausible, as most participants with OO received years of intensive therapy that could alter brain networks to align with typical function or work around ASD-related neural dysfunction. Individuals ages 8 to 21 years with high-functioning ASD (n = 23, OO (n = 16, or typical development (TD; n = 20 completed a functional MRI scan while performing a sentence comprehension task. Results indicated similar activations in frontal and temporal regions (left middle frontal, left supramarginal, and right superior temporal gyri and posterior cingulate in OO and ASD groups, where both differed from the TD group. Furthermore, the OO group showed heightened “compensatory” activation in numerous left- and right-lateralized regions (left precentral/postcentral gyri, right precentral gyrus, left inferior parietal lobule, right supramarginal gyrus, left superior temporal/parahippocampal gyrus, left middle occipital gyrus and cerebellum, relative to both ASD and TD groups. Behaviorally normalized language abilities in OO individuals appear to utilize atypical brain networks, with increased recruitment of language-specific as well as right homologue and other systems. Early intensive learning and experience may normalize behavioral language performance in OO, but some brain regions involved in language processing may continue to display characteristics that are more similar to ASD than typical development, while others show characteristics not like ASD or typical development.

  7. Universal brain signature of proficient reading: Evidence from four contrasting languages.

    Science.gov (United States)

    Rueckl, Jay G; Paz-Alonso, Pedro M; Molfese, Peter J; Kuo, Wen-Jui; Bick, Atira; Frost, Stephen J; Hancock, Roeland; Wu, Denise H; Mencl, William Einar; Duñabeitia, Jon Andoni; Lee, Jun-Ren; Oliver, Myriam; Zevin, Jason D; Hoeft, Fumiko; Carreiras, Manuel; Tzeng, Ovid J L; Pugh, Kenneth R; Frost, Ram

    2015-12-15

    We propose and test a theoretical perspective in which a universal hallmark of successful literacy acquisition is the convergence of the speech and orthographic processing systems onto a common network of neural structures, regardless of how spoken words are represented orthographically in a writing system. During functional MRI, skilled adult readers of four distinct and highly contrasting languages, Spanish, English, Hebrew, and Chinese, performed an identical semantic categorization task to spoken and written words. Results from three complementary analytic approaches demonstrate limited language variation, with speech-print convergence emerging as a common brain signature of reading proficiency across the wide spectrum of selected languages, whether their writing system is alphabetic or logographic, whether it is opaque or transparent, and regardless of the phonological and morphological structure it represents.

  8. The Virtual Brain: a simulator of primate brain network dynamics.

    Science.gov (United States)

    Sanz Leon, Paula; Knock, Stuart A; Woodman, M Marmaduke; Domide, Lia; Mersmann, Jochen; McIntosh, Anthony R; Jirsa, Viktor

    2013-01-01

    We present The Virtual Brain (TVB), a neuroinformatics platform for full brain network simulations using biologically realistic connectivity. This simulation environment enables the model-based inference of neurophysiological mechanisms across different brain scales that underlie the generation of macroscopic neuroimaging signals including functional MRI (fMRI), EEG and MEG. Researchers from different backgrounds can benefit from an integrative software platform including a supporting framework for data management (generation, organization, storage, integration and sharing) and a simulation core written in Python. TVB allows the reproduction and evaluation of personalized configurations of the brain by using individual subject data. This personalization facilitates an exploration of the consequences of pathological changes in the system, permitting to investigate potential ways to counteract such unfavorable processes. The architecture of TVB supports interaction with MATLAB packages, for example, the well known Brain Connectivity Toolbox. TVB can be used in a client-server configuration, such that it can be remotely accessed through the Internet thanks to its web-based HTML5, JS, and WebGL graphical user interface. TVB is also accessible as a standalone cross-platform Python library and application, and users can interact with the scientific core through the scripting interface IDLE, enabling easy modeling, development and debugging of the scientific kernel. This second interface makes TVB extensible by combining it with other libraries and modules developed by the Python scientific community. In this article, we describe the theoretical background and foundations that led to the development of TVB, the architecture and features of its major software components as well as potential neuroscience applications.

  9. The Virtual Brain: a simulator of primate brain network dynamics

    Science.gov (United States)

    Sanz Leon, Paula; Knock, Stuart A.; Woodman, M. Marmaduke; Domide, Lia; Mersmann, Jochen; McIntosh, Anthony R.; Jirsa, Viktor

    2013-01-01

    We present The Virtual Brain (TVB), a neuroinformatics platform for full brain network simulations using biologically realistic connectivity. This simulation environment enables the model-based inference of neurophysiological mechanisms across different brain scales that underlie the generation of macroscopic neuroimaging signals including functional MRI (fMRI), EEG and MEG. Researchers from different backgrounds can benefit from an integrative software platform including a supporting framework for data management (generation, organization, storage, integration and sharing) and a simulation core written in Python. TVB allows the reproduction and evaluation of personalized configurations of the brain by using individual subject data. This personalization facilitates an exploration of the consequences of pathological changes in the system, permitting to investigate potential ways to counteract such unfavorable processes. The architecture of TVB supports interaction with MATLAB packages, for example, the well known Brain Connectivity Toolbox. TVB can be used in a client-server configuration, such that it can be remotely accessed through the Internet thanks to its web-based HTML5, JS, and WebGL graphical user interface. TVB is also accessible as a standalone cross-platform Python library and application, and users can interact with the scientific core through the scripting interface IDLE, enabling easy modeling, development and debugging of the scientific kernel. This second interface makes TVB extensible by combining it with other libraries and modules developed by the Python scientific community. In this article, we describe the theoretical background and foundations that led to the development of TVB, the architecture and features of its major software components as well as potential neuroscience applications. PMID:23781198

  10. Consciousness, cognition and brain networks: New perspectives.

    Science.gov (United States)

    Aldana, E M; Valverde, J L; Fábregas, N

    2016-10-01

    A detailed analysis of the literature on consciousness and cognition mechanisms based on the neural networks theory is presented. The immune and inflammatory response to the anesthetic-surgical procedure induces modulation of neuronal plasticity by influencing higher cognitive functions. Anesthetic drugs can cause unconsciousness, producing a functional disruption of cortical and thalamic cortical integration complex. The external and internal perceptions are processed through an intricate network of neural connections, involving the higher nervous activity centers, especially the cerebral cortex. This requires an integrated model, formed by neural networks and their interactions with highly specialized regions, through large-scale networks, which are distributed throughout the brain collecting information flow of these perceptions. Functional and effective connectivity between large-scale networks, are essential for consciousness, unconsciousness and cognition. It is what is called the "human connectome" or map neural networks. Copyright © 2014 Sociedad Española de Anestesiología, Reanimación y Terapéutica del Dolor. Publicado por Elsevier España, S.L.U. All rights reserved.

  11. The development of brain network architecture.

    Science.gov (United States)

    Wierenga, Lara M; van den Heuvel, Martijn P; van Dijk, Sarai; Rijks, Yvonne; de Reus, Marcel A; Durston, Sarah

    2016-02-01

    Brain connectivity shows protracted development throughout childhood and adolescence, and, as such, the topology of brain networks changes during this period. The complexity of these changes with development is reflected by regional differences in maturation. This study explored age-related changes in network topology and regional developmental patterns during childhood and adolescence. We acquired two sets of Diffusion Weighted Imaging-scans and anatomical T1-weighted scans. The first dataset included 85 typically developing individuals (53 males; 32 females), aged between 7 and 23 years and was acquired on a Philips Achieva 1.5 Tesla scanner. A second dataset (N = 38) was acquired on a different (but identical) 1.5 T scanner and was used for independent replication of our results. We reconstructed whole brain networks using tractography. We operationalized fiber tract development as changes in mean diffusivity and radial diffusivity with age. Most fibers showed maturational changes in mean and radial diffusivity values throughout childhood and adolescence, likely reflecting increasing white matter integrity. The largest age-related changes were observed in association fibers within and between the frontal and parietal lobes. Furthermore, there was a simultaneous age-related decrease in average path length (P maturational model where connections between unimodal regions strengthen in childhood, followed by connections from these unimodal regions to association regions, while adolescence is characterized by the strengthening of connections between association regions within the frontal and parietal cortex. Hum Brain Mapp 37:717-729, 2016. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

  12. Diagnostic work up for language testing in patients undergoing awake craniotomy for brain lesions in language areas.

    Science.gov (United States)

    Bilotta, Federico; Stazi, Elisabetta; Titi, Luca; Lalli, Diana; Delfini, Roberto; Santoro, Antonio; Rosa, Giovanni

    2014-06-01

    Awake craniotomy is the technique of choice in patients with brain tumours adjacent to primary and accessory language areas (Broca's and Wernicke's areas). Language testing should be aimed to detect preoperative deficits, to promptly identify the occurrence of new intraoperative impairments and to establish the course of postoperative language status. Aim of this case series is to describe our experience with a dedicated language testing work up to evaluate patients with or at risk for language disturbances undergoing awake craniotomy for brain tumour resection. Pre- and intra operative testing was accomplished with 8 tests. Intraoperative evaluation was accomplished when patients were fully cooperative (Ramsey awake craniotomy for brain tumour resection with preoperative language disturbances or at risk for postoperative language deficits. This approach allows a systematic evaluation and recording of language function status and can be accomplished even when a neuropsychologist or speech therapist are not involved in the operation crew.

  13. Switching Language Modes: Complementary Brain Patterns for Formulaic and Propositional Language.

    Science.gov (United States)

    Sidtis, John J; Van Lancker Sidtis, Diana; Dhawan, Vijay; Eidelberg, David

    2018-04-01

    Language has been modeled as a rule governed behavior for generating an unlimited number of novel utterances using phonological, syntactic, and lexical processes. This view of language as essentially propositional is expanding as a contributory role of formulaic expressions (e.g., you know, have a nice day, how are you?) is increasingly recognized. The basic features of the functional anatomy of this language system have been described by studies of brain damage: left lateralization for propositional language and greater right lateralization and basal ganglia involvement for formulaic expressions. Positron emission tomography (PET) studies of cerebral blood flow (CBF) have established a cortical-subcortical pattern of brain activity predictive of syllable rate during phonological/lexical repetition. The same analytic approach was applied to analyzing brain images obtained during spontaneous monologues. Sixteen normal, right-handed, native English speakers underwent PET scanning during several language tasks. Speech rate for the repetition of phonological/lexical items was predicted by increased CBF in the left inferior frontal region and decreased CBF in the head of the right caudate nucleus, replicating previous results. A complementary cortical-subcortical pattern (CBF increased in the right inferior frontal region and decreased in the left caudate) was predictive of the use of speech formulas during monologue speech. The use of propositional language during the monologues was associated with strong left lateralization (increased CBF at the left inferior frontal region and decreased CBF at the right inferior frontal region). Normal communication involves the integration of two language modes, formulaic and novel, that have different neural substrates.

  14. Recognition of sign language gestures using neural networks

    Directory of Open Access Journals (Sweden)

    Simon Vamplew

    2007-04-01

    Full Text Available This paper describes the structure and performance of the SLARTI sign language recognition system developed at the University of Tasmania. SLARTI uses a modular architecture consisting of multiple feature-recognition neural networks and a nearest-neighbour classifier to recognise Australian sign language (Auslan hand gestures.

  15. Recognition of sign language gestures using neural networks

    OpenAIRE

    Simon Vamplew

    2007-01-01

    This paper describes the structure and performance of the SLARTI sign language recognition system developed at the University of Tasmania. SLARTI uses a modular architecture consisting of multiple feature-recognition neural networks and a nearest-neighbour classifier to recognise Australian sign language (Auslan) hand gestures.

  16. The Influence of Japanese Anime Language to Chinese Network Buzzwords

    Directory of Open Access Journals (Sweden)

    Cai Jin Chang

    2016-01-01

    Full Text Available In this paper, the influence of Japanese anime language to Chinese network buzzwords was studied, and the maturity and rigor were quoted. The Japanese anime language refined, creative can be so popular in the factors of psychologies also was analyzed. According to these studies, some suggestions were put forward that how to standardize the network buzzwords and how to raise its taste.

  17. Spectral properties of the temporal evolution of brain network structure.

    Science.gov (United States)

    Wang, Rong; Zhang, Zhen-Zhen; Ma, Jun; Yang, Yong; Lin, Pan; Wu, Ying

    2015-12-01

    The temporal evolution properties of the brain network are crucial for complex brain processes. In this paper, we investigate the differences in the dynamic brain network during resting and visual stimulation states in a task-positive subnetwork, task-negative subnetwork, and whole-brain network. The dynamic brain network is first constructed from human functional magnetic resonance imaging data based on the sliding window method, and then the eigenvalues corresponding to the network are calculated. We use eigenvalue analysis to analyze the global properties of eigenvalues and the random matrix theory (RMT) method to measure the local properties. For global properties, the shifting of the eigenvalue distribution and the decrease in the largest eigenvalue are linked to visual stimulation in all networks. For local properties, the short-range correlation in eigenvalues as measured by the nearest neighbor spacing distribution is not always sensitive to visual stimulation. However, the long-range correlation in eigenvalues as evaluated by spectral rigidity and number variance not only predicts the universal behavior of the dynamic brain network but also suggests non-consistent changes in different networks. These results demonstrate that the dynamic brain network is more random for the task-positive subnetwork and whole-brain network under visual stimulation but is more regular for the task-negative subnetwork. Our findings provide deeper insight into the importance of spectral properties in the functional brain network, especially the incomparable role of RMT in revealing the intrinsic properties of complex systems.

  18. Language and reading development in the brain today: neuromarkers and the case for prediction.

    Science.gov (United States)

    Buchweitz, Augusto

    2016-01-01

    The goal of this article is to provide an account of language development in the brain using the new information about brain function gleaned from cognitive neuroscience. This account goes beyond describing the association between language and specific brain areas to advocate the possibility of predicting language outcomes using brain-imaging data. The goal is to address the current evidence about language development in the brain and prediction of language outcomes. Recent studies will be discussed in the light of the evidence generated for predicting language outcomes and using new methods of analysis of brain data. The present account of brain behavior will address: (1) the development of a hardwired brain circuit for spoken language; (2) the neural adaptation that follows reading instruction and fosters the "grafting" of visual processing areas of the brain onto the hardwired circuit of spoken language; and (3) the prediction of language development and the possibility of translational neuroscience. Brain imaging has allowed for the identification of neural indices (neuromarkers) that reflect typical and atypical language development; the possibility of predicting risk for language disorders has emerged. A mandate to develop a bridge between neuroscience and health and cognition-related outcomes may pave the way for translational neuroscience. Copyright © 2016 Sociedade Brasileira de Pediatria. Published by Elsevier Editora Ltda. All rights reserved.

  19. A comparison of brain activity associated with language production in brain tumor patients with left and right sided language laterality.

    Science.gov (United States)

    Jansma, J M; Ramsey, N; Rutten, G J

    2015-12-01

    Language dominance is an important factor for clinical decision making in brain tumor surgery. Functional MRI can provide detailed information about the organization of language in the brain. One often used measure derived from fMRI data is the laterality index (LI). The LI is typically based on the ratio between left and right brain activity in a specific region associated with language. Nearly all fMRI language studies show language-related activity in both hemispheres, and as a result the LI shows a large range of values. The clinical significance of the variation in language laterality as measured with the LI is still under debate. In this study, we tested two hypotheses in relation to the LI, measured in Broca's region, and it's right hemisphere homologue: 1: the level of activity in Broca's and it's right hemisphere homologue is mirrored for subjects with an equal but opposite LI; 2: the whole brain language activation pattern differs between subjects with an equal but opposite LI. One hundred sixty-three glioma and meningioma patients performed a verb generation task as part of a standard clinical protocol. We calculated the LI in the pars orbitalis, pars triangularis and pars opercularis of the left inferior frontal gyrus, referred to as Broca's region from here on. In our database, 21 patients showed right lateralized activity, with a moderate average level (-0.32). A second group of 21 patients was selected from the remaining group, for equal but opposite LI (0.32). We compared the level and distribution of activity associated with language production in the left and right hemisphere in these two groups. Patients with left sided laterality showed a significantly higher level of activity in Broca's region than the patients with right sided laterality. However, both groups showed no difference in level of activity in Broca's homologue region in the right hemisphere. Also, we did not see any difference in the pattern of activity between patients with left

  20. Pediatric traumatic brain injury: language outcomes and their relationship to the arcuate fasciculus.

    Science.gov (United States)

    Liégeois, Frédérique J; Mahony, Kate; Connelly, Alan; Pigdon, Lauren; Tournier, Jacques-Donald; Morgan, Angela T

    2013-12-01

    Pediatric traumatic brain injury (TBI) may result in long-lasting language impairments alongside dysarthria, a motor-speech disorder. Whether this co-morbidity is due to the functional links between speech and language networks, or to widespread damage affecting both motor and language tracts, remains unknown. Here we investigated language function and diffusion metrics (using diffusion-weighted tractography) within the arcuate fasciculus, the uncinate fasciculus, and the corpus callosum in 32 young people after TBI (approximately half with dysarthria) and age-matched healthy controls (n=17). Only participants with dysarthria showed impairments in language, affecting sentence formulation and semantic association. In the whole TBI group, sentence formulation was best predicted by combined corpus callosum and left arcuate volumes, suggesting this "dual blow" seriously reduces the potential for functional reorganisation. Word comprehension was predicted by fractional anisotropy in the right arcuate. The co-morbidity between dysarthria and language deficits therefore seems to be the consequence of multiple tract damage. Copyright © 2013 Elsevier Inc. All rights reserved.

  1. Brain networks that track musical structure.

    Science.gov (United States)

    Janata, Petr

    2005-12-01

    As the functional neuroimaging literature grows, it becomes increasingly apparent that music and musical activities engage diverse regions of the brain. In this paper I discuss two studies to illustrate that exactly which brain areas are observed to be responsive to musical stimuli and tasks depends on the tasks and the methods used to describe the tasks and the stimuli. In one study, subjects listened to polyphonic music and were asked to either orient their attention selectively to individual instruments or in a divided or holistic manner across multiple instruments. The network of brain areas that was recruited changed subtly with changes in the task instructions. The focus of the second study was to identify brain regions that follow the pattern of movement of a continuous melody through the tonal space defined by the major and minor keys of Western tonal music. Such an area was identified in the rostral medial prefrontal cortex. This observation is discussed in the context of other neuroimaging studies that implicate this region in inwardly directed mental states involving decisions about the self, autobiographical memory, the cognitive regulation of emotion, affective responses to musical stimuli, and familiarity judgments about musical stimuli. Together with observations that these regions are among the last to atrophy in Alzheimer disease, and that these patients appear to remain responsive to autobiographically salient musical stimuli, very early evidence is emerging from the literature for the hypothesis that the rostral medial prefrontal cortex is a node that is important for binding music with memories within a broader music-responsive network.

  2. Second language acquisition after traumatic brain injury: a case study.

    Science.gov (United States)

    Połczyńska-Fiszer, M; Mazaux, J M

    2008-01-01

    Post-traumatic language and memory impairment, as well as a subsequent recovery in monolinguals have been widely documented in the literature, yet little is known about learning the second language after a severe head trauma followed by coma, as well as the relationship of this process with cognitive recovery, psychological status and quality of life. The present study investigates the relationship of learning the second language (English) in the process of rehabilitation, with quality of life in a Polish female university student who, as a result of a car accident, suffered a major closed-head injury and was comatose for a month. The subject was enrolled in an English learning program nine months after the trauma. The experiment lasted six months and comprised monthly meetings. The patient improved the major components of the second language, including vocabulary. Within the 6 months, the subject was gradually capable of learning additional and more complex lexical items. Learning the second language after traumatic brain injury may positively influence emotional well-being, self-esteem, and, perhaps, recovery of quality of life. A long-term beneficial effect of learning L2 was a consequential improvement of the patient's memory.

  3. Potential brain language reorganization in a boy with refractory epilepsy; an fNIRS–EEG and fMRI comparison

    Directory of Open Access Journals (Sweden)

    Phetsamone Vannasing

    2016-01-01

    Full Text Available As part of a presurgical investigation for a resection of a tumor located in the left temporal brain region, we evaluated pre- and postsurgical language lateralization in a right-handed boy with refractory epilepsy. In this study, we compared functional near infrared spectroscopy (fNIRS results obtained while the participant performed expressive and receptive language tasks with those obtained using functional magnetic resonance imaging (fMRI. This case study illustrates the potential for NIRS to contribute favorably to the localization of language functions in children with epilepsy and cognitive or behavioral problems and its potential advantages over fMRI in presurgical assessment. Moreover, it suggests that fNIRS is sensitive in localizing an atypical language network or potential brain reorganization related to epilepsy in young patients.

  4. Broca's arrow: evolution, prediction, and language in the brain.

    Science.gov (United States)

    Cooper, David L

    2006-01-01

    Brodmann's areas 44 and 45 in the human brain, also known as Broca's area, have long been associated with language functions, especially in the left hemisphere. However, the precise role Broca's area plays in human language has not been established with certainty. Broca's area has homologs in the great apes and in area F5 in monkeys, which suggests that its original function was not linguistic at all. In fact, great ape and hominid brains show very similar left-over-right asymmetries in Broca's area homologs as well as in other areas, such as homologs to Wernicke's area, that are normally associated with language in modern humans. Moreover, the so-called mirror neurons are located in Broca's area in great apes and area F5 in monkeys, which seem to provide a representation of cause and effect in a primate's environment, particularly its social environment. Humans appear to have these mirror neurons in Broca's area as well. Similarly, genetic evidence related to the FOXP2 gene implicates Broca's area in linguistic function and dysfunction, but the gene itself is a highly conserved developmental gene in vertebrates and is shared with only two or three differences between humans and great apes, five between humans and mice, and eight between humans and songbirds. Taking neurons and portions of the brain as discrete computational segments in the sense of constituting specific Turing machines, this evidence points to a predictive motor and conceptual function for Broca's area in primates, especially for social concepts. In human language, this is consistent with evidence from typological and cognitive linguistics. (c) 2006 Wiley-Liss, Inc.

  5. [Language Functions in the Frontal Association Area: Brain Mechanisms That Create Language].

    Science.gov (United States)

    Yamamoto, Kayako; Sakai, Kuniyoshi L

    2016-11-01

    Broca's area is known to be critically involved in language processing for more than 150 years. Recent neuroimaging techniques, including functional magnetic resonance imaging (fMRI) and diffusion MRI, enabled the subdivision of Broca's area based on both functional and anatomical aspects. Networks among the frontal association areas, especially the left inferior frontal gyrus (IFG), and other cortical regions in the temporal/parietal association areas, are also important for language-related information processing. Here, we review how neuroimaging studies, combined with research paradigms based on theoretical linguistics, have contributed to clarifying the critical roles of the left IFG in syntactic processing and those of language-related networks, including cortical and cerebellar regions.

  6. Neuroimaging correlates of language network impairment and reorganization in temporal lobe epilepsy

    Science.gov (United States)

    Balter, S.; Lin, G.; Leyden, K.M.; Paul, B.M.; McDonald, C.R.

    2016-01-01

    Advanced, noninvasive imaging has revolutionized our understanding of language networks in the brain and is reshaping our approach to the presurgical evaluation of patients with epilepsy. Functional magnetic resonance imaging (fMRI) has had the greatest impact, unveiling the complexity of language organization and reorganization in patients with epilepsy both pre- and postoperatively, while volumetric MRI and diffusion tensor imaging have led to a greater appreciation of structural and microstructural correlates of language dysfunction in different epilepsy syndromes. In this article, we review recent literature describing how unimodal and multimodal imaging has advanced our knowledge of language networks and their plasticity in epilepsy, with a focus on the most frequently studied epilepsy syndrome in adults, temporal lobe epilepsy (TLE). We also describe how new analytic techniques (i.e., graph theory) are leading to a refined characterization of abnormal brain connectivity, and how subject-specific imaging profiles combined with clinical data may enhance the prediction of both seizure and language outcomes following surgical interventions. PMID:27393391

  7. Brain white matter structure and language ability in preschool-aged children.

    Science.gov (United States)

    Walton, Matthew; Dewey, Deborah; Lebel, Catherine

    2018-01-01

    Brain alterations are associated with reading and language difficulties in older children, but little research has investigated relationships between early language skills and brain white matter structure during the preschool period. We studied 68 children aged 3.0-5.6 years who underwent diffusion tensor imaging and participated in assessments of Phonological Processing and Speeded Naming. Tract-based spatial statistics and tractography revealed relationships between Phonological Processing and diffusion parameters in bilateral ventral white matter pathways and the corpus callosum. Phonological Processing was positively correlated with fractional anisotropy and negatively correlated with mean diffusivity. The relationships observed in left ventral pathways are consistent with studies in older children, and demonstrate that structural markers for language performance are apparent as young as 3 years of age. Our findings in right hemisphere areas that are not as commonly found in adult studies suggest that young children rely on a widespread network for language processing that becomes more specialized with age. Copyright © 2017 Elsevier Inc. All rights reserved.

  8. Brain tumor segmentation with Deep Neural Networks.

    Science.gov (United States)

    Havaei, Mohammad; Davy, Axel; Warde-Farley, David; Biard, Antoine; Courville, Aaron; Bengio, Yoshua; Pal, Chris; Jodoin, Pierre-Marc; Larochelle, Hugo

    2017-01-01

    In this paper, we present a fully automatic brain tumor segmentation method based on Deep Neural Networks (DNNs). The proposed networks are tailored to glioblastomas (both low and high grade) pictured in MR images. By their very nature, these tumors can appear anywhere in the brain and have almost any kind of shape, size, and contrast. These reasons motivate our exploration of a machine learning solution that exploits a flexible, high capacity DNN while being extremely efficient. Here, we give a description of different model choices that we've found to be necessary for obtaining competitive performance. We explore in particular different architectures based on Convolutional Neural Networks (CNN), i.e. DNNs specifically adapted to image data. We present a novel CNN architecture which differs from those traditionally used in computer vision. Our CNN exploits both local features as well as more global contextual features simultaneously. Also, different from most traditional uses of CNNs, our networks use a final layer that is a convolutional implementation of a fully connected layer which allows a 40 fold speed up. We also describe a 2-phase training procedure that allows us to tackle difficulties related to the imbalance of tumor labels. Finally, we explore a cascade architecture in which the output of a basic CNN is treated as an additional source of information for a subsequent CNN. Results reported on the 2013 BRATS test data-set reveal that our architecture improves over the currently published state-of-the-art while being over 30 times faster. Copyright © 2016 Elsevier B.V. All rights reserved.

  9. Brain networks: small-worlds, after all?

    Energy Technology Data Exchange (ETDEWEB)

    Muller, Lyle; Destexhe, Alain; Rudolph-Lilith, Michelle [Unité de Neurosciences, Information et Complexité (UNIC), Centre National de la Recherche Scientifique (CNRS), 1 Avenue de la Terrasse, Gif-sur-Yvette (France)

    2014-10-01

    Since its introduction, the ‘small-world’ effect has played a central role in network science, particularly in the analysis of the complex networks of the nervous system. From the cellular level to that of interconnected cortical regions, many analyses have revealed small-world properties in the networks of the brain. In this work, we revisit the quantification of small-worldness in neural graphs. We find that neural graphs fall into the ‘borderline’ regime of small-worldness, residing close to that of a random graph, especially when the degree sequence of the network is taken into account. We then apply recently introducted analytical expressions for clustering and distance measures, to study this borderline small-worldness regime. We derive theoretical bounds for the minimal and maximal small-worldness index for a given graph, and by semi-analytical means, study the small-worldness index itself. With this approach, we find that graphs with small-worldness equivalent to that observed in experimental data are dominated by their random component. These results provide the first thorough analysis suggesting that neural graphs may reside far away from the maximally small-world regime. (paper)

  10. Brain networks: small-worlds, after all?

    International Nuclear Information System (INIS)

    Muller, Lyle; Destexhe, Alain; Rudolph-Lilith, Michelle

    2014-01-01

    Since its introduction, the ‘small-world’ effect has played a central role in network science, particularly in the analysis of the complex networks of the nervous system. From the cellular level to that of interconnected cortical regions, many analyses have revealed small-world properties in the networks of the brain. In this work, we revisit the quantification of small-worldness in neural graphs. We find that neural graphs fall into the ‘borderline’ regime of small-worldness, residing close to that of a random graph, especially when the degree sequence of the network is taken into account. We then apply recently introducted analytical expressions for clustering and distance measures, to study this borderline small-worldness regime. We derive theoretical bounds for the minimal and maximal small-worldness index for a given graph, and by semi-analytical means, study the small-worldness index itself. With this approach, we find that graphs with small-worldness equivalent to that observed in experimental data are dominated by their random component. These results provide the first thorough analysis suggesting that neural graphs may reside far away from the maximally small-world regime. (paper)

  11. MaLT - Combined Motor and Language Therapy Tool for Brain Injury Patients Using Kinect.

    Science.gov (United States)

    Wairagkar, Maitreyee; McCrindle, Rachel; Robson, Holly; Meteyard, Lotte; Sperrin, Malcom; Smith, Andy; Pugh, Moyra

    2017-03-23

    The functional connectivity and structural proximity of elements of the language and motor systems result in frequent co-morbidity post brain injury. Although rehabilitation services are becoming increasingly multidisciplinary and "integrated", treatment for language and motor functions often occurs in isolation. Thus, behavioural therapies which promote neural reorganisation do not reflect the high intersystem connectivity of the neurologically intact brain. As such, there is a pressing need for rehabilitation tools which better reflect and target the impaired cognitive networks. The objective of this research is to develop a combined high dosage therapy tool for language and motor rehabilitation. The rehabilitation therapy tool developed, MaLT (Motor and Language Therapy), comprises a suite of computer games targeting both language and motor therapy that use the Kinect sensor as an interaction device. The games developed are intended for use in the home environment over prolonged periods of time. In order to track patients' engagement with the games and their rehabilitation progress, the game records patient performance data for the therapist to interrogate. MaLT incorporates Kinect-based games, a database of objects and language parameters, and a reporting tool for therapists. Games have been developed that target four major language therapy tasks involving single word comprehension, initial phoneme identification, rhyme identification and a naming task. These tasks have 8 levels each increasing in difficulty. A database of 750 objects is used to programmatically generate appropriate questions for the game, providing both targeted therapy and unique gameplay every time. The design of the games has been informed by therapists and by discussions with a Public Patient Involvement (PPI) group. Pilot MaLT trials have been conducted with three stroke survivors for the duration of 6 to 8 weeks. Patients' performance is monitored through MaLT's reporting facility

  12. Languages for Software-Defined Networks

    Science.gov (United States)

    2013-02-01

    switches, firewalls, and middleboxes) with closed and proprietary configuration inter- faces. Software - Defined Networks ( SDN ) are poised to change...how- ever, have seen growing interest in software - defined networks ( SDNs ), in which a logically-centralized controller manages the packet-processing...switches, firewalls, and middleboxes) with closed and proprietary configuration interfaces. Software - Defined Networks ( SDN ) are poised to change this

  13. Functional brain networks and prediction models in childhood epilepsy

    NARCIS (Netherlands)

    Diessen, E.G.A.L. van

    2015-01-01

    Modern network science revolutionized the field of neuroscience and revealed significant insights into the organization of the brain. Throughout this thesis we applied a network analytical approach to improve our understanding of the pathological mechanisms underlying focal epilepsy. The presented

  14. Topological organization of functional brain networks in healthy children: differences in relation to age, sex, and intelligence.

    Science.gov (United States)

    Wu, Kai; Taki, Yasuyuki; Sato, Kazunori; Hashizume, Hiroshi; Sassa, Yuko; Takeuchi, Hikaru; Thyreau, Benjamin; He, Yong; Evans, Alan C; Li, Xiaobo; Kawashima, Ryuta; Fukuda, Hiroshi

    2013-01-01

    Recent studies have demonstrated developmental changes of functional brain networks derived from functional connectivity using graph theoretical analysis, which has been rapidly translated to studies of brain network organization. However, little is known about sex- and IQ-related differences in the topological organization of functional brain networks during development. In this study, resting-state fMRI (rs-fMRI) was used to map the functional brain networks in 51 healthy children. We then investigated the effects of age, sex, and IQ on economic small-world properties and regional nodal properties of the functional brain networks. At a global level of whole networks, we found significant age-related increases in the small-worldness and local efficiency, significant higher values of the global efficiency in boys compared with girls, and no significant IQ-related difference. Age-related increases in the regional nodal properties were found predominately in the frontal brain regions, whereas the parietal, temporal, and occipital brain regions showed age-related decreases. Significant sex-related differences in the regional nodal properties were found in various brain regions, primarily related to the default mode, language, and vision systems. Positive correlations between IQ and the regional nodal properties were found in several brain regions related to the attention system, whereas negative correlations were found in various brain regions primarily involved in the default mode, emotion, and language systems. Together, our findings of the network topology of the functional brain networks in healthy children and its relationship with age, sex, and IQ bring new insights into the understanding of brain maturation and cognitive development during childhood and adolescence.

  15. Topological organization of functional brain networks in healthy children: differences in relation to age, sex, and intelligence.

    Directory of Open Access Journals (Sweden)

    Kai Wu

    Full Text Available Recent studies have demonstrated developmental changes of functional brain networks derived from functional connectivity using graph theoretical analysis, which has been rapidly translated to studies of brain network organization. However, little is known about sex- and IQ-related differences in the topological organization of functional brain networks during development. In this study, resting-state fMRI (rs-fMRI was used to map the functional brain networks in 51 healthy children. We then investigated the effects of age, sex, and IQ on economic small-world properties and regional nodal properties of the functional brain networks. At a global level of whole networks, we found significant age-related increases in the small-worldness and local efficiency, significant higher values of the global efficiency in boys compared with girls, and no significant IQ-related difference. Age-related increases in the regional nodal properties were found predominately in the frontal brain regions, whereas the parietal, temporal, and occipital brain regions showed age-related decreases. Significant sex-related differences in the regional nodal properties were found in various brain regions, primarily related to the default mode, language, and vision systems. Positive correlations between IQ and the regional nodal properties were found in several brain regions related to the attention system, whereas negative correlations were found in various brain regions primarily involved in the default mode, emotion, and language systems. Together, our findings of the network topology of the functional brain networks in healthy children and its relationship with age, sex, and IQ bring new insights into the understanding of brain maturation and cognitive development during childhood and adolescence.

  16. Multiple brain networks underpinning word learning from fluent speech revealed by independent component analysis.

    Science.gov (United States)

    López-Barroso, Diana; Ripollés, Pablo; Marco-Pallarés, Josep; Mohammadi, Bahram; Münte, Thomas F; Bachoud-Lévi, Anne-Catherine; Rodriguez-Fornells, Antoni; de Diego-Balaguer, Ruth

    2015-04-15

    Although neuroimaging studies using standard subtraction-based analysis from functional magnetic resonance imaging (fMRI) have suggested that frontal and temporal regions are involved in word learning from fluent speech, the possible contribution of different brain networks during this type of learning is still largely unknown. Indeed, univariate fMRI analyses cannot identify the full extent of distributed networks that are engaged by a complex task such as word learning. Here we used Independent Component Analysis (ICA) to characterize the different brain networks subserving word learning from an artificial language speech stream. Results were replicated in a second cohort of participants with a different linguistic background. Four spatially independent networks were associated with the task in both cohorts: (i) a dorsal Auditory-Premotor network; (ii) a dorsal Sensory-Motor network; (iii) a dorsal Fronto-Parietal network; and (iv) a ventral Fronto-Temporal network. The level of engagement of these networks varied through the learning period with only the dorsal Auditory-Premotor network being engaged across all blocks. In addition, the connectivity strength of this network in the second block of the learning phase correlated with the individual variability in word learning performance. These findings suggest that: (i) word learning relies on segregated connectivity patterns involving dorsal and ventral networks; and (ii) specifically, the dorsal auditory-premotor network connectivity strength is directly correlated with word learning performance. Copyright © 2015 Elsevier Inc. All rights reserved.

  17. Approach of Complex Networks for the Determination of Brain Death

    International Nuclear Information System (INIS)

    Sun Wei-Gang; Cao Jian-Ting; Wang Ru-Bin

    2011-01-01

    In clinical practice, brain death is the irreversible end of all brain activity. Compared to current statistical methods for the determination of brain death, we focus on the approach of complex networks for real-world electroencephalography in its determination. Brain functional networks constructed by correlation analysis are derived, and statistical network quantities used for distinguishing the patients in coma or brain death state, such as average strength, clustering coefficient and average path length, are calculated. Numerical results show that the values of network quantities of patients in coma state are larger than those of patients in brain death state. Our findings might provide valuable insights on the determination of brain death. (cross-disciplinary physics and related areas of science and technology)

  18. Language, Learning, and Identity in Social Networking Sites for Language Learning: The Case of Busuu

    Science.gov (United States)

    Alvarez Valencia, Jose Aldemar

    2014-01-01

    Recent progress in the discipline of computer applications such as the advent of web-based communication, afforded by the Web 2.0, has paved the way for novel applications in language learning, namely, social networking. Social networking has challenged the area of Computer Mediated Communication (CMC) to expand its research palette in order to…

  19. Language, gesture, and handedness: Evidence for independent lateralized networks.

    Science.gov (United States)

    Häberling, Isabelle S; Corballis, Paul M; Corballis, Michael C

    2016-09-01

    Language, gesture, and handedness are in most people represented in the left cerebral hemisphere. To explore the relations among these attributes, we collected fMRI images in a large sample of left- and right-handers while they performed language tasks and watched action sequences. Regions of interest included the frontal and parietal areas previously identified as comprising an action-observation network, and the frontal and temporal areas comprising the primary areas for language production and comprehension. All of the language areas and most of the action-observation areas showed an overall left-hemispheric bias, despite the participation of equal numbers of left- and right-handers. A factor analysis of the laterality indices derived from the different areas during the tasks indicated three independent networks, one associated with language, one associated with handedness, and one representing action observation independent of handedness. Areas 44 and 45, which together make up Broca's area, were part of the language and action-observation networks, but were not included in the part of the action observation network that was related to handedness, which in turn was strongly linked to areas in the parietal lobe. These results suggest an evolutionary scenario in which the primate mirror neuron system (MNS) became increasingly lateralized, and later fissioned onto subsystems with one mediating language and the other mediating the execution and observation of manual actions. The second network is further subdivided into one dependent on hand preference and one that is not, providing new insight into the tripartite system of language, handedness, and praxis. Copyright © 2016 Elsevier Ltd. All rights reserved.

  20. Child gender influences paternal behavior, language, and brain function.

    Science.gov (United States)

    Mascaro, Jennifer S; Rentscher, Kelly E; Hackett, Patrick D; Mehl, Matthias R; Rilling, James K

    2017-06-01

    Multiple lines of research indicate that fathers often treat boys and girls differently in ways that impact child outcomes. The complex picture that has emerged, however, is obscured by methodological challenges inherent to the study of parental caregiving, and no studies to date have examined the possibility that gender differences in observed real-world paternal behavior are related to differential paternal brain responses to male and female children. Here we compare fathers of daughters and fathers of sons in terms of naturalistically observed everyday caregiving behavior and neural responses to child picture stimuli. Compared with fathers of sons, fathers of daughters were more attentively engaged with their daughters, sang more to their daughters, used more analytical language and language related to sadness and the body with their daughters, and had a stronger neural response to their daughter's happy facial expressions in areas of the brain important for reward and emotion regulation (medial and lateral orbitofrontal cortex [OFC]). In contrast, fathers of sons engaged in more rough and tumble play (RTP), used more achievement language with their sons, and had a stronger neural response to their son's neutral facial expressions in the medial OFC (mOFC). Whereas the mOFC response to happy faces was negatively related to RTP, the mOFC response to neutral faces was positively related to RTP, specifically for fathers of boys. These results indicate that real-world paternal behavior and brain function differ as a function of child gender. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  1. Abnormal neural activities of directional brain networks in patients with long-term bilateral hearing loss.

    Science.gov (United States)

    Xu, Long-Chun; Zhang, Gang; Zou, Yue; Zhang, Min-Feng; Zhang, Dong-Sheng; Ma, Hua; Zhao, Wen-Bo; Zhang, Guang-Yu

    2017-10-13

    The objective of the study is to provide some implications for rehabilitation of hearing impairment by investigating changes of neural activities of directional brain networks in patients with long-term bilateral hearing loss. Firstly, we implemented neuropsychological tests of 21 subjects (11 patients with long-term bilateral hearing loss, and 10 subjects with normal hearing), and these tests revealed significant differences between the deaf group and the controls. Then we constructed the individual specific virtual brain based on functional magnetic resonance data of participants by utilizing effective connectivity and multivariate regression methods. We exerted the stimulating signal to the primary auditory cortices of the virtual brain and observed the brain region activations. We found that patients with long-term bilateral hearing loss presented weaker brain region activations in the auditory and language networks, but enhanced neural activities in the default mode network as compared with normally hearing subjects. Especially, the right cerebral hemisphere presented more changes than the left. Additionally, weaker neural activities in the primary auditor cortices were also strongly associated with poorer cognitive performance. Finally, causal analysis revealed several interactional circuits among activated brain regions, and these interregional causal interactions implied that abnormal neural activities of the directional brain networks in the deaf patients impacted cognitive function.

  2. Age of second language acquisition in multilinguals has an impact on gray matter volume in language-associated brain areas.

    Science.gov (United States)

    Kaiser, Anelis; Eppenberger, Leila S; Smieskova, Renata; Borgwardt, Stefan; Kuenzli, Esther; Radue, Ernst-Wilhelm; Nitsch, Cordula; Bendfeldt, Kerstin

    2015-01-01

    Numerous structural studies have established that experience shapes and reshapes the brain throughout a lifetime. The impact of early development, however, is still a matter of debate. Further clues may come from studying multilinguals who acquired their second language at different ages. We investigated adult multilinguals who spoke three languages fluently, where the third language was learned in classroom settings, not before the age of 9 years. Multilinguals exposed to two languages simultaneously from birth (SiM) were contrasted with multinguals who acquired their first two languages successively (SuM). Whole brain voxel based morphometry revealed that, relative to SuM, SiM have significantly lower gray matter volume in several language-associated cortical areas in both hemispheres: bilaterally in medial and inferior frontal gyrus, in the right medial temporal gyrus and inferior posterior parietal gyrus, as well as in the left inferior temporal gyrus. Thus, as shown by others, successive language learning increases the volume of language-associated cortical areas. In brains exposed early on and simultaneously to more than one language, however, learning of additional languages seems to have less impact. We conclude that - at least with respect to language acquisition - early developmental influences are maintained and have an effect on experience-dependent plasticity well into adulthood.

  3. Age of second language acquisition in multilinguals has an impact on grey matter volume in language-associated brain areas

    Directory of Open Access Journals (Sweden)

    Anelis eKaiser

    2015-06-01

    Full Text Available Numerous structural studies have established that experience shapes and reshapes the brain throughout a lifetime. The impact of early development, however, is still a matter of debate. Further clues may come from studying multilinguals who acquired their second language at different ages. We investigated adult multilinguals who spoke three languages fluently, where the third language was learned in classroom settings, not before the age of 9 years. Multilinguals exposed to 2 languages simultaneously from birth (SiM were contrasted with multinguals who acquired their first two languages successively (SuM. Whole brain voxel based morphometry revealed that, relative to SuM, SiM have significantly lower grey matter volume in several language-associated cortical areas in both hemispheres: bilaterally in medial and inferior frontal gyrus, in the right medial temporal gyrus and inferior posterior parietal gyrus, as well as in the left inferior frontal gyrus. Thus, as shown by others, successive language learning increases the volume of language-associated cortical areas. In brains exposed early on and simultaneously to more than one language, however, learning of additional languages seems to have less impact. We conclude that - at least with respect to language acquisition - early developmental influences are maintained and influence experience-dependent plasticity well into adulthood.

  4. Mapping human whole-brain structural networks with diffusion MRI.

    Directory of Open Access Journals (Sweden)

    Patric Hagmann

    Full Text Available Understanding the large-scale structural network formed by neurons is a major challenge in system neuroscience. A detailed connectivity map covering the entire brain would therefore be of great value. Based on diffusion MRI, we propose an efficient methodology to generate large, comprehensive and individual white matter connectional datasets of the living or dead, human or animal brain. This non-invasive tool enables us to study the basic and potentially complex network properties of the entire brain. For two human subjects we find that their individual brain networks have an exponential node degree distribution and that their global organization is in the form of a small world.

  5. Brain Networks Implicated in Seasonal Affective Disorder

    DEFF Research Database (Denmark)

    Nørgaard, Martin; Ganz, Melanie; Svarer, Claus

    2017-01-01

    , patients with SAD fail to globally downregulate their cerebral serotonin transporter (5-HTT) in winter, and that this effect seemed to be particularly pronounced in female S-carriers of the 5-HTTLPR genotype. The purpose of this study was to identify a 5-HTT brain network that accounts for the adaption...... without SAD; it included the right superior frontal gyrus, brainstem, globus pallidus (bilaterally) and the left hippocampus. Across seasons, female S' carriers without SAD showed nominally higher 5-HTT levels in these regions compared to female S' carriers with SAD, but the group difference was only...... winter compared to female S' carriers without SAD. Limitations: The study is preliminary and limited by small sample size in the SAD group (N = 6). Conclusions: These findings provide novel exploratory evidence for a wintertime state-dependent difference in 5-HTT levels that may leave SAD females...

  6. Brain signal variability as a window into the bidirectionality between music and language processing: moving from a linear to a nonlinear model.

    Science.gov (United States)

    Hutka, Stefanie; Bidelman, Gavin M; Moreno, Sylvain

    2013-12-30

    There is convincing empirical evidence for bidirectional transfer between music and language, such that experience in either domain can improve mental processes required by the other. This music-language relationship has been studied using linear models (e.g., comparing mean neural activity) that conceptualize brain activity as a static entity. The linear approach limits how we can understand the brain's processing of music and language because the brain is a nonlinear system. Furthermore, there is evidence that the networks supporting music and language processing interact in a nonlinear manner. We therefore posit that the neural processing and transfer between the domains of language and music are best viewed through the lens of a nonlinear framework. Nonlinear analysis of neurophysiological activity may yield new insight into the commonalities, differences, and bidirectionality between these two cognitive domains not measurable in the local output of a cortical patch. We thus propose a novel application of brain signal variability (BSV) analysis, based on mutual information and signal entropy, to better understand the bidirectionality of music-to-language transfer in the context of a nonlinear framework. This approach will extend current methods by offering a nuanced, network-level understanding of the brain complexity involved in music-language transfer.

  7. Functional connectivity in task-negative network of the Deaf: effects of sign language experience

    Directory of Open Access Journals (Sweden)

    Evie Malaia

    2014-06-01

    Full Text Available Prior studies investigating cortical processing in Deaf signers suggest that life-long experience with sign language and/or auditory deprivation may alter the brain’s anatomical structure and the function of brain regions typically recruited for auditory processing (Emmorey et al., 2010; Pénicaud et al., 2013 inter alia. We report the first investigation of the task-negative network in Deaf signers and its functional connectivity—the temporal correlations among spatially remote neurophysiological events. We show that Deaf signers manifest increased functional connectivity between posterior cingulate/precuneus and left medial temporal gyrus (MTG, but also inferior parietal lobe and medial temporal gyrus in the right hemisphere- areas that have been found to show functional recruitment specifically during sign language processing. These findings suggest that the organization of the brain at the level of inter-network connectivity is likely affected by experience with processing visual language, although sensory deprivation could be another source of the difference. We hypothesize that connectivity alterations in the task negative network reflect predictive/automatized processing of the visual signal.

  8. BrainNetVis: analysis and visualization of brain functional networks.

    Science.gov (United States)

    Tsiaras, Vassilis; Andreou, Dimitris; Tollis, Ioannis G

    2009-01-01

    BrainNetVis is an application, written in Java, that displays and analyzes synchronization networks from brain signals. The program implements a number of network indices and visualization techniques. We demonstrate its use through a case study of left hand and foot motor imagery. The data sets were provided by the Berlin BCI group. Using this program we managed to find differences between the average left hand and foot synchronization networks by comparing them with the average idle state synchronization network.

  9. Brain functional plasticity associated with the emergence of expertise in extreme language control.

    Science.gov (United States)

    Hervais-Adelman, Alexis; Moser-Mercer, Barbara; Golestani, Narly

    2015-07-01

    We used functional magnetic resonance imaging (fMRI) to longitudinally examine brain plasticity arising from long-term, intensive simultaneous interpretation training. Simultaneous interpretation is a bilingual task with heavy executive control demands. We compared brain responses observed during simultaneous interpretation with those observed during simultaneous speech repetition (shadowing) in a group of trainee simultaneous interpreters, at the beginning and at the end of their professional training program. Age, sex and language-proficiency matched controls were scanned at similar intervals. Using multivariate pattern classification, we found distributed patterns of changes in functional responses from the first to second scan that distinguished the interpreters from the controls. We also found reduced recruitment of the right caudate nucleus during simultaneous interpretation as a result of training. Such practice-related change is consistent with decreased demands on multilingual language control as the task becomes more automatized with practice. These results demonstrate the impact of simultaneous interpretation training on the brain functional response in a cerebral structure that is not specifically linguistic, but that is known to be involved in learning, in motor control, and in a variety of domain-general executive functions. Along with results of recent studies showing functional and structural adaptations in the caudate nuclei of experts in a broad range of domains, our results underline the importance of this structure as a central node in expertise-related networks. Copyright © 2015 Elsevier Inc. All rights reserved.

  10. Disrupted functional brain networks in autistic toddlers

    NARCIS (Netherlands)

    Boersma, M.; Kemner, C.; Reus, M.A. de; Collin, G; Snijders, T.M.; Hofman, D.; Buitelaar, J.K.; Stam, C.J.; Heuvel, M.P. van den

    2013-01-01

    Communication and integration of information between brain regions plays a key role in healthy brain function. Conversely, disruption in brain communication may lead to cognitive and behavioral problems. Autism is a neurodevelopmental disorder that is characterized by impaired social interactions

  11. Resolving structural variability in network models and the brain.

    Directory of Open Access Journals (Sweden)

    Florian Klimm

    2014-03-01

    Full Text Available Large-scale white matter pathways crisscrossing the cortex create a complex pattern of connectivity that underlies human cognitive function. Generative mechanisms for this architecture have been difficult to identify in part because little is known in general about mechanistic drivers of structured networks. Here we contrast network properties derived from diffusion spectrum imaging data of the human brain with 13 synthetic network models chosen to probe the roles of physical network embedding and temporal network growth. We characterize both the empirical and synthetic networks using familiar graph metrics, but presented here in a more complete statistical form, as scatter plots and distributions, to reveal the full range of variability of each measure across scales in the network. We focus specifically on the degree distribution, degree assortativity, hierarchy, topological Rentian scaling, and topological fractal scaling--in addition to several summary statistics, including the mean clustering coefficient, the shortest path-length, and the network diameter. The models are investigated in a progressive, branching sequence, aimed at capturing different elements thought to be important in the brain, and range from simple random and regular networks, to models that incorporate specific growth rules and constraints. We find that synthetic models that constrain the network nodes to be physically embedded in anatomical brain regions tend to produce distributions that are most similar to the corresponding measurements for the brain. We also find that network models hardcoded to display one network property (e.g., assortativity do not in general simultaneously display a second (e.g., hierarchy. This relative independence of network properties suggests that multiple neurobiological mechanisms might be at play in the development of human brain network architecture. Together, the network models that we develop and employ provide a potentially useful

  12. Attention-based Memory Selection Recurrent Network for Language Modeling

    OpenAIRE

    Liu, Da-Rong; Chuang, Shun-Po; Lee, Hung-yi

    2016-01-01

    Recurrent neural networks (RNNs) have achieved great success in language modeling. However, since the RNNs have fixed size of memory, their memory cannot store all the information about the words it have seen before in the sentence, and thus the useful long-term information may be ignored when predicting the next words. In this paper, we propose Attention-based Memory Selection Recurrent Network (AMSRN), in which the model can review the information stored in the memory at each previous time ...

  13. Natural Language Processing with Small Feed-Forward Networks

    OpenAIRE

    Botha, Jan A.; Pitler, Emily; Ma, Ji; Bakalov, Anton; Salcianu, Alex; Weiss, David; McDonald, Ryan; Petrov, Slav

    2017-01-01

    We show that small and shallow feed-forward neural networks can achieve near state-of-the-art results on a range of unstructured and structured language processing tasks while being considerably cheaper in memory and computational requirements than deep recurrent models. Motivated by resource-constrained environments like mobile phones, we showcase simple techniques for obtaining such small neural network models, and investigate different tradeoffs when deciding how to allocate a small memory...

  14. Unmasking Language Lateralization in Human Brain Intrinsic Activity

    Science.gov (United States)

    McAvoy, Mark; Mitra, Anish; Coalson, Rebecca S.; d'Avossa, Giovanni; Keidel, James L.; Petersen, Steven E.; Raichle, Marcus E.

    2016-01-01

    Lateralization of function is a fundamental feature of the human brain as exemplified by the left hemisphere dominance of language. Despite the prominence of lateralization in the lesion, split-brain and task-based fMRI literature, surprisingly little asymmetry has been revealed in the increasingly popular functional imaging studies of spontaneous fluctuations in the fMRI BOLD signal (so-called resting-state fMRI). Here, we show the global signal, an often discarded component of the BOLD signal in resting-state studies, reveals a leftward asymmetry that maps onto regions preferential for semantic processing in left frontal and temporal cortex and the right cerebellum and a rightward asymmetry that maps onto putative attention-related regions in right frontal, temporoparietal, and parietal cortex. Hemispheric asymmetries in the global signal resulted from amplitude modulation of the spontaneous fluctuations. To confirm these findings obtained from normal, healthy, right-handed subjects in the resting-state, we had them perform 2 semantic processing tasks: synonym and numerical magnitude judgment and sentence comprehension. In addition to establishing a new technique for studying lateralization through functional imaging of the resting-state, our findings shed new light on the physiology of the global brain signal. PMID:25636911

  15. A functional MRI study of language networks in left medial temporal lobe epilepsy

    International Nuclear Information System (INIS)

    Yu Aihong; Wang Xiaoyi; Xu Guoqing; Li Yongjie; Qin Wen; Li Kuncheng; Wang, Yuping

    2011-01-01

    Purpose: The purpose of this study was to investigate the abnormality of language networks in left medial temporal lobe epilepsy (MTLE) using fMRI. Materials and methods: Eight patients with left MTLE and 15 healthy subjects were evaluated. An auditory semantic judgment (AJ) paradigm was used. The fMRI data were collected on a 3T MR system and analyzed by AFNI (analysis of functional neuroimages) to generate the activation map. Results: Behavioral data showed that the reaction time of the left MTLE patients was significantly longer than that of controls on the AJ task (t = -3.396, P < 0.05). The left MTLE patients also exhibited diffusively decreased activation in the AJ task. Right hemisphere dominance of Broca's and Wernicke's areas was demonstrated in left MTLE patients. Conclusions: Long-term activation of spikes in left MTLE patients results in language impairment, which is associated with an abnormality of the brain neural network.

  16. Hyper-connectivity of functional networks for brain disease diagnosis.

    Science.gov (United States)

    Jie, Biao; Wee, Chong-Yaw; Shen, Dinggang; Zhang, Daoqiang

    2016-08-01

    Exploring structural and functional interactions among various brain regions enables better understanding of pathological underpinnings of neurological disorders. Brain connectivity network, as a simplified representation of those structural and functional interactions, has been widely used for diagnosis and classification of neurodegenerative diseases, especially for Alzheimer's disease (AD) and its early stage - mild cognitive impairment (MCI). However, the conventional functional connectivity network is usually constructed based on the pairwise correlation among different brain regions and thus ignores their higher-order relationships. Such loss of high-order information could be important for disease diagnosis, since neurologically a brain region predominantly interacts with more than one other brain regions. Accordingly, in this paper, we propose a novel framework for estimating the hyper-connectivity network of brain functions and then use this hyper-network for brain disease diagnosis. Here, the functional connectivity hyper-network denotes a network where each of its edges representing the interactions among multiple brain regions (i.e., an edge can connect with more than two brain regions), which can be naturally represented by a hyper-graph. Specifically, we first construct connectivity hyper-networks from the resting-state fMRI (R-fMRI) time series by using sparse representation. Then, we extract three sets of brain-region specific features from the connectivity hyper-networks, and further exploit a manifold regularized multi-task feature selection method to jointly select the most discriminative features. Finally, we use multi-kernel support vector machine (SVM) for classification. The experimental results on both MCI dataset and attention deficit hyperactivity disorder (ADHD) dataset demonstrate that, compared with the conventional connectivity network-based methods, the proposed method can not only improve the classification performance, but also help

  17. Resection of highly language-eloquent brain lesions based purely on rTMS language mapping without awake surgery.

    Science.gov (United States)

    Ille, Sebastian; Sollmann, Nico; Butenschoen, Vicki M; Meyer, Bernhard; Ringel, Florian; Krieg, Sandro M

    2016-12-01

    The resection of left-sided perisylvian brain lesions harbours the risk of postoperative language impairment. Therefore the individual patient's language distribution is investigated by intraoperative direct cortical stimulation (DCS) during awake surgery. Yet, not all patients qualify for awake surgery. Non-invasive language mapping by repetitive navigated transcranial magnetic stimulation (rTMS) has frequently shown a high correlation in comparison with the results of DCS language mapping in terms of language-negative brain regions. The present study analyses the extent of resection (EOR) and functional outcome of patients who underwent left-sided perisylvian resection of brain lesions based purely on rTMS language mapping. Four patients with left-sided perisylvian brain lesions (two gliomas WHO III, one glioblastoma, one cavernous angioma) underwent rTMS language mapping prior to surgery. Data from rTMS language mapping and rTMS-based diffusion tensor imaging fibre tracking (DTI-FT) were transferred to the intraoperative neuronavigation system. Preoperatively, 5 days after surgery (POD5), and 3 months after surgery (POM3) clinical follow-up examinations were performed. No patient suffered from a new surgery-related aphasia at POM3. Three patients underwent complete resection immediately, while one patient required a second rTMS-based resection some days later to achieve the final, complete resection. The present study shows for the first time the feasibility of successfully resecting language-eloquent brain lesions based purely on the results of negative language maps provided by rTMS language mapping and rTMS-based DTI-FT. In very select cases, this technique can provide a rescue strategy with an optimal functional outcome and EOR when awake surgery is not feasible.

  18. Macroscopic networks in the human brain: mapping connectivity in healthy and damaged brains

    NARCIS (Netherlands)

    Nijhuis, E.H.J.

    2013-01-01

    The human brain contains a network of interconnected neurons. Recent advances in functional and structural in-vivo magnetic resonance neuroimaging (MRI) techniques have provided opportunities to model the networks of the human brain on a macroscopic scale. This dissertation investigates the

  19. Brain Connectivity Networks and the Aesthetic Experience of Music.

    Science.gov (United States)

    Reybrouck, Mark; Vuust, Peter; Brattico, Elvira

    2018-06-12

    Listening to music is above all a human experience, which becomes an aesthetic experience when an individual immerses himself/herself in the music, dedicating attention to perceptual-cognitive-affective interpretation and evaluation. The study of these processes where the individual perceives, understands, enjoys and evaluates a set of auditory stimuli has mainly been focused on the effect of music on specific brain structures, as measured with neurophysiology and neuroimaging techniques. The very recent application of network science algorithms to brain research allows an insight into the functional connectivity between brain regions. These studies in network neuroscience have identified distinct circuits that function during goal-directed tasks and resting states. We review recent neuroimaging findings which indicate that music listening is traceable in terms of network connectivity and activations of target regions in the brain, in particular between the auditory cortex, the reward brain system and brain regions active during mind wandering.

  20. Learners' Attitudes toward Foreign Language Practice on Social Network Sites

    Science.gov (United States)

    Villafuerte, Jhonny; Romero, Asier

    2017-01-01

    This work aims to study learners' attitudes towards practicing English Language on Social Networks Sites (SNS). The sample involved 110 students from the University Laica Eloy Alfaro de Manabi in Ecuador, and the University of the Basque Country in Spain. The instrument applied was a Likert scale questionnaire designed Ad hoc by the researchers,…

  1. Recurrent Artificial Neural Networks and Finite State Natural Language Processing.

    Science.gov (United States)

    Moisl, Hermann

    It is argued that pessimistic assessments of the adequacy of artificial neural networks (ANNs) for natural language processing (NLP) on the grounds that they have a finite state architecture are unjustified, and that their adequacy in this regard is an empirical issue. First, arguments that counter standard objections to finite state NLP on the…

  2. Language Learning through Social Networks: Perceptions and Reality

    Science.gov (United States)

    Lin, Chin-Hsi; Warschauer, Mark; Blake, Robert

    2016-01-01

    Language Learning Social Network Sites (LLSNSs) have attracted millions of users around the world. However, little is known about how people participate in these sites and what they learn from them. This study investigated learners' attitudes, usage, and progress in a major LLSNS through a survey of 4,174 as well as 20 individual case studies. The…

  3. Business Process Modeling Languages Supporting Collaborative Networks

    NARCIS (Netherlands)

    Soleimani Malekan, H.; Afsarmanesh, H.; Hammoudi, S.; Maciaszek, L.A.; Cordeiro, J.; Dietz, J.L.G.

    2013-01-01

    Formalizing the definition of Business Processes (BPs) performed within each enterprise is fundamental for effective deployment of their competencies and capabilities within Collaborative Networks (CN). In our approach, every enterprise in the CN is represented by its set of BPs, so that other

  4. Language Development and Brain Magnetic Resonance Imaging Characteristics in Preschool Children with Cerebral Palsy

    Science.gov (United States)

    Choi, Ja Young; Choi, Yoon Seong; Park, Eun Sook

    2017-01-01

    Purpose: The purpose of this study was to investigate characteristics of language development in relation to brain magnetic resonance imaging (MRI) characteristics and the other contributing factors to language development in children with cerebral palsy (CP). Method: The study included 172 children with CP who underwent brain MRI and language…

  5. From the Left to the Right: How the Brain Compensates Progressive Loss of Language Function

    Science.gov (United States)

    Thiel, Alexander; Habedank, Birgit; Herholz, Karl; Kessler, Josef; Winhuisen, Lutz; Haupt, Walter F.; Heiss, Wolf-Dieter

    2006-01-01

    In normal right-handed subjects language production usually is a function of the left brain hemisphere. Patients with aphasia following brain damage to the left hemisphere have a considerable potential to compensate for the loss of this function. Sometimes, but not always, areas of the right hemisphere which are homologous to language areas of the…

  6. Network-dependent modulation of brain activity during sleep.

    Science.gov (United States)

    Watanabe, Takamitsu; Kan, Shigeyuki; Koike, Takahiko; Misaki, Masaya; Konishi, Seiki; Miyauchi, Satoru; Miyahsita, Yasushi; Masuda, Naoki

    2014-09-01

    Brain activity dynamically changes even during sleep. A line of neuroimaging studies has reported changes in functional connectivity and regional activity across different sleep stages such as slow-wave sleep (SWS) and rapid-eye-movement (REM) sleep. However, it remains unclear whether and how the large-scale network activity of human brains changes within a given sleep stage. Here, we investigated modulation of network activity within sleep stages by applying the pairwise maximum entropy model to brain activity obtained by functional magnetic resonance imaging from sleeping healthy subjects. We found that the brain activity of individual brain regions and functional interactions between pairs of regions significantly increased in the default-mode network during SWS and decreased during REM sleep. In contrast, the network activity of the fronto-parietal and sensory-motor networks showed the opposite pattern. Furthermore, in the three networks, the amount of the activity changes throughout REM sleep was negatively correlated with that throughout SWS. The present findings suggest that the brain activity is dynamically modulated even in a sleep stage and that the pattern of modulation depends on the type of the large-scale brain networks. Copyright © 2014 Elsevier Inc. All rights reserved.

  7. A network of genes, genetic disorders, and brain areas.

    Directory of Open Access Journals (Sweden)

    Satoru Hayasaka

    Full Text Available The network-based approach has been used to describe the relationship among genes and various phenotypes, producing a network describing complex biological relationships. Such networks can be constructed by aggregating previously reported associations in the literature from various databases. In this work, we applied the network-based approach to investigate how different brain areas are associated to genetic disorders and genes. In particular, a tripartite network with genes, genetic diseases, and brain areas was constructed based on the associations among them reported in the literature through text mining. In the resulting network, a disproportionately large number of gene-disease and disease-brain associations were attributed to a small subset of genes, diseases, and brain areas. Furthermore, a small number of brain areas were found to be associated with a large number of the same genes and diseases. These core brain regions encompassed the areas identified by the previous genome-wide association studies, and suggest potential areas of focus in the future imaging genetics research. The approach outlined in this work demonstrates the utility of the network-based approach in studying genetic effects on the brain.

  8. Role of physical and mental training in brain network configuration

    Directory of Open Access Journals (Sweden)

    Philip P. Foster

    2015-06-01

    Full Text Available Continuous remodeling of proteins of excitatory neurons is fine-tuning the scaling and strength of excitatory synapses up or down via regulation of intra-cellular metabolic and regulatory networks of the genome-transcriptome-proteome interface. Alzheimer's disease is a model of energy cost-driven small-world network disorder as the network global efficiency is impaired by the deposition of an informed agent, the amyloid-β, selectively targeting high-degree nodes. In schizophrenia, the interconnectivity and density of rich-club networks are significantly reduced. Training-induced homeostatic synaptogenesis-enhancement produces a reconfiguration of brain networks into greater small-worldness. Creation of synaptic connections in a macro-network, and, at the intra-cellular scale, micro-networks regulate the physiological mechanisms for the preferential attachment of synapses. The strongest molecular relationship of exercise and functional connectivity was identified for brain-derived neurotrophic factor (BDNF. The allele variant, rs7294919, also shows a powerful relationship with the hippocampal volume. How the brain achieves this unique quest of reconfiguration remains a puzzle. What are the underlying mechanisms of synaptogenesis promoting communications brain ↔ muscle and brainbrain in such trainings? What is the respective role of independent mental, physical or combined-mental-physical trainings? Physical practice seems to be playing an instrumental role in the cognitive enhancement (brain ↔ muscle com.. However, mental training, meditation or virtual reality (films, games require only minimal motor activity and cardio-respiratory stimulation. Therefore, other potential paths (brainbrain com. molding brain networks are nonetheless essential. Patients with motor neuron disease/injury (e.g. amyotrophic lateral sclerosis, traumatism also achieve successful cognitive enhancement albeit they may only elicit mental practice

  9. Brain and Social Networks: Fundamental Building Blocks of Human Experience.

    Science.gov (United States)

    Falk, Emily B; Bassett, Danielle S

    2017-09-01

    How do brains shape social networks, and how do social ties shape the brain? Social networks are complex webs by which ideas spread among people. Brains comprise webs by which information is processed and transmitted among neural units. While brain activity and structure offer biological mechanisms for human behaviors, social networks offer external inducers or modulators of those behaviors. Together, these two axes represent fundamental contributors to human experience. Integrating foundational knowledge from social and developmental psychology and sociology on how individuals function within dyads, groups, and societies with recent advances in network neuroscience can offer new insights into both domains. Here, we use the example of how ideas and behaviors spread to illustrate the potential of multilayer network models. Copyright © 2017 Elsevier Ltd. All rights reserved.

  10. Properties of language networks and language systems. Comment on "Approaching human language with complex networks" by Cong and Liu

    Science.gov (United States)

    Yu, Shuiyuan; Xu, Chunshan

    2014-12-01

    Language is generally considered a defining feature of human beings, a key medium for interpersonal communication, a fundamental tool for human thinking and an important vehicle for culture transmission. For the anthropoids to evolve into human being, the emergence of linguistic system is a vital step. Then, how can language serve functions so complicated and so important? To answer this question, it is necessary to probe into a central topic in linguistics: the structure of language, which has been inevitably involved in various fields of linguistic research-the functions of languages, the evolution of languages, the typology of languages, etc.

  11. Hemispheric asymmetry of electroencephalography-based functional brain networks.

    Science.gov (United States)

    Jalili, Mahdi

    2014-11-12

    Electroencephalography (EEG)-based functional brain networks have been investigated frequently in health and disease. It has been shown that a number of graph theory metrics are disrupted in brain disorders. EEG-based brain networks are often studied in the whole-brain framework, where all the nodes are grouped into a single network. In this study, we studied the brain networks in two hemispheres and assessed whether there are any hemispheric-specific patterns in the properties of the networks. To this end, resting state closed-eyes EEGs from 44 healthy individuals were processed and the network structures were extracted separately for each hemisphere. We examined neurophysiologically meaningful graph theory metrics: global and local efficiency measures. The global efficiency did not show any hemispheric asymmetry, whereas the local connectivity showed rightward asymmetry for a range of intermediate density values for the constructed networks. Furthermore, the age of the participants showed significant direct correlations with the global efficiency of the left hemisphere, but only in the right hemisphere, with local connectivity. These results suggest that only local connectivity of EEG-based functional networks is associated with brain hemispheres.

  12. Scalable Brain Network Construction on White Matter Fibers.

    Science.gov (United States)

    Chung, Moo K; Adluru, Nagesh; Dalton, Kim M; Alexander, Andrew L; Davidson, Richard J

    2011-02-12

    DTI offers a unique opportunity to characterize the structural connectivity of the human brain non-invasively by tracing white matter fiber tracts. Whole brain tractography studies routinely generate up to half million tracts per brain, which serves as edges in an extremely large 3D graph with up to half million edges. Currently there is no agreed-upon method for constructing the brain structural network graphs out of large number of white matter tracts. In this paper, we present a scalable iterative framework called the ε-neighbor method for building a network graph and apply it to testing abnormal connectivity in autism.

  13. Brain signal variability as a window into the bidirectionality between music and language processing: Moving from a linear to a nonlinear model

    Directory of Open Access Journals (Sweden)

    Stefanie Andrea Hutka

    2013-12-01

    Full Text Available There is convincing empirical evidence for bidirectional transfer between music and language, such that experience in either domain can improve mental processes required by the other. This music-language relationship has been studied using linear models (e.g., comparing mean neural activity that conceptualize brain activity as a static entity. The linear approach limits how we can understand the brain’s processing of music and language because the brain is a nonlinear system. Furthermore, there is evidence that the networks supporting music and language processing interact in a nonlinear manner. We therefore posit that the neural processing and transfer between the domains of language and music are best viewed through the lens of a nonlinear framework. Nonlinear analysis of neurophysiological activity may yield new insight into the commonalities, differences, and bidirectionality between these two cognitive domains not measurable in the local output of a cortical patch. We thus propose a novel application of brain signal variability (BSV analysis, based on mutual information and signal entropy, to better understand the bidirectionality of music-to-language transfer in the context of a nonlinear framework. This approach will extend current methods by offering a nuanced, network-level understanding of the brain complexity involved in music-language transfer.

  14. Development of large-scale functional brain networks in children.

    Directory of Open Access Journals (Sweden)

    Kaustubh Supekar

    2009-07-01

    Full Text Available The ontogeny of large-scale functional organization of the human brain is not well understood. Here we use network analysis of intrinsic functional connectivity to characterize the organization of brain networks in 23 children (ages 7-9 y and 22 young-adults (ages 19-22 y. Comparison of network properties, including path-length, clustering-coefficient, hierarchy, and regional connectivity, revealed that although children and young-adults' brains have similar "small-world" organization at the global level, they differ significantly in hierarchical organization and interregional connectivity. We found that subcortical areas were more strongly connected with primary sensory, association, and paralimbic areas in children, whereas young-adults showed stronger cortico-cortical connectivity between paralimbic, limbic, and association areas. Further, combined analysis of functional connectivity with wiring distance measures derived from white-matter fiber tracking revealed that the development of large-scale brain networks is characterized by weakening of short-range functional connectivity and strengthening of long-range functional connectivity. Importantly, our findings show that the dynamic process of over-connectivity followed by pruning, which rewires connectivity at the neuronal level, also operates at the systems level, helping to reconfigure and rebalance subcortical and paralimbic connectivity in the developing brain. Our study demonstrates the usefulness of network analysis of brain connectivity to elucidate key principles underlying functional brain maturation, paving the way for novel studies of disrupted brain connectivity in neurodevelopmental disorders such as autism.

  15. Development of large-scale functional brain networks in children.

    Science.gov (United States)

    Supekar, Kaustubh; Musen, Mark; Menon, Vinod

    2009-07-01

    The ontogeny of large-scale functional organization of the human brain is not well understood. Here we use network analysis of intrinsic functional connectivity to characterize the organization of brain networks in 23 children (ages 7-9 y) and 22 young-adults (ages 19-22 y). Comparison of network properties, including path-length, clustering-coefficient, hierarchy, and regional connectivity, revealed that although children and young-adults' brains have similar "small-world" organization at the global level, they differ significantly in hierarchical organization and interregional connectivity. We found that subcortical areas were more strongly connected with primary sensory, association, and paralimbic areas in children, whereas young-adults showed stronger cortico-cortical connectivity between paralimbic, limbic, and association areas. Further, combined analysis of functional connectivity with wiring distance measures derived from white-matter fiber tracking revealed that the development of large-scale brain networks is characterized by weakening of short-range functional connectivity and strengthening of long-range functional connectivity. Importantly, our findings show that the dynamic process of over-connectivity followed by pruning, which rewires connectivity at the neuronal level, also operates at the systems level, helping to reconfigure and rebalance subcortical and paralimbic connectivity in the developing brain. Our study demonstrates the usefulness of network analysis of brain connectivity to elucidate key principles underlying functional brain maturation, paving the way for novel studies of disrupted brain connectivity in neurodevelopmental disorders such as autism.

  16. Japanese Language Proficiency, Social Networking, and Language Use during Study Abroad: Learners' Perspectives

    Science.gov (United States)

    Dewey, Dan P.; Bown, Jennifer; Eggett, Dennis

    2012-01-01

    This study examines the self-perceived speaking proficiency development of 204 learners of Japanese who studied abroad in Japan and analyzes connections between self-reported social network development, language use, and speaking development. Learners perceived that they gained the most in areas associated with the intermediate and advanced levels…

  17. Extending network approach to language dynamics and human cognition. Comment on "Approaching human language with complex networks" by Cong and Liu

    Science.gov (United States)

    Gong, Tao; Shuai, Lan; Wu, Yicheng

    2014-12-01

    By analyzing complex networks constructed from authentic language data, Cong and Liu [1] advance linguistics research into the big data era. The network approach has revealed many intrinsic generalities and crucial differences at both the macro and micro scales between human languages. The axiom behind this research is that language is a complex adaptive system [2]. Although many lexical, semantic, or syntactic features have been discovered by means of analyzing the static and dynamic linguistic networks of world languages, available network-based language studies have not explicitly addressed the evolutionary dynamics of language systems and the correlations between language and human cognition. This commentary aims to provide some insights on how to use the network approach to study these issues.

  18. Role of physical and mental training in brain network configuration.

    Science.gov (United States)

    Foster, Philip P

    2015-01-01

    It is hypothesized that the topology of brain networks is constructed by connecting nodes which may be continuously remodeled by appropriate training. Efficiency of physical and/or mental training on the brain relies on the flexibility of networks' architecture molded by local remodeling of proteins and synapses of excitatory neurons producing transformations in network topology. Continuous remodeling of proteins of excitatory neurons is fine-tuning the scaling and strength of excitatory synapses up or down via regulation of intra-cellular metabolic and regulatory networks of the genome-transcriptome-proteome interface. Alzheimer's disease is a model of "energy cost-driven small-world network disorder" with dysfunction of high-energy cost wiring as the network global efficiency is impaired by the deposition of an informed agent, the amyloid-β, selectively targeting high-degree nodes. In schizophrenia, the interconnectivity and density of rich-club networks are significantly reduced. Training-induced homeostatic synaptogenesis-enhancement, presumably via reconfiguration of brain networks into greater small-worldness, appears essential in learning, memory, and executive functions. A macroscopic cartography of creation-removal of synaptic connections in a macro-network, and at the intra-cellular scale, micro-networks regulate the physiological mechanisms for the preferential attachment of synapses. The strongest molecular relationship of exercise and functional connectivity was identified for brain-derived neurotrophic factor (BDNF). The allele variant, rs7294919, also shows a powerful relationship with the hippocampal volume. How the brain achieves this unique quest of reconfiguration remains a puzzle. What are the underlying mechanisms of synaptogenesis promoting communications brain ↔ muscle and brainbrain in such trainings? What is the respective role of independent mental, physical, or combined-mental-physical trainings? Physical practice seems to be

  19. Toward the Language-Ready Brain: Biological Evolution and Primate Comparisons.

    Science.gov (United States)

    Arbib, Michael A

    2017-02-01

    The approach to language evolution suggested here focuses on three questions: How did the human brain evolve so that humans can develop, use, and acquire languages? How can the evolutionary quest be informed by studying brain, behavior, and social interaction in monkeys, apes, and humans? How can computational modeling advance these studies? I hypothesize that the brain is language ready in that the earliest humans had protolanguages but not languages (i.e., communication systems endowed with rich and open-ended lexicons and grammars supporting a compositional semantics), and that it took cultural evolution to yield societies (a cultural constructed niche) in which language-ready brains could become language-using brains. The mirror system hypothesis is a well-developed example of this approach, but I offer it here not as a closed theory but as an evolving framework for the development and analysis of conflicting subhypotheses in the hope of their eventual integration. I also stress that computational modeling helps us understand the evolving role of mirror neurons, not in and of themselves, but only in their interaction with systems "beyond the mirror." Because a theory of evolution needs a clear characterization of what it is that evolved, I also outline ideas for research in neurolinguistics to complement studies of the evolution of the language-ready brain. A clear challenge is to go beyond models of speech comprehension to include sign language and models of production, and to link language to visuomotor interaction with the physical and social world.

  20. Dynamic neural network reorganization associated with second language vocabulary acquisition: a multimodal imaging study.

    Science.gov (United States)

    Hosoda, Chihiro; Tanaka, Kanji; Nariai, Tadashi; Honda, Manabu; Hanakawa, Takashi

    2013-08-21

    It remains unsettled whether human language relies exclusively on innately privileged brain structure in the left hemisphere or is more flexibly shaped through experiences, which induce neuroplastic changes in potentially relevant neural circuits. Here we show that learning of second language (L2) vocabulary and its cessation can induce bidirectional changes in the mirror-reverse of the traditional language areas. A cross-sectional study identified that gray matter volume in the inferior frontal gyrus pars opercularis (IFGop) and connectivity of the IFGop with the caudate nucleus and the superior temporal gyrus/supramarginal (STG/SMG), predominantly in the right hemisphere, were positively correlated with L2 vocabulary competence. We then implemented a cohort study involving 16 weeks of L2 training in university students. Brain structure before training did not predict the later gain in L2 ability. However, training intervention did increase IFGop volume and reorganization of white matter including the IFGop-caudate and IFGop-STG/SMG pathways in the right hemisphere. These "positive" plastic changes were correlated with the gain in L2 ability in the trained group but were not observed in the control group. We propose that the right hemispheric network can be reorganized into language-related areas through use-dependent plasticity in young adults, reflecting a repertoire of flexible reorganization of the neural substrates responding to linguistic experiences.

  1. Hierarchical organization of brain functional networks during visual tasks.

    Science.gov (United States)

    Zhuo, Zhao; Cai, Shi-Min; Fu, Zhong-Qian; Zhang, Jie

    2011-09-01

    The functional network of the brain is known to demonstrate modular structure over different hierarchical scales. In this paper, we systematically investigated the hierarchical modular organizations of the brain functional networks that are derived from the extent of phase synchronization among high-resolution EEG time series during a visual task. In particular, we compare the modular structure of the functional network from EEG channels with that of the anatomical parcellation of the brain cortex. Our results show that the modular architectures of brain functional networks correspond well to those from the anatomical structures over different levels of hierarchy. Most importantly, we find that the consistency between the modular structures of the functional network and the anatomical network becomes more pronounced in terms of vision, sensory, vision-temporal, motor cortices during the visual task, which implies that the strong modularity in these areas forms the functional basis for the visual task. The structure-function relationship further reveals that the phase synchronization of EEG time series in the same anatomical group is much stronger than that of EEG time series from different anatomical groups during the task and that the hierarchical organization of functional brain network may be a consequence of functional segmentation of the brain cortex.

  2. Episodic memory in aspects of large-scale brain networks

    Science.gov (United States)

    Jeong, Woorim; Chung, Chun Kee; Kim, June Sic

    2015-01-01

    Understanding human episodic memory in aspects of large-scale brain networks has become one of the central themes in neuroscience over the last decade. Traditionally, episodic memory was regarded as mostly relying on medial temporal lobe (MTL) structures. However, recent studies have suggested involvement of more widely distributed cortical network and the importance of its interactive roles in the memory process. Both direct and indirect neuro-modulations of the memory network have been tried in experimental treatments of memory disorders. In this review, we focus on the functional organization of the MTL and other neocortical areas in episodic memory. Task-related neuroimaging studies together with lesion studies suggested that specific sub-regions of the MTL are responsible for specific components of memory. However, recent studies have emphasized that connectivity within MTL structures and even their network dynamics with other cortical areas are essential in the memory process. Resting-state functional network studies also have revealed that memory function is subserved by not only the MTL system but also a distributed network, particularly the default-mode network (DMN). Furthermore, researchers have begun to investigate memory networks throughout the entire brain not restricted to the specific resting-state network (RSN). Altered patterns of functional connectivity (FC) among distributed brain regions were observed in patients with memory impairments. Recently, studies have shown that brain stimulation may impact memory through modulating functional networks, carrying future implications of a novel interventional therapy for memory impairment. PMID:26321939

  3. Episodic memory in aspects of large-scale brain networks

    Directory of Open Access Journals (Sweden)

    Woorim eJeong

    2015-08-01

    Full Text Available Understanding human episodic memory in aspects of large-scale brain networks has become one of the central themes in neuroscience over the last decade. Traditionally, episodic memory was regarded as mostly relying on medial temporal lobe (MTL structures. However, recent studies have suggested involvement of more widely distributed cortical network and the importance of its interactive roles in the memory process. Both direct and indirect neuro-modulations of the memory network have been tried in experimental treatments of memory disorders. In this review, we focus on the functional organization of the MTL and other neocortical areas in episodic memory. Task-related neuroimaging studies together with lesion studies suggested that specific sub-regions of the MTL are responsible for specific components of memory. However, recent studies have emphasized that connectivity within MTL structures and even their network dynamics with other cortical areas are essential in the memory process. Resting-state functional network studies also have revealed that memory function is subserved by not only the MTL system but also a distributed network, particularly the default-mode network. Furthermore, researchers have begun to investigate memory networks throughout the entire brain not restricted to the specific resting-state network. Altered patterns of functional connectivity among distributed brain regions were observed in patients with memory impairments. Recently, studies have shown that brain stimulation may impact memory through modulating functional networks, carrying future implications of a novel interventional therapy for memory impairment.

  4. A permutation testing framework to compare groups of brain networks.

    Science.gov (United States)

    Simpson, Sean L; Lyday, Robert G; Hayasaka, Satoru; Marsh, Anthony P; Laurienti, Paul J

    2013-01-01

    Brain network analyses have moved to the forefront of neuroimaging research over the last decade. However, methods for statistically comparing groups of networks have lagged behind. These comparisons have great appeal for researchers interested in gaining further insight into complex brain function and how it changes across different mental states and disease conditions. Current comparison approaches generally either rely on a summary metric or on mass-univariate nodal or edge-based comparisons that ignore the inherent topological properties of the network, yielding little power and failing to make network level comparisons. Gleaning deeper insights into normal and abnormal changes in complex brain function demands methods that take advantage of the wealth of data present in an entire brain network. Here we propose a permutation testing framework that allows comparing groups of networks while incorporating topological features inherent in each individual network. We validate our approach using simulated data with known group differences. We then apply the method to functional brain networks derived from fMRI data.

  5. Human brain networks function in connectome-specific harmonic waves.

    Science.gov (United States)

    Atasoy, Selen; Donnelly, Isaac; Pearson, Joel

    2016-01-21

    A key characteristic of human brain activity is coherent, spatially distributed oscillations forming behaviour-dependent brain networks. However, a fundamental principle underlying these networks remains unknown. Here we report that functional networks of the human brain are predicted by harmonic patterns, ubiquitous throughout nature, steered by the anatomy of the human cerebral cortex, the human connectome. We introduce a new technique extending the Fourier basis to the human connectome. In this new frequency-specific representation of cortical activity, that we call 'connectome harmonics', oscillatory networks of the human brain at rest match harmonic wave patterns of certain frequencies. We demonstrate a neural mechanism behind the self-organization of connectome harmonics with a continuous neural field model of excitatory-inhibitory interactions on the connectome. Remarkably, the critical relation between the neural field patterns and the delicate excitation-inhibition balance fits the neurophysiological changes observed during the loss and recovery of consciousness.

  6. Structural Brain Network Disturbances in the Psychosis Spectrum

    NARCIS (Netherlands)

    van Dellen, Edwin; Bohlken, Marc M; Draaisma, Laurijn; Tewarie, Prejaas K; van Lutterveld, Remko; Mandl, René; Stam, Cornelis J; Sommer, Iris E

    2016-01-01

    BACKGROUND: Individuals with subclinical psychotic symptoms provide a unique window on the pathophysiology of psychotic experiences as these individuals are free of confounders such as hospitalization, negative and cognitive symptoms and medication use. Brain network disturbances of white matter

  7. Disruption of structural covariance networks for language in autism is modulated by verbal ability.

    Science.gov (United States)

    Sharda, Megha; Khundrakpam, Budhachandra S; Evans, Alan C; Singh, Nandini C

    2016-03-01

    The presence of widespread speech and language deficits is a core feature of autism spectrum disorders (ASD). These impairments have often been attributed to altered connections between brain regions. Recent developments in anatomical correlation-based approaches to map structural covariance offer an effective way of studying such connections in vivo. In this study, we employed such a structural covariance network (SCN)-based approach to investigate the integrity of anatomical networks in fronto-temporal brain regions of twenty children with ASD compared to an age and gender-matched control group of twenty-two children. Our findings reflected large-scale disruption of inter and intrahemispheric covariance in left frontal SCNs in the ASD group compared to controls, but no differences in right fronto-temporal SCNs. Interhemispheric covariance in left-seeded networks was further found to be modulated by verbal ability of the participants irrespective of autism diagnosis, suggesting that language function might be related to the strength of interhemispheric structural covariance between frontal regions. Additionally, regional cortical thickening was observed in right frontal and left posterior regions, which was predicted by decreasing symptom severity and increasing verbal ability in ASD. These findings unify reports of regional differences in cortical morphology in ASD. They also suggest that reduced left hemisphere asymmetry and increased frontal growth may not only reflect neurodevelopmental aberrations but also compensatory mechanisms.

  8. The Efficiency of a Small-World Functional Brain Network

    Institute of Scientific and Technical Information of China (English)

    ZHAO Qing-Bai; ZHANG Xiao-Fei; SUI Dan-Ni; ZHOU Zhi-Jin; CHEN Qi-Cai; TANG Yi-Yuan

    2012-01-01

    We investigate whether the small-world topology of a functional brain network means high information processing efficiency by calculating the correlation between the small-world measures of a functional brain network and behavioral reaction during an imagery task.Functional brain networks are constructed by multichannel eventrelated potential data,in which the electrodes are the nodes and the functional connectivities between them are the edges.The results show that the correlation between small-world measures and reaction time is task-specific,such that in global imagery,there is a positive correlation between the clustering coefficient and reaction time,while in local imagery the average path length is positively correlated with the reaction time.This suggests that the efficiency of a functional brain network is task-dependent.%We investigate whether the small-world topology of a functional brain network means high information processing efficiency by calculating the correlation between the small-world measures of a functional brain network and behavioral reaction during an imagery task. Functional brain networks are constructed by multichannel event-related potential data, in which the electrodes are the nodes and the functional connectivities between them are the edges. The results show that the correlation between small-world measures and reaction time is task-specific, such that in global imagery, there is a positive correlation between the clustering coefficient and reaction time, while in local imagery the average path length is positively correlated with the reaction time. This suggests that the efficiency of a functional brain network is task-dependent.

  9. Default, Cognitive, and Affective Brain Networks in Human Tinnitus

    Science.gov (United States)

    2015-10-01

    AWARD NUMBER: W81XWH-13-1-0491 TITLE: Default, Cognitive, and Affective Brain Networks in Human Tinnitus PRINCIPAL INVESTIGATOR: Jennifer R...SUBTITLE 5a. CONTRACT NUMBER Default, Cognitive and Affective Brain Networks in Human Tinnitus 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6...Release; Distribution Unlimited 13. SUPPLEMENTARY NOTES 14. ABSTRACT Tinnitus is a major health problem among those currently and formerly in military

  10. Gender differences in brain networks supporting empathy.

    Science.gov (United States)

    Schulte-Rüther, Martin; Markowitsch, Hans J; Shah, N Jon; Fink, Gereon R; Piefke, Martina

    2008-08-01

    Females frequently score higher on standard tests of empathy, social sensitivity, and emotion recognition than do males. It remains to be clarified, however, whether these gender differences are associated with gender specific neural mechanisms of emotional social cognition. We investigated gender differences in an emotion attribution task using functional magnetic resonance imaging. Subjects either focused on their own emotional response to emotion expressing faces (SELF-task) or evaluated the emotional state expressed by the faces (OTHER-task). Behaviorally, females rated SELF-related emotions significantly stronger than males. Across the sexes, SELF- and OTHER-related processing of facial expressions activated a network of medial and lateral prefrontal, temporal, and parietal brain regions involved in emotional perspective taking. During SELF-related processing, females recruited the right inferior frontal cortex and superior temporal sulcus stronger than males. In contrast, there was increased neural activity in the left temporoparietal junction in males (relative to females). When performing the OTHER-task, females showed increased activation of the right inferior frontal cortex while there were no differential activations in males. The data suggest that females recruit areas containing mirror neurons to a higher degree than males during both SELF- and OTHER-related processing in empathic face-to-face interactions. This may underlie facilitated emotional "contagion" in females. Together with the observation that males differentially rely on the left temporoparietal junction (an area mediating the distinction between the SELF and OTHERS) the data suggest that females and males rely on different strategies when assessing their own emotions in response to other people.

  11. The social brain is not enough: on the importance of the ecological brain for the origin of language

    Directory of Open Access Journals (Sweden)

    Francesco Ferretti

    2016-08-01

    Full Text Available In this paper, I assume that the study of the origin of language is strictly connected to the analysis of the traits that distinguish human language from animal communication. Usually, human language is said to be unique in the animal kingdom because it enables and/or requires intentionality or mindreading. By emphasizing the importance of mindreading, the social brain hypothesis has provided major insights within the origin of language debate. However, as studies on non-human primates have demonstrated that intentional forms of communication are already present in these species to a greater or lesser extent, I maintain that the social brain is a necessary but not a sufficient condition to explain the uniqueness of language. In this paper, I suggest that the distinctive feature of human communication resides in the ability to tell stories, and that the origin of language should be traced with respect to the capacity to produce discourses, rather than phrases or words. As narrative requires the ability to link events distant from one another in space and time, my proposal is that in order to explain the origin of language, we need to appeal to both the social brain and the ecological brain – that is, the cognitive devices which allow us to mentally travel in space and time.

  12. The Social Brain Is Not Enough: On the Importance of the Ecological Brain for the Origin of Language.

    Science.gov (United States)

    Ferretti, Francesco

    2016-01-01

    In this paper, I assume that the study of the origin of language is strictly connected to the analysis of the traits that distinguish human language from animal communication. Usually, human language is said to be unique in the animal kingdom because it enables and/or requires intentionality or mindreading. By emphasizing the importance of mindreading, the social brain hypothesis has provided major insights within the origin of language debate. However, as studies on non-human primates have demonstrated that intentional forms of communication are already present in these species to a greater or lesser extent, I maintain that the social brain is a necessary but not a sufficient condition to explain the uniqueness of language. In this paper, I suggest that the distinctive feature of human communication resides in the ability to tell stories, and that the origin of language should be traced with respect to the capacity to produce discourses, rather than phrases or words. As narrative requires the ability to link events distant from one another in space and time, my proposal is that in order to explain the origin of language, we need to appeal to both the social brain and the ecological brain - that is, the cognitive devices which allow us to mentally travel in space and time.

  13. Spontaneous brain network activity: Analysis of its temporal complexity

    Directory of Open Access Journals (Sweden)

    Mangor Pedersen

    2017-06-01

    Full Text Available The brain operates in a complex way. The temporal complexity underlying macroscopic and spontaneous brain network activity is still to be understood. In this study, we explored the brain’s complexity by combining functional connectivity, graph theory, and entropy analyses in 25 healthy people using task-free functional magnetic resonance imaging. We calculated the pairwise instantaneous phase synchrony between 8,192 brain nodes for a total of 200 time points. This resulted in graphs for which time series of clustering coefficients (the “cliquiness” of a node and participation coefficients (the between-module connectivity of a node were estimated. For these two network metrics, sample entropy was calculated. The procedure produced a number of results: (1 Entropy is higher for the participation coefficient than for the clustering coefficient. (2 The average clustering coefficient is negatively related to its associated entropy, whereas the average participation coefficient is positively related to its associated entropy. (3 The level of entropy is network-specific to the participation coefficient, but not to the clustering coefficient. High entropy for the participation coefficient was observed in the default-mode, visual, and motor networks. These results were further validated using an independent replication dataset. Our work confirms that brain networks are temporally complex. Entropy is a good candidate metric to explore temporal network alterations in diseases with paroxysmal brain disruptions, including schizophrenia and epilepsy. In recent years, connectomics has provided significant insights into the topological complexity of brain networks. However, the temporal complexity of brain networks still remains somewhat poorly understood. In this study we used entropy analysis to demonstrate that the properties of network segregation (the clustering coefficient and integration (the participation coefficient are temporally complex

  14. Imaging network level language recovery after left PCA stroke.

    Science.gov (United States)

    Sebastian, Rajani; Long, Charltien; Purcell, Jeremy J; Faria, Andreia V; Lindquist, Martin; Jarso, Samson; Race, David; Davis, Cameron; Posner, Joseph; Wright, Amy; Hillis, Argye E

    2016-05-11

    The neural mechanisms that support aphasia recovery are not yet fully understood. Our goal was to evaluate longitudinal changes in naming recovery in participants with posterior cerebral artery (PCA) stroke using a case-by-case analysis. Using task based and resting state functional magnetic resonance imaging (fMRI) and detailed language testing, we longitudinally studied the recovery of the naming network in four participants with PCA stroke with naming deficits at the acute (0 week), sub acute (3-5 weeks), and chronic time point (5-7 months) post stroke. Behavioral and imaging analyses (task related and resting state functional connectivity) were carried out to elucidate longitudinal changes in naming recovery. Behavioral and imaging analysis revealed that an improvement in naming accuracy from the acute to the chronic stage was reflected by increased connectivity within and between left and right hemisphere "language" regions. One participant who had persistent moderate naming deficit showed weak and decreasing connectivity longitudinally within and between left and right hemisphere language regions. These findings emphasize a network view of aphasia recovery, and show that the degree of inter- and intra- hemispheric balance between the language-specific regions is necessary for optimal recovery of naming, at least in participants with PCA stroke.

  15. A Programming Language Approach to Safety in Home Networks

    DEFF Research Database (Denmark)

    Mortensen, Kjeld Høyer; Schougaard, Kari Rye; Schultz, Ulrik Pagh

    , even in a worst-case scenario where an unauthorized user gains remote control of the facilities. We address this safety issue at the programming language level by restricting the operations that can be performed on devices according to the physical location of the user initiating the request......-based restrictions on operations. This model has been implemented in a middleware for home AV devices written in Java, using infrared communication and a FireWire network to implement location awareness....

  16. A Programming Language Approach to Safety in Home Networks

    DEFF Research Database (Denmark)

    Mortensen, Kjeld Høyer; Schougaard, Kari Sofie Fogh; Schultz, Ulrik Pagh

    2003-01-01

    , even in a worst-case scenario where an unauthorized user gains remote control of the facilities. We address this safety issue at the programming language level by restricting the operations that can be performed on devices according to the physical location of the user initiating the request......-based restrictions on operations. This model has been implemented in a middleware for home AV devices written in Java, using infrared communication and a FireWire network to implement location awareness....

  17. Brain Network Analysis from High-Resolution EEG Signals

    Science.gov (United States)

    de Vico Fallani, Fabrizio; Babiloni, Fabio

    Over the last decade, there has been a growing interest in the detection of the functional connectivity in the brain from different neuroelectromagnetic and hemodynamic signals recorded by several neuro-imaging devices such as the functional Magnetic Resonance Imaging (fMRI) scanner, electroencephalography (EEG) and magnetoencephalography (MEG) apparatus. Many methods have been proposed and discussed in the literature with the aim of estimating the functional relationships among different cerebral structures. However, the necessity of an objective comprehension of the network composed by the functional links of different brain regions is assuming an essential role in the Neuroscience. Consequently, there is a wide interest in the development and validation of mathematical tools that are appropriate to spot significant features that could describe concisely the structure of the estimated cerebral networks. The extraction of salient characteristics from brain connectivity patterns is an open challenging topic, since often the estimated cerebral networks have a relative large size and complex structure. Recently, it was realized that the functional connectivity networks estimated from actual brain-imaging technologies (MEG, fMRI and EEG) can be analyzed by means of the graph theory. Since a graph is a mathematical representation of a network, which is essentially reduced to nodes and connections between them, the use of a theoretical graph approach seems relevant and useful as firstly demonstrated on a set of anatomical brain networks. In those studies, the authors have employed two characteristic measures, the average shortest path L and the clustering index C, to extract respectively the global and local properties of the network structure. They have found that anatomical brain networks exhibit many local connections (i.e. a high C) and few random long distance connections (i.e. a low L). These values identify a particular model that interpolate between a regular

  18. Optimal Brain Surgeon on Artificial Neural Networks in

    DEFF Research Database (Denmark)

    Christiansen, Niels Hørbye; Job, Jonas Hultmann; Klyver, Katrine

    2012-01-01

    It is shown how the procedure know as optimal brain surgeon can be used to trim and optimize artificial neural networks in nonlinear structural dynamics. Beside optimizing the neural network, and thereby minimizing computational cost in simulation, the surgery procedure can also serve as a quick...

  19. Write to read: the brain's universal reading and writing network.

    Science.gov (United States)

    Perfetti, Charles A; Tan, Li-Hai

    2013-02-01

    Do differences in writing systems translate into differences in the brain's reading network? Or is this network universal, relatively impervious to variation in writing systems? A new study adds intriguing evidence to these questions by showing that reading handwritten words activates a pre-motor area across writing systems. Copyright © 2012 Elsevier Ltd. All rights reserved.

  20. Using network science in the language sciences and clinic.

    Science.gov (United States)

    Vitevitch, Michael S; Castro, Nichol

    2015-02-01

    A number of variables—word frequency, word length—have long been known to influence language processing. This study briefly reviews the effects in speech perception and production of two more recently examined variables: phonotactic probability and neighbourhood density. It then describes a new approach to study language, network science, which is an interdisciplinary field drawing from mathematics, computer science, physics and other disciplines. In this approach, nodes represent individual entities in a system (i.e. phonological word-forms in the lexicon), links between nodes represent relationships between nodes (i.e. phonological neighbours) and various measures enable researchers to assess the micro-level (i.e. the individual word), the macro-level (i.e. characteristics about the whole system) and the meso-level (i.e. how an individual fits into smaller sub-groups in the larger system). Although research on individual lexical characteristics such as word-frequency has increased understanding of language processing, these measures only assess the "micro-level". Using network science, researchers can examine words at various levels in the system and how each word relates to the many other words stored in the lexicon. Several new findings using the network science approach are summarized to illustrate how this approach can be used to advance basic research as well as clinical practice.

  1. Social Networking Sites (SNSs- Shifting Paradigm of English Language Usage

    Directory of Open Access Journals (Sweden)

    Hetal K. Kachhia

    2014-06-01

    Full Text Available English is the globally accepted language in various nations and professions in different varieties, but the English that has acquired a wider spectrum than all these Englishes is the English used in Social Networking Sites which has changed the scenario of English language usage due to the ease in its accessibility and the kind of flexibility of language usage. The English used in Social Networking Sites like Facebook and Twitter has changed the linguistic behaviour of the people by producing a number of acronyms like BFF, FB etc, creating new verb forms like ‘to tweet’ or nouns like ‘tweeple’ or producing many compound nouns such as ‘netiquette’, changing the meaning of traditional verbs and nouns by introducing new meanings to them, e.g. the word ‘friend’ is used to refer “someone to an online list of acquaintances”, and by making use of prefixes like ‘un’ for the purpose of conveying the meaning of negation as in ‘unlike a comment/update’ by ignoring its original prefix ‘dis’ for referring the antonym of ‘like’. By emphasizing on the aim of communication, grammar and vocabulary are put on the peripheral value in Social Networking Sites. Therefore, the focal point of this paper is to study the changes in the linguistic behaviour of the people caused by the SNSs.

  2. Altered resting state brain networks in Parkinson's disease.

    Directory of Open Access Journals (Sweden)

    Martin Göttlich

    Full Text Available Parkinson's disease (PD is a neurodegenerative disorder affecting dopaminergic neurons in the substantia nigra leading to dysfunctional cortico-striato-thalamic-cortical loops. In addition to the characteristic motor symptoms, PD patients often show cognitive impairments, affective changes and other non-motor symptoms, suggesting system-wide effects on brain function. Here, we used functional magnetic resonance imaging and graph-theory based analysis methods to investigate altered whole-brain intrinsic functional connectivity in PD patients (n = 37 compared to healthy controls (n = 20. Global network properties indicated less efficient processing in PD. Analysis of brain network modules pointed to increased connectivity within the sensorimotor network, but decreased interaction of the visual network with other brain modules. We found lower connectivity mainly between the cuneus and the ventral caudate, medial orbitofrontal cortex and the temporal lobe. To identify regions of altered connectivity, we mapped the degree of intrinsic functional connectivity both on ROI- and on voxel-level across the brain. Compared to healthy controls, PD patients showed lower connectedness in the medial and middle orbitofrontal cortex. The degree of connectivity was also decreased in the occipital lobe (cuneus and calcarine, but increased in the superior parietal cortex, posterior cingulate gyrus, supramarginal gyrus and supplementary motor area. Our results on global network and module properties indicated that PD manifests as a disconnection syndrome. This was most apparent in the visual network module. The higher connectedness within the sensorimotor module in PD patients may be related to compensation mechanism in order to overcome the functional deficit of the striato-cortical motor loops or to loss of mutual inhibition between brain networks. Abnormal connectivity in the visual network may be related to adaptation and compensation processes as a consequence

  3. Information dynamics of brain-heart physiological networks during sleep

    Science.gov (United States)

    Faes, L.; Nollo, G.; Jurysta, F.; Marinazzo, D.

    2014-10-01

    This study proposes an integrated approach, framed in the emerging fields of network physiology and information dynamics, for the quantitative analysis of brain-heart interaction networks during sleep. With this approach, the time series of cardiac vagal autonomic activity and brain wave activities measured respectively as the normalized high frequency component of heart rate variability and the EEG power in the δ, θ, α, σ, and β bands, are considered as realizations of the stochastic processes describing the dynamics of the heart system and of different brain sub-systems. Entropy-based measures are exploited to quantify the predictive information carried by each (sub)system, and to dissect this information into a part actively stored in the system and a part transferred to it from the other connected systems. The application of this approach to polysomnographic recordings of ten healthy subjects led us to identify a structured network of sleep brain-brain and brain-heart interactions, with the node described by the β EEG power acting as a hub which conveys the largest amount of information flowing between the heart and brain nodes. This network was found to be sustained mostly by the transitions across different sleep stages, as the information transfer was weaker during specific stages than during the whole night, and vanished progressively when moving from light sleep to deep sleep and to REM sleep.

  4. Development of the Intrinsic Language Network in Preschool Children from Ages 3 to 5 Years.

    Science.gov (United States)

    Xiao, Yaqiong; Brauer, Jens; Lauckner, Mark; Zhai, Hongchang; Jia, Fucang; Margulies, Daniel S; Friederici, Angela D

    2016-01-01

    Resting state studies of spontaneous fluctuations in the functional magnetic resonance imaging (fMRI) blood oxygen level dependent signal have shown great potential in mapping the intrinsic functional connectivity of the human brain underlying cognitive functions. The aim of the present study was to explore the developmental changes in functional networks of the developing human brain exemplified with the language network in typically developing preschool children. To this end, resting-sate fMRI data were obtained from native Chinese children at ages of 3 and 5 years, 15 in each age group. Resting-state functional connectivity (RSFC) was analyzed for four regions of interest; these are the left and right anterior superior temporal gyrus (aSTG), left posterior superior temporal gyrus (pSTG), and left inferior frontal gyrus (IFG). The comparison of these RSFC maps between 3- and 5-year-olds revealed that RSFC decreases in the right aSTG and increases in the left hemisphere between aSTG seed and IFG, between pSTG seed and IFG, as well as between IFG seed and posterior superior temporal sulcus. In a subsequent analysis, functional asymmetry of the language network seeding in aSTG, pSTG and IFG was further investigated. The results showed an increase of left lateralization in both RSFC of pSTG and of IFG from ages 3 to 5 years. The IFG showed a leftward lateralized trend in 3-year-olds, while pSTG demonstrated rightward asymmetry in 5-year-olds. These findings suggest clear developmental trajectories of the language network between 3- and 5-year-olds revealed as a function of age, characterized by increasing long-range connections and dynamic hemispheric lateralization with age. Our study provides new insights into the developmental changes of a well-established functional network in young children and also offers a basis for future cross-culture and cross-age studies of the resting-state language network.

  5. Development of the Intrinsic Language Network in Preschool Children from Ages 3 to 5 Years.

    Directory of Open Access Journals (Sweden)

    Yaqiong Xiao

    Full Text Available Resting state studies of spontaneous fluctuations in the functional magnetic resonance imaging (fMRI blood oxygen level dependent signal have shown great potential in mapping the intrinsic functional connectivity of the human brain underlying cognitive functions. The aim of the present study was to explore the developmental changes in functional networks of the developing human brain exemplified with the language network in typically developing preschool children. To this end, resting-sate fMRI data were obtained from native Chinese children at ages of 3 and 5 years, 15 in each age group. Resting-state functional connectivity (RSFC was analyzed for four regions of interest; these are the left and right anterior superior temporal gyrus (aSTG, left posterior superior temporal gyrus (pSTG, and left inferior frontal gyrus (IFG. The comparison of these RSFC maps between 3- and 5-year-olds revealed that RSFC decreases in the right aSTG and increases in the left hemisphere between aSTG seed and IFG, between pSTG seed and IFG, as well as between IFG seed and posterior superior temporal sulcus. In a subsequent analysis, functional asymmetry of the language network seeding in aSTG, pSTG and IFG was further investigated. The results showed an increase of left lateralization in both RSFC of pSTG and of IFG from ages 3 to 5 years. The IFG showed a leftward lateralized trend in 3-year-olds, while pSTG demonstrated rightward asymmetry in 5-year-olds. These findings suggest clear developmental trajectories of the language network between 3- and 5-year-olds revealed as a function of age, characterized by increasing long-range connections and dynamic hemispheric lateralization with age. Our study provides new insights into the developmental changes of a well-established functional network in young children and also offers a basis for future cross-culture and cross-age studies of the resting-state language network.

  6. Development of human brain structural networks through infancy and childhood.

    Science.gov (United States)

    Huang, Hao; Shu, Ni; Mishra, Virendra; Jeon, Tina; Chalak, Lina; Wang, Zhiyue J; Rollins, Nancy; Gong, Gaolang; Cheng, Hua; Peng, Yun; Dong, Qi; He, Yong

    2015-05-01

    During human brain development through infancy and childhood, microstructural and macrostructural changes take place to reshape the brain's structural networks and better adapt them to sophisticated functional and cognitive requirements. However, structural topological configuration of the human brain during this specific development period is not well understood. In this study, diffusion magnetic resonance image (dMRI) of 25 neonates, 13 toddlers, and 25 preadolescents were acquired to characterize network dynamics at these 3 landmark cross-sectional ages during early childhood. dMRI tractography was used to construct human brain structural networks, and the underlying topological properties were quantified by graph-theory approaches. Modular organization and small-world attributes are evident at birth with several important topological metrics increasing monotonically during development. Most significant increases of regional nodes occur in the posterior cingulate cortex, which plays a pivotal role in the functional default mode network. Positive correlations exist between nodal efficiencies and fractional anisotropy of the white matter traced from these nodes, while correlation slopes vary among the brain regions. These results reveal substantial topological reorganization of human brain structural networks through infancy and childhood, which is likely to be the outcome of both heterogeneous strengthening of the major white matter tracts and pruning of other axonal fibers. © The Author 2013. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  7. Development of Human Brain Structural Networks Through Infancy and Childhood

    Science.gov (United States)

    Huang, Hao; Shu, Ni; Mishra, Virendra; Jeon, Tina; Chalak, Lina; Wang, Zhiyue J.; Rollins, Nancy; Gong, Gaolang; Cheng, Hua; Peng, Yun; Dong, Qi; He, Yong

    2015-01-01

    During human brain development through infancy and childhood, microstructural and macrostructural changes take place to reshape the brain's structural networks and better adapt them to sophisticated functional and cognitive requirements. However, structural topological configuration of the human brain during this specific development period is not well understood. In this study, diffusion magnetic resonance image (dMRI) of 25 neonates, 13 toddlers, and 25 preadolescents were acquired to characterize network dynamics at these 3 landmark cross-sectional ages during early childhood. dMRI tractography was used to construct human brain structural networks, and the underlying topological properties were quantified by graph-theory approaches. Modular organization and small-world attributes are evident at birth with several important topological metrics increasing monotonically during development. Most significant increases of regional nodes occur in the posterior cingulate cortex, which plays a pivotal role in the functional default mode network. Positive correlations exist between nodal efficiencies and fractional anisotropy of the white matter traced from these nodes, while correlation slopes vary among the brain regions. These results reveal substantial topological reorganization of human brain structural networks through infancy and childhood, which is likely to be the outcome of both heterogeneous strengthening of the major white matter tracts and pruning of other axonal fibers. PMID:24335033

  8. Complex modular structure of large-scale brain networks

    Science.gov (United States)

    Valencia, M.; Pastor, M. A.; Fernández-Seara, M. A.; Artieda, J.; Martinerie, J.; Chavez, M.

    2009-06-01

    Modular structure is ubiquitous among real-world networks from related proteins to social groups. Here we analyze the modular organization of brain networks at a large scale (voxel level) extracted from functional magnetic resonance imaging signals. By using a random-walk-based method, we unveil the modularity of brain webs and show modules with a spatial distribution that matches anatomical structures with functional significance. The functional role of each node in the network is studied by analyzing its patterns of inter- and intramodular connections. Results suggest that the modular architecture constitutes the structural basis for the coexistence of functional integration of distant and specialized brain areas during normal brain activities at rest.

  9. THE IMPACT OF POVERTY ON THE DEVELOPMENT OF BRAIN NETWORKS

    Directory of Open Access Journals (Sweden)

    Sebastian J Lipina

    2012-08-01

    Full Text Available Although the study of brain development in non-human animals is an old one, recent imaging methods have allowed non-invasive studies of the grey and white matter of the human brain over the lifespan. Classic animal studies show clearly that impoverished environments reduce cortical grey matter in relation to complex environments and cognitive and imaging studies in humans suggest which networks may be most influenced by poverty. Studies have been clear in showing the plasticity of many brain systems, but whether sensitivity to learning differs over the lifespan and for which networks is still unclear. A major task for current research is a successful integration of these methods to understand how development and learning shape the neural networks underlying achievements in literacy, numeracy, and attention. This paper seeks to foster further integration by reviewing the currents state of knowledge relating brain changes to behavior and indicating possible future directions.

  10. The brain's default network: anatomy, function, and relevance to disease.

    Science.gov (United States)

    Buckner, Randy L; Andrews-Hanna, Jessica R; Schacter, Daniel L

    2008-03-01

    Thirty years of brain imaging research has converged to define the brain's default network-a novel and only recently appreciated brain system that participates in internal modes of cognition. Here we synthesize past observations to provide strong evidence that the default network is a specific, anatomically defined brain system preferentially active when individuals are not focused on the external environment. Analysis of connectional anatomy in the monkey supports the presence of an interconnected brain system. Providing insight into function, the default network is active when individuals are engaged in internally focused tasks including autobiographical memory retrieval, envisioning the future, and conceiving the perspectives of others. Probing the functional anatomy of the network in detail reveals that it is best understood as multiple interacting subsystems. The medial temporal lobe subsystem provides information from prior experiences in the form of memories and associations that are the building blocks of mental simulation. The medial prefrontal subsystem facilitates the flexible use of this information during the construction of self-relevant mental simulations. These two subsystems converge on important nodes of integration including the posterior cingulate cortex. The implications of these functional and anatomical observations are discussed in relation to possible adaptive roles of the default network for using past experiences to plan for the future, navigate social interactions, and maximize the utility of moments when we are not otherwise engaged by the external world. We conclude by discussing the relevance of the default network for understanding mental disorders including autism, schizophrenia, and Alzheimer's disease.

  11. An algebraic topological method for multimodal brain networks comparison

    Directory of Open Access Journals (Sweden)

    Tiago eSimas

    2015-07-01

    Full Text Available Understanding brain connectivity is one of the most important issues in neuroscience. Nonetheless, connectivity data can reflect either functional relationships of brain activities or anatomical connections between brain areas. Although both representations should be related, this relationship is not straightforward. We have devised a powerful method that allows different operations between networks that share the same set of nodes, by embedding them in a common metric space, enforcing transitivity to the graph topology. Here, we apply this method to construct an aggregated network from a set of functional graphs, each one from a different subject. Once this aggregated functional network is constructed, we use again our method to compare it with the structural connectivity to identify particular brain regions that differ in both modalities (anatomical and functional. Remarkably, these brain regions include functional areas that form part of the classical resting state networks. We conclude that our method -based on the comparison of the aggregated functional network- reveals some emerging features that could not be observed when the comparison is performed with the classical averaged functional network.

  12. Neural networks underlying language and social cognition during self-other processing in Autism spectrum disorders.

    Science.gov (United States)

    Kana, Rajesh K; Sartin, Emma B; Stevens, Carl; Deshpande, Hrishikesh D; Klein, Christopher; Klinger, Mark R; Klinger, Laura Grofer

    2017-07-28

    The social communication impairments defining autism spectrum disorders (ASD) may be built upon core deficits in perspective-taking, language processing, and self-other representation. Self-referential processing entails the ability to incorporate self-awareness, self-judgment, and self-memory in information processing. Very few studies have examined the neural bases of integrating self-other representation and semantic processing in individuals with ASD. The main objective of this functional MRI study is to examine the role of language and social brain networks in self-other processing in young adults with ASD. Nineteen high-functioning male adults with ASD and 19 age-sex-and-IQ-matched typically developing (TD) control participants made "yes" or "no" judgments of whether an adjective, presented visually, described them (self) or their favorite teacher (other). Both ASD and TD participants showed significantly increased activity in the medial prefrontal cortex (MPFC) during self and other processing relative to letter search. Analyses of group differences revealed significantly reduced activity in left inferior frontal gyrus (LIFG), and left inferior parietal lobule (LIPL) in ASD participants, relative to TD controls. ASD participants also showed significantly weaker functional connectivity of the anterior cingulate cortex (ACC) with several brain areas while processing self-related words. The LIFG and IPL are important regions functionally at the intersection of language and social roles; reduced recruitment of these regions in ASD participants may suggest poor level of semantic and social processing. In addition, poor connectivity of the ACC may suggest the difficulty in meeting the linguistic and social demands of this task in ASD. Overall, this study provides new evidence of the altered recruitment of the neural networks underlying language and social cognition in ASD. Published by Elsevier Ltd.

  13. A systematic literature review of sex differences in childhood language and brain development.

    Science.gov (United States)

    Etchell, Andrew; Adhikari, Aditi; Weinberg, Lauren S; Choo, Ai Leen; Garnett, Emily O; Chow, Ho Ming; Chang, Soo-Eun

    2018-06-01

    The extent of sex differences in childhood language development is unclear. We conducted a systematic literature review synthesizing results from studies examining sex differences in brain structure and function relevant to language development during childhood. We searched PubMed and Scopus databases, and this returned a total of 46 published studies meeting criteria for inclusion that directly examined sex differences in brain development relevant to language function in children. The results indicate that: (a) sex differences in brain structure or function do not necessarily lead to differences in language task performance; (b) evidence for sex differences in brain and language development are limited; (c) when present, sex differences often interact with a variety of factors such as age and task. Overall, the magnitude of sexual dimorphism of brain developmental trajectories associated with language is not as significant as previously thought. Sex differences were found, however, in studies employing tighter age ranges. This suggests that sex differences may be more prominent during certain developmental stages but are negligible in other stages, likely due to different rates of maturation between the sexes. More research is needed to improve our understanding of how sex differences may arise due to the influence of sex hormones and developmental stages, and how these differences may lead to differences in various language task performance. These studies are expected to provide normative information that may be used in studies examining neurodevelopmental disorders that frequently affect more males than females, and also often affect language development. Copyright © 2018 Elsevier Ltd. All rights reserved.

  14. Brain responses in 4-month-old infants are already language specific.

    Science.gov (United States)

    Friederici, Angela D; Friedrich, Manuela; Christophe, Anne

    2007-07-17

    Language is the most important faculty that distinguishes humans from other animals. Infants learn their native language fast and effortlessly during the first years of life, as a function of the linguistic input in their environment. Behavioral studies reported the discrimination of melodic contours [1] and stress patterns [2, 3] in 1-4-month-olds. Behavioral [4, 5] and brain measures [6-8] have shown language-independent discrimination of phonetic contrasts at that age. Language-specific discrimination, however, has been reported for phonetic contrasts only for 6-12-month-olds [9-12]. Here we demonstrate language-specific discrimination of stress patterns in 4-month-old German and French infants by using electrophysiological brain measures. We compare the processing of disyllabic words differing in their rhythmic structure, mimicking German words being stressed on the first syllable, e.g., pápa/daddy[13], and French ones being stressed on the second syllable, e.g., papá/daddy. Event-related brain potentials reveal that experience with German and French differentially affects the brain responses of 4-month-old infants, with each language group displaying a processing advantage for the rhythmic structure typical in its native language. These data indicate language-specific neural representations of word forms in the infant brain as early as 4 months of age.

  15. An ANOVA approach for statistical comparisons of brain networks.

    Science.gov (United States)

    Fraiman, Daniel; Fraiman, Ricardo

    2018-03-16

    The study of brain networks has developed extensively over the last couple of decades. By contrast, techniques for the statistical analysis of these networks are less developed. In this paper, we focus on the statistical comparison of brain networks in a nonparametric framework and discuss the associated detection and identification problems. We tested network differences between groups with an analysis of variance (ANOVA) test we developed specifically for networks. We also propose and analyse the behaviour of a new statistical procedure designed to identify different subnetworks. As an example, we show the application of this tool in resting-state fMRI data obtained from the Human Connectome Project. We identify, among other variables, that the amount of sleep the days before the scan is a relevant variable that must be controlled. Finally, we discuss the potential bias in neuroimaging findings that is generated by some behavioural and brain structure variables. Our method can also be applied to other kind of networks such as protein interaction networks, gene networks or social networks.

  16. On characterizing population commonalities and subject variations in brain networks.

    Science.gov (United States)

    Ghanbari, Yasser; Bloy, Luke; Tunc, Birkan; Shankar, Varsha; Roberts, Timothy P L; Edgar, J Christopher; Schultz, Robert T; Verma, Ragini

    2017-05-01

    Brain networks based on resting state connectivity as well as inter-regional anatomical pathways obtained using diffusion imaging have provided insight into pathology and development. Such work has underscored the need for methods that can extract sub-networks that can accurately capture the connectivity patterns of the underlying population while simultaneously describing the variation of sub-networks at the subject level. We have designed a multi-layer graph clustering method that extracts clusters of nodes, called 'network hubs', which display higher levels of connectivity within the cluster than to the rest of the brain. The method determines an atlas of network hubs that describes the population, as well as weights that characterize subject-wise variation in terms of within- and between-hub connectivity. This lowers the dimensionality of brain networks, thereby providing a representation amenable to statistical analyses. The applicability of the proposed technique is demonstrated by extracting an atlas of network hubs for a population of typically developing controls (TDCs) as well as children with autism spectrum disorder (ASD), and using the structural and functional networks of a population to determine the subject-level variation of these hubs and their inter-connectivity. These hubs are then used to compare ASD and TDCs. Our method is generalizable to any population whose connectivity (structural or functional) can be captured via non-negative network graphs. Copyright © 2015 Elsevier B.V. All rights reserved.

  17. The Virtual Brain: a simulator of primate brain network dynamics

    Directory of Open Access Journals (Sweden)

    Paula eSanz Leon

    2013-06-01

    Full Text Available We present TheVirtualBrain (TVB, a neuroinformatics platform for full brainnetwork simulations using biologically realistic connectivity. This simulationenvironment enables the model-based inference of neurophysiological mechanismsacross different brain scales that underlie the generation of macroscopicneuroimaging signals including functional MRI (fMRI, EEG and MEG. Researchersfrom different backgrounds can benefit from an integrative software platformincluding a supporting framework for data management (generation,organization, storage, integration and sharing and a simulation core writtenin Python. TVB allows the reproduction and evaluation of personalizedconfigurations of the brain by using individual subject data. Thispersonalization facilitates an exploration of the consequences of pathologicalchanges in the system, permitting to investigate potential ways to counteractsuch unfavorable processes. The architecture of TVB supports interaction withMATLAB packages, for example, the well known Brain Connectivity Toolbox. TVBcan be used in a client-server configuration, such that it can be remotelyaccessed through the Internet thanks to its web-basedHTML5, JS and WebGL graphical user interface. TVB is alsoaccessible as a standalone cross-platform Python library and application, andusers can interact with the scientific core through the scripting interfaceIDLE, enabling easy modeling, development and debugging of the scientifickernel. This second interface makes TVB extensible by combining it with otherlibraries and modules developed by the Python scientific community. In this article, we describe the theoretical background and foundations that led to thedevelopment of TVB, the architecture and features of its major softwarecomponents as well as potential neuroscience applications.

  18. Structural brain changes linked to delayed first language acquisition in congenitally deaf individuals.

    Science.gov (United States)

    Pénicaud, Sidonie; Klein, Denise; Zatorre, Robert J; Chen, Jen-Kai; Witcher, Pamela; Hyde, Krista; Mayberry, Rachel I

    2013-02-01

    Early language experience is essential for the development of a high level of linguistic proficiency in adulthood and in a recent functional Magnetic Resonance Imaging (fMRI) experiment, we showed that a delayed acquisition of a first language results in changes in the functional organization of the adult brain (Mayberry et al., 2011). The present study extends the question to explore if delayed acquisition of a first language also modulates the structural development of the brain. To this end, we carried out anatomical MRI in the same group of congenitally deaf individuals who varied in the age of acquisition of a first language, American Sign Language -ASL (Mayberry et al., 2011) and used a neuroanatomical technique, Voxel-Based Morphometry (VBM), to explore changes in gray and white matter concentrations across the brain related to the age of first language acquisition. The results show that delayed acquisition of a first language is associated with changes in tissue concentration in the occipital cortex close to the area that has been found to show functional recruitment during language processing in these deaf individuals with a late age of acquisition. These findings suggest that a lack of early language experience affects not only the functional but also the anatomical organization of the brain. Copyright © 2012 Elsevier Inc. All rights reserved.

  19. Influences of brain development and ageing on cortical interactive networks.

    Science.gov (United States)

    Zhu, Chengyu; Guo, Xiaoli; Jin, Zheng; Sun, Junfeng; Qiu, Yihong; Zhu, Yisheng; Tong, Shanbao

    2011-02-01

    To study the effect of brain development and ageing on the pattern of cortical interactive networks. By causality analysis of multichannel electroencephalograph (EEG) with partial directed coherence (PDC), we investigated the different neural networks involved in the whole cortex as well as the anterior and posterior areas in three age groups, i.e., children (0-10 years), mid-aged adults (26-38 years) and the elderly (56-80 years). By comparing the cortical interactive networks in different age groups, the following findings were concluded: (1) the cortical interactive network in the right hemisphere develops earlier than its left counterpart in the development stage; (2) the cortical interactive network of anterior cortex, especially at C3 and F3, is demonstrated to undergo far more extensive changes, compared with the posterior area during brain development and ageing; (3) the asymmetry of the cortical interactive networks declines during ageing with more loss of connectivity in the left frontal and central areas. The age-related variation of cortical interactive networks from resting EEG provides new insights into brain development and ageing. Our findings demonstrated that the PDC analysis of EEG is a powerful approach for characterizing the cortical functional connectivity during brain development and ageing. Copyright © 2010 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

  20. BrainNet Viewer: a network visualization tool for human brain connectomics.

    Science.gov (United States)

    Xia, Mingrui; Wang, Jinhui; He, Yong

    2013-01-01

    The human brain is a complex system whose topological organization can be represented using connectomics. Recent studies have shown that human connectomes can be constructed using various neuroimaging technologies and further characterized using sophisticated analytic strategies, such as graph theory. These methods reveal the intriguing topological architectures of human brain networks in healthy populations and explore the changes throughout normal development and aging and under various pathological conditions. However, given the huge complexity of this methodology, toolboxes for graph-based network visualization are still lacking. Here, using MATLAB with a graphical user interface (GUI), we developed a graph-theoretical network visualization toolbox, called BrainNet Viewer, to illustrate human connectomes as ball-and-stick models. Within this toolbox, several combinations of defined files with connectome information can be loaded to display different combinations of brain surface, nodes and edges. In addition, display properties, such as the color and size of network elements or the layout of the figure, can be adjusted within a comprehensive but easy-to-use settings panel. Moreover, BrainNet Viewer draws the brain surface, nodes and edges in sequence and displays brain networks in multiple views, as required by the user. The figure can be manipulated with certain interaction functions to display more detailed information. Furthermore, the figures can be exported as commonly used image file formats or demonstration video for further use. BrainNet Viewer helps researchers to visualize brain networks in an easy, flexible and quick manner, and this software is freely available on the NITRC website (www.nitrc.org/projects/bnv/).

  1. BrainNet Viewer: a network visualization tool for human brain connectomics.

    Directory of Open Access Journals (Sweden)

    Mingrui Xia

    Full Text Available The human brain is a complex system whose topological organization can be represented using connectomics. Recent studies have shown that human connectomes can be constructed using various neuroimaging technologies and further characterized using sophisticated analytic strategies, such as graph theory. These methods reveal the intriguing topological architectures of human brain networks in healthy populations and explore the changes throughout normal development and aging and under various pathological conditions. However, given the huge complexity of this methodology, toolboxes for graph-based network visualization are still lacking. Here, using MATLAB with a graphical user interface (GUI, we developed a graph-theoretical network visualization toolbox, called BrainNet Viewer, to illustrate human connectomes as ball-and-stick models. Within this toolbox, several combinations of defined files with connectome information can be loaded to display different combinations of brain surface, nodes and edges. In addition, display properties, such as the color and size of network elements or the layout of the figure, can be adjusted within a comprehensive but easy-to-use settings panel. Moreover, BrainNet Viewer draws the brain surface, nodes and edges in sequence and displays brain networks in multiple views, as required by the user. The figure can be manipulated with certain interaction functions to display more detailed information. Furthermore, the figures can be exported as commonly used image file formats or demonstration video for further use. BrainNet Viewer helps researchers to visualize brain networks in an easy, flexible and quick manner, and this software is freely available on the NITRC website (www.nitrc.org/projects/bnv/.

  2. Neural networks involved in learning lexical-semantic and syntactic information in a second language.

    Science.gov (United States)

    Mueller, Jutta L; Rueschemeyer, Shirley-Ann; Ono, Kentaro; Sugiura, Motoaki; Sadato, Norihiro; Nakamura, Akinori

    2014-01-01

    The present study used functional magnetic resonance imaging (fMRI) to investigate the neural correlates of language acquisition in a realistic learning environment. Japanese native speakers were trained in a miniature version of German prior to fMRI scanning. During scanning they listened to (1) familiar sentences, (2) sentences including a novel sentence structure, and (3) sentences containing a novel word while visual context provided referential information. Learning-related decreases of brain activation over time were found in a mainly left-hemispheric network comprising classical frontal and temporal language areas as well as parietal and subcortical regions and were largely overlapping for novel words and the novel sentence structure in initial stages of learning. Differences occurred at later stages of learning during which content-specific activation patterns in prefrontal, parietal and temporal cortices emerged. The results are taken as evidence for a domain-general network supporting the initial stages of language learning which dynamically adapts as learners become proficient.

  3. [Research on brain white matter network in cerebral palsy infant].

    Science.gov (United States)

    Li, Jun; Yang, Cheng; Wang, Yuanjun; Nie, Shengdong

    2017-10-01

    Present study used diffusion tensor image and tractography to construct brain white matter networks of 15 cerebral palsy infants and 30 healthy infants that matched for age and gender. After white matter network analysis, we found that both cerebral palsy and healthy infants had a small-world topology in white matter network, but cerebral palsy infants exhibited abnormal topological organization: increased shortest path length but decreased normalize clustering coefficient, global efficiency and local efficiency. Furthermore, we also found that white matter network hub regions were located in the left cuneus, precuneus, and left posterior cingulate gyrus. However, some abnormal nodes existed in the frontal, temporal, occipital and parietal lobes of cerebral palsy infants. These results indicated that the white matter networks for cerebral palsy infants were disrupted, which was consistent with previous studies about the abnormal brain white matter areas. This work could help us further study the pathogenesis of cerebral palsy infants.

  4. Speaking in Multiple Languages: Neural Correlates of Language Proficiency in Multilingual Word Production

    Science.gov (United States)

    Videsott, Gerda; Herrnberger, Barbel; Hoenig, Klaus; Schilly, Edgar; Grothe, Jo; Wiater, Werner; Spitzer, Manfred; Kiefer, Markus

    2010-01-01

    The human brain has the fascinating ability to represent and to process several languages. Although the first and further languages activate partially different brain networks, the linguistic factors underlying these differences in language processing have to be further specified. We investigated the neural correlates of language proficiency in a…

  5. Sensitivity analysis of human brain structural network construction

    Directory of Open Access Journals (Sweden)

    Kuang Wei

    2017-12-01

    Full Text Available Network neuroscience leverages diffusion-weighted magnetic resonance imaging and tractography to quantify structural connectivity of the human brain. However, scientists and practitioners lack a clear understanding of the effects of varying tractography parameters on the constructed structural networks. With diffusion images from the Human Connectome Project (HCP, we characterize how structural networks are impacted by the spatial resolution of brain atlases, total number of tractography streamlines, and grey matter dilation with various graph metrics. We demonstrate how injudicious combinations of highly refined brain parcellations and low numbers of streamlines may inadvertently lead to disconnected network models with isolated nodes. Furthermore, we provide solutions to significantly reduce the likelihood of generating disconnected networks. In addition, for different tractography parameters, we investigate the distributions of values taken by various graph metrics across the population of HCP subjects. Analyzing the ranks of individual subjects within the graph metric distributions, we find that the ranks of individuals are affected differently by atlas scale changes. Our work serves as a guideline for researchers to optimize the selection of tractography parameters and illustrates how biological characteristics of the brain derived in network neuroscience studies can be affected by the choice of atlas parcellation schemes. Diffusion tractography has been proven to be a promising noninvasive technique to study the network properties of the human brain. However, how various tractography and network construction parameters affect network properties has not been studied using a large cohort of high-quality data. We utilize data provided by the Human Connectome Project to characterize the changes to network properties induced by varying the brain parcellation atlas scales, the number of reconstructed tractography tracks, and the degree of grey

  6. Brain network disturbance related to posttraumatic stress and traumatic brain injury in veterans.

    Science.gov (United States)

    Spielberg, Jeffrey M; McGlinchey, Regina E; Milberg, William P; Salat, David H

    2015-08-01

    Understanding the neural causes and consequences of posttraumatic stress disorder (PTSD) and mild traumatic brain injury (mTBI) is a high research priority, given the high rates of associated disability and suicide. Despite remarkable progress in elucidating the brain mechanisms of PTSD and mTBI, a comprehensive understanding of these conditions at the level of brain networks has yet to be achieved. The present study sought to identify functional brain networks and topological properties (measures of network organization and function) related to current PTSD severity and mTBI. Graph theoretic tools were used to analyze resting-state functional magnetic resonance imaging data from 208 veterans of Operation Enduring Freedom, Operation Iraqi Freedom, and Operation New Dawn, all of whom had experienced a traumatic event qualifying for PTSD criterion A. Analyses identified brain networks and topological network properties linked to current PTSD symptom severity, mTBI, and the interaction between PTSD and mTBI. Two brain networks were identified in which weaker connectivity was linked to higher PTSD re-experiencing symptoms, one of which was present only in veterans with comorbid mTBI. Re-experiencing was also linked to worse functional segregation (necessary for specialized processing) and diminished influence of key regions on the network, including the hippocampus. Findings of this study demonstrate that PTSD re-experiencing symptoms are linked to weakened connectivity in a network involved in providing contextual information. A similar relationship was found in a separate network typically engaged in the gating of working memory, but only in veterans with mTBI. Published by Elsevier Inc.

  7. Co-Localisation of Abnormal Brain Structure and Function in Specific Language Impairment

    Science.gov (United States)

    Badcock, Nicholas A.; Bishop, Dorothy V. M.; Hardiman, Mervyn J.; Barry, Johanna G.; Watkins, Kate E.

    2012-01-01

    We assessed the relationship between brain structure and function in 10 individuals with specific language impairment (SLI), compared to six unaffected siblings, and 16 unrelated control participants with typical language. Voxel-based morphometry indicated that grey matter in the SLI group, relative to controls, was increased in the left inferior…

  8. Brain networks underlying mental imagery of auditory and visual information.

    Science.gov (United States)

    Zvyagintsev, Mikhail; Clemens, Benjamin; Chechko, Natalya; Mathiak, Krystyna A; Sack, Alexander T; Mathiak, Klaus

    2013-05-01

    Mental imagery is a complex cognitive process that resembles the experience of perceiving an object when this object is not physically present to the senses. It has been shown that, depending on the sensory nature of the object, mental imagery also involves correspondent sensory neural mechanisms. However, it remains unclear which areas of the brain subserve supramodal imagery processes that are independent of the object modality, and which brain areas are involved in modality-specific imagery processes. Here, we conducted a functional magnetic resonance imaging study to reveal supramodal and modality-specific networks of mental imagery for auditory and visual information. A common supramodal brain network independent of imagery modality, two separate modality-specific networks for imagery of auditory and visual information, and a common deactivation network were identified. The supramodal network included brain areas related to attention, memory retrieval, motor preparation and semantic processing, as well as areas considered to be part of the default-mode network and multisensory integration areas. The modality-specific networks comprised brain areas involved in processing of respective modality-specific sensory information. Interestingly, we found that imagery of auditory information led to a relative deactivation within the modality-specific areas for visual imagery, and vice versa. In addition, mental imagery of both auditory and visual information widely suppressed the activity of primary sensory and motor areas, for example deactivation network. These findings have important implications for understanding the mechanisms that are involved in generation of mental imagery. © 2013 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

  9. Left hemisphere regions are critical for language in the face of early left focal brain injury

    OpenAIRE

    Raja Beharelle, Anjali; Dick, Anthony Steven; Josse, Goulven; Solodkin, Ana; Huttenlocher, Peter R.; Levine, Susan C.; Small, Steven L.

    2010-01-01

    A predominant theory regarding early stroke and its effect on language development, is that early left hemisphere lesions trigger compensatory processes that allow the right hemisphere to assume dominant language functions, and this is thought to underlie the near normal language development observed after early stroke. To test this theory, we used functional magnetic resonance imaging to examine brain activity during category fluency in participants who had sustained pre- or perinatal left h...

  10. Age of language acquisition and cortical language organization in multilingual patients undergoing awake brain mapping.

    Science.gov (United States)

    Fernández-Coello, Alejandro; Havas, Viktória; Juncadella, Montserrat; Sierpowska, Joanna; Rodríguez-Fornells, Antoni; Gabarrós, Andreu

    2017-06-01

    OBJECTIVE Most knowledge regarding the anatomical organization of multilingualism is based on aphasiology and functional imaging studies. However, the results have still to be validated by the gold standard approach, namely electrical stimulation mapping (ESM) during awake neurosurgical procedures. In this ESM study the authors describe language representation in a highly specific group of 13 multilingual individuals, focusing on how age of acquisition may influence the cortical organization of language. METHODS Thirteen patients who had a high degree of proficiency in multiple languages and were harboring lesions within the dominant, left hemisphere underwent ESM while being operated on under awake conditions. Demographic and language data were recorded in relation to age of language acquisition (for native languages and early- and late-acquired languages), neuropsychological pre- and postoperative language testing, the number and location of language sites, and overlapping distribution in terms of language acquisition time. Lesion growth patterns and histopathological characteristics, location, and size were also recorded. The distribution of language sites was analyzed with respect to age of acquisition and overlap. RESULTS The functional language-related sites were distributed in the frontal (55%), temporal (29%), and parietal lobes (16%). The total number of native language sites was 47. Early-acquired languages (including native languages) were represented in 97 sites (55 overlapped) and late-acquired languages in 70 sites (45 overlapped). The overlapping distribution was 20% for early-early, 71% for early-late, and 9% for late-late. The average lesion size (maximum diameter) was 3.3 cm. There were 5 fast-growing and 7 slow-growing lesions. CONCLUSIONS Cortical language distribution in multilingual patients is not homogeneous, and it is influenced by age of acquisition. Early-acquired languages have a greater cortical representation than languages acquired

  11. Sleeping of a Complex Brain Networks with Hierarchical Organization

    Science.gov (United States)

    Zhang, Ying-Yue; Yang, Qiu-Ying; Chen, Tian-Lun

    2009-01-01

    The dynamical behavior in the cortical brain network of macaque is studied by modeling each cortical area with a subnetwork of interacting excitable neurons. We characterize the system by studying how to perform the transition, which is now topology-dependent, from the active state to that with no activity. This could be a naive model for the wakening and sleeping of a brain-like system, i.e., a multi-component system with two different dynamical behavior.

  12. Delayed convergence between brain network structure and function in rolandic epilepsy

    Directory of Open Access Journals (Sweden)

    Rene MH Besseling

    2014-09-01

    Full Text Available Introduction Rolandic epilepsy (RE manifests during a critical phase of brain development, and has been associated with language impairments. Concordant abnormalities in structural and functional connectivity (SC and FC have been described before. As SC and FC are under mutual influence, the current study investigates abnormalities in the SC-FC synergy in RE. Methods Twenty-two children with RE (age, mean±SD: 11.3±2.0 y and 22 healthy controls (age 10.5±1.6 y underwent structural, diffusion weighted, and functional MRI at 3T. The probabilistic anatomical landmarks atlas was used to parcellate the (subcortical gray matter. Constrained spherical deconvolution tractography and correlation of time series were used to assess SC and FC, respectively. The SC-FC correlation was assessed as a function of age for the non-zero structural connections over a range of sparsity values (0.01-0.75. A modularity analysis was performed on the mean SC network of the controls to localize potential global effects to subnetworks. SC and FC were also assessed separately using graph analysis.Results The SC-FC correlation was significantly reduced in children with RE compared to healthy controls, especially for the youngest participants. This effect was most pronounced in a left and a right centro-temporal network, as well as in a medial parietal network. Graph analysis revealed no prominent abnormalities in SC or FC network organization.Conclusion Since SC and FC converge during normal maturation, our finding of reduced SC-FC correlation illustrates impaired synergy between brain structure and function. More specifically, since this effect was most pronounced in the youngest participants, RE may represent a developmental disorder of delayed brain network maturation. The observed effects seem especially attributable to medial parietal connections, which forms an intermediate between bilateral centro-temporal modules of epileptiform activity, and bear relevance for

  13. Brain network dysregulation, emotion, and complaints after mild traumatic brain injury.

    Science.gov (United States)

    van der Horn, Harm J; Liemburg, Edith J; Scheenen, Myrthe E; de Koning, Myrthe E; Marsman, Jan-Bernard C; Spikman, Jacoba M; van der Naalt, Joukje

    2016-04-01

    To assess the role of brain networks in emotion regulation and post-traumatic complaints in the sub-acute phase after non-complicated mild traumatic brain injury (mTBI). Fifty-four patients with mTBI (34 with and 20 without complaints) and 20 healthy controls (group-matched for age, sex, education, and handedness) were included. Resting-state fMRI was performed at four weeks post-injury. Static and dynamic functional connectivity were studied within and between the default mode, executive (frontoparietal and bilateral frontal network), and salience network. The hospital anxiety and depression scale (HADS) was used to measure anxiety (HADS-A) and depression (HADS-D). Regarding within-network functional connectivity, none of the selected brain networks were different between groups. Regarding between-network interactions, patients with complaints exhibited lower functional connectivity between the bilateral frontal and salience network compared to patients without complaints. In the total patient group, higher HADS-D scores were related to lower functional connectivity between the bilateral frontal network and both the right frontoparietal and salience network, and to higher connectivity between the right frontoparietal and salience network. Furthermore, whereas higher HADS-D scores were associated with lower connectivity within the parietal midline areas of the bilateral frontal network, higher HADS-A scores were related to lower connectivity within medial prefrontal areas of the bilateral frontal network. Functional interactions of the executive and salience networks were related to emotion regulation and complaints after mTBI, with a key role for the bilateral frontal network. These findings may have implications for future studies on the effect of psychological interventions. © 2016 Wiley Periodicals, Inc.

  14. Mapping Language Function in the Brain: A Review of the Recent Literature.

    Science.gov (United States)

    Crafton, Robert E.; Kido, Elissa

    2000-01-01

    Considers the potential importance of brain study for composition instruction, briefly describes functional imaging techniques, and reviews the findings of recent brain-mapping studies investigating the neurocognitive systems involved in language function. Presents a review of the recent literature and considers the possible implications of this…

  15. Shadows of Music-Language Interaction on Low Frequency Brain Oscillatory Patterns

    Science.gov (United States)

    Carrus, Elisa; Koelsch, Stefan; Bhattacharya, Joydeep

    2011-01-01

    Electrophysiological studies investigating similarities between music and language perception have relied exclusively on the signal averaging technique, which does not adequately represent oscillatory aspects of electrical brain activity that are relevant for higher cognition. The current study investigated the patterns of brain oscillations…

  16. Small-world human brain networks: Perspectives and challenges.

    Science.gov (United States)

    Liao, Xuhong; Vasilakos, Athanasios V; He, Yong

    2017-06-01

    Modelling the human brain as a complex network has provided a powerful mathematical framework to characterize the structural and functional architectures of the brain. In the past decade, the combination of non-invasive neuroimaging techniques and graph theoretical approaches enable us to map human structural and functional connectivity patterns (i.e., connectome) at the macroscopic level. One of the most influential findings is that human brain networks exhibit prominent small-world organization. Such a network architecture in the human brain facilitates efficient information segregation and integration at low wiring and energy costs, which presumably results from natural selection under the pressure of a cost-efficiency balance. Moreover, the small-world organization undergoes continuous changes during normal development and ageing and exhibits dramatic alterations in neurological and psychiatric disorders. In this review, we survey recent advances regarding the small-world architecture in human brain networks and highlight the potential implications and applications in multidisciplinary fields, including cognitive neuroscience, medicine and engineering. Finally, we highlight several challenging issues and areas for future research in this rapidly growing field. Copyright © 2017 Elsevier Ltd. All rights reserved.

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

    Science.gov (United States)

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

    2017-08-01

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

  18. Brain tumor classification using Probabilistic Neural Network

    African Journals Online (AJOL)

    pc

    2018-03-05

    Mar 5, 2018 ... Baghdad, Iraq. 1sami.hasan@coie.nahrainuniv.edu.iq ... The Human brain is the most amazing and complex thing known in the world [1]. ... achieved using gray level co-occurrence matrix (GLCM). This work is aimed to ...

  19. Riemannian multi-manifold modeling and clustering in brain networks

    Science.gov (United States)

    Slavakis, Konstantinos; Salsabilian, Shiva; Wack, David S.; Muldoon, Sarah F.; Baidoo-Williams, Henry E.; Vettel, Jean M.; Cieslak, Matthew; Grafton, Scott T.

    2017-08-01

    This paper introduces Riemannian multi-manifold modeling in the context of brain-network analytics: Brainnetwork time-series yield features which are modeled as points lying in or close to a union of a finite number of submanifolds within a known Riemannian manifold. Distinguishing disparate time series amounts thus to clustering multiple Riemannian submanifolds. To this end, two feature-generation schemes for brain-network time series are put forth. The first one is motivated by Granger-causality arguments and uses an auto-regressive moving average model to map low-rank linear vector subspaces, spanned by column vectors of appropriately defined observability matrices, to points into the Grassmann manifold. The second one utilizes (non-linear) dependencies among network nodes by introducing kernel-based partial correlations to generate points in the manifold of positivedefinite matrices. Based on recently developed research on clustering Riemannian submanifolds, an algorithm is provided for distinguishing time series based on their Riemannian-geometry properties. Numerical tests on time series, synthetically generated from real brain-network structural connectivity matrices, reveal that the proposed scheme outperforms classical and state-of-the-art techniques in clustering brain-network states/structures.

  20. Interfaces, syntactic movement, and neural activation: A new perspective on the implementation of language in the brain

    DEFF Research Database (Denmark)

    Christensen, Ken Ramshøj

    2008-01-01

    Studies of language deficits as well as neuroimaging studies indicate that syntactic processing of displaced constituents is implemented in the brain as a distributed cortical network of modules. The data from the present fMRI study on two types of syntactic movement in Danish offers further...... support for such a distributed syntactic network. These results, together with the results from a number of other fMRI studies in the literature, form the basis for the Domain Hypothesis according to which differential activation in the subcomponents of the cortical network reflects computation...... of different syntactic domains—the interface levels between syntax, semantics, and pragmatics. The activation patters result from the interaction between movement and target domain, not (non-) canonicity or working memory per se. Specifically, movement to the CP-domain activates areas including Broca's area...

  1. Hemispheric language dominance measured by repetitive navigated transcranial magnetic stimulation and postoperative course of language function in brain tumor patients.

    Science.gov (United States)

    Ille, Sebastian; Kulchytska, Nataliia; Sollmann, Nico; Wittig, Regina; Beurskens, Eva; Butenschoen, Vicki M; Ringel, Florian; Vajkoczy, Peter; Meyer, Bernhard; Picht, Thomas; Krieg, Sandro M

    2016-10-01

    The resection of left-sided perisylvian brain lesions harbors the risk of postoperative aphasia. Because it is known that language function can shift between hemispheres in brain tumor patients, the preoperative knowledge of the patient's language dominance could be helpful. We therefore investigated the hemispheric language dominance by repetitive navigated transcranial magnetic stimulation (rTMS) and surgery-related deficits of language function. We pooled the bicentric language mapping data of 80 patients undergoing the resection of left-sided perisylvian brain lesions in our two university neurosurgical departments. We calculated error rates (ERs; ER = errors per stimulations) for both hemispheres and defined the hemispheric dominance ratio (HDR) as the quotient of the left- and right-sided ER (HDR >1= left dominant; HDR right dominant). The course of the patient's language function was evaluated and correlated with the preoperative HDR. Only three of 80 patients (4%) presented with permanent surgery-related aphasia and 24 patients (30%) with transient surgery-related aphasia. The mean HDR (± standard deviation) of patients with new aphasia after five days was significantly higher (1.68±1.07) than the HDR of patients with no new language deficit (1.37±1.08) (p=0.0482). With a predefined cut-off value of 0.5 for HDR, we achieved a sensitivity for predicting new aphasia of 100%. A higher preoperative HDR significantly correlates with an increased risk for transient aphasia. Moreover, the intensive preoperative workup in this study led to a considerably low rate of permanent aphasia. Copyright © 2016 Elsevier Ltd. All rights reserved.

  2. Language and spatial frames of reference in mind and brain.

    Science.gov (United States)

    Gallistel, C R.

    2002-08-01

    Some language communities routinely use allocentric reference directions (e.g. 'uphill-downhill') where speakers of European languages would use egocentric references ('left-right'). Previous experiments have suggested that the different language groups use different reference frames in non-linguistic tasks involving the recreation of oriented arrays. However, a recent paper argues that manipulating test conditions produces similar effects in monolingual English speakers, and in animals.

  3. Evaluation of the factors influencing brain language laterality in presurgical planning.

    Science.gov (United States)

    Batouli, Seyed Amir Hossein; Hasani, Nafiseh; Gheisari, Sara; Behzad, Ebrahim; Oghabian, Mohammad Ali

    2016-10-01

    Brain lesions cause functional deficits, and one treatment for this condition is lesion resection. In most cases, presurgical planning (PSP) and the information from laterality indices are necessary for maximum preservation of the critical functions after surgery. Language laterality index (LI) is reliably estimated using functional magnetic resonance imaging (fMRI); however, this measure is under the influence of some external factors. In this study, we investigated the influence of a number of factors on language LI, using data from 120 patients (mean age=35.65 (±13.4) years) who underwent fMRI for PSP. Using two proposed language tasks from our previous works, brain left hemisphere was showed to be dominant for the language function, although a higher LI was obtained using the "Word Generation" task, compared to the "Reverse Word Reading". In addition, decline of LIs with age, and lower LI when the lesion invaded brain language area were observed. Meanwhile, gender, lesion side (affected hemisphere), LI calculation strategy, and fMRI analysis Z-values did not statistically show any influences on the LIs. Although fMRI is widely used to estimate language LI, it is shown here that in order to present a reliable language LI and to correctly select the dominant hemisphere of the brain, the influence of external factors should be carefully considered. Copyright © 2016 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

  4. Neuroplasticity as a function of second language learning: anatomical changes in the human brain.

    Science.gov (United States)

    Li, Ping; Legault, Jennifer; Litcofsky, Kaitlyn A

    2014-09-01

    The brain has an extraordinary ability to functionally and physically change or reconfigure its structure in response to environmental stimulus, cognitive demand, or behavioral experience. This property, known as neuroplasticity, has been examined extensively in many domains. But how does neuroplasticity occur in the brain as a function of an individual's experience with a second language? It is not until recently that we have gained some understanding of this question by examining the anatomical changes as well as functional neural patterns that are induced by the learning and use of multiple languages. In this article we review emerging evidence regarding how structural neuroplasticity occurs in the brain as a result of one's bilingual experience. Our review aims at identifying the processes and mechanisms that drive experience-dependent anatomical changes, and integrating structural imaging evidence with current knowledge of functional neural plasticity of language and other cognitive skills. The evidence reviewed so far portrays a picture that is highly consistent with structural neuroplasticity observed for other domains: second language experience-induced brain changes, including increased gray matter (GM) density and white matter (WM) integrity, can be found in children, young adults, and the elderly; can occur rapidly with short-term language learning or training; and are sensitive to age, age of acquisition, proficiency or performance level, language-specific characteristics, and individual differences. We conclude with a theoretical perspective on neuroplasticity in language and bilingualism, and point to future directions for research. Copyright © 2014 Elsevier Ltd. All rights reserved.

  5. Validation of network communicability metrics for the analysis of brain structural networks.

    Directory of Open Access Journals (Sweden)

    Jennifer Andreotti

    Full Text Available Computational network analysis provides new methods to analyze the brain's structural organization based on diffusion imaging tractography data. Networks are characterized by global and local metrics that have recently given promising insights into diagnosis and the further understanding of psychiatric and neurologic disorders. Most of these metrics are based on the idea that information in a network flows along the shortest paths. In contrast to this notion, communicability is a broader measure of connectivity which assumes that information could flow along all possible paths between two nodes. In our work, the features of network metrics related to communicability were explored for the first time in the healthy structural brain network. In addition, the sensitivity of such metrics was analysed using simulated lesions to specific nodes and network connections. Results showed advantages of communicability over conventional metrics in detecting densely connected nodes as well as subsets of nodes vulnerable to lesions. In addition, communicability centrality was shown to be widely affected by the lesions and the changes were negatively correlated with the distance from lesion site. In summary, our analysis suggests that communicability metrics that may provide an insight into the integrative properties of the structural brain network and that these metrics may be useful for the analysis of brain networks in the presence of lesions. Nevertheless, the interpretation of communicability is not straightforward; hence these metrics should be used as a supplement to the more standard connectivity network metrics.

  6. Common and distinct brain networks underlying verbal and visual creativity.

    Science.gov (United States)

    Zhu, Wenfeng; Chen, Qunlin; Xia, Lingxiang; Beaty, Roger E; Yang, Wenjing; Tian, Fang; Sun, Jiangzhou; Cao, Guikang; Zhang, Qinglin; Chen, Xu; Qiu, Jiang

    2017-04-01

    Creativity is imperative to the progression of human civilization, prosperity, and well-being. Past creative researches tends to emphasize the default mode network (DMN) or the frontoparietal network (FPN) somewhat exclusively. However, little is known about how these networks interact to contribute to creativity and whether common or distinct brain networks are responsible for visual and verbal creativity. Here, we use functional connectivity analysis of resting-state functional magnetic resonance imaging data to investigate visual and verbal creativity-related regions and networks in 282 healthy subjects. We found that functional connectivity within the bilateral superior parietal cortex of the FPN was negatively associated with visual and verbal creativity. The strength of connectivity between the DMN and FPN was positively related to both creative domains. Visual creativity was negatively correlated with functional connectivity within the precuneus of the pDMN and right middle frontal gyrus of the FPN, and verbal creativity was negatively correlated with functional connectivity within the medial prefrontal cortex of the aDMN. Critically, the FPN mediated the relationship between the aDMN and verbal creativity, and it also mediated the relationship between the pDMN and visual creativity. Taken together, decreased within-network connectivity of the FPN and DMN may allow for flexible between-network coupling in the highly creative brain. These findings provide indirect evidence for the cooperative role of the default and executive control networks in creativity, extending past research by revealing common and distinct brain systems underlying verbal and visual creative cognition. Hum Brain Mapp 38:2094-2111, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  7. Towards a Computational Comparative Neuroprimatology: Framing the language-ready brain

    Science.gov (United States)

    Arbib, Michael A.

    2016-03-01

    We make the case for developing a Computational Comparative Neuroprimatology to inform the analysis of the function and evolution of the human brain. First, we update the mirror system hypothesis on the evolution of the language-ready brain by (i) modeling action and action recognition and opportunistic scheduling of macaque brains to hypothesize the nature of the last common ancestor of macaque and human (LCA-m); and then we (ii) introduce dynamic brain modeling to show how apes could acquire gesture through ontogenetic ritualization, hypothesizing the nature of evolution from LCA-m to the last common ancestor of chimpanzee and human (LCA-c). We then (iii) hypothesize the role of imitation, pantomime, protosign and protospeech in biological and cultural evolution from LCA-c to Homo sapiens with a language-ready brain. Second, we suggest how cultural evolution in Homo sapiens led from protolanguages to full languages with grammar and compositional semantics. Third, we assess the similarities and differences between the dorsal and ventral streams in audition and vision as the basis for presenting and comparing two models of language processing in the human brain: A model of (i) the auditory dorsal and ventral streams in sentence comprehension; and (ii) the visual dorsal and ventral streams in defining ;what language is about; in both production and perception of utterances related to visual scenes provide the basis for (iii) a first step towards a synthesis and a look at challenges for further research.

  8. Speech networks at rest and in action: interactions between functional brain networks controlling speech production

    Science.gov (United States)

    Fuertinger, Stefan

    2015-01-01

    Speech production is one of the most complex human behaviors. Although brain activation during speaking has been well investigated, our understanding of interactions between the brain regions and neural networks remains scarce. We combined seed-based interregional correlation analysis with graph theoretical analysis of functional MRI data during the resting state and sentence production in healthy subjects to investigate the interface and topology of functional networks originating from the key brain regions controlling speech, i.e., the laryngeal/orofacial motor cortex, inferior frontal and superior temporal gyri, supplementary motor area, cingulate cortex, putamen, and thalamus. During both resting and speaking, the interactions between these networks were bilaterally distributed and centered on the sensorimotor brain regions. However, speech production preferentially recruited the inferior parietal lobule (IPL) and cerebellum into the large-scale network, suggesting the importance of these regions in facilitation of the transition from the resting state to speaking. Furthermore, the cerebellum (lobule VI) was the most prominent region showing functional influences on speech-network integration and segregation. Although networks were bilaterally distributed, interregional connectivity during speaking was stronger in the left vs. right hemisphere, which may have underlined a more homogeneous overlap between the examined networks in the left hemisphere. Among these, the laryngeal motor cortex (LMC) established a core network that fully overlapped with all other speech-related networks, determining the extent of network interactions. Our data demonstrate complex interactions of large-scale brain networks controlling speech production and point to the critical role of the LMC, IPL, and cerebellum in the formation of speech production network. PMID:25673742

  9. Speech networks at rest and in action: interactions between functional brain networks controlling speech production.

    Science.gov (United States)

    Simonyan, Kristina; Fuertinger, Stefan

    2015-04-01

    Speech production is one of the most complex human behaviors. Although brain activation during speaking has been well investigated, our understanding of interactions between the brain regions and neural networks remains scarce. We combined seed-based interregional correlation analysis with graph theoretical analysis of functional MRI data during the resting state and sentence production in healthy subjects to investigate the interface and topology of functional networks originating from the key brain regions controlling speech, i.e., the laryngeal/orofacial motor cortex, inferior frontal and superior temporal gyri, supplementary motor area, cingulate cortex, putamen, and thalamus. During both resting and speaking, the interactions between these networks were bilaterally distributed and centered on the sensorimotor brain regions. However, speech production preferentially recruited the inferior parietal lobule (IPL) and cerebellum into the large-scale network, suggesting the importance of these regions in facilitation of the transition from the resting state to speaking. Furthermore, the cerebellum (lobule VI) was the most prominent region showing functional influences on speech-network integration and segregation. Although networks were bilaterally distributed, interregional connectivity during speaking was stronger in the left vs. right hemisphere, which may have underlined a more homogeneous overlap between the examined networks in the left hemisphere. Among these, the laryngeal motor cortex (LMC) established a core network that fully overlapped with all other speech-related networks, determining the extent of network interactions. Our data demonstrate complex interactions of large-scale brain networks controlling speech production and point to the critical role of the LMC, IPL, and cerebellum in the formation of speech production network. Copyright © 2015 the American Physiological Society.

  10. Rehabilitation of People with a Brain Injury Through the Lens of Networked Learning

    DEFF Research Database (Denmark)

    Konnerup, Ulla; Castro Rojas, Maria Dolores; Bygholm, Ann

    2016-01-01

    This paper will demonstrate how avatar-mediated interactions and learning in networks might lead to identity formation and rehabilitation of language after a brain injury. With references to Vygotsky's notion of the social origins of higher mental functions (1978) and Hutchins claims that cognition...... the development of rehabilitation; and, conversely, how our finding add perspectives to NL. In doing so, we will introduce the concept of locale framework, and how a specific setting and embodied interactions might trigger autobiographical memory, relearning communication competencies and renegotiation...

  11. Resting state brain networks in the prairie vole.

    Science.gov (United States)

    Ortiz, Juan J; Portillo, Wendy; Paredes, Raul G; Young, Larry J; Alcauter, Sarael

    2018-01-19

    Resting state functional magnetic resonance imaging (rsfMRI) has shown the hierarchical organization of the human brain into large-scale complex networks, referred as resting state networks. This technique has turned into a promising translational research tool after the finding of similar resting state networks in non-human primates, rodents and other animal models of great value for neuroscience. Here, we demonstrate and characterize the presence of resting states networks in Microtus ochrogaster, the prairie vole, an extraordinary animal model to study complex human-like social behavior, with potential implications for the research of normal social development, addiction and neuropsychiatric disorders. Independent component analysis of rsfMRI data from isoflurane-anestethized prairie voles resulted in cortical and subcortical networks, including primary motor and sensory networks, but also included putative salience and default mode networks. We further discuss how future research could help to close the gap between the properties of the large scale functional organization and the underlying neurobiology of several aspects of social cognition. These results contribute to the evidence of preserved resting state brain networks across species and provide the foundations to explore the use of rsfMRI in the prairie vole for basic and translational research.

  12. Time Course of Brain Network Reconfiguration Supporting Inhibitory Control.

    Science.gov (United States)

    Popov, Tzvetan; Westner, Britta U; Silton, Rebecca L; Sass, Sarah M; Spielberg, Jeffrey M; Rockstroh, Brigitte; Heller, Wendy; Miller, Gregory A

    2018-05-02

    Hemodynamic research has recently clarified key nodes and links in brain networks implementing inhibitory control. Although fMRI methods are optimized for identifying the structure of brain networks, the relatively slow temporal course of fMRI limits the ability to characterize network operation. The latter is crucial for developing a mechanistic understanding of how brain networks shift dynamically to support inhibitory control. To address this critical gap, we applied spectrally resolved Granger causality (GC) and random forest machine learning tools to human EEG data in two large samples of adults (test sample n = 96, replication sample n = 237, total N = 333, both sexes) who performed a color-word Stroop task. Time-frequency analysis confirmed that recruitment of inhibitory control accompanied by slower behavioral responses was related to changes in theta and alpha/beta power. GC analyses revealed directionally asymmetric exchanges within frontal and between frontal and parietal brain areas: top-down influence of superior frontal gyrus (SFG) over both dorsal ACC (dACC) and inferior frontal gyrus (IFG), dACC control over middle frontal gyrus (MFG), and frontal-parietal exchanges (IFG, precuneus, MFG). Predictive analytics confirmed a combination of behavioral and brain-derived variables as the best set of predictors of inhibitory control demands, with SFG theta bearing higher classification importance than dACC theta and posterior beta tracking the onset of behavioral response. The present results provide mechanistic insight into the biological implementation of a psychological phenomenon: inhibitory control is implemented by dynamic routing processes during which the target response is upregulated via theta-mediated effective connectivity within key PFC nodes and via beta-mediated motor preparation. SIGNIFICANCE STATEMENT Hemodynamic neuroimaging research has recently clarified regional structures in brain networks supporting inhibitory control. However, due to

  13. Foundational perspectives on causality in large-scale brain networks

    Science.gov (United States)

    Mannino, Michael; Bressler, Steven L.

    2015-12-01

    A profusion of recent work in cognitive neuroscience has been concerned with the endeavor to uncover causal influences in large-scale brain networks. However, despite the fact that many papers give a nod to the important theoretical challenges posed by the concept of causality, this explosion of research has generally not been accompanied by a rigorous conceptual analysis of the nature of causality in the brain. This review provides both a descriptive and prescriptive account of the nature of causality as found within and between large-scale brain networks. In short, it seeks to clarify the concept of causality in large-scale brain networks both philosophically and scientifically. This is accomplished by briefly reviewing the rich philosophical history of work on causality, especially focusing on contributions by David Hume, Immanuel Kant, Bertrand Russell, and Christopher Hitchcock. We go on to discuss the impact that various interpretations of modern physics have had on our understanding of causality. Throughout all this, a central focus is the distinction between theories of deterministic causality (DC), whereby causes uniquely determine their effects, and probabilistic causality (PC), whereby causes change the probability of occurrence of their effects. We argue that, given the topological complexity of its large-scale connectivity, the brain should be considered as a complex system and its causal influences treated as probabilistic in nature. We conclude that PC is well suited for explaining causality in the brain for three reasons: (1) brain causality is often mutual; (2) connectional convergence dictates that only rarely is the activity of one neuronal population uniquely determined by another one; and (3) the causal influences exerted between neuronal populations may not have observable effects. A number of different techniques are currently available to characterize causal influence in the brain. Typically, these techniques quantify the statistical

  14. Primary progressive aphasia: A dementia of the language network

    Directory of Open Access Journals (Sweden)

    Marsel Mesulam

    Full Text Available ABSTRACT Primary progressive aphasia (PPA is a clinical syndrome diagnosed when three core criteria are met. First, there should be a language impairment (i.e., aphasia that interferes with the usage or comprehension of words. Second, the neurological work-up should determine that the disease is neurodegenerative, and therefore progressive. Third, the aphasia should arise in relative isolation, without equivalent deficits of comportment or episodic memory. The language impairment can be fluent or non-fluent and may or may not interfere with word comprehension. Memory for recent events is preserved although memory scores obtained in verbally mediated tests may be abnormal. Minor changes in personality and behavior may be present but are not the leading factors that bring the patient to medical attention or that limit daily living activities. This distinctive clinical pattern is most conspicuous in the initial stages of the disease, and reflects a relatively selective atrophy of the language network, usually located in the left hemisphere. There are different clinical variants of PPA, each with a characteristic pattern of atrophy. The underlying neuropathological diseases are heterogeneous and can include Alzheimer's disease as well as frontotemporal lobar degeneration. The clinician's task is to recognize PPA and differentiate it from other neurodegenerative phenotypes, use biomarkers to surmise the nature of the underlying neuropathology, and institute the most fitting multimodal interventions.

  15. An Analysis of Social Network Websites for Language Learning: Implications for Teaching and Learning English as a Second Language

    Science.gov (United States)

    Liu, M.; Abe, K.; Cao, M. W.; Liu, S.; Ok, D. U.; Park, J.; Parrish, C.; Sardegna, V. G.

    2015-01-01

    Although educators are excited about the potential of social network sites for language learning (SNSLL), there is a lack of understanding of how SNSLL can be used to facilitate teaching and learning for English as Second language (ESL) instructors and students. The purpose of this study was to examine the affordances of four selected SNSLL…

  16. The Co-evolution of Language and the Brain: A Review of Two Contrastive Views (Pinker & Deacon)

    DEFF Research Database (Denmark)

    Christensen, Ken Ramshøj

    2001-01-01

    in a larger symbolic computational chain controlled by regions in the frontal parts of the brain. To Deacon, a symbolic learning algorithm drives language acquisition. The increase in size of the human brain in relation to the body may be due to a “cognitive arms race”. Both Pinker and Deacon agree......This article is a review of two contrastive views on the co-evolution of language and the brain – The Language Instinct by Steven Pinker (1994) and The Symbolic Species by Terrence Deacon (1997). As language is a trait unique to mankind it can not be equated with nonlinguistic communication – human...... or nonhuman. This points to a special human brain architecture. Pinker’s claim is that certain areas on the left side of the brain constitute a language organ and that language acquisition is instinctual. To Deacon, however, those areas are non-language-specific computational centers. Moreover, they are parts...

  17. The emergence of grammar in a language-ready brain. Comment on "Towards a Computational Comparative Neuroprimatology: Framing the language-ready brain" by Michael A. Arbib

    Science.gov (United States)

    Hawkins, John A.

    2016-03-01

    Arbib makes the interesting proposal [3, §1.6] that the first Homo sapiens could have been ;language-ready;, without possessing the kind of rich lexicon, grammar and compositional semantics that we see in the world's languages today. This early language readiness would have consisted of a set of ;protolanguage; abilities, which he enumerates (1-7 in §1.6), supported by brain mechanisms unique to humans. The transition to full ;language; (properties 8-11 in §1.6 and §3) would have required no changes in the genome, he argues, but could have resulted from cultural evolution plus some measure of Baldwinian evolution favoring offspring with greater linguistic skill. The full picture is set out in [1].

  18. A network of networks model to study phase synchronization using structural connection matrix of human brain

    Science.gov (United States)

    Ferrari, F. A. S.; Viana, R. L.; Reis, A. S.; Iarosz, K. C.; Caldas, I. L.; Batista, A. M.

    2018-04-01

    The cerebral cortex plays a key role in complex cortical functions. It can be divided into areas according to their function (motor, sensory and association areas). In this paper, the cerebral cortex is described as a network of networks (cortex network), we consider that each cortical area is composed of a network with small-world property (cortical network). The neurons are assumed to have bursting properties with the dynamics described by the Rulkov model. We study the phase synchronization of the cortex network and the cortical networks. In our simulations, we verify that synchronization in cortex network is not homogeneous. Besides, we focus on the suppression of neural phase synchronization. Synchronization can be related to undesired and pathological abnormal rhythms in the brain. For this reason, we consider the delayed feedback control to suppress the synchronization. We show that delayed feedback control is efficient to suppress synchronous behavior in our network model when an appropriate signal intensity and time delay are defined.

  19. Rounding of abrupt phase transitions in brain networks

    International Nuclear Information System (INIS)

    Martín, Paula Villa; Moretti, Paolo; Muñoz, Miguel A

    2015-01-01

    The observation of critical-like behavior in cortical networks represents a major step forward in elucidating how the brain manages information. Understanding the origin and functionality of critical-like dynamics, as well as its robustness, is a major challenge in contemporary neuroscience. Here, we present an extensive numerical study of a family of simple dynamical models, which describe activity propagation in brain networks through the integration of different neighboring spiking potentials, mimicking basic neural interactions. The requirement of signal integration may lead to discontinuous phase transitions in networks that are well described by the mean-field approximation, thus preventing the emergence of critical points in such systems. Brain networks, however, are finite dimensional and exhibit a heterogeneous hierarchical structure that cannot be encoded in mean-field models. Here we propose that, as a consequence of the presence of such a heterogeneous substrate with its concomitant structural disorder, critical-like features may emerge even in the presence of integration. These conclusions may prove significant in explaining the observation of traits of critical behavior in large-scale measurements of brain activity. (paper)

  20. Supervised dictionary learning for inferring concurrent brain networks.

    Science.gov (United States)

    Zhao, Shijie; Han, Junwei; Lv, Jinglei; Jiang, Xi; Hu, Xintao; Zhao, Yu; Ge, Bao; Guo, Lei; Liu, Tianming

    2015-10-01

    Task-based fMRI (tfMRI) has been widely used to explore functional brain networks via predefined stimulus paradigm in the fMRI scan. Traditionally, the general linear model (GLM) has been a dominant approach to detect task-evoked networks. However, GLM focuses on task-evoked or event-evoked brain responses and possibly ignores the intrinsic brain functions. In comparison, dictionary learning and sparse coding methods have attracted much attention recently, and these methods have shown the promise of automatically and systematically decomposing fMRI signals into meaningful task-evoked and intrinsic concurrent networks. Nevertheless, two notable limitations of current data-driven dictionary learning method are that the prior knowledge of task paradigm is not sufficiently utilized and that the establishment of correspondences among dictionary atoms in different brains have been challenging. In this paper, we propose a novel supervised dictionary learning and sparse coding method for inferring functional networks from tfMRI data, which takes both of the advantages of model-driven method and data-driven method. The basic idea is to fix the task stimulus curves as predefined model-driven dictionary atoms and only optimize the other portion of data-driven dictionary atoms. Application of this novel methodology on the publicly available human connectome project (HCP) tfMRI datasets has achieved promising results.

  1. Patterns recognition of electric brain activity using artificial neural networks

    Science.gov (United States)

    Musatov, V. Yu.; Pchelintseva, S. V.; Runnova, A. E.; Hramov, A. E.

    2017-04-01

    An approach for the recognition of various cognitive processes in the brain activity in the perception of ambiguous images. On the basis of developed theoretical background and the experimental data, we propose a new classification of oscillating patterns in the human EEG by using an artificial neural network approach. After learning of the artificial neural network reliably identified cube recognition processes, for example, left-handed or right-oriented Necker cube with different intensity of their edges, construct an artificial neural network based on Perceptron architecture and demonstrate its effectiveness in the pattern recognition of the EEG in the experimental.

  2. Abnormal Functional Lateralization and Activity of Language Brain Areas in Typical Specific Language Impairment (Developmental Dysphasia)

    Science.gov (United States)

    de Guibert, Clement; Maumet, Camille; Jannin, Pierre; Ferre, Jean-Christophe; Treguier, Catherine; Barillot, Christian; Le Rumeur, Elisabeth; Allaire, Catherine; Biraben, Arnaud

    2011-01-01

    Atypical functional lateralization and specialization for language have been proposed to account for developmental language disorders, yet results from functional neuroimaging studies are sparse and inconsistent. This functional magnetic resonance imaging study compared children with a specific subtype of specific language impairment affecting…

  3. 5-HTTLPR differentially predicts brain network responses to emotional faces

    DEFF Research Database (Denmark)

    Fisher, Patrick M; Grady, Cheryl L; Madsen, Martin K

    2015-01-01

    The effects of the 5-HTTLPR polymorphism on neural responses to emotionally salient faces have been studied extensively, focusing on amygdala reactivity and amygdala-prefrontal interactions. Despite compelling evidence that emotional face paradigms engage a distributed network of brain regions...... to fearful faces was significantly greater in S' carriers compared to LA LA individuals. These findings provide novel evidence for emotion-specific 5-HTTLPR effects on the response of a distributed set of brain regions including areas responsive to emotionally salient stimuli and critical components...... involved in emotion, cognitive and visual processing, less is known about 5-HTTLPR effects on broader network responses. To address this, we evaluated 5-HTTLPR differences in the whole-brain response to an emotional faces paradigm including neutral, angry and fearful faces using functional magnetic...

  4. Whole brain and brain regional coexpression network interactions associated with predisposition to alcohol consumption.

    Directory of Open Access Journals (Sweden)

    Lauren A Vanderlinden

    Full Text Available To identify brain transcriptional networks that may predispose an animal to consume alcohol, we used weighted gene coexpression network analysis (WGCNA. Candidate coexpression modules are those with an eigengene expression level that correlates significantly with the level of alcohol consumption across a panel of BXD recombinant inbred mouse strains, and that share a genomic region that regulates the module transcript expression levels (mQTL with a genomic region that regulates alcohol consumption (bQTL. To address a controversy regarding utility of gene expression profiles from whole brain, vs specific brain regions, as indicators of the relationship of gene expression to phenotype, we compared candidate coexpression modules from whole brain gene expression data (gathered with Affymetrix 430 v2 arrays in the Colorado laboratories and from gene expression data from 6 brain regions (nucleus accumbens (NA; prefrontal cortex (PFC; ventral tegmental area (VTA; striatum (ST; hippocampus (HP; cerebellum (CB available from GeneNetwork. The candidate modules were used to construct candidate eigengene networks across brain regions, resulting in three "meta-modules", composed of candidate modules from two or more brain regions (NA, PFC, ST, VTA and whole brain. To mitigate the potential influence of chromosomal location of transcripts and cis-eQTLs in linkage disequilibrium, we calculated a semi-partial correlation of the transcripts in the meta-modules with alcohol consumption conditional on the transcripts' cis-eQTLs. The function of transcripts that retained the correlation with the phenotype after correction for the strong genetic influence, implicates processes of protein metabolism in the ER and Golgi as influencing susceptibility to variation in alcohol consumption. Integration of these data with human GWAS provides further information on the function of polymorphisms associated with alcohol-related traits.

  5. Language Development and Brain Magnetic Resonance Imaging Characteristics in Preschool Children With Cerebral Palsy.

    Science.gov (United States)

    Choi, Ja Young; Choi, Yoon Seong; Park, Eun Sook

    2017-05-24

    The purpose of this study was to investigate characteristics of language development in relation to brain magnetic resonance imaging (MRI) characteristics and the other contributing factors to language development in children with cerebral palsy (CP). The study included 172 children with CP who underwent brain MRI and language assessments between 3 and 7 years of age. The MRI characteristics were categorized as normal, malformation, periventricular white matter lesion (PVWL), deep gray matter lesion, focal infarct, cortical/subcortical lesion, and others. Neurodevelopmental outcomes such as ambulatory status, manual ability, cognitive function, and accompanying impairments were assessed. Both receptive and expressive language development quotients (DQs) were significantly related to PVWL or deep gray matter lesion severity. In multivariable analysis, only cognitive function was significantly related to receptive language development, whereas ambulatory status and cognitive function were significantly associated with expressive language development. More than one third of the children had a language developmental discrepancy between receptive and expressive DQs. Children with cortical/subcortical lesions were at high risk for this discrepancy. Cognitive function is a key factor for both receptive and expressive language development. In children with PVWL or deep gray matter lesion, lesion severity seems to be useful to predict language development.

  6. Network dynamics in the healthy and epileptic developing brain

    Directory of Open Access Journals (Sweden)

    Richard Rosch

    2018-03-01

    Full Text Available Electroencephalography (EEG allows recording of cortical activity at high temporal resolution. EEG recordings can be summarized along different dimensions using network-level quantitative measures, such as channel-to-channel correlation, or band power distributions across channels. These reveal network patterns that unfold over a range of different timescales and can be tracked dynamically. Here we describe the dynamics of network state transitions in EEG recordings of spontaneous brain activity in normally developing infants and infants with severe early infantile epileptic encephalopathies (n = 8, age: 1–8 months. We describe differences in measures of EEG dynamics derived from band power, and correlation-based summaries of network-wide brain activity. We further show that EEGs from different patient groups and controls may be distinguishable on a small set of the novel quantitative measures introduced here, which describe dynamic network state switching. Quantitative measures related to the sharpness of switching from one correlation pattern to another show the largest differences between groups. These findings reveal that the early epileptic encephalopathies are associated with characteristic dynamic features at the network level. Quantitative network-based analyses like the one presented here may in the future inform the clinical use of quantitative EEG for diagnosis.

  7. Imaging structural and functional brain networks in temporal lobe epilepsy

    Science.gov (United States)

    Bernhardt, Boris C.; Hong, SeokJun; Bernasconi, Andrea; Bernasconi, Neda

    2013-01-01

    Early imaging studies in temporal lobe epilepsy (TLE) focused on the search for mesial temporal sclerosis, as its surgical removal results in clinically meaningful improvement in about 70% of patients. Nevertheless, a considerable subgroup of patients continues to suffer from post-operative seizures. Although the reasons for surgical failure are not fully understood, electrophysiological and imaging data suggest that anomalies extending beyond the temporal lobe may have negative impact on outcome. This hypothesis has revived the concept of human epilepsy as a disorder of distributed brain networks. Recent methodological advances in non-invasive neuroimaging have led to quantify structural and functional networks in vivo. While structural networks can be inferred from diffusion MRI tractography and inter-regional covariance patterns of structural measures such as cortical thickness, functional connectivity is generally computed based on statistical dependencies of neurophysiological time-series, measured through functional MRI or electroencephalographic techniques. This review considers the application of advanced analytical methods in structural and functional connectivity analyses in TLE. We will specifically highlight findings from graph-theoretical analysis that allow assessing the topological organization of brain networks. These studies have provided compelling evidence that TLE is a system disorder with profound alterations in local and distributed networks. In addition, there is emerging evidence for the utility of network properties as clinical diagnostic markers. Nowadays, a network perspective is considered to be essential to the understanding of the development, progression, and management of epilepsy. PMID:24098281

  8. Imaging structural and functional brain networks in temporal lobe epilepsy

    Directory of Open Access Journals (Sweden)

    Boris eBernhardt

    2013-10-01

    Full Text Available Early imaging studies in temporal lobe epilepsy (TLE focused on the search for mesial temporal sclerosis, as its surgical removal results in clinically meaningful improvement in about 70% of patients. Nevertheless, a considerable subgroup of patients continues to suffer from post-operative seizures. Although the reasons for surgical failure are not fully understood, electrophysiological and imaging data suggest that anomalies extending beyond the temporal lobe may have negative impact on outcome. This hypothesis has revived the concept of human epilepsy as a disorder of distributed brain networks. Recent methodological advances in non-invasive neuroimaging have led to quantify structural and functional networks in vivo. While structural networks can be inferred from diffusion MRI tractography and inter-regional covariance patterns of structural measures such as cortical thickness, functional connectivity is generally computed based on statistical dependencies of neurophysiological time-series, measured through functional MRI or electroencephalographic techniques. This review considers the application of advanced analytical methods in structural and functional connectivity analyses in TLE. We will specifically highlight findings from graph-theoretical analysis that allow assessing topological organization of brain networks. These studies have provided compelling evidence that TLE is a system disorder with profound alterations in local and distributed networks. In addition, there is emerging evidence for the utility of network properties as clinical diagnostic markers. Nowadays, a network perspective is considered to be essential to the understanding of the development, progression, and management of epilepsy.

  9. Imaging structural and functional brain networks in temporal lobe epilepsy.

    Science.gov (United States)

    Bernhardt, Boris C; Hong, Seokjun; Bernasconi, Andrea; Bernasconi, Neda

    2013-10-01

    Early imaging studies in temporal lobe epilepsy (TLE) focused on the search for mesial temporal sclerosis, as its surgical removal results in clinically meaningful improvement in about 70% of patients. Nevertheless, a considerable subgroup of patients continues to suffer from post-operative seizures. Although the reasons for surgical failure are not fully understood, electrophysiological and imaging data suggest that anomalies extending beyond the temporal lobe may have negative impact on outcome. This hypothesis has revived the concept of human epilepsy as a disorder of distributed brain networks. Recent methodological advances in non-invasive neuroimaging have led to quantify structural and functional networks in vivo. While structural networks can be inferred from diffusion MRI tractography and inter-regional covariance patterns of structural measures such as cortical thickness, functional connectivity is generally computed based on statistical dependencies of neurophysiological time-series, measured through functional MRI or electroencephalographic techniques. This review considers the application of advanced analytical methods in structural and functional connectivity analyses in TLE. We will specifically highlight findings from graph-theoretical analysis that allow assessing the topological organization of brain networks. These studies have provided compelling evidence that TLE is a system disorder with profound alterations in local and distributed networks. In addition, there is emerging evidence for the utility of network properties as clinical diagnostic markers. Nowadays, a network perspective is considered to be essential to the understanding of the development, progression, and management of epilepsy.

  10. Flow distributions and spatial correlations in human brain capillary networks

    Science.gov (United States)

    Lorthois, Sylvie; Peyrounette, Myriam; Larue, Anne; Le Borgne, Tanguy

    2015-11-01

    The vascular system of the human brain cortex is composed of a space filling mesh-like capillary network connected upstream and downstream to branched quasi-fractal arterioles and venules. The distribution of blood flow rates in these networks may affect the efficiency of oxygen transfer processes. Here, we investigate the distribution and correlation properties of blood flow velocities from numerical simulations in large 3D human intra-cortical vascular network (10000 segments) obtained from an anatomical database. In each segment, flow is solved from a 1D non-linear model taking account of the complex rheological properties of blood flow in microcirculation to deduce blood pressure, blood flow and red blood cell volume fraction distributions throughout the network. The network structural complexity is found to impart broad and spatially correlated Lagrangian velocity distributions, leading to power law transit time distributions. The origins of this behavior (existence of velocity correlations in capillary networks, influence of the coupling with the feeding arterioles and draining veins, topological disorder, complex blood rheology) are studied by comparison with results obtained in various model capillary networks of controlled disorder. ERC BrainMicroFlow GA615102, ERC ReactiveFronts GA648377.

  11. Sex differences in normal age trajectories of functional brain networks.

    Science.gov (United States)

    Scheinost, Dustin; Finn, Emily S; Tokoglu, Fuyuze; Shen, Xilin; Papademetris, Xenophon; Hampson, Michelle; Constable, R Todd

    2015-04-01

    Resting-state functional magnetic resonance image (rs-fMRI) is increasingly used to study functional brain networks. Nevertheless, variability in these networks due to factors such as sex and aging is not fully understood. This study explored sex differences in normal age trajectories of resting-state networks (RSNs) using a novel voxel-wise measure of functional connectivity, the intrinsic connectivity distribution (ICD). Males and females showed differential patterns of changing connectivity in large-scale RSNs during normal aging from early adulthood to late middle-age. In some networks, such as the default-mode network, males and females both showed decreases in connectivity with age, albeit at different rates. In other networks, such as the fronto-parietal network, males and females showed divergent connectivity trajectories with age. Main effects of sex and age were found in many of the same regions showing sex-related differences in aging. Finally, these sex differences in aging trajectories were robust to choice of preprocessing strategy, such as global signal regression. Our findings resolve some discrepancies in the literature, especially with respect to the trajectory of connectivity in the default mode, which can be explained by our observed interactions between sex and aging. Overall, results indicate that RSNs show different aging trajectories for males and females. Characterizing effects of sex and age on RSNs are critical first steps in understanding the functional organization of the human brain. © 2014 Wiley Periodicals, Inc.

  12. Brain and Behavioral Assessment of Executive Functions for Self-Regulating Levels of Language in Reading Brain.

    Science.gov (United States)

    Berninger, Virginia W; Richards, Todd L; Abbott, Robert D

    2017-11-01

    This brief research report examines brain-behavioral relationships specific to levels of language in the complex reading brain. The first specific aim was to examine prior findings for significant fMRI connectivity from four seeds (left precuneus, left occipital temporal, left supramarginal, left inferior frontal) for each of four levels of language-subword, word (word-specific spelling or affixed words), syntax (with and without homonym foils or affix foils), and multi-sentence text to identify significant fMRI connectivity (a) unique to the lower level of language when compared to the immediately higher adjacent level of language across subword-word, word-syntax, and syntax-text comparisons; and (b) involving a brain region associated with executive functions. The second specific aim was to correlate the magnitude of that connectivity with standard scores on tests of Focused Attention (D-K EFS Color Word Form Inhibition) and Switching Attention (Wolf & Denckla Rapid Automatic Switching). Seven correlations were significant. Focused Attention was significantly correlated with the word level (word-specific spellings of real words) fMRI task in left cingulum from left inferior frontal seed. Switching Attention was significantly correlated with the (a) subword level (grapheme-phoneme correspondence) fMRI task in left and right Cerebellum V from left supramarginal seed; (b) the word level (word-specific spelling) fMRI task in right Cerebellum V from left precuneus seed; (c) the syntax level (with and without homonym foils) fMRI task in right Cerebellum V from left precuneus seed and from left supramarginal seed; and (d) syntax level (with and without affix foils) fMRI task in right Cerebellum V from left precuneus seed. Results are discussed in reference to neuropsychological assessment of supervisory attention (focused and switching) for specific levels of language related to reading acquisition in students with and without language-related specific learning

  13. Pro-cognitive drug effects modulate functional brain network organization

    Science.gov (United States)

    Giessing, Carsten; Thiel, Christiane M.

    2012-01-01

    Previous studies document that cholinergic and noradrenergic drugs improve attention, memory and cognitive control in healthy subjects and patients with neuropsychiatric disorders. In humans neural mechanisms of cholinergic and noradrenergic modulation have mainly been analyzed by investigating drug-induced changes of task-related neural activity measured with functional magnetic resonance imaging (fMRI). Endogenous neural activity has often been neglected. Further, although drugs affect the coupling between neurons, only a few human studies have explicitly addressed how drugs modulate the functional connectome, i.e., the functional neural interactions within the brain. These studies have mainly focused on synchronization or correlation of brain activations. Recently, there are some drug studies using graph theory and other new mathematical approaches to model the brain as a complex network of interconnected processing nodes. Using such measures it is possible to detect not only focal, but also subtle, widely distributed drug effects on functional network topology. Most important, graph theoretical measures also quantify whether drug-induced changes in topology or network organization facilitate or hinder information processing. Several studies could show that functional brain integration is highly correlated with behavioral performance suggesting that cholinergic and noradrenergic drugs which improve measures of cognitive performance should increase functional network integration. The purpose of this paper is to show that graph theory provides a mathematical tool to develop theory-driven biomarkers of pro-cognitive drug effects, and also to discuss how these approaches can contribute to the understanding of the role of cholinergic and noradrenergic modulation in the human brain. Finally we discuss the “global workspace” theory as a theoretical framework of pro-cognitive drug effects and argue that pro-cognitive effects of cholinergic and noradrenergic drugs

  14. Stone tools, language and the brain in human evolution

    Science.gov (United States)

    Stout, Dietrich; Chaminade, Thierry

    2012-01-01

    Long-standing speculations and more recent hypotheses propose a variety of possible evolutionary connections between language, gesture and tool use. These arguments have received important new support from neuroscientific research on praxis, observational action understanding and vocal language demonstrating substantial functional/anatomical overlap between these behaviours. However, valid reasons for scepticism remain as well as substantial differences in detail between alternative evolutionary hypotheses. Here, we review the current status of alternative ‘gestural’ and ‘technological’ hypotheses of language origins, drawing on current evidence of the neural bases of speech and tool use generally, and on recent studies of the neural correlates of Palaeolithic technology specifically. PMID:22106428

  15. Meeting the memory challenges of brain-scale network simulation

    Directory of Open Access Journals (Sweden)

    Susanne eKunkel

    2012-01-01

    Full Text Available The development of high-performance simulation software is crucial for studying the brain connectome. Using connectome data to generate neurocomputational models requires software capable of coping with models on a variety of scales: from the microscale, investigating plasticity and dynamics of circuits in local networks, to the macroscale, investigating the interactions between distinct brain regions. Prior to any serious dynamical investigation, the first task of network simulations is to check the consistency of data integrated in the connectome and constrain ranges for yet unknown parameters. Thanks to distributed computing techniques, it is possible today to routinely simulate local cortical networks of around 10^5 neurons with up to 10^9 synapses on clusters and multi-processor shared-memory machines. However, brain-scale networks are one or two orders of magnitude larger than such local networks, in terms of numbers of neurons and synapses as well as in terms of computational load. Such networks have been studied in individual studies, but the underlying simulation technologies have neither been described in sufficient detail to be reproducible nor made publicly available. Here, we discover that as the network model sizes approach the regime of meso- and macroscale simulations, memory consumption on individual compute nodes becomes a critical bottleneck. This is especially relevant on modern supercomputers such as the Bluegene/P architecture where the available working memory per CPU core is rather limited. We develop a simple linear model to analyze the memory consumption of the constituent components of a neuronal simulator as a function of network size and the number of cores used. This approach has multiple benefits. The model enables identification of key contributing components to memory saturation and prediction of the effects of potential improvements to code before any implementation takes place.

  16. Lectures in Supercomputational Neurosciences Dynamics in Complex Brain Networks

    CERN Document Server

    Graben, Peter beim; Thiel, Marco; Kurths, Jürgen

    2008-01-01

    Computational Neuroscience is a burgeoning field of research where only the combined effort of neuroscientists, biologists, psychologists, physicists, mathematicians, computer scientists, engineers and other specialists, e.g. from linguistics and medicine, seem to be able to expand the limits of our knowledge. The present volume is an introduction, largely from the physicists' perspective, to the subject matter with in-depth contributions by system neuroscientists. A conceptual model for complex networks of neurons is introduced that incorporates many important features of the real brain, such as various types of neurons, various brain areas, inhibitory and excitatory coupling and the plasticity of the network. The computational implementation on supercomputers, which is introduced and discussed in detail in this book, will enable the readers to modify and adapt the algortihm for their own research. Worked-out examples of applications are presented for networks of Morris-Lecar neurons to model the cortical co...

  17. Functional brain networks underlying detection and integration of disconfirmatory evidence.

    Science.gov (United States)

    Lavigne, Katie M; Metzak, Paul D; Woodward, Todd S

    2015-05-15

    Processing evidence that disconfirms a prior interpretation is a fundamental aspect of belief revision, and has clear social and clinical relevance. This complex cognitive process requires (at minimum) an alerting stage and an integration stage, and in the current functional magnetic resonance imaging (fMRI) study, we used multivariate analysis methodology on two datasets in an attempt to separate these sequentially-activated cognitive stages and link them to distinct functional brain networks. Thirty-nine healthy participants completed one of two versions of an evidence integration experiment involving rating two consecutive animal images, both of which consisted of two intact images of animal faces morphed together at different ratios (e.g., 70/30 bird/dolphin followed by 10/90 bird/dolphin). The two versions of the experiment differed primarily in terms of stimulus presentation and timing, which facilitated functional interpretation of brain networks based on differences in the hemodynamic response shapes between versions. The data were analyzed using constrained principal component analysis for fMRI (fMRI-CPCA), which allows distinct, simultaneously active task-based networks to be separated, and these were interpreted using both temporal (task-based hemodynamic response shapes) and spatial (dominant brain regions) information. Three networks showed increased activity during integration of disconfirmatory relative to confirmatory evidence: (1) a network involved in alerting to the requirement to revise an interpretation, identified as the salience network (dorsal anterior cingulate cortex and bilateral insula); (2) a sensorimotor response-related network (pre- and post-central gyri, supplementary motor area, and thalamus); and (3) an integration network involving rostral prefrontal, orbitofrontal and posterior parietal cortex. These three networks were staggered in their peak activity (alerting, responding, then integrating), but at certain time points (e

  18. A functional MRI study of language networks in left medial temporal lobe epilepsy

    Energy Technology Data Exchange (ETDEWEB)

    Yu Aihong, E-mail: yuaihong163@tom.com [Department of Radiology, the 4th Medical College of Peking University, Beijing Jishuitan Hospital, Beijing 100035 (China); Wang Xiaoyi; Xu Guoqing [Beijing Normal University, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing 100875 (China); Li Yongjie [Beijing Institute of Functional Neurosurgery, Xuanwu Hospital, Capital University of Medical Sciences, Beijing 100053 (China); Qin Wen; Li Kuncheng [Department of Radiology, Xuanwu Hospital, Capital University of Medical Sciences (China); Wang, Yuping [Department of Neurology, Xuanwu Hospital, Capital University of Medical Sciences (China)

    2011-11-15

    Purpose: The purpose of this study was to investigate the abnormality of language networks in left medial temporal lobe epilepsy (MTLE) using fMRI. Materials and methods: Eight patients with left MTLE and 15 healthy subjects were evaluated. An auditory semantic judgment (AJ) paradigm was used. The fMRI data were collected on a 3T MR system and analyzed by AFNI (analysis of functional neuroimages) to generate the activation map. Results: Behavioral data showed that the reaction time of the left MTLE patients was significantly longer than that of controls on the AJ task (t = -3.396, P < 0.05). The left MTLE patients also exhibited diffusively decreased activation in the AJ task. Right hemisphere dominance of Broca's and Wernicke's areas was demonstrated in left MTLE patients. Conclusions: Long-term activation of spikes in left MTLE patients results in language impairment, which is associated with an abnormality of the brain neural network.

  19. Altered brain network modules induce helplessness in major depressive disorder.

    Science.gov (United States)

    Peng, Daihui; Shi, Feng; Shen, Ting; Peng, Ziwen; Zhang, Chen; Liu, Xiaohua; Qiu, Meihui; Liu, Jun; Jiang, Kaida; Fang, Yiru; Shen, Dinggang

    2014-10-01

    The abnormal brain functional connectivity (FC) has been assumed to be a pathophysiological aspect of major depressive disorder (MDD). However, it is poorly understood, regarding the underlying patterns of global FC network and their relationships with the clinical characteristics of MDD. Resting-state functional magnetic resonance imaging data were acquired from 16 first episode, medication-naïve MDD patients and 16 healthy control subjects. The global FC network was constructed using 90 brain regions. The global topological patterns, e.g., small-worldness and modularity, and their relationships with depressive characteristics were investigated. Furthermore, the participant coefficient and module degree of MDD patients were measured to reflect the regional roles in module network, and the impairment of FC was examined by network based statistic. Small-world property was not altered in MDD. However, MDD patients exhibited 5 atypically reorganized modules compared to the controls. A positive relationship was also found among MDD patients between the intra-module I and helplessness factor evaluated via the Hamilton Depression Scale. Specifically, eight regions exhibited the abnormal participant coefficient or module degree, e.g., left superior orbital frontal cortex and right amygdala. The decreased FC was identified among the sub-network of 24 brain regions, e.g., frontal cortex, supplementary motor area, amygdala, thalamus, and hippocampus. The limited size of MDD samples precluded meaningful study of distinct clinical characteristics in relation to aberrant FC. The results revealed altered patterns of brain module network at the global level in MDD patients, which might contribute to the feelings of helplessness. Copyright © 2014 Elsevier B.V. All rights reserved.

  20. Reverse engineering GTPase programming languages with reconstituted signaling networks.

    Science.gov (United States)

    Coyle, Scott M

    2016-07-02

    The Ras superfamily GTPases represent one of the most prolific signaling currencies used in Eukaryotes. With these remarkable molecules, evolution has built GTPase networks that control diverse cellular processes such as growth, morphology, motility and trafficking. (1-4) Our knowledge of the individual players that underlie the function of these networks is deep; decades of biochemical and structural data has provided a mechanistic understanding of the molecules that turn GTPases ON and OFF, as well as how those GTPase states signal by controlling the assembly of downstream effectors. However, we know less about how these different activities work together as a system to specify complex dynamic signaling outcomes. Decoding this molecular "programming language" would help us understand how different species and cell types have used the same GTPase machinery in different ways to accomplish different tasks, and would also provide new insights as to how mutations to these networks can cause disease. We recently developed a bead-based microscopy assay to watch reconstituted H-Ras signaling systems at work under arbitrary configurations of regulators and effectors. (5) Here we highlight key observations and insights from this study and propose extensions to our method to further study this and other GTPase signaling systems.

  1. Statistical physics, neural networks, brain studies

    International Nuclear Information System (INIS)

    Toulouse, G.

    1999-01-01

    An overview of some aspects of a vast domain, located at the crossroads of physics, biology and computer science is presented: (1) During the last fifteen years, physicists advancing along various pathways have come into contact with biology (computational neurosciences) and engineering (formal neural nets). (2) This move may actually be viewed as one component in a larger picture. A prominent trend of recent years, observable over many countries, has been the establishment of interdisciplinary centers devoted to the study of: cognitive sciences; natural and artificial intelligence; brain, mind and behaviour; perception and action; learning and memory; robotics; man-machine communication, etc. What are the promising lines of development? What opportunities for physicists? An attempt will be made to address such questions and related issues

  2. The Bilingual Brain: Human Evolution and Second Language Acquisition

    Directory of Open Access Journals (Sweden)

    L. Kirk Hagen

    2008-01-01

    Full Text Available For the past half-century, psycholinguistic research has concerned itself with two mysteries of human cognition: (1 that children universally acquire a highly abstract, computationally complex set of linguistic rules rapidly and effortlessly, and (2 that second language acquisition (SLA among adults is, conversely, slow, laborious, highly variable, and virtually never results in native fluency. We now have a decent, if approximate, understanding of the biological foundations of first language acquisition, thanks in large part to Lenneberg's (1964, 1984 seminal work on the critical period hypothesis. More recently, the elements of a promising theory of language and evolution have emerged as well (see e.g. Bickerton, 1981, 1990; Leiberman, 1984, 1987. I argue here that the empirical foundations of an evolutionary theory of language are now solid enough to support an account of bilingualism and adult SLA as well. Specifically, I will show that evidence from the environment of evolutionary adaptation of paleolithic humans suggests that for our nomadic ancestors, the ability to master a language early in life was an eminently useful adaptation. However, the ability to acquire another language in adulthood was not, and consequently was not selected for propagation.

  3. Using Social Networking Sites as a Platform for Second Language Instruction

    Science.gov (United States)

    Prichard, Caleb

    2013-01-01

    Social networking sites (SNSs) are increasingly used to communicate and to maintain relationships with people around the globe, and their usage has certainly led to incidental language gains for second language (L2) users. Language instructors are just beginning to utilize SNS sites to manage their courses or to have students practice language…

  4. Paradoxes of Social Networking in a Structured Web 2.0 Language Learning Community

    Science.gov (United States)

    Loiseau, Mathieu; Zourou, Katerina

    2012-01-01

    This paper critically inquires into social networking as a set of mechanisms and associated practices developed in a structured Web 2.0 language learning community. This type of community can be roughly described as learning spaces featuring (more or less) structured language learning resources displaying at least some notions of language learning…

  5. The correlation of metrics in complex networks with applications in functional brain networks

    International Nuclear Information System (INIS)

    Li, C; Wang, H; Van Mieghem, P; De Haan, W; Stam, C J

    2011-01-01

    An increasing number of network metrics have been applied in network analysis. If metric relations were known better, we could more effectively characterize networks by a small set of metrics to discover the association between network properties/metrics and network functioning. In this paper, we investigate the linear correlation coefficients between widely studied network metrics in three network models (Bárabasi–Albert graphs, Erdös–Rényi random graphs and Watts–Strogatz small-world graphs) as well as in functional brain networks of healthy subjects. The metric correlations, which we have observed and theoretically explained, motivate us to propose a small representative set of metrics by including only one metric from each subset of mutually strongly dependent metrics. The following contributions are considered important. (a) A network with a given degree distribution can indeed be characterized by a small representative set of metrics. (b) Unweighted networks, which are obtained from weighted functional brain networks with a fixed threshold, and Erdös–Rényi random graphs follow a similar degree distribution. Moreover, their metric correlations and the resultant representative metrics are similar as well. This verifies the influence of degree distribution on metric correlations. (c) Most metric correlations can be explained analytically. (d) Interestingly, the most studied metrics so far, the average shortest path length and the clustering coefficient, are strongly correlated and, thus, redundant. Whereas spectral metrics, though only studied recently in the context of complex networks, seem to be essential in network characterizations. This representative set of metrics tends to both sufficiently and effectively characterize networks with a given degree distribution. In the study of a specific network, however, we have to at least consider the representative set so that important network properties will not be neglected

  6. Mapping human brain networks with cortico-cortical evoked potentials

    Science.gov (United States)

    Keller, Corey J.; Honey, Christopher J.; Mégevand, Pierre; Entz, Laszlo; Ulbert, Istvan; Mehta, Ashesh D.

    2014-01-01

    The cerebral cortex forms a sheet of neurons organized into a network of interconnected modules that is highly expanded in humans and presumably enables our most refined sensory and cognitive abilities. The links of this network form a fundamental aspect of its organization, and a great deal of research is focusing on understanding how information flows within and between different regions. However, an often-overlooked element of this connectivity regards a causal, hierarchical structure of regions, whereby certain nodes of the cortical network may exert greater influence over the others. While this is difficult to ascertain non-invasively, patients undergoing invasive electrode monitoring for epilepsy provide a unique window into this aspect of cortical organization. In this review, we highlight the potential for cortico-cortical evoked potential (CCEP) mapping to directly measure neuronal propagation across large-scale brain networks with spatio-temporal resolution that is superior to traditional neuroimaging methods. We first introduce effective connectivity and discuss the mechanisms underlying CCEP generation. Next, we highlight how CCEP mapping has begun to provide insight into the neural basis of non-invasive imaging signals. Finally, we present a novel approach to perturbing and measuring brain network function during cognitive processing. The direct measurement of CCEPs in response to electrical stimulation represents a potentially powerful clinical and basic science tool for probing the large-scale networks of the human cerebral cortex. PMID:25180306

  7. Developmental changes in organization of structural brain networks.

    Science.gov (United States)

    Khundrakpam, Budhachandra S; Reid, Andrew; Brauer, Jens; Carbonell, Felix; Lewis, John; Ameis, Stephanie; Karama, Sherif; Lee, Junki; Chen, Zhang; Das, Samir; Evans, Alan C

    2013-09-01

    Recent findings from developmental neuroimaging studies suggest that the enhancement of cognitive processes during development may be the result of a fine-tuning of the structural and functional organization of brain with maturation. However, the details regarding the developmental trajectory of large-scale structural brain networks are not yet understood. Here, we used graph theory to examine developmental changes in the organization of structural brain networks in 203 normally growing children and adolescents. Structural brain networks were constructed using interregional correlations in cortical thickness for 4 age groups (early childhood: 4.8-8.4 year; late childhood: 8.5-11.3 year; early adolescence: 11.4-14.7 year; late adolescence: 14.8-18.3 year). Late childhood showed prominent changes in topological properties, specifically a significant reduction in local efficiency, modularity, and increased global efficiency, suggesting a shift of topological organization toward a more random configuration. An increase in number and span of distribution of connector hubs was found in this age group. Finally, inter-regional connectivity analysis and graph-theoretic measures indicated early maturation of primary sensorimotor regions and protracted development of higher order association and paralimbic regions. Our finding reveals a time window of plasticity occurring during late childhood which may accommodate crucial changes during puberty and the new developmental tasks that an adolescent faces.

  8. The globularization hypothesis of the language-ready brain as a developmental frame for prosodic bootstrapping theories of language acquisition

    Directory of Open Access Journals (Sweden)

    Aritz eIrurtzun

    2015-12-01

    Full Text Available In recent research Boeckx & Benítez-Burraco (2014a,b have advanced the hypothesis that our species-specific language-ready brain should be understood as the outcome of developmental changes that occurred in our species after the split from Neanderthals-Denisovans, which resulted in a more globular braincase configuration in comparison to our closest relatives, who had elongated endocasts. According to these authors, the development of a globular brain is an essential ingredient for the language faculty and in particular, it is the centrality occupied by the thalamus in a globular brain that allows its modulatory or regulatory role, essential for syntactico-semantic computations. Their hypothesis is that the syntactico-semantic capacities arise in humans as a consequence of a process of globularization, which significantly takes place postnatally (cf. Neubauer et al. (2010. In this paper, I show that Boeckx & Benítez-Burraco’s hypothesis makes an interesting developmental prediction regarding the path of language acquisition: it teases apart the onset of phonological acquisition and the onset of syntactic acquisition (the latter starting significantly later, after globularization. I argue that this hypothesis provides a developmental rationale for the prosodic bootstrapping hypothesis of language acquisition (cf. i.a. Gleitman & Wanner (1982; Mehler et al. (1988, et seq.; Gervain & Werker (2013, which claim that prosodic cues are employed for syntactic parsing. The literature converges in the observation that a large amount of such prosodic cues (in particular, rhythmic cues are already acquired before the completion of the globularization phase, which paves the way for the premises of prosodic bootstrapping hypothesis, allowing babies to have a rich knowledge of the prosody of their target language before they can start parsing the primary linguistic data syntactically.

  9. The "Globularization Hypothesis" of the Language-ready Brain as a Developmental Frame for Prosodic Bootstrapping Theories of Language Acquisition.

    Science.gov (United States)

    Irurtzun, Aritz

    2015-01-01

    In recent research (Boeckx and Benítez-Burraco, 2014a,b) have advanced the hypothesis that our species-specific language-ready brain should be understood as the outcome of developmental changes that occurred in our species after the split from Neanderthals-Denisovans, which resulted in a more globular braincase configuration in comparison to our closest relatives, who had elongated endocasts. According to these authors, the development of a globular brain is an essential ingredient for the language faculty and in particular, it is the centrality occupied by the thalamus in a globular brain that allows its modulatory or regulatory role, essential for syntactico-semantic computations. Their hypothesis is that the syntactico-semantic capacities arise in humans as a consequence of a process of globularization, which significantly takes place postnatally (cf. Neubauer et al., 2010). In this paper, I show that Boeckx and Benítez-Burraco's hypothesis makes an interesting developmental prediction regarding the path of language acquisition: it teases apart the onset of phonological acquisition and the onset of syntactic acquisition (the latter starting significantly later, after globularization). I argue that this hypothesis provides a developmental rationale for the prosodic bootstrapping hypothesis of language acquisition (cf. i.a. Gleitman and Wanner, 1982; Mehler et al., 1988, et seq.; Gervain and Werker, 2013), which claim that prosodic cues are employed for syntactic parsing. The literature converges in the observation that a large amount of such prosodic cues (in particular, rhythmic cues) are already acquired before the completion of the globularization phase, which paves the way for the premises of the prosodic bootstrapping hypothesis, allowing babies to have a rich knowledge of the prosody of their target language before they can start parsing the primary linguistic data syntactically.

  10. Multilayer modeling and analysis of human brain networks

    Science.gov (United States)

    2017-01-01

    Abstract Understanding how the human brain is structured, and how its architecture is related to function, is of paramount importance for a variety of applications, including but not limited to new ways to prevent, deal with, and cure brain diseases, such as Alzheimer’s or Parkinson’s, and psychiatric disorders, such as schizophrenia. The recent advances in structural and functional neuroimaging, together with the increasing attitude toward interdisciplinary approaches involving computer science, mathematics, and physics, are fostering interesting results from computational neuroscience that are quite often based on the analysis of complex network representation of the human brain. In recent years, this representation experienced a theoretical and computational revolution that is breaching neuroscience, allowing us to cope with the increasing complexity of the human brain across multiple scales and in multiple dimensions and to model structural and functional connectivity from new perspectives, often combined with each other. In this work, we will review the main achievements obtained from interdisciplinary research based on magnetic resonance imaging and establish de facto, the birth of multilayer network analysis and modeling of the human brain. PMID:28327916

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

    Science.gov (United States)

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

    2018-05-01

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

  12. Disruption of functional networks in dyslexia: A whole-brain, data-driven analysis of connectivity

    Science.gov (United States)

    Finn, Emily S.; Shen, Xilin; Holahan, John M.; Scheinost, Dustin; Lacadie, Cheryl; Papademetris, Xenophon; Shaywitz, Sally E.; Shaywitz, Bennett A.; Constable, R. Todd

    2013-01-01

    Background Functional connectivity analyses of fMRI data are a powerful tool for characterizing brain networks and how they are disrupted in neural disorders. However, many such analyses examine only one or a small number of a priori seed regions. Studies that consider the whole brain frequently rely on anatomic atlases to define network nodes, which may result in mixing distinct activation timecourses within a single node. Here, we improve upon previous methods by using a data-driven brain parcellation to compare connectivity profiles of dyslexic (DYS) versus non-impaired (NI) readers in the first whole-brain functional connectivity analysis of dyslexia. Methods Whole-brain connectivity was assessed in children (n = 75; 43 NI, 32 DYS) and adult (n = 104; 64 NI, 40 DYS) readers. Results Compared to NI readers, DYS readers showed divergent connectivity within the visual pathway and between visual association areas and prefrontal attention areas; increased right-hemisphere connectivity; reduced connectivity in the visual word-form area (part of the left fusiform gyrus specialized for printed words); and persistent connectivity to anterior language regions around the inferior frontal gyrus. Conclusions Together, findings suggest that NI readers are better able to integrate visual information and modulate their attention to visual stimuli, allowing them to recognize words based on their visual properties, while DYS readers recruit altered reading circuits and rely on laborious phonology-based “sounding out” strategies into adulthood. These results deepen our understanding of the neural basis of dyslexia and highlight the importance of synchrony between diverse brain regions for successful reading. PMID:24124929

  13. Construction of Individual Morphological Brain Networks with Multiple Morphometric Features

    Directory of Open Access Journals (Sweden)

    Chunlan Yang

    2017-04-01

    Full Text Available In recent years, researchers have increased attentions to the morphological brain network, which is generally constructed by measuring the mathematical correlation across regions using a certain morphometric feature, such as regional cortical thickness and voxel intensity. However, cerebral structure can be characterized by various factors, such as regional volume, surface area, and curvature. Moreover, most of the morphological brain networks are population-based, which has limitations in the investigations of individual difference and clinical applications. Hence, we have extended previous studies by proposing a novel method for realizing the construction of an individual-based morphological brain network through a combination of multiple morphometric features. In particular, interregional connections are estimated using our newly introduced feature vectors, namely, the Pearson correlation coefficient of the concatenation of seven morphometric features. Experiments were performed on a healthy cohort of 55 subjects (24 males aged from 20 to 29 and 31 females aged from 20 to 28 each scanned twice, and reproducibility was evaluated through test–retest reliability. The robustness of morphometric features was measured firstly to select the more reproducible features to form the connectomes. Then the topological properties were analyzed and compared with previous reports of different modalities. Small-worldness was observed in all the subjects at the range of the entire network sparsity (20–40%, and configurations were comparable with previous findings at the sparsity of 23%. The spatial distributions of the hub were found to be significantly influenced by the individual variances, and the hubs obtained by averaging across subjects and sparsities showed correspondence with previous reports. The intraclass coefficient of graphic properties (clustering coefficient = 0.83, characteristic path length = 0.81, betweenness centrality = 0.78 indicates

  14. MULTIPLE INTELLIGENCE THEORY AND FOREIGN LANGUAGE LEARNING:A BRAIN-BASED PERSPECTIVE

    Directory of Open Access Journals (Sweden)

    Jane Arnold

    2004-06-01

    Full Text Available Gardner's Multiple Intelligences theory is presented as a cognitive perspective on intelligence which has profound implications for education in general. More specifically, it has led to the application of eight of these frames to language teaching and learning. In this chapter, we will argue in favour of the application of MIT to the EFL classroom, using as support some of the major insights for language teaching from brain science.

  15. LANGUAGE COMPETENCE OF STUDENT TOWARD RIGHT HEMISPHER BRAIN FUNCTION : A Neuropragmatic Study

    OpenAIRE

    Handoko, Handoko; Sastra, Gusdi; Revita, Ike

    2015-01-01

    It has been known that the right hemisphere is contributed to language processing, especially in macro level, including macrostructure or discourse processing. This research is aimed at evaluating the students’ ability in language processing concerning macrostructure and the right hemispher brain function. This research is based on Dharmaperwira-prins method “Right Hemisphere Communication Assessment” (Pemeriksaan Komunikasi Hemisfer Kanan/PKHK). Research on students’ ability in macrostructur...

  16. The brain's router: a cortical network model of serial processing in the primate brain

    NARCIS (Netherlands)

    Zylberberg, Ariel; Fernández Slezak, Diego; Roelfsema, Pieter R.; Dehaene, Stanislas; Sigman, Mariano

    2010-01-01

    The human brain efficiently solves certain operations such as object recognition and categorization through a massively parallel network of dedicated processors. However, human cognition also relies on the ability to perform an arbitrarily large set of tasks by flexibly recombining different

  17. Considerations on command and response language features for a network of heterogeneous autonomous computers

    Science.gov (United States)

    Engelberg, N.; Shaw, C., III

    1984-01-01

    The design of a uniform command language to be used in a local area network of heterogeneous, autonomous nodes is considered. After examining the major characteristics of such a network, and after considering the profile of a scientist using the computers on the net as an investigative aid, a set of reasonable requirements for the command language are derived. Taking into account the possible inefficiencies in implementing a guest-layered network operating system and command language on a heterogeneous net, the authors examine command language naming, process/procedure invocation, parameter acquisition, help and response facilities, and other features found in single-node command languages, and conclude that some features may extend simply to the network case, others extend after some restrictions are imposed, and still others require modifications. In addition, it is noted that some requirements considered reasonable (user accounting reports, for example) demand further study before they can be efficiently implemented on a network of the sort described.

  18. Evidence for Functional Networks within the Human Brain's White Matter.

    Science.gov (United States)

    Peer, Michael; Nitzan, Mor; Bick, Atira S; Levin, Netta; Arzy, Shahar

    2017-07-05

    brain. However, most fMRI studies ignored a major part of the brain, the white-matter, discarding signals from it as arising from noise. Here we use resting-state fMRI data from 176 subjects to show that signals from the human white-matter contain meaningful information. We identify 12 functional networks composed of interacting long-distance white-matter tracts. Moreover, we show that these networks are highly correlated to resting-state gray-matter networks, highlighting their functional role. Our findings enable reinterpretation of many existing fMRI datasets, and suggest a new way to explore the white-matter role in cognition and its disturbances in neuropsychiatric disorders. Copyright © 2017 the authors 0270-6474/17/376394-14$15.00/0.

  19. Electrical brain responses in language-impaired children reveal grammar-specific deficits.

    Directory of Open Access Journals (Sweden)

    Elisabeth Fonteneau

    2008-03-01

    Full Text Available Scientific and public fascination with human language have included intensive scrutiny of language disorders as a new window onto the biological foundations of language and its evolutionary origins. Specific language impairment (SLI, which affects over 7% of children, is one such disorder. SLI has received robust scientific attention, in part because of its recent linkage to a specific gene and loci on chromosomes and in part because of the prevailing question regarding the scope of its language impairment: Does the disorder impact the general ability to segment and process language or a specific ability to compute grammar? Here we provide novel electrophysiological data showing a domain-specific deficit within the grammar of language that has been hitherto undetectable through behavioural data alone.We presented participants with Grammatical(G-SLI, age-matched controls, and younger child and adult controls, with questions containing syntactic violations and sentences containing semantic violations. Electrophysiological brain responses revealed a selective impairment to only neural circuitry that is specific to grammatical processing in G-SLI. Furthermore, the participants with G-SLI appeared to be partially compensating for their syntactic deficit by using neural circuitry associated with semantic processing and all non-grammar-specific and low-level auditory neural responses were normal.The findings indicate that grammatical neural circuitry underlying language is a developmentally unique system in the functional architecture of the brain, and this complex higher cognitive system can be selectively impaired. The findings advance fundamental understanding about how cognitive systems develop and all human language is represented and processed in the brain.

  20. Large-Scale Functional Brain Network Reorganization During Taoist Meditation.

    Science.gov (United States)

    Jao, Tun; Li, Chia-Wei; Vértes, Petra E; Wu, Changwei Wesley; Achard, Sophie; Hsieh, Chao-Hsien; Liou, Chien-Hui; Chen, Jyh-Horng; Bullmore, Edward T

    2016-02-01

    Meditation induces a distinct and reversible mental state that provides insights into brain correlates of consciousness. We explored brain network changes related to meditation by graph theoretical analysis of resting-state functional magnetic resonance imaging data. Eighteen Taoist meditators with varying levels of expertise were scanned using a within-subjects counterbalanced design during resting and meditation states. State-related differences in network topology were measured globally and at the level of individual nodes and edges. Although measures of global network topology, such as small-worldness, were unchanged, meditation was characterized by an extensive and expertise-dependent reorganization of the hubs (highly connected nodes) and edges (functional connections). Areas of sensory cortex, especially the bilateral primary visual and auditory cortices, and the bilateral temporopolar areas, which had the highest degree (or connectivity) during the resting state, showed the biggest decrease during meditation. Conversely, bilateral thalamus and components of the default mode network, mainly the bilateral precuneus and posterior cingulate cortex, had low degree in the resting state but increased degree during meditation. Additionally, these changes in nodal degree were accompanied by reorganization of anatomical orientation of the edges. During meditation, long-distance longitudinal (antero-posterior) edges increased proportionally, whereas orthogonal long-distance transverse (right-left) edges connecting bilaterally homologous cortices decreased. Our findings suggest that transient changes in consciousness associated with meditation introduce convergent changes in the topological and spatial properties of brain functional networks, and the anatomical pattern of integration might be as important as the global level of integration when considering the network basis for human consciousness.

  1. Energy landscapes of resting-state brain networks

    Directory of Open Access Journals (Sweden)

    Takamitsu eWatanabe

    2014-02-01

    Full Text Available During rest, the human brain performs essential functions such as memory maintenance, which are associated with resting-state brain networks (RSNs including the default-mode network (DMN and frontoparietal network (FPN. Previous studies based on spiking-neuron network models and their reduced models, as well as those based on imaging data, suggest that resting-state network activity can be captured as attractor dynamics, i.e., dynamics of the brain state toward an attractive state and transitions between different attractors. Here, we analyze the energy landscapes of the RSNs by applying the maximum entropy model, or equivalently the Ising spin model, to human RSN data. We use the previously estimated parameter values to define the energy landscape, and the disconnectivity graph method to estimate the number of local energy minima (equivalent to attractors in attractor dynamics, the basin size, and hierarchical relationships among the different local minima. In both of the DMN and FPN, low-energy local minima tended to have large basins. A majority of the network states belonged to a basin of one of a few local minima. Therefore, a small number of local minima constituted the backbone of each RSN. In the DMN, the energy landscape consisted of two groups of low-energy local minima that are separated by a relatively high energy barrier. Within each group, the activity patterns of the local minima were similar, and different minima were connected by relatively low energy barriers. In the FPN, all dominant energy were separated by relatively low energy barriers such that they formed a single coarse-grained global minimum. Our results indicate that multistable attractor dynamics may underlie the DMN, but not the FPN, and assist memory maintenance with different memory states.

  2. Dynamic functional brain networks involved in simple visual discrimination learning.

    Science.gov (United States)

    Fidalgo, Camino; Conejo, Nélida María; González-Pardo, Héctor; Arias, Jorge Luis

    2014-10-01

    Visual discrimination tasks have been widely used to evaluate many types of learning and memory processes. However, little is known about the brain regions involved at different stages of visual discrimination learning. We used cytochrome c oxidase histochemistry to evaluate changes in regional brain oxidative metabolism during visual discrimination learning in a water-T maze at different time points during training. As compared with control groups, the results of the present study reveal the gradual activation of cortical (prefrontal and temporal cortices) and subcortical brain regions (including the striatum and the hippocampus) associated to the mastery of a simple visual discrimination task. On the other hand, the brain regions involved and their functional interactions changed progressively over days of training. Regions associated with novelty, emotion, visuo-spatial orientation and motor aspects of the behavioral task seem to be relevant during the earlier phase of training, whereas a brain network comprising the prefrontal cortex was found along the whole learning process. This study highlights the relevance of functional interactions among brain regions to investigate learning and memory processes. Copyright © 2014 Elsevier Inc. All rights reserved.

  3. Brain Structure Network Analysis in Patients with Obstructive Sleep Apnea.

    Directory of Open Access Journals (Sweden)

    Yun-Gang Luo

    Full Text Available Childhood obstructive sleep apnea (OSA is a sleeping disorder commonly affecting school-aged children and is characterized by repeated episodes of blockage of the upper airway during sleep. In this study, we performed a graph theoretical analysis on the brain morphometric correlation network in 25 OSA patients (OSA group; 5 female; mean age, 10.1 ± 1.8 years and investigated the topological alterations in global and regional properties compared with 20 healthy control individuals (CON group; 6 females; mean age, 10.4 ± 1.8 years. A structural correlation network based on regional gray matter volume was constructed respectively for each group. Our results revealed a significantly decreased mean local efficiency in the OSA group over the density range of 0.32-0.44 (p < 0.05. Regionally, the OSAs showed a tendency of decreased betweenness centrality in the left angular gyrus, and a tendency of decreased degree in the right lingual and inferior frontal (orbital part gyrus (p < 0.005, uncorrected. We also found that the network hubs in OSA and controls were distributed differently. To the best of our knowledge, this is the first study that characterizes the brain structure network in OSA patients and invests the alteration of topological properties of gray matter volume structural network. This study may help to provide new evidence for understanding the neuropathophysiology of OSA from a topological perspective.

  4. Brain Structure Network Analysis in Patients with Obstructive Sleep Apnea.

    Science.gov (United States)

    Luo, Yun-Gang; Wang, Defeng; Liu, Kai; Weng, Jian; Guan, Yuefeng; Chan, Kate C C; Chu, Winnie C W; Shi, Lin

    2015-01-01

    Childhood obstructive sleep apnea (OSA) is a sleeping disorder commonly affecting school-aged children and is characterized by repeated episodes of blockage of the upper airway during sleep. In this study, we performed a graph theoretical analysis on the brain morphometric correlation network in 25 OSA patients (OSA group; 5 female; mean age, 10.1 ± 1.8 years) and investigated the topological alterations in global and regional properties compared with 20 healthy control individuals (CON group; 6 females; mean age, 10.4 ± 1.8 years). A structural correlation network based on regional gray matter volume was constructed respectively for each group. Our results revealed a significantly decreased mean local efficiency in the OSA group over the density range of 0.32-0.44 (p gyrus, and a tendency of decreased degree in the right lingual and inferior frontal (orbital part) gyrus (p < 0.005, uncorrected). We also found that the network hubs in OSA and controls were distributed differently. To the best of our knowledge, this is the first study that characterizes the brain structure network in OSA patients and invests the alteration of topological properties of gray matter volume structural network. This study may help to provide new evidence for understanding the neuropathophysiology of OSA from a topological perspective.

  5. How does language change as a lexical network? An investigation based on written Chinese word co-occurrence networks

    Science.gov (United States)

    Chen, Heng; Chen, Xinying

    2018-01-01

    Language is a complex adaptive system, but how does it change? For investigating this process, four diachronic Chinese word co-occurrence networks have been built based on texts that were written during the last 2,000 years. By comparing the network indicators that are associated with the hierarchical features in language networks, we learn that the hierarchy of Chinese lexical networks has indeed evolved over time at three different levels. The connections of words at the micro level are continually weakening; the number of words in the meso-level communities has increased significantly; and the network is expanding at the macro level. This means that more and more words tend to be connected to medium-central words and form different communities. Meanwhile, fewer high-central words link these communities into a highly efficient small-world network. Understanding this process may be crucial for understanding the increasing structural complexity of the language system. PMID:29489837

  6. Improving the Reliability of Network Metrics in Structural Brain Networks by Integrating Different Network Weighting Strategies into a Single Graph

    Directory of Open Access Journals (Sweden)

    Stavros I. Dimitriadis

    2017-12-01

    Full Text Available Structural brain networks estimated from diffusion MRI (dMRI via tractography have been widely studied in healthy controls and patients with neurological and psychiatric diseases. However, few studies have addressed the reliability of derived network metrics both node-specific and network-wide. Different network weighting strategies (NWS can be adopted to weight the strength of connection between two nodes yielding structural brain networks that are almost fully-weighted. Here, we scanned five healthy participants five times each, using a diffusion-weighted MRI protocol and computed edges between 90 regions of interest (ROI from the Automated Anatomical Labeling (AAL template. The edges were weighted according to nine different methods. We propose a linear combination of these nine NWS into a single graph using an appropriate diffusion distance metric. We refer to the resulting weighted graph as an Integrated Weighted Structural Brain Network (ISWBN. Additionally, we consider a topological filtering scheme that maximizes the information flow in the brain network under the constraint of the overall cost of the surviving connections. We compared each of the nine NWS and the ISWBN based on the improvement of: (a intra-class correlation coefficient (ICC of well-known network metrics, both node-wise and per network level; and (b the recognition accuracy of each subject compared to the remainder of the cohort, as an attempt to access the uniqueness of the structural brain network for each subject, after first applying our proposed topological filtering scheme. Based on a threshold where the network level ICC should be >0.90, our findings revealed that six out of nine NWS lead to unreliable results at the network level, while all nine NWS were unreliable at the node level. In comparison, our proposed ISWBN performed as well as the best performing individual NWS at the network level, and the ICC was higher compared to all individual NWS at the node

  7. Volumetric multimodality neural network for brain tumor segmentation

    Science.gov (United States)

    Silvana Castillo, Laura; Alexandra Daza, Laura; Carlos Rivera, Luis; Arbeláez, Pablo

    2017-11-01

    Brain lesion segmentation is one of the hardest tasks to be solved in computer vision with an emphasis on the medical field. We present a convolutional neural network that produces a semantic segmentation of brain tumors, capable of processing volumetric data along with information from multiple MRI modalities at the same time. This results in the ability to learn from small training datasets and highly imbalanced data. Our method is based on DeepMedic, the state of the art in brain lesion segmentation. We develop a new architecture with more convolutional layers, organized in three parallel pathways with different input resolution, and additional fully connected layers. We tested our method over the 2015 BraTS Challenge dataset, reaching an average dice coefficient of 84%, while the standard DeepMedic implementation reached 74%.

  8. Brain anatomy alterations associated with Social Networking Site (SNS) addiction

    OpenAIRE

    He, Qinghua; Turel, Ofir; Bechara, Antoine

    2017-01-01

    This study relies on knowledge regarding the neuroplasticity of dual-system components that govern addiction and excessive behavior and suggests that alterations in the grey matter volumes, i.e., brain morphology, of specific regions of interest are associated with technology-related addictions. Using voxel based morphometry (VBM) applied to structural Magnetic Resonance Imaging (MRI) scans of twenty social network site (SNS) users with varying degrees of SNS addiction, we show that SNS addic...

  9. State-related functional integration and functional segregation brain networks in schizophrenia.

    Science.gov (United States)

    Yu, Qingbao; Sui, Jing; Kiehl, Kent A; Pearlson, Godfrey; Calhoun, Vince D

    2013-11-01

    Altered topological properties of brain connectivity networks have emerged as important features of schizophrenia. The aim of this study was to investigate how the state-related modulations to graph measures of functional integration and functional segregation brain networks are disrupted in schizophrenia. Firstly, resting state and auditory oddball discrimination (AOD) fMRI data of healthy controls (HCs) and schizophrenia patients (SZs) were decomposed into spatially independent components (ICs) by group independent component analysis (ICA). Then, weighted positive and negative functional integration (inter-component networks) and functional segregation (intra-component networks) brain networks were built in each subject. Subsequently, connectivity strength, clustering coefficient, and global efficiency of all brain networks were statistically compared between groups (HCs and SZs) in each state and between states (rest and AOD) within group. We found that graph measures of negative functional integration brain network and several positive functional segregation brain networks were altered in schizophrenia during AOD task. The metrics of positive functional integration brain network and one positive functional segregation brain network were higher during the resting state than during the AOD task only in HCs. These findings imply that state-related characteristics of both functional integration and functional segregation brain networks are impaired in schizophrenia which provides new insight into the altered brain performance in this brain disorder. © 2013.

  10. NCOG-06. Usability and validity of a phone battery to assess language functions in brain tumor patients undergoing awake surgery

    NARCIS (Netherlands)

    Witte, E. de; Piai, V.; Dronkers, N.F.; Berger, M.S.

    2016-01-01

    Introduction: A wake surgery in eloquent brain regions is performed to preserve language functions. Although in general no major permanent language deficits are found after awake brain surgery, clinically relevant impairments are detected (Satoer et al., 2014). Unfortunately, follow-up of tumor

  11. Natural language acquisition in large scale neural semantic networks

    Science.gov (United States)

    Ealey, Douglas

    This thesis puts forward the view that a purely signal- based approach to natural language processing is both plausible and desirable. By questioning the veracity of symbolic representations of meaning, it argues for a unified, non-symbolic model of knowledge representation that is both biologically plausible and, potentially, highly efficient. Processes to generate a grounded, neural form of this model-dubbed the semantic filter-are discussed. The combined effects of local neural organisation, coincident with perceptual maturation, are used to hypothesise its nature. This theoretical model is then validated in light of a number of fundamental neurological constraints and milestones. The mechanisms of semantic and episodic development that the model predicts are then used to explain linguistic properties, such as propositions and verbs, syntax and scripting. To mimic the growth of locally densely connected structures upon an unbounded neural substrate, a system is developed that can grow arbitrarily large, data- dependant structures composed of individual self- organising neural networks. The maturational nature of the data used results in a structure in which the perception of concepts is refined by the networks, but demarcated by subsequent structure. As a consequence, the overall structure shows significant memory and computational benefits, as predicted by the cognitive and neural models. Furthermore, the localised nature of the neural architecture also avoids the increasing error sensitivity and redundancy of traditional systems as the training domain grows. The semantic and episodic filters have been demonstrated to perform as well, or better, than more specialist networks, whilst using significantly larger vocabularies, more complex sentence forms and more natural corpora.

  12. Bilingualism alters brain functional connectivity between "control" regions and "language" regions: Evidence from bimodal bilinguals.

    Science.gov (United States)

    Li, Le; Abutalebi, Jubin; Zou, Lijuan; Yan, Xin; Liu, Lanfang; Feng, Xiaoxia; Wang, Ruiming; Guo, Taomei; Ding, Guosheng

    2015-05-01

    Previous neuroimaging studies have revealed that bilingualism induces both structural and functional neuroplasticity in the dorsal anterior cingulate cortex (dACC) and the left caudate nucleus (LCN), both of which are associated with cognitive control. Since these "control" regions should work together with other language regions during language processing, we hypothesized that bilingualism may also alter the functional interaction between the dACC/LCN and language regions. Here we tested this hypothesis by exploring the functional connectivity (FC) in bimodal bilinguals and monolinguals using functional MRI when they either performed a picture naming task with spoken language or were in resting state. We found that for bimodal bilinguals who use spoken and sign languages, the FC of the dACC with regions involved in spoken language (e.g. the left superior temporal gyrus) was stronger in performing the task, but weaker in the resting state as compared to monolinguals. For the LCN, its intrinsic FC with sign language regions including the left inferior temporo-occipital part and right inferior and superior parietal lobules was increased in the bilinguals. These results demonstrate that bilingual experience may alter the brain functional interaction between "control" regions and "language" regions. For different control regions, the FC alters in different ways. The findings also deepen our understanding of the functional roles of the dACC and LCN in language processing. Copyright © 2015 Elsevier Ltd. All rights reserved.

  13. Enhanced Network Efficiency of Functional Brain Networks in Primary Insomnia Patients

    Directory of Open Access Journals (Sweden)

    Xiaofen Ma

    2018-02-01

    Full Text Available Accumulating evidence from neuroimaging studies suggests that primary insomnia (PI affects interregional neural coordination of multiple interacting functional brain networks. However, a complete understanding of the whole-brain network organization from a system-level perspective in PI is still lacking. To this end, we investigated in topological organization changes in brain functional networks in PI. 36 PI patients and 38 age-, sex-, and education-matched healthy controls were recruited. All participants underwent a series of neuropsychological assessments and resting-state functional magnetic resonance imaging scans. Individual whole-brain functional network were constructed and analyzed using graph theory-based network approaches. There were no significant differences with respect to age, sex, or education between groups (P > 0.05. Graph-based analyses revealed that participants with PI had a significantly higher total number of edges (P = 0.022, global efficiency (P = 0.014, and normalized global efficiency (P = 0.002, and a significantly lower normalized local efficiency (P = 0.042 compared with controls. Locally, several prefrontal and parietal regions, the superior temporal gyrus, and the thalamus exhibited higher nodal efficiency in participants with PI (P < 0.05, false discovery rate corrected. In addition, most of these regions showed increased functional connectivity in PI patients (P < 0.05, corrected. Finally, altered network efficiency was correlated with neuropsychological variables of the Epworth Sleepiness Scale and Insomnia Severity Index in patients with PI. PI is associated with abnormal organization of large-scale functional brain networks, which may account for memory and emotional dysfunction in people with PI. These findings provide novel implications for neural substrates associated with PI.

  14. Language Impairments in ASD Resulting from a Failed Domestication of the Human Brain

    Science.gov (United States)

    Benítez-Burraco, Antonio; Lattanzi, Wanda; Murphy, Elliot

    2016-01-01

    Autism spectrum disorders (ASD) are pervasive neurodevelopmental disorders entailing social and cognitive deficits, including marked problems with language. Numerous genes have been associated with ASD, but it is unclear how language deficits arise from gene mutation or dysregulation. It is also unclear why ASD shows such high prevalence within human populations. Interestingly, the emergence of a modern faculty of language has been hypothesized to be linked to changes in the human brain/skull, but also to the process of self-domestication of the human species. It is our intention to show that people with ASD exhibit less marked domesticated traits at the morphological, physiological, and behavioral levels. We also discuss many ASD candidates represented among the genes known to be involved in the “domestication syndrome” (the constellation of traits exhibited by domesticated mammals, which seemingly results from the hypofunction of the neural crest) and among the set of genes involved in language function closely connected to them. Moreover, many of these genes show altered expression profiles in the brain of autists. In addition, some candidates for domestication and language-readiness show the same expression profile in people with ASD and chimps in different brain areas involved in language processing. Similarities regarding the brain oscillatory behavior of these areas can be expected too. We conclude that ASD may represent an abnormal ontogenetic itinerary for the human faculty of language resulting in part from changes in genes important for the “domestication syndrome” and, ultimately, from the normal functioning of the neural crest. PMID:27621700

  15. Towards a Computational Comparative Neuroprimatology: Framing the language-ready brain.

    Science.gov (United States)

    Arbib, Michael A

    2016-03-01

    We make the case for developing a Computational Comparative Neuroprimatology to inform the analysis of the function and evolution of the human brain. First, we update the mirror system hypothesis on the evolution of the language-ready brain by (i) modeling action and action recognition and opportunistic scheduling of macaque brains to hypothesize the nature of the last common ancestor of macaque and human (LCA-m); and then we (ii) introduce dynamic brain modeling to show how apes could acquire gesture through ontogenetic ritualization, hypothesizing the nature of evolution from LCA-m to the last common ancestor of chimpanzee and human (LCA-c). We then (iii) hypothesize the role of imitation, pantomime, protosign and protospeech in biological and cultural evolution from LCA-c to Homo sapiens with a language-ready brain. Second, we suggest how cultural evolution in Homo sapiens led from protolanguages to full languages with grammar and compositional semantics. Third, we assess the similarities and differences between the dorsal and ventral streams in audition and vision as the basis for presenting and comparing two models of language processing in the human brain: A model of (i) the auditory dorsal and ventral streams in sentence comprehension; and (ii) the visual dorsal and ventral streams in defining "what language is about" in both production and perception of utterances related to visual scenes provide the basis for (iii) a first step towards a synthesis and a look at challenges for further research. Copyright © 2015 Elsevier B.V. All rights reserved.

  16. Reproducibility of graph metrics of human brain functional networks.

    Science.gov (United States)

    Deuker, Lorena; Bullmore, Edward T; Smith, Marie; Christensen, Soren; Nathan, Pradeep J; Rockstroh, Brigitte; Bassett, Danielle S

    2009-10-01

    Graph theory provides many metrics of complex network organization that can be applied to analysis of brain networks derived from neuroimaging data. Here we investigated the test-retest reliability of graph metrics of functional networks derived from magnetoencephalography (MEG) data recorded in two sessions from 16 healthy volunteers who were studied at rest and during performance of the n-back working memory task in each session. For each subject's data at each session, we used a wavelet filter to estimate the mutual information (MI) between each pair of MEG sensors in each of the classical frequency intervals from gamma to low delta in the overall range 1-60 Hz. Undirected binary graphs were generated by thresholding the MI matrix and 8 global network metrics were estimated: the clustering coefficient, path length, small-worldness, efficiency, cost-efficiency, assortativity, hierarchy, and synchronizability. Reliability of each graph metric was assessed using the intraclass correlation (ICC). Good reliability was demonstrated for most metrics applied to the n-back data (mean ICC=0.62). Reliability was greater for metrics in lower frequency networks. Higher frequency gamma- and beta-band networks were less reliable at a global level but demonstrated high reliability of nodal metrics in frontal and parietal regions. Performance of the n-back task was associated with greater reliability than measurements on resting state data. Task practice was also associated with greater reliability. Collectively these results suggest that graph metrics are sufficiently reliable to be considered for future longitudinal studies of functional brain network changes.

  17. Spatial dependencies between large-scale brain networks.

    Directory of Open Access Journals (Sweden)

    Robert Leech

    Full Text Available Functional neuroimaging reveals both increases (task-positive and decreases (task-negative in neural activation with many tasks. Many studies show a temporal relationship between task positive and task negative networks that is important for efficient cognitive functioning. Here we provide evidence for a spatial relationship between task positive and negative networks. There are strong spatial similarities between many reported task negative brain networks, termed the default mode network, which is typically assumed to be a spatially fixed network. However, this is not the case. The spatial structure of the DMN varies depending on what specific task is being performed. We test whether there is a fundamental spatial relationship between task positive and negative networks. Specifically, we hypothesize that the distance between task positive and negative voxels is consistent despite different spatial patterns of activation and deactivation evoked by different cognitive tasks. We show significantly reduced variability in the distance between within-condition task positive and task negative voxels than across-condition distances for four different sensory, motor and cognitive tasks--implying that deactivation patterns are spatially dependent on activation patterns (and vice versa, and that both are modulated by specific task demands. We also show a similar relationship between positively and negatively correlated networks from a third 'rest' dataset, in the absence of a specific task. We propose that this spatial relationship may be the macroscopic analogue of microscopic neuronal organization reported in sensory cortical systems, and that this organization may reflect homeostatic plasticity necessary for efficient brain function.

  18. Dynamic reconfiguration of human brain functional networks through neurofeedback.

    Science.gov (United States)

    Haller, Sven; Kopel, Rotem; Jhooti, Permi; Haas, Tanja; Scharnowski, Frank; Lovblad, Karl-Olof; Scheffler, Klaus; Van De Ville, Dimitri

    2013-11-01

    Recent fMRI studies demonstrated that functional connectivity is altered following cognitive tasks (e.g., learning) or due to various neurological disorders. We tested whether real-time fMRI-based neurofeedback can be a tool to voluntarily reconfigure brain network interactions. To disentangle learning-related from regulation-related effects, we first trained participants to voluntarily regulate activity in the auditory cortex (training phase) and subsequently asked participants to exert learned voluntary self-regulation in the absence of feedback (transfer phase without learning). Using independent component analysis (ICA), we found network reconfigurations (increases in functional network connectivity) during the neurofeedback training phase between the auditory target region and (1) the auditory pathway; (2) visual regions related to visual feedback processing; (3) insula related to introspection and self-regulation and (4) working memory and high-level visual attention areas related to cognitive effort. Interestingly, the auditory target region was identified as the hub of the reconfigured functional networks without a-priori assumptions. During the transfer phase, we again found specific functional connectivity reconfiguration between auditory and attention network confirming the specific effect of self-regulation on functional connectivity. Functional connectivity to working memory related networks was no longer altered consistent with the absent demand on working memory. We demonstrate that neurofeedback learning is mediated by widespread changes in functional connectivity. In contrast, applying learned self-regulation involves more limited and specific network changes in an auditory setup intended as a model for tinnitus. Hence, neurofeedback training might be used to promote recovery from neurological disorders that are linked to abnormal patterns of brain connectivity. Copyright © 2013 Elsevier Inc. All rights reserved.

  19. Prediction signatures in the brain: Semantic pre-activation during language comprehension

    Directory of Open Access Journals (Sweden)

    Burkhard Maess

    2016-11-01

    Full Text Available There is broad agreement that context-based predictions facilitate lexical-semantic processing. A robust index of semantic prediction during language comprehension is an evoked response, known as the N400, whose amplitude is modulated as a function of semantic context. However, the underlying neural mechanisms that utilize relations of the prior context and the embedded word within it are largely unknown. We measured magnetoencephalography (MEG data while participants were listening to simple German sentences in which the verbs were either highly predictive for the occurrence of a particular noun (i.e., provided context or not. The identical set of nouns was presented in both conditions. Hence, differences for the evoked responses of the nouns can only be due to differences in the earlier context. We observed a reduction of the N400 response for highly predicted nouns. Interestingly, the opposite pattern was observed for the preceding verbs: Highly predictive (that is more informative verbs yielded stronger neural magnitude compared to less predictive verbs. A negative correlation between the N400 effect of the verb and that of the noun was found in a distributed brain network, indicating an integral relation between the predictive power of the verb and the processing of the subsequent noun. This network consisted of left hemispheric superior and middle temporal areas and a subcortical area; the parahippocampus. Enhanced activity for highly predictive relative to less predictive verbs, likely reflects establishing semantic features associated with the expected nouns, that is a pre-activation of the expected nouns.

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

    Science.gov (United States)

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

    2011-05-15

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

  1. Hallucination- and speech-specific hypercoupling in frontotemporal auditory and language networks in schizophrenia using combined task-based fMRI data: An fBIRN study.

    Science.gov (United States)

    Lavigne, Katie M; Woodward, Todd S

    2018-04-01

    Hypercoupling of activity in speech-perception-specific brain networks has been proposed to play a role in the generation of auditory-verbal hallucinations (AVHs) in schizophrenia; however, it is unclear whether this hypercoupling extends to nonverbal auditory perception. We investigated this by comparing schizophrenia patients with and without AVHs, and healthy controls, on task-based functional magnetic resonance imaging (fMRI) data combining verbal speech perception (SP), inner verbal thought generation (VTG), and nonverbal auditory oddball detection (AO). Data from two previously published fMRI studies were simultaneously analyzed using group constrained principal component analysis for fMRI (group fMRI-CPCA), which allowed for comparison of task-related functional brain networks across groups and tasks while holding the brain networks under study constant, leading to determination of the degree to which networks are common to verbal and nonverbal perception conditions, and which show coordinated hyperactivity in hallucinations. Three functional brain networks emerged: (a) auditory-motor, (b) language processing, and (c) default-mode (DMN) networks. Combining the AO and sentence tasks allowed the auditory-motor and language networks to separately emerge, whereas they were aggregated when individual tasks were analyzed. AVH patients showed greater coordinated activity (deactivity for DMN regions) than non-AVH patients during SP in all networks, but this did not extend to VTG or AO. This suggests that the hypercoupling in AVH patients in speech-perception-related brain networks is specific to perceived speech, and does not extend to perceived nonspeech or inner verbal thought generation. © 2017 Wiley Periodicals, Inc.

  2. Brain Mechanisms of Affective Language Comprehension in Autism Spectrum Disorders

    Science.gov (United States)

    2016-10-01

    in effort or project goals. NICHD R01 Hippocampal- memory network development and episodic memory in early childhood Role: co-I; time committed...functional development of hippocampal- memory network in early childhood and its relation to episodic memory abilities. What other organizations were involved...1-0457 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Dr. Donald Joseph Bolger 5d. PROJECT NUMBER 5e. TASK NUMBER 5f. WORK UNIT NUMBER 7. PERFORMING

  3. Early changes in brain structure correlate with language outcomes in children with neonatal encephalopathy.

    Science.gov (United States)

    Shapiro, Kevin A; Kim, Hosung; Mandelli, Maria Luisa; Rogers, Elizabeth E; Gano, Dawn; Ferriero, Donna M; Barkovich, A James; Gorno-Tempini, Maria Luisa; Glass, Hannah C; Xu, Duan

    2017-01-01

    Global patterns of brain injury correlate with motor, cognitive, and language outcomes in survivors of neonatal encephalopathy (NE). However, it is still unclear whether local changes in brain structure predict specific deficits. We therefore examined whether differences in brain structure at 6 months of age are associated with neurodevelopmental outcomes in this population. We enrolled 32 children with NE, performed structural brain MR imaging at 6 months, and assessed neurodevelopmental outcomes at 30 months. All subjects underwent T1-weighted imaging at 3 T using a 3D IR-SPGR sequence. Images were normalized in intensity and nonlinearly registered to a template constructed specifically for this population, creating a deformation field map. We then used deformation based morphometry (DBM) to correlate variation in the local volume of gray and white matter with composite scores on the Bayley Scales of Infant and Toddler Development (Bayley-III) at 30 months. Our general linear model included gestational age, sex, birth weight, and treatment with hypothermia as covariates. Regional brain volume was significantly associated with language scores, particularly in perisylvian cortical regions including the left supramarginal gyrus, posterior superior and middle temporal gyri, and right insula, as well as inferior frontoparietal subcortical white matter. We did not find significant correlations between regional brain volume and motor or cognitive scale scores. We conclude that, in children with a history of NE, local changes in the volume of perisylvian gray and white matter at 6 months are correlated with language outcome at 30 months. Quantitative measures of brain volume on early MRI may help identify infants at risk for poor language outcomes.

  4. Early changes in brain structure correlate with language outcomes in children with neonatal encephalopathy

    Directory of Open Access Journals (Sweden)

    Kevin A. Shapiro

    2017-01-01

    Full Text Available Global patterns of brain injury correlate with motor, cognitive, and language outcomes in survivors of neonatal encephalopathy (NE. However, it is still unclear whether local changes in brain structure predict specific deficits. We therefore examined whether differences in brain structure at 6 months of age are associated with neurodevelopmental outcomes in this population. We enrolled 32 children with NE, performed structural brain MR imaging at 6 months, and assessed neurodevelopmental outcomes at 30 months. All subjects underwent T1-weighted imaging at 3 T using a 3D IR-SPGR sequence. Images were normalized in intensity and nonlinearly registered to a template constructed specifically for this population, creating a deformation field map. We then used deformation based morphometry (DBM to correlate variation in the local volume of gray and white matter with composite scores on the Bayley Scales of Infant and Toddler Development (Bayley-III at 30 months. Our general linear model included gestational age, sex, birth weight, and treatment with hypothermia as covariates. Regional brain volume was significantly associated with language scores, particularly in perisylvian cortical regions including the left supramarginal gyrus, posterior superior and middle temporal gyri, and right insula, as well as inferior frontoparietal subcortical white matter. We did not find significant correlations between regional brain volume and motor or cognitive scale scores. We conclude that, in children with a history of NE, local changes in the volume of perisylvian gray and white matter at 6 months are correlated with language outcome at 30 months. Quantitative measures of brain volume on early MRI may help identify infants at risk for poor language outcomes.

  5. A step beyond local observations with a dialog aware bidirectional GRU network for Spoken Language Understanding

    OpenAIRE

    Vukotic , Vedran; Raymond , Christian; Gravier , Guillaume

    2016-01-01

    International audience; Architectures of Recurrent Neural Networks (RNN) recently become a very popular choice for Spoken Language Understanding (SLU) problems; however, they represent a big family of different architectures that can furthermore be combined to form more complex neural networks. In this work, we compare different recurrent networks, such as simple Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) networks, Gated Memory Units (GRU) and their bidirectional versions,...

  6. Action and Language Mechanisms in the Brain: Data, Models and Neuroinformatics

    Science.gov (United States)

    Bonaiuto, James J.; Bornkessel-Schlesewsky, Ina; Kemmerer, David; MacWhinney, Brian; Nielsen, Finn Årup; Oztop, Erhan

    2014-01-01

    We assess the challenges of studying action and language mechanisms in the brain, both singly and in relation to each other to provide a novel perspective on neuroinformatics, integrating the development of databases for encoding – separately or together – neurocomputational models and empirical data that serve systems and cognitive neuroscience. PMID:24234916

  7. Action and Language Mechanisms in the Brain: Data, Models and Neuroinformatics

    DEFF Research Database (Denmark)

    Arbib, Michael A.; Bonaiuto, James J.; Bornkessel-Schlesewsky, Ina

    2014-01-01

    We assess the challenges of studying action and language mechanisms in the brain, both singly and in relation to each other to provide a novel perspective on neuroinformatics, integrating the development of databases for encoding - separately or together - neurocomputational models and empirical ...

  8. Wanted: Tesseract. One Hypothesis on Languages, Cultures, and Ethics for Mind, Brain, and Education

    Science.gov (United States)

    Della Chiesa, Bruno

    2010-01-01

    For potential consideration by the Mind, Brain, and Education community, here is a modest but provocative hypothesis regarding the relationships between acquisition of languages, awareness of cultures, and development of ethics in human beings. Starting from the basic idea according to which "a fish does not know what water is," and using both…

  9. Temporal Dynamics of Late Second Language Acquisition: Evidence from Event-Related Brain Potentials

    Science.gov (United States)

    Steinhauer, Karsten; White, Erin J.; Drury, John E.

    2009-01-01

    The ways in which age of acquisition (AoA) may affect (morpho)syntax in second language acquisition (SLA) are discussed. We suggest that event-related brain potentials (ERPs) provide an appropriate online measure to test some such effects. ERP findings of the past decade are reviewed with a focus on recent and ongoing research. It is concluded…

  10. Large-scale brain networks in affective and social neuroscience: Towards an integrative functional architecture of the brain

    Science.gov (United States)

    Barrett, Lisa Feldman; Satpute, Ajay

    2013-01-01

    Understanding how a human brain creates a human mind ultimately depends on mapping psychological categories and concepts to physical measurements of neural response. Although it has long been assumed that emotional, social, and cognitive phenomena are realized in the operations of separate brain regions or brain networks, we demonstrate that it is possible to understand the body of neuroimaging evidence using a framework that relies on domain general, distributed structure-function mappings. We review current research in affective and social neuroscience and argue that the emerging science of large-scale intrinsic brain networks provides a coherent framework for a domain-general functional architecture of the human brain. PMID:23352202

  11. Social Rewards and Social Networks in the Human Brain.

    Science.gov (United States)

    Fareri, Dominic S; Delgado, Mauricio R

    2014-08-01

    The rapid development of social media and social networking sites in human society within the past decade has brought about an increased focus on the value of social relationships and being connected with others. Research suggests that we pursue socially valued or rewarding outcomes-approval, acceptance, reciprocity-as a means toward learning about others and fulfilling social needs of forming meaningful relationships. Focusing largely on recent advances in the human neuroimaging literature, we review findings highlighting the neural circuitry and processes that underlie pursuit of valued rewarding outcomes across non-social and social domains. We additionally discuss emerging human neuroimaging evidence supporting the idea that social rewards provide a gateway to establishing relationships and forming social networks. Characterizing the link between social network, brain, and behavior can potentially identify contributing factors to maladaptive influences on decision making within social situations. © The Author(s) 2014.

  12. Hierarchical brain networks active in approach and avoidance goal pursuit.

    Science.gov (United States)

    Spielberg, Jeffrey M; Heller, Wendy; Miller, Gregory A

    2013-01-01

    Effective approach/avoidance goal pursuit is critical for attaining long-term health and well-being. Research on the neural correlates of key goal-pursuit processes (e.g., motivation) has long been of interest, with lateralization in prefrontal cortex being a particularly fruitful target of investigation. However, this literature has often been limited by a lack of spatial specificity and has not delineated the precise aspects of approach/avoidance motivation involved. Additionally, the relationships among brain regions (i.e., network connectivity) vital to goal-pursuit remain largely unexplored. Specificity in location, process, and network relationship is vital for moving beyond gross characterizations of function and identifying the precise cortical mechanisms involved in motivation. The present paper integrates research using more spatially specific methodologies (e.g., functional magnetic resonance imaging) with the rich psychological literature on approach/avoidance to propose an integrative network model that takes advantage of the strengths of each of these literatures.

  13. Cortical atrophy and language network reorganization associated with a novel progranulin mutation.

    Science.gov (United States)

    Cruchaga, Carlos; Fernández-Seara, Maria A; Seijo-Martínez, Manuel; Samaranch, Lluis; Lorenzo, Elena; Hinrichs, Anthony; Irigoyen, Jaione; Maestro, Cristina; Prieto, Elena; Martí-Climent, Josep M; Arbizu, Javier; Pastor, Maria A; Pastor, Pau

    2009-08-01

    Progressive nonfluent aphasia (PNFA) is an early stage of frontotemporal degeneration. We identified a novel Cys521Tyr progranulin gene variant in a PNFA family that potentially disrupts disulphide bridging causing protein misfolding. To identify early neurodegeneration changes, we performed neuropsychological and neuroimaging studies in 6 family members (MRI [magnetic resonance imaging], fMRI [functional MRI], and 18f-fluorodeoxygenlucose positron emission tomography, including 4 mutation carriers, and in 9 unrelated controls. Voxel-based morphometry (VBM) of the carriers compared with controls showed significant cortical atrophy in language areas. Grey matter loss was distributed mainly in frontal lobes, being more prominent on the left. Clusters were located in the superior frontal gyri, left inferior frontal gyrus, left middle frontal gyrus, left middle temporal gyri and left posterior parietal areas, concordant with (18)FDG-PET hypometabolic areas. fMRI during semantic and phonemic covert word generation (CWGTs) and word listening tasks (WLTs) showed recruitment of attentional and working memory networks in the carriers indicative of functional reorganization. During CWGTs, activation in left prefrontal cortex and bilateral anterior insulae was present whereas WLT recruited mesial prefrontal and anterior temporal cortex. These findings suggest that Cys521Tyr could be associated with early brain impairment not limited to language areas and compensated by recruitment of bilateral auxiliary cortical areas.

  14. Functional brain networks develop from a "local to distributed" organization.

    Directory of Open Access Journals (Sweden)

    Damien A Fair

    2009-05-01

    Full Text Available The mature human brain is organized into a collection of specialized functional networks that flexibly interact to support various cognitive functions. Studies of development often attempt to identify the organizing principles that guide the maturation of these functional networks. In this report, we combine resting state functional connectivity MRI (rs-fcMRI, graph analysis, community detection, and spring-embedding visualization techniques to analyze four separate networks defined in earlier studies. As we have previously reported, we find, across development, a trend toward 'segregation' (a general decrease in correlation strength between regions close in anatomical space and 'integration' (an increased correlation strength between selected regions distant in space. The generalization of these earlier trends across multiple networks suggests that this is a general developmental principle for changes in functional connectivity that would extend to large-scale graph theoretic analyses of large-scale brain networks. Communities in children are predominantly arranged by anatomical proximity, while communities in adults predominantly reflect functional relationships, as defined from adult fMRI studies. In sum, over development, the organization of multiple functional networks shifts from a local anatomical emphasis in children to a more "distributed" architecture in young adults. We argue that this "local to distributed" developmental characterization has important implications for understanding the development of neural systems underlying cognition. Further, graph metrics (e.g., clustering coefficients and average path lengths are similar in child and adult graphs, with both showing "small-world"-like properties, while community detection by modularity optimization reveals stable communities within the graphs that are clearly different between young children and young adults. These observations suggest that early school age children and adults

  15. Functional brain networks develop from a "local to distributed" organization.

    Science.gov (United States)

    Fair, Damien A; Cohen, Alexander L; Power, Jonathan D; Dosenbach, Nico U F; Church, Jessica A; Miezin, Francis M; Schlaggar, Bradley L; Petersen, Steven E

    2009-05-01

    The mature human brain is organized into a collection of specialized functional networks that flexibly interact to support various cognitive functions. Studies of development often attempt to identify the organizing principles that guide the maturation of these functional networks. In this report, we combine resting state functional connectivity MRI (rs-fcMRI), graph analysis, community detection, and spring-embedding visualization techniques to analyze four separate networks defined in earlier studies. As we have previously reported, we find, across development, a trend toward 'segregation' (a general decrease in correlation strength) between regions close in anatomical space and 'integration' (an increased correlation strength) between selected regions distant in space. The generalization of these earlier trends across multiple networks suggests that this is a general developmental principle for changes in functional connectivity that would extend to large-scale graph theoretic analyses of large-scale brain networks. Communities in children are predominantly arranged by anatomical proximity, while communities in adults predominantly reflect functional relationships, as defined from adult fMRI studies. In sum, over development, the organization of multiple functional networks shifts from a local anatomical emphasis in children to a more "distributed" architecture in young adults. We argue that this "local to distributed" developmental characterization has important implications for understanding the development of neural systems underlying cognition. Further, graph metrics (e.g., clustering coefficients and average path lengths) are similar in child and adult graphs, with both showing "small-world"-like properties, while community detection by modularity optimization reveals stable communities within the graphs that are clearly different between young children and young adults. These observations suggest that early school age children and adults both have

  16. Constructing fine-granularity functional brain network atlases via deep convolutional autoencoder.

    Science.gov (United States)

    Zhao, Yu; Dong, Qinglin; Chen, Hanbo; Iraji, Armin; Li, Yujie; Makkie, Milad; Kou, Zhifeng; Liu, Tianming

    2017-12-01

    State-of-the-art functional brain network reconstruction methods such as independent component analysis (ICA) or sparse coding of whole-brain fMRI data can effectively infer many thousands of volumetric brain network maps from a large number of human brains. However, due to the variability of individual brain networks and the large scale of such networks needed for statistically meaningful group-level analysis, it is still a challenging and open problem to derive group-wise common networks as network atlases. Inspired by the superior spatial pattern description ability of the deep convolutional neural networks (CNNs), a novel deep 3D convolutional autoencoder (CAE) network is designed here to extract spatial brain network features effectively, based on which an Apache Spark enabled computational framework is developed for fast clustering of larger number of network maps into fine-granularity atlases. To evaluate this framework, 10 resting state networks (RSNs) were manually labeled from the sparsely decomposed networks of Human Connectome Project (HCP) fMRI data and 5275 network training samples were obtained, in total. Then the deep CAE models are trained by these functional networks' spatial maps, and the learned features are used to refine the original 10 RSNs into 17 network atlases that possess fine-granularity functional network patterns. Interestingly, it turned out that some manually mislabeled outliers in training networks can be corrected by the deep CAE derived features. More importantly, fine granularities of networks can be identified and they reveal unique network patterns specific to different brain task states. By further applying this method to a dataset of mild traumatic brain injury study, it shows that the technique can effectively identify abnormal small networks in brain injury patients in comparison with controls. In general, our work presents a promising deep learning and big data analysis solution for modeling functional connectomes, with

  17. Network Analysis of Resting State EEG in the Developing Young Brain: Structure Comes With Maturation

    NARCIS (Netherlands)

    Boersma, M.; Smit, D.J.A.; de Bie, H.M.A.; van Baal, G.C.M.; Boomsma, D.I.; de Geus, E.J.C.; Delemarre-van de Waal, H.A.; Stam, C.J.

    2011-01-01

    During childhood, brain structure and function changes substantially. Recently, graph theory has been introduced to model connectivity in the brain. Small-world networks, such as the brain, combine optimal properties of both ordered and random networks, i.e., high clustering and short path lengths.

  18. Association between structural brain network efficiency and intelligence increases during adolescence

    NARCIS (Netherlands)

    Koenis, Marinka M G; Brouwer, Rachel M; Swagerman, Suzanne C; van Soelen, Inge L C; Boomsma, Dorret I; Hulshoff Pol, Hilleke E

    2018-01-01

    Adolescence represents an important period during which considerable changes in the brain take place, including increases in integrity of white matter bundles, and increasing efficiency of the structural brain network. A more efficient structural brain network has been associated with higher

  19. The Impact of Reading Intervention on Brain Responses Underlying Language in Children With Autism.

    Science.gov (United States)

    Murdaugh, Donna L; Deshpande, Hrishikesh D; Kana, Rajesh K

    2016-01-01

    Deficits in language comprehension have been widely reported in children with autism spectrum disorders (ASD), with behavioral and neuroimaging studies finding increased reliance on visuospatial processing to aid in language comprehension. However, no study to date, has taken advantage of this strength in visuospatial processing to improve language comprehension difficulties in ASD. This study used a translational neuroimaging approach to test the role of a visual imagery-based reading intervention in improving the brain circuitry underlying language processing in children with ASD. Functional magnetic resonance imaging (MRI), in a longitudinal study design, was used to investigate intervention-related change in sentence comprehension, brain activation, and functional connectivity in three groups of participants (age 8-13 years): an experimental group of ASD children (ASD-EXP), a wait-list control group of ASD children (ASD-WLC), and a group of typically developing control children. After intervention, the ASD-EXP group showed significant increase in activity in visual and language areas and right-hemisphere language area homologues, putamen, and thalamus, suggestive of compensatory routes to increase proficiency in reading comprehension. Additionally, ASD children who had the most improvement in reading comprehension after intervention showed greater functional connectivity between left-hemisphere language areas, the middle temporal gyrus and inferior frontal gyrus while reading high imagery sentences. Thus, the findings of this study, which support the principles of dual coding theory [Paivio 2007], suggest the potential of a strength-based reading intervention in changing brain responses and facilitating better reading comprehension in ASD children. © 2015 International Society for Autism Research, Wiley Periodicals, Inc.

  20. Brain white matter structure and COMT gene are linked to second-language learning in adults.

    Science.gov (United States)

    Mamiya, Ping C; Richards, Todd L; Coe, Bradley P; Eichler, Evan E; Kuhl, Patricia K

    2016-06-28

    Adult human brains retain the capacity to undergo tissue reorganization during second-language learning. Brain-imaging studies show a relationship between neuroanatomical properties and learning for adults exposed to a second language. However, the role of genetic factors in this relationship has not been investigated. The goal of the current study was twofold: (i) to characterize the relationship between brain white matter fiber-tract properties and second-language immersion using diffusion tensor imaging, and (ii) to determine whether polymorphisms in the catechol-O-methyltransferase (COMT) gene affect the relationship. We recruited incoming Chinese students enrolled in the University of Washington and scanned their brains one time. We measured the diffusion properties of the white matter fiber tracts and correlated them with the number of days each student had been in the immersion program at the time of the brain scan. We found that higher numbers of days in the English immersion program correlated with higher fractional anisotropy and lower radial diffusivity in the right superior longitudinal fasciculus. We show that fractional anisotropy declined once the subjects finished the immersion program. The relationship between brain white matter fiber-tract properties and immersion varied in subjects with different COMT genotypes. Subjects with the Methionine (Met)/Valine (Val) and Val/Val genotypes showed higher fractional anisotropy and lower radial diffusivity during immersion, which reversed immediately after immersion ended, whereas those with the Met/Met genotype did not show these relationships. Statistical modeling revealed that subjects' grades in the language immersion program were best predicted by fractional anisotropy and COMT genotype.

  1. Partially Overlapping Brain Networks for Singing and Cello Playing

    Directory of Open Access Journals (Sweden)

    Melanie Segado

    2018-05-01

    Full Text Available This research uses an MR-Compatible cello to compare functional brain activation during singing and cello playing within the same individuals to determine the extent to which arbitrary auditory-motor associations, like those required to play the cello, co-opt functional brain networks that evolved for singing. Musical instrument playing and singing both require highly specific associations between sounds and movements. Because these are both used to produce musical sounds, it is often assumed in the literature that their neural underpinnings are highly similar. However, singing is an evolutionarily old human trait, and the auditory-motor associations used for singing are also used for speech and non-speech vocalizations. This sets it apart from the arbitrary auditory-motor associations required to play musical instruments. The pitch range of the cello is similar to that of the human voice, but cello playing is completely independent of the vocal apparatus, and can therefore be used to dissociate the auditory-vocal network from that of the auditory-motor network. While in the MR-Scanner, 11 expert cellists listened to and subsequently produced individual tones either by singing or cello playing. All participants were able to sing and play the target tones in tune (<50C deviation from target. We found that brain activity during cello playing directly overlaps with brain activity during singing in many areas within the auditory-vocal network. These include primary motor, dorsal pre-motor, and supplementary motor cortices (M1, dPMC, SMA,the primary and periprimary auditory cortices within the superior temporal gyrus (STG including Heschl's gyrus, anterior insula (aINS, anterior cingulate cortex (ACC, and intraparietal sulcus (IPS, and Cerebellum but, notably, exclude the periaqueductal gray (PAG and basal ganglia (Putamen. Second, we found that activity within the overlapping areas is positively correlated with, and therefore likely contributing to

  2. Second Language Word Learning through Repetition and Imitation: Functional Networks as a Function of Learning Phase and Language Distance.

    Science.gov (United States)

    Ghazi-Saidi, Ladan; Ansaldo, Ana Ines

    2017-01-01

    Introduction and Aim : Repetition and imitation are among the oldest second language (L2) teaching approaches and are frequently used in the context of L2 learning and language therapy, despite some heavy criticism. Current neuroimaging techniques allow the neural mechanisms underlying repetition and imitation to be examined. This fMRI study examines the influence of verbal repetition and imitation on network configuration. Integration changes within and between the cognitive control and language networks were studied, in a pair of linguistically close languages (Spanish and French), and compared to our previous work on a distant language pair (Ghazi-Saidi et al., 2013). Methods : Twelve healthy native Spanish-speaking (L1) adults, and 12 healthy native Persian-speaking adults learned 130 new French (L2) words, through a computerized audiovisual repetition and imitation program. The program presented colored photos of objects. Participants were instructed to look at each photo and pronounce its name as closely as possible to the native template (imitate). Repetition was encouraged as many times as necessary to learn the object's name; phonological cues were provided if necessary. Participants practiced for 15 min, over 30 days, and were tested while naming the same items during fMRI scanning, at week 1 (shallow learning phase) and week 4 (consolidation phase) of training. To compare this set of data with our previous work on Persian speakers, a similar data analysis plan including accuracy rates (AR), response times (RT), and functional integration values for the language and cognitive control network at each measure point was included, with further L1-L2 direct comparisons across the two populations. Results and Discussion : The evidence shows that learning L2 words through repetition induces neuroplasticity at the network level. Specifically, L2 word learners showed increased network integration after 3 weeks of training, with both close and distant language

  3. A brain network processing the age of faces.

    Directory of Open Access Journals (Sweden)

    György A Homola

    Full Text Available Age is one of the most salient aspects in faces and of fundamental cognitive and social relevance. Although face processing has been studied extensively, brain regions responsive to age have yet to be localized. Using evocative face morphs and fMRI, we segregate two areas extending beyond the previously established face-sensitive core network, centered on the inferior temporal sulci and angular gyri bilaterally, both of which process changes of facial age. By means of probabilistic tractography, we compare their patterns of functional activation and structural connectivity. The ventral portion of Wernicke's understudied perpendicular association fasciculus is shown to interconnect the two areas, and activation within these clusters is related to the probability of fiber connectivity between them. In addition, post-hoc age-rating competence is found to be associated with high response magnitudes in the left angular gyrus. Our results provide the first evidence that facial age has a distinct representation pattern in the posterior human brain. We propose that particular face-sensitive nodes interact with additional object-unselective quantification modules to obtain individual estimates of facial age. This brain network processing the age of faces differs from the cortical areas that have previously been linked to less developmental but instantly changeable face aspects. Our probabilistic method of associating activations with connectivity patterns reveals an exemplary link that can be used to further study, assess and quantify structure-function relationships.

  4. A SPECT study of language and brain reorganization three years after pediatric brain injury.

    Science.gov (United States)

    Chiu Wong, Stephanie B; Chapman, Sandra B; Cook, Lois G; Anand, Raksha; Gamino, Jacquelyn F; Devous, Michael D

    2006-01-01

    Using single photon emission computed tomography (SPECT), we investigated brain plasticity in children 3 years after sustaining a severe traumatic brain injury (TBI). First, we assessed brain perfusion patterns (i.e., the extent of brain blood flow to regions of the brain) at rest in eight children who suffered severe TBI as compared to perfusion patterns in eight normally developing children. Second, we examined differences in perfusion between children with severe TBI who showed good versus poor recovery in complex discourse skills. Specifically, the children were asked to produce and abstract core meaning for two stories in the form of a lesson. Inconsistent with our predictions, children with severe TBI showed areas of increased perfusion as compared to normally developing controls. Adult studies have shown the reverse pattern with TBI associated with reduced perfusion. With regard to the second aim and consistent with previously identified brain-discourse relations, we found a strong positive association between perfusion in right frontal regions and discourse abstraction abilities, with higher perfusion linked to better discourse outcomes and lower perfusion linked to poorer discourse outcomes. Furthermore, brain-discourse patterns of increased perfusion in left frontal regions were associated with lower discourse abstraction ability. The results are discussed in terms of how brain changes may represent adaptive and maladaptive plasticity. The findings offer direction for future studies of brain plasticity in response to neurocognitive treatments.

  5. On the Relationship between Brain Laterality and Language Proficiency in L2: A Replication Study

    Directory of Open Access Journals (Sweden)

    Nima Shakouri

    2016-06-01

    Full Text Available The present paper attempted to investigate whether there is any significant relationship between participants' brain laterality and L2 proficiency level. To carry out the experiment, 30 participants administered in the present study. Fifteen of them did not have any English language learning experience and were at the start of language learning, while the rest had attended L2 learning classes for about 2 years in a popular English language center, located in Bandar-e Anzali, Iran. Finally, the researchers concluded that the activity of the right hemisphere went up by the increase in language proficiency among bilinguals. Thereupon, the result of the paper was at variance with Albert and Obler's (1978 early work on hemispheric differentiation, which indicated that bilinguals were less hemispheric dominant than monolinguals.

  6. Fetal brain extracellular matrix boosts neuronal network formation in 3D bioengineered model of cortical brain tissue.

    Science.gov (United States)

    Sood, Disha; Chwalek, Karolina; Stuntz, Emily; Pouli, Dimitra; Du, Chuang; Tang-Schomer, Min; Georgakoudi, Irene; Black, Lauren D; Kaplan, David L

    2016-01-01

    The extracellular matrix (ECM) constituting up to 20% of the organ volume is a significant component of the brain due to its instructive role in the compartmentalization of functional microdomains in every brain structure. The composition, quantity and structure of ECM changes dramatically during the development of an organism greatly contributing to the remarkably sophisticated architecture and function of the brain. Since fetal brain is highly plastic, we hypothesize that the fetal brain ECM may contain cues promoting neural growth and differentiation, highly desired in regenerative medicine. Thus, we studied the effect of brain-derived fetal and adult ECM complemented with matricellular proteins on cortical neurons using in vitro 3D bioengineered model of cortical brain tissue. The tested parameters included neuronal network density, cell viability, calcium signaling and electrophysiology. Both, adult and fetal brain ECM as well as matricellular proteins significantly improved neural network formation as compared to single component, collagen I matrix. Additionally, the brain ECM improved cell viability and lowered glutamate release. The fetal brain ECM induced superior neural network formation, calcium signaling and spontaneous spiking activity over adult brain ECM. This study highlights the difference in the neuroinductive properties of fetal and adult brain ECM and suggests that delineating the basis for this divergence may have implications for regenerative medicine.

  7. Sigmund Freud-early network theories of the brain.

    Science.gov (United States)

    Surbeck, Werner; Killeen, Tim; Vetter, Johannes; Hildebrandt, Gerhard

    2018-06-01

    Since the early days of modern neuroscience, psychological models of brain function have been a key component in the development of new knowledge. These models aim to provide a framework that allows the integration of discoveries derived from the fundamental disciplines of neuroscience, including anatomy and physiology, as well as clinical neurology and psychiatry. During the initial stages of his career, Sigmund Freud (1856-1939), became actively involved in these nascent fields with a burgeoning interest in functional neuroanatomy. In contrast to his contemporaries, Freud was convinced that cognition could not be localised to separate modules and that the brain processes cognition not in a merely serial manner but in a parallel and dynamic fashion-anticipating fundamental aspects of current network theories of brain function. This article aims to shed light on Freud's seminal, yet oft-overlooked, early work on functional neuroanatomy and his reasons for finally abandoning the conventional neuroscientific "brain-based" reference frame in order to conceptualise the mind from a purely psychological perspective.

  8. The minimum spanning tree : An unbiased method for brain network analysis

    NARCIS (Netherlands)

    Tewarie, P.; van Dellen, E.; Hillebrand, A.; Stam, C. J.

    2015-01-01

    The brain is increasingly studied with graph theoretical approaches, which can be used to characterize network topology. However, studies on brain networks have reported contradictory findings, and do not easily converge to a clear concept of the structural and functional network organization of the

  9. Emotion Regulation and Complex Brain Networks: Association Between Expressive Suppression and Efficiency in the Fronto-Parietal Network and Default-Mode Network

    Directory of Open Access Journals (Sweden)

    Junhao Pan

    2018-03-01

    Full Text Available Emotion regulation (ER refers to the “implementation of a conscious or non-conscious goal to start, stop or otherwise modulate the trajectory of an emotion” (Etkin et al., 2015. Whereas multiple brain areas have been found to be involved in ER, relatively little is known about whether and how ER is associated with the global functioning of brain networks. Recent advances in brain connectivity research using graph-theory based analysis have shown that the brain can be organized into complex networks composed of functionally or structurally connected brain areas. Global efficiency is one graphic metric indicating the efficiency of information exchange among brain areas and is utilized to measure global functioning of brain networks. The present study examined the relationship between trait measures of ER (expressive suppression (ES and cognitive reappraisal (CR and global efficiency in resting-state functional brain networks (the whole brain network and ten predefined networks using structural equation modeling (SEM. The results showed that ES was reliably associated with efficiency in the fronto-parietal network and default-mode network. The finding advances the understanding of neural substrates of ER, revealing the relationship between ES and efficient organization of brain networks.

  10. Dismissing Attachment Characteristics Dynamically Modulate Brain Networks Subserving Social Aversion.

    Science.gov (United States)

    Krause, Anna Linda; Borchardt, Viola; Li, Meng; van Tol, Marie-José; Demenescu, Liliana Ramona; Strauss, Bernhard; Kirchmann, Helmut; Buchheim, Anna; Metzger, Coraline D; Nolte, Tobias; Walter, Martin

    2016-01-01

    Attachment patterns influence actions, thoughts and feeling through a person's "inner working model". Speech charged with attachment-dependent content was proposed to modulate the activation of cognitive-emotional schemata in listeners. We performed a 7 Tesla rest-task-rest functional magnetic resonance imaging (fMRI)-experiment, presenting auditory narratives prototypical of dismissing attachment representations to investigate their effect on 23 healthy males. We then examined effects of participants' attachment style and childhood trauma on brain state changes using seed-based functional connectivity (FC) analyses, and finally tested whether subjective differences in responsivity to narratives could be predicted by baseline network states. In comparison to a baseline state, we observed increased FC in a previously described "social aversion network" including dorsal anterior cingulated cortex (dACC) and left anterior middle temporal gyrus (aMTG) specifically after exposure to insecure-dismissing attachment narratives. Increased dACC-seeded FC within the social aversion network was positively related to the participants' avoidant attachment style and presence of a history of childhood trauma. Anxious attachment style on the other hand was positively correlated with FC between the dACC and a region outside of the "social aversion network", namely the dorsolateral prefrontal cortex, which suggests decreased network segregation as a function of anxious attachment. Finally, the extent of subjective experience of friendliness towards the dismissing narrative was predicted by low baseline FC-values between hippocampus and inferior parietal lobule (IPL). Taken together, our study demonstrates an activation of networks related to social aversion in terms of increased connectivity after listening to insecure-dismissing attachment narratives. A causal interrelation of brain state changes and subsequent changes in social reactivity was further supported by our observation of

  11. Using stochastic language models (SLM) to map lexical, syntactic, and phonological information processing in the brain.

    Science.gov (United States)

    Lopopolo, Alessandro; Frank, Stefan L; van den Bosch, Antal; Willems, Roel M

    2017-01-01

    Language comprehension involves the simultaneous processing of information at the phonological, syntactic, and lexical level. We track these three distinct streams of information in the brain by using stochastic measures derived from computational language models to detect neural correlates of phoneme, part-of-speech, and word processing in an fMRI experiment. Probabilistic language models have proven to be useful tools for studying how language is processed as a sequence of symbols unfolding in time. Conditional probabilities between sequences of words are at the basis of probabilistic measures such as surprisal and perplexity which have been successfully used as predictors of several behavioural and neural correlates of sentence processing. Here we computed perplexity from sequences of words and their parts of speech, and their phonemic transcriptions. Brain activity time-locked to each word is regressed on the three model-derived measures. We observe that the brain keeps track of the statistical structure of lexical, syntactic and phonological information in distinct areas.

  12. A Novel Human Body Area Network for Brain Diseases Analysis.

    Science.gov (United States)

    Lin, Kai; Xu, Tianlang

    2016-10-01

    Development of wireless sensor and mobile communication technology provide an unprecedented opportunity for realizing smart and interactive healthcare systems. Designing such systems aims to remotely monitor the health and diagnose the diseases for users. In this paper, we design a novel human body area network for brain diseases analysis, which is named BABDA. Considering the brain is one of the most complex organs in the human body, the BABDA system provides four function modules to ensure the high quality of the analysis result, which includes initial data collection, data correction, data transmission and comprehensive data analysis. The performance evaluation conducted in a realistic environment with several criteria shows the availability and practicability of the BABDA system.

  13. Path finding using the multi-layer network description language

    NARCIS (Netherlands)

    Dijkstra, F.; van der Ham, J.; Grosso, P.; de Laat, C.

    2008-01-01

    Research networks are providing their users with dedicated network connections. Users want these connects to go through different domains, so the research networks are challenged to find valid network connections through multiple domains. The challenge arises if the different network domains use

  14. Metabolic Brain Network Analysis of Hypothyroidism Symptom Based on [18F]FDG-PET of Rats.

    Science.gov (United States)

    Wan, Hongkai; Tan, Ziyu; Zheng, Qiang; Yu, Jing

    2018-03-12

    Recent researches have demonstrated the value of using 2-deoxy-2-[ 18 F]fluoro-D-glucose ([ 18 F]FDG) positron emission tomography (PET) imaging to reveal the hypothyroidism-related damages in local brain regions. However, the influence of hypothyroidism on the entire brain network is barely studied. This study focuses on the application of graph theory on analyzing functional brain networks of the hypothyroidism symptom. For both the hypothyroidism and the control groups of Wistar rats, the functional brain networks were constructed by thresholding the glucose metabolism correlation matrices of 58 brain regions. The network topological properties (including the small-world properties and the nodal centralities) were calculated and compared between the two groups. We found that the rat brains, like human brains, have typical properties of the small-world network in both the hypothyroidism and the control groups. However, the hypothyroidism group demonstrated lower global efficiency and decreased local cliquishness of the brain network, indicating hypothyroidism-related impairment to the brain network. The hypothyroidism group also has decreased nodal centrality in the left posterior hippocampus, the right hypothalamus, pituitary, pons, and medulla. This observation accorded with the hypothyroidism-related functional disorder of hypothalamus-pituitary-thyroid (HPT) feedback regulation mechanism. Our research quantitatively confirms that hypothyroidism hampers brain cognitive function by causing impairment to the brain network of glucose metabolism. This study reveals the feasibility and validity of applying graph theory method to preclinical [ 18 F]FDG-PET images and facilitates future study on human subjects.

  15. Selective deficit of second language: a case study of a brain-damaged Arabic-Hebrew bilingual patient

    Directory of Open Access Journals (Sweden)

    Ibrahim Raphiq

    2009-03-01

    Full Text Available Abstract Background An understanding of how two languages are represented in the human brain is best obtained from studies of bilingual patients who have sustained brain damage. The primary goal of the present study was to determine whether one or both languages of an Arabic-Hebrew bilingual individual are disrupted following brain damage. I present a case study of a bilingual patient, proficient in Arabic and Hebrew, who had sustained brain damage as a result of an intracranial hemorrhage related to herpes encephalitis. Methods The patient's performance on several linguistic tasks carried out in the first language (Arabic and in the second language (Hebrew was assessed, and his performance in the two languages was compared. Results The patient displayed somewhat different symptomatologies in the two languages. The results revealed dissociation between the two languages in terms of both the types and the magnitude of errors, pointing to aphasic symptoms in both languages, with Hebrew being the more impaired. Further analysis disclosed that this dissociation was apparently caused not by damage to his semantic system, but rather by damage at the lexical level. Conclusion The results suggest that the principles governing the organization of lexical representations in the brain are not similar for the two languages.

  16. Network Dynamics with BrainX3: A Large-Scale Simulation of the Human Brain Network with Real-Time Interaction

    OpenAIRE

    Xerxes D. Arsiwalla; Riccardo eZucca; Alberto eBetella; Enrique eMartinez; David eDalmazzo; Pedro eOmedas; Gustavo eDeco; Gustavo eDeco; Paul F.M.J. Verschure; Paul F.M.J. Verschure

    2015-01-01

    BrainX3 is a large-scale simulation of human brain activity with real-time interaction, rendered in 3D in a virtual reality environment, which combines computational power with human intuition for the exploration and analysis of complex dynamical networks. We ground this simulation on structural connectivity obtained from diffusion spectrum imaging data and model it on neuronal population dynamics. Users can interact with BrainX3 in real-time by perturbing brain regions with transient stimula...

  17. Network dynamics with BrainX3: a large-scale simulation of the human brain network with real-time interaction

    OpenAIRE

    Arsiwalla, Xerxes D.; Zucca, Riccardo; Betella, Alberto; Martínez, Enrique, 1961-; Dalmazzo, David; Omedas, Pedro; Deco, Gustavo; Verschure, Paul F. M. J.

    2015-01-01

    BrainX3 is a large-scale simulation of human brain activity with real-time interaction, rendered in 3D in a virtual reality environment, which combines computational power with human intuition for the exploration and analysis of complex dynamical networks. We ground this simulation on structural connectivity obtained from diffusion spectrum imaging data and model it on neuronal population dynamics. Users can interact with BrainX3 in real-time by perturbing brain regions with transient stimula...

  18. Meditation is associated with increased brain network integration.

    Science.gov (United States)

    van Lutterveld, Remko; van Dellen, Edwin; Pal, Prasanta; Yang, Hua; Stam, Cornelis Jan; Brewer, Judson

    2017-09-01

    This study aims to identify novel quantitative EEG measures associated with mindfulness meditation. As there is some evidence that meditation is associated with higher integration of brain networks, we focused on EEG measures of network integration. Sixteen novice meditators and sixteen experienced meditators participated in the study. Novice meditators performed a basic meditation practice that supported effortless awareness, which is an important quality of experience related to mindfulness practices, while their EEG was recorded. Experienced meditators performed a self-selected meditation practice that supported effortless awareness. Network integration was analyzed with maximum betweenness centrality and leaf fraction (which both correlate positively with network integration) as well as with diameter and average eccentricity (which both correlate negatively with network integration), based on a phase-lag index (PLI) and minimum spanning tree (MST) approach. Differences between groups were assessed using repeated-measures ANOVA for the theta (4-8 Hz), alpha (8-13 Hz) and lower beta (13-20 Hz) frequency bands. Maximum betweenness centrality was significantly higher in experienced meditators than in novices (P = 0.012) in the alpha band. In the same frequency band, leaf fraction showed a trend toward being significantly higher in experienced meditators than in novices (P = 0.056), while diameter and average eccentricity were significantly lower in experienced meditators than in novices (P = 0.016 and P = 0.028 respectively). No significant differences between groups were observed for the theta and beta frequency bands. These results show that alpha band functional network topology is better integrated in experienced meditators than in novice meditators during meditation. This novel finding provides the rationale to investigate the temporal relation between measures of functional connectivity network integration and meditation quality, for example using

  19. Analysis of structure-function network decoupling in the brain systems of spastic diplegic cerebral palsy.

    Science.gov (United States)

    Lee, Dongha; Pae, Chongwon; Lee, Jong Doo; Park, Eun Sook; Cho, Sung-Rae; Um, Min-Hee; Lee, Seung-Koo; Oh, Maeng-Keun; Park, Hae-Jeong

    2017-10-01

    Manifestation of the functionalities from the structural brain network is becoming increasingly important to understand a brain disease. With the aim of investigating the differential structure-function couplings according to network systems, we investigated the structural and functional brain networks of patients with spastic diplegic cerebral palsy with periventricular leukomalacia compared to healthy controls. The structural and functional networks of the whole brain and motor system, constructed using deterministic and probabilistic tractography of diffusion tensor magnetic resonance images and Pearson and partial correlation analyses of resting-state functional magnetic resonance images, showed differential embedding of functional networks in the structural networks in patients. In the whole-brain network of patients, significantly reduced global network efficiency compared to healthy controls were found in the structural networks but not in the functional networks, resulting in reduced structural-functional coupling. On the contrary, the motor network of patients had a significantly lower functional network efficiency over the intact structural network and a lower structure-function coupling than the control group. This reduced coupling but reverse directionality in the whole-brain and motor networks of patients was prominent particularly between the probabilistic structural and partial correlation-based functional networks. Intact (or less deficient) functional network over impaired structural networks of the whole brain and highly impaired functional network topology over the intact structural motor network might subserve relatively preserved cognitions and impaired motor functions in cerebral palsy. This study suggests that the structure-function relationship, evaluated specifically using sparse functional connectivity, may reveal important clues to functional reorganization in cerebral palsy. Hum Brain Mapp 38:5292-5306, 2017. © 2017 Wiley Periodicals

  20. Domain-General Brain Regions Do Not Track Linguistic Input as Closely as Language-Selective Regions.

    Science.gov (United States)

    Blank, Idan A; Fedorenko, Evelina

    2017-10-11

    Language comprehension engages a cortical network of left frontal and temporal regions. Activity in this network is language-selective, showing virtually no modulation by nonlinguistic tasks. In addition, language comprehension engages a second network consisting of bilateral frontal, parietal, cingulate, and insular regions. Activity in this "multiple demand" (MD) network scales with comprehension difficulty, but also with cognitive effort across a wide range of nonlinguistic tasks in a domain-general fashion. Given the functional dissociation between the language and MD networks, their respective contributions to comprehension are likely distinct, yet such differences remain elusive. Prior neuroimaging studies have suggested that activity in each network covaries with some linguistic features that, behaviorally, influence on-line processing and comprehension. This sensitivity of the language and MD networks to local input characteristics has often been interpreted, implicitly or explicitly, as evidence that both networks track linguistic input closely, and in a manner consistent across individuals. Here, we used fMRI to directly test this assumption by comparing the BOLD signal time courses in each network across different people ( n = 45, men and women) listening to the same story. Language network activity showed fewer individual differences, indicative of closer input tracking, whereas MD network activity was more idiosyncratic and, moreover, showed lower reliability within an individual across repetitions of a story. These findings constrain cognitive models of language comprehension by suggesting a novel distinction between the processes implemented in the language and MD networks. SIGNIFICANCE STATEMENT Language comprehension recruits both language-specific mechanisms and domain-general mechanisms that are engaged in many cognitive processes. In the human cortex, language-selective mechanisms are implemented in the left-lateralized "core language network

  1. Brain signatures of early lexical and morphological learning of a new language.

    Science.gov (United States)

    Havas, Viktória; Laine, Matti; Rodríguez Fornells, Antoni

    2017-07-01

    Morphology is an important part of language processing but little is known about how adult second language learners acquire morphological rules. Using a word-picture associative learning task, we have previously shown that a brief exposure to novel words with embedded morphological structure (suffix for natural gender) is enough for language learners to acquire the hidden morphological rule. Here we used this paradigm to study the brain signatures of early morphological learning in a novel language in adults. Behavioural measures indicated successful lexical (word stem) and morphological (gender suffix) learning. A day after the learning phase, event-related brain potentials registered during a recognition memory task revealed enhanced N400 and P600 components for stem and suffix violations, respectively. An additional effect observed with combined suffix and stem violations was an enhancement of an early N2 component, most probably related to conflict-detection processes. Successful morphological learning was also evident in the ERP responses to the subsequent rule-generalization task with new stems, where violation of the morphological rule was associated with an early (250-400ms) and late positivity (750-900ms). Overall, these findings tend to converge with lexical and morphosyntactic violation effects observed in L1 processing, suggesting that even after a short exposure, adult language learners can acquire both novel words and novel morphological rules. Copyright © 2017 Elsevier Ltd. All rights reserved.

  2. Drawing Inspiration from Human Brain Networks: Construction of Interconnected Virtual Networks.

    Science.gov (United States)

    Murakami, Masaya; Kominami, Daichi; Leibnitz, Kenji; Murata, Masayuki

    2018-04-08

    Virtualization of wireless sensor networks (WSN) is widely considered as a foundational block of edge/fog computing, which is a key technology that can help realize next-generation Internet of things (IoT) networks. In such scenarios, multiple IoT devices and service modules will be virtually deployed and interconnected over the Internet. Moreover, application services are expected to be more sophisticated and complex, thereby increasing the number of modifications required for the construction of network topologies. Therefore, it is imperative to establish a method for constructing a virtualized WSN (VWSN) topology that achieves low latency on information transmission and high resilience against network failures, while keeping the topological construction cost low. In this study, we draw inspiration from inter-modular connectivity in human brain networks, which achieves high performance when dealing with large-scale networks composed of a large number of modules (i.e., regions) and nodes (i.e., neurons). We propose a method for assigning inter-modular links based on a connectivity model observed in the cerebral cortex of the brain, known as the exponential distance rule (EDR) model. We then choose endpoint nodes of these links by controlling inter-modular assortativity, which characterizes the topological connectivity of brain networks. We test our proposed methods using simulation experiments. The results show that the proposed method based on the EDR model can construct a VWSN topology with an optimal combination of communication efficiency, robustness, and construction cost. Regarding the selection of endpoint nodes for the inter-modular links, the results also show that high assortativity enhances the robustness and communication efficiency because of the existence of inter-modular links of two high-degree nodes.

  3. Drawing Inspiration from Human Brain Networks: Construction of Interconnected Virtual Networks

    Directory of Open Access Journals (Sweden)

    Masaya Murakami

    2018-04-01

    Full Text Available Virtualization of wireless sensor networks (WSN is widely considered as a foundational block of edge/fog computing, which is a key technology that can help realize next-generation Internet of things (IoT networks. In such scenarios, multiple IoT devices and service modules will be virtually deployed and interconnected over the Internet. Moreover, application services are expected to be more sophisticated and complex, thereby increasing the number of modifications required for the construction of network topologies. Therefore, it is imperative to establish a method for constructing a virtualized WSN (VWSN topology that achieves low latency on information transmission and high resilience against network failures, while keeping the topological construction cost low. In this study, we draw inspiration from inter-modular connectivity in human brain networks, which achieves high performance when dealing with large-scale networks composed of a large number of modules (i.e., regions and nodes (i.e., neurons. We propose a method for assigning inter-modular links based on a connectivity model observed in the cerebral cortex of the brain, known as the exponential distance rule (EDR model. We then choose endpoint nodes of these links by controlling inter-modular assortativity, which characterizes the topological connectivity of brain networks. We test our proposed methods using simulation experiments. The results show that the proposed method based on the EDR model can construct a VWSN topology with an optimal combination of communication efficiency, robustness, and construction cost. Regarding the selection of endpoint nodes for the inter-modular links, the results also show that high assortativity enhances the robustness and communication efficiency because of the existence of inter-modular links of two high-degree nodes.

  4. Finding language in the matter of the brain: origins of the clinical aphasia examination.

    Science.gov (United States)

    Roth, Heidi L

    2002-12-01

    The origins of the aphasia examination can be traced back to the 19th century when physicians and scientists began to understand how higher mental functions such as language could be localized in the brain. Paul Broca, Carl Wernicke, and Hughlings Jackson developed different models of brain function, and each contributed important insights to the study of aphasia. Broca's contributions were influenced by the fundamental question of whether higher mental function could be localized in the brain at all; Wernicke's contributions were influenced by an attempt to unite more mechanistic and physiological principles to a model of higher brain functions; and Jackson's contributions were influenced by British association psychology. In addition to reviewing the origins of the aphasia examination, this article reviews the historical context in which these contributors worked, the factors that affected the reception of their views, and the manner in which their views have affected the aphasia examination and understanding of aphasia today.

  5. The Effectiveness of Social Media Network Telegram in Teaching English Language Pronunciation to Iranian EFL Learners

    Science.gov (United States)

    Xodabande, Ismail

    2017-01-01

    In recent years, the expansion of digital technologies, multimedia, and social networks, dramatically transformed our lives. Education in general and the area of foreign language teaching and learning have also benefited hugely from those developments and advances. As a result, the face of language learning is changing and new technologies provide…

  6. Investigating the Use of a Smartphone Social Networking Application on Language Learning

    Science.gov (United States)

    Sung, Ko-Yin; Poole, Frederick

    2017-01-01

    This study explored college students' use of a popular smartphone social networking application, WeChat, in a tandem language learning project. The research questions included (1) How do Chinese-English dyads utilize the WeChat app for weekly language learning?, and (2) What are the perceptions of the Chinese-English dyads on the use of the WeChat…

  7. Visual language recognition with a feed-forward network of spiking neurons

    Energy Technology Data Exchange (ETDEWEB)

    Rasmussen, Craig E [Los Alamos National Laboratory; Garrett, Kenyan [Los Alamos National Laboratory; Sottile, Matthew [GALOIS; Shreyas, Ns [INDIANA UNIV.

    2010-01-01

    An analogy is made and exploited between the recognition of visual objects and language parsing. A subset of regular languages is used to define a one-dimensional 'visual' language, in which the words are translational and scale invariant. This allows an exploration of the viewpoint invariant languages that can be solved by a network of concurrent, hierarchically connected processors. A language family is defined that is hierarchically tiling system recognizable (HREC). As inspired by nature, an algorithm is presented that constructs a cellular automaton that recognizes strings from a language in the HREC family. It is demonstrated how a language recognizer can be implemented from the cellular automaton using a feed-forward network of spiking neurons. This parser recognizes fixed-length strings from the language in parallel and as the computation is pipelined, a new string can be parsed in each new interval of time. The analogy with formal language theory allows inferences to be drawn regarding what class of objects can be recognized by visual cortex operating in purely feed-forward fashion and what class of objects requires a more complicated network architecture.

  8. Quetiapine modulates functional connectivity in brain aggression networks.

    Science.gov (United States)

    Klasen, Martin; Zvyagintsev, Mikhail; Schwenzer, Michael; Mathiak, Krystyna A; Sarkheil, Pegah; Weber, René; Mathiak, Klaus

    2013-07-15

    Aggressive behavior is associated with dysfunctions in an affective regulation network encompassing amygdala and prefrontal areas such as orbitofrontal (OFC), anterior cingulate (ACC), and dorsolateral prefrontal cortex (DLPFC). In particular, prefrontal regions have been postulated to control amygdala activity by inhibitory projections, and this process may be disrupted in aggressive individuals. The atypical antipsychotic quetiapine successfully attenuates aggressive behavior in various disorders; the underlying neural processes, however, are unknown. A strengthened functional coupling in the prefrontal-amygdala system may account for these anti-aggressive effects. An inhibition of this network has been reported for virtual aggression in violent video games as well. However, there have been so far no in-vivo observations of pharmacological influences on corticolimbic projections during human aggressive behavior. In a double-blind, placebo-controlled study, quetiapine and placebo were administered for three successive days prior to an fMRI experiment. In this experiment, functional brain connectivity was assessed during virtual aggressive behavior in a violent video game and an aggression-free control task in a non-violent modification. Quetiapine increased the functional connectivity of ACC and DLPFC with the amygdala during virtual aggression, whereas OFC-amygdala coupling was attenuated. These effects were observed neither for placebo nor for the non-violent control. These results demonstrate for the first time a pharmacological modification of aggression-related human brain networks in a naturalistic setting. The violence-specific modulation of prefrontal-amygdala networks appears to control aggressive behavior and provides a neurobiological model for the anti-aggressive effects of quetiapine. Copyright © 2013 Elsevier Inc. All rights reserved.

  9. Brain network response underlying decisions about abstract reinforcers.

    Science.gov (United States)

    Mills-Finnerty, Colleen; Hanson, Catherine; Hanson, Stephen Jose

    2014-12-01

    Decision making studies typically use tasks that involve concrete action-outcome contingencies, in which subjects do something and get something. No studies have addressed decision making involving abstract reinforcers, where there are no action-outcome contingencies and choices are entirely hypothetical. The present study examines these kinds of choices, as well as whether the same biases that exist for concrete reinforcer decisions, specifically framing effects, also apply during abstract reinforcer decisions. We use both General Linear Model as well as Bayes network connectivity analysis using the Independent Multi-sample Greedy Equivalence Search (IMaGES) algorithm to examine network response underlying choices for abstract reinforcers under positive and negative framing. We find for the first time that abstract reinforcer decisions activate the same network of brain regions as concrete reinforcer decisions, including the striatum, insula, anterior cingulate, and VMPFC, results that are further supported via comparison to a meta-analysis of decision making studies. Positive and negative framing activated different parts of this network, with stronger activation in VMPFC during negative framing and in DLPFC during positive, suggesting different decision making pathways depending on frame. These results were further clarified using connectivity analysis, which revealed stronger connections between anterior cingulate, insula, and accumbens during negative framing compared to positive. Taken together, these results suggest that not only do abstract reinforcer decisions rely on the same brain substrates as concrete reinforcers, but that the response underlying framing effects on abstract reinforcers also resemble those for concrete reinforcers, specifically increased limbic system connectivity during negative frames. Copyright © 2014 Elsevier Inc. All rights reserved.

  10. The structural, connectomic and network covariance of the human brain.

    Science.gov (United States)

    Irimia, Andrei; Van Horn, John D

    2013-02-01

    Though it is widely appreciated that complex structural, functional and morphological relationships exist between distinct areas of the human cerebral cortex, the extent to which such relationships coincide remains insufficiently appreciated. Here we determine the extent to which correlations between brain regions are modulated by either structural, connectomic or network-theoretic properties using a structural neuroimaging data set of magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) volumes acquired from N=110 healthy human adults. To identify the linear relationships between all available pairs of regions, we use canonical correlation analysis to test whether a statistically significant correlation exists between each pair of cortical parcels as quantified via structural, connectomic or network-theoretic measures. In addition to this, we investigate (1) how each group of canonical variables (whether structural, connectomic or network-theoretic) contributes to the overall correlation and, additionally, (2) whether each individual variable makes a significant contribution to the test of the omnibus null hypothesis according to which no correlation between regions exists across subjects. We find that, although region-to-region correlations are extensively modulated by structural and connectomic measures, there are appreciable differences in how these two groups of measures drive inter-regional correlation patterns. Additionally, our results indicate that the network-theoretic properties of the cortex are strong modulators of region-to-region covariance. Our findings are useful for understanding the structural and connectomic relationship between various parts of the brain, and can inform theoretical and computational models of cortical information processing. Published by Elsevier Inc.

  11. Morphological features of the neonatal brain support development of subsequent cognitive, language, and motor abilities.

    Science.gov (United States)

    Spann, Marisa N; Bansal, Ravi; Rosen, Tove S; Peterson, Bradley S

    2014-09-01

    Knowledge of the role of brain maturation in the development of cognitive abilities derives primarily from studies of school-age children to adults. Little is known about the morphological features of the neonatal brain that support the subsequent development of abilities in early childhood, when maturation of the brain and these abilities are the most dynamic. The goal of our study was to determine whether brain morphology during the neonatal period supports early cognitive development through 2 years of age. We correlated morphological features of the cerebral surface assessed using deformation-based measures (surface distances) of high-resolution MRI scans for 33 healthy neonates, scanned between the first to sixth week of postmenstrual life, with subsequent measures of their motor, language, and cognitive abilities at ages 6, 12, 18, and 24 months. We found that morphological features of the cerebral surface of the frontal, mesial prefrontal, temporal, and occipital regions correlated with subsequent motor scores, posterior parietal regions correlated with subsequent language scores, and temporal and occipital regions correlated with subsequent cognitive scores. Measures of the anterior and middle portions of the cingulate gyrus correlated with scores across all three domains of ability. Most of the significant findings were inverse correlations located bilaterally in the brain. The inverse correlations may suggest either that a more protracted morphological maturation or smaller local volumes of neonatal brain tissue supports better performance on measures of subsequent motor, language, and cognitive abilities throughout the first 2 years of postnatal life. The correlations of morphological measures of the cingulate with measures of performance across all domains of ability suggest that the cingulate supports a broad range of skills in infancy and early childhood, similar to its functions in older children and adults. Copyright © 2014 Wiley Periodicals, Inc.

  12. Finding influential nodes for integration in brain networks using optimal percolation theory.

    Science.gov (United States)

    Del Ferraro, Gino; Moreno, Andrea; Min, Byungjoon; Morone, Flaviano; Pérez-Ramírez, Úrsula; Pérez-Cervera, Laura; Parra, Lucas C; Holodny, Andrei; Canals, Santiago; Makse, Hernán A

    2018-06-11

    Global integration of information in the brain results from complex interactions of segregated brain networks. Identifying the most influential neuronal populations that efficiently bind these networks is a fundamental problem of systems neuroscience. Here, we apply optimal percolation theory and pharmacogenetic interventions in vivo to predict and subsequently target nodes that are essential for global integration of a memory network in rodents. The theory predicts that integration in the memory network is mediated by a set of low-degree nodes located in the nucleus accumbens. This result is confirmed with pharmacogenetic inactivation of the nucleus accumbens, which eliminates the formation of the memory network, while inactivations of other brain areas leave the network intact. Thus, optimal percolation theory predicts essential nodes in brain networks. This could be used to identify targets of interventions to modulate brain function.

  13. A Mapping Between Structural and Functional Brain Networks.

    Science.gov (United States)

    Meier, Jil; Tewarie, Prejaas; Hillebrand, Arjan; Douw, Linda; van Dijk, Bob W; Stufflebeam, Steven M; Van Mieghem, Piet

    2016-05-01

    The relationship between structural and functional brain networks is still highly debated. Most previous studies have used a single functional imaging modality to analyze this relationship. In this work, we use multimodal data, from functional MRI, magnetoencephalography, and diffusion tensor imaging, and assume that there exists a mapping between the connectivity matrices of the resting-state functional and structural networks. We investigate this mapping employing group averaged as well as individual data. We indeed find a significantly high goodness of fit level for this structure-function mapping. Our analysis suggests that a functional connection is shaped by all walks up to the diameter in the structural network in both modality cases. When analyzing the inverse mapping, from function to structure, longer walks in the functional network also seem to possess minor influence on the structural connection strengths. Even though similar overall properties for the structure-function mapping are found for different functional modalities, our results indicate that the structure-function relationship is modality dependent.

  14. Brain networks and their origins. Comment on “Understanding brain networks and brain organization” by Luiz Pessoa

    Science.gov (United States)

    Cisek, Paul

    2014-09-01

    Nearly every textbook on psychology or neuroscience contains theories of function described with box and arrow diagrams. Sometimes, the boxes stand for purely theoretical constructs, such as attention or working memory, and sometimes they also correspond to specific brain regions or systems, such as parietal or prefrontal cortex, and the arrows between them to known anatomical pathways. It is common for scientists (present company included) to summarize their theories in this way and to think of the brain as a set of interacting modules with clearly distinguishable functions.

  15. A more randomly organized grey matter network is associated with deteriorating language and global cognition in individuals with subjective cognitive decline.

    Science.gov (United States)

    Verfaillie, Sander C J; Slot, Rosalinde E R; Dicks, Ellen; Prins, Niels D; Overbeek, Jozefien M; Teunissen, Charlotte E; Scheltens, Philip; Barkhof, Frederik; van der Flier, Wiesje M; Tijms, Betty M

    2018-03-30

    Grey matter network disruptions in Alzheimer's disease (AD) are associated with worse cognitive impairment cross-sectionally. Our aim was to investigate whether indications of a more random network organization are associated with longitudinal decline in specific cognitive functions in individuals with subjective cognitive decline (SCD). We included 231 individuals with SCD who had annually repeated neuropsychological assessment (3 ± 1 years; n = 646 neuropsychological investigations) available from the Amsterdam Dementia Cohort (54% male, age: 63 ± 9, MMSE: 28 ± 2). Single-subject grey matter networks were extracted from baseline 3D-T1 MRI scans and we computed basic network (size, degree, connectivity density) and higher-order (path length, clustering, betweenness centrality, normalized path length [lambda] and normalized clustering [gamma]) parameters at whole brain and/or regional levels. We tested associations of network parameters with baseline and annual cognition (memory, attention, executive functioning, language composite scores, and global cognition [all domains with MMSE]) using linear mixed models, adjusted for age, sex, education, scanner and total gray matter volume. Lower network size was associated with steeper decline in language (β ± SE = 0.12 ± 0.05, p organized grey matter network was associated with a steeper decline of cognitive functioning, possibly indicating the start of cognitive impairment. © 2018 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

  16. Brain Interaction during Cooperation: Evaluating Local Properties of Multiple-Brain Network.

    Science.gov (United States)

    Sciaraffa, Nicolina; Borghini, Gianluca; Aricò, Pietro; Di Flumeri, Gianluca; Colosimo, Alfredo; Bezerianos, Anastasios; Thakor, Nitish V; Babiloni, Fabio

    2017-07-21

    Subjects' interaction is the core of most human activities. This is the reason why a lack of coordination is often the cause of missing goals, more than individual failure. While there are different subjective and objective measures to assess the level of mental effort required by subjects while facing a situation that is getting harder, that is, mental workload, to define an objective measure based on how and if team members are interacting is not so straightforward. In this study, behavioral, subjective and synchronized electroencephalographic data were collected from couples involved in a cooperative task to describe the relationship between task difficulty and team coordination, in the sense of interaction aimed at cooperatively performing the assignment. Multiple-brain connectivity analysis provided information about the whole interacting system. The results showed that averaged local properties of a brain network were affected by task difficulty. In particular, strength changed significantly with task difficulty and clustering coefficients strongly correlated with the workload itself. In particular, a higher workload corresponded to lower clustering values over the central and parietal brain areas. Such results has been interpreted as less efficient organization of the network when the subjects' activities, due to high workload tendencies, were less coordinated.

  17. Organic and Non-Organic Language Disorders after Awake Brain Surgery

    Directory of Open Access Journals (Sweden)

    Elke De Witte

    2014-04-01

    Full Text Available INTRODUCTION: Awake surgery with Direct Electrical Stimulation (DES is considered the ‘gold standard’ to resect brain tumours in the language dominant hemisphere (De Witte & Mariën, 2013. Although transient language impairments are common in the immediate postoperative phase, permanent postoperative language deficits seem to be rare (Duffau, 2007. Milian et al. (2014 stated that most patients tolerate the awake procedure well and would undergo a similar procedure again. However, postoperative psychological symptoms including recurrent distressing dreams and persistent avoidance of stimuli have been recorded following awake surgery (Goebel, Nabavi, Schubert, & Mehdorn, 2010; Milian et al., 2014. To the best of our knowledge, psychogenic language disturbances have never been described after awake surgery. In general, only a handful of non-organic, psychogenic language disorders have been reported in the literature (De Letter et al., 2012. We report three patients with left brain tumours (see table 1 who presented linguistic symptoms after awake surgery that were incompatible with the lesion location, suggesting a psychogenic origin. METHODS: Neurocognitive (language, memory, executive functions investigations were carried out before, during and after awake surgery (6 weeks, 6 months postsurgery on the basis of standardised tests. Pre- and postoperative (fMRI images, DTI results and intraoperative DES findings were analysed. A selection of tasks was used to map language intraoperatively (De Witte et al., 2013. In the postoperative phase spontaneous speech and behavioural phenomena to errors were video-recorded. RESULTS: Preoperative language tests did not reveal any speech or language problems. Intraoperatively, eloquent sites were mapped and preserved enabling good language skills at the end of the awake procedure. However, assessments in the first weeks postsurgery disclosed language and behavioural symptoms that support the hypothesis of a

  18. Task vs. rest-different network configurations between the coactivation and the resting-state brain networks.

    Science.gov (United States)

    Di, Xin; Gohel, Suril; Kim, Eun H; Biswal, Bharat B

    2013-01-01

    There is a growing interest in studies of human brain networks using resting-state functional magnetic resonance imaging (fMRI). However, it is unclear whether and how brain networks measured during the resting-state exhibit comparable properties to brain networks during task performance. In the present study, we investigated meta-analytic coactivation patterns among brain regions based upon published neuroimaging studies, and compared the coactivation network configurations with those in the resting-state network. The strength of resting-state functional connectivity between two regions were strongly correlated with the coactivation strength. However, the coactivation network showed greater global efficiency, smaller mean clustering coefficient, and lower modularity compared with the resting-state network, which suggest a more efficient global information transmission and between system integrations during task performing. Hub shifts were also observed within the thalamus and the left inferior temporal cortex. The thalamus and the left inferior temporal cortex exhibited higher and lower degrees, respectively in the coactivation network compared with the resting-state network. These results shed light regarding the reconfiguration of the brain networks between task and resting-state conditions, and highlight the role of the thalamus in change of network configurations in task vs. rest.

  19. Task vs. rest—different network configurations between the coactivation and the resting-state brain networks

    Science.gov (United States)

    Di, Xin; Gohel, Suril; Kim, Eun H.; Biswal, Bharat B.

    2013-01-01

    There is a growing interest in studies of human brain networks using resting-state functional magnetic resonance imaging (fMRI). However, it is unclear whether and how brain networks measured during the resting-state exhibit comparable properties to brain networks during task performance. In the present study, we investigated meta-analytic coactivation patterns among brain regions based upon published neuroimaging studies, and compared the coactivation network configurations with those in the resting-state network. The strength of resting-state functional connectivity between two regions were strongly correlated with the coactivation strength. However, the coactivation network showed greater global efficiency, smaller mean clustering coefficient, and lower modularity compared with the resting-state network, which suggest a more efficient global information transmission and between system integrations during task performing. Hub shifts were also observed within the thalamus and the left inferior temporal cortex. The thalamus and the left inferior temporal cortex exhibited higher and lower degrees, respectively in the coactivation network compared with the resting-state network. These results shed light regarding the reconfiguration of the brain networks between task and resting-state conditions, and highlight the role of the thalamus in change of network configurations in task vs. rest. PMID:24062654

  20. Prereader to beginning reader: changes induced by reading acquisition in print and speech brain networks.

    Science.gov (United States)

    Chyl, Katarzyna; Kossowski, Bartosz; Dębska, Agnieszka; Łuniewska, Magdalena; Banaszkiewicz, Anna; Żelechowska, Agata; Frost, Stephen J; Mencl, William Einar; Wypych, Marek; Marchewka, Artur; Pugh, Kenneth R; Jednoróg, Katarzyna

    2018-01-01

    Literacy acquisition is a demanding process that induces significant changes in the brain, especially in the spoken and written language networks. Nevertheless, large-scale paediatric fMRI studies are still limited. We analyzed fMRI data to show how individual differences in reading performance correlate with brain activation for speech and print in 111 children attending kindergarten or first grade and examined group differences between a matched subset of emergent-readers and prereaders. Across the entire cohort, individual differences analysis revealed that reading skill was positively correlated with the magnitude of activation difference between words and symbol strings in left superior temporal, inferior frontal and fusiform gyri. Group comparisons of the matched subset of pre- and emergent-readers showed higher activity for emergent-readers in left inferior frontal, precentral, and postcentral gyri. Individual differences in activation for natural versus vocoded speech were also positively correlated with reading skill, primarily in the left temporal cortex. However, in contrast to studies on adult illiterates, group comparisons revealed higher activity in prereaders compared to readers in the frontal lobes. Print-speech coactivation was observed only in readers and individual differences analyses revealed a positive correlation between convergence and reading skill in the left superior temporal sulcus. These results emphasise that a child's brain undergoes several modifications to both visual and oral language systems in the process of learning to read. They also suggest that print-speech convergence is a hallmark of acquiring literacy. © 2017 Association for Child and Adolescent Mental Health.

  1. Optimal brain network synchrony visualization: application in an alcoholism paradigm.

    Science.gov (United States)

    Sakkalis, Vangelis; Tsiaras, Vassilis; Zervakis, Michalis; Tollis, Ioannis

    2007-01-01

    Although Electroencephalographic (EEG) signal synchronization studies have been a topic of increasing interest lately, there is no similar effort in the visualization of such measures. In this direction a graph-theoretic approach devised to study and stress the coupling dynamics of task-performing dynamical networks is proposed. Both linear and nonlinear interdependence measures are investigated in an alcoholism paradigm during mental rehearsal of pictures, which is known to reflect synchronization impairment. More specifically, the widely used magnitude squared coherence; phase synchronization and a robust nonlinear state-space generalized synchronization assessment method are investigated. This paper mostly focuses on a signal-based technique of selecting the optimal visualization threshold using surrogate datasets to correctly identify the most significant correlation patterns. Furthermore, a graph statistical parameter attempts to capture and quantify collective motifs present in the functional brain network. The results are in accordance with previous psychophysiology studies suggesting that an alcoholic subject has impaired synchronization of brain activity and loss of lateralization during the rehearsal process, most prominently in alpha (8-12 Hz) band, as compared to a control subject. Lower beta (13-30 Hz) synchronization was also evident in the alcoholic subject.

  2. The effects of music on brain functional networks: a network analysis.

    Science.gov (United States)

    Wu, J; Zhang, J; Ding, X; Li, R; Zhou, C

    2013-10-10

    The human brain can dynamically adapt to the changing surroundings. To explore this issue, we adopted graph theoretical tools to examine changes in electroencephalography (EEG) functional networks while listening to music. Three different excerpts of Chinese Guqin music were played to 16 non-musician subjects. For the main frequency intervals, synchronizations between all pair-wise combinations of EEG electrodes were evaluated with phase lag index (PLI). Then, weighted connectivity networks were created and their organizations were characterized in terms of an average clustering coefficient and characteristic path length. We found an enhanced synchronization level in the alpha2 band during music listening. Music perception showed a decrease of both normalized clustering coefficient and path length in the alpha2 band. Moreover, differences in network measures were not observed between musical excerpts. These experimental results demonstrate an increase of functional connectivity as well as a more random network structure in the alpha2 band during music perception. The present study offers support for the effects of music on human brain functional networks with a trend toward a more efficient but less economical architecture. Copyright © 2013 IBRO. Published by Elsevier Ltd. All rights reserved.

  3. Classification of CT brain images based on deep learning networks.

    Science.gov (United States)

    Gao, Xiaohong W; Hui, Rui; Tian, Zengmin

    2017-01-01

    While computerised tomography (CT) may have been the first imaging tool to study human brain, it has not yet been implemented into clinical decision making process for diagnosis of Alzheimer's disease (AD). On the other hand, with the nature of being prevalent, inexpensive and non-invasive, CT does present diagnostic features of AD to a great extent. This study explores the significance and impact on the application of the burgeoning deep learning techniques to the task of classification of CT brain images, in particular utilising convolutional neural network (CNN), aiming at providing supplementary information for the early diagnosis of Alzheimer's disease. Towards this end, three categories of CT images (N = 285) are clustered into three groups, which are AD, lesion (e.g. tumour) and normal ageing. In addition, considering the characteristics of this collection with larger thickness along the direction of depth (z) (~3-5 mm), an advanced CNN architecture is established integrating both 2D and 3D CNN networks. The fusion of the two CNN networks is subsequently coordinated based on the average of Softmax scores obtained from both networks consolidating 2D images along spatial axial directions and 3D segmented blocks respectively. As a result, the classification accuracy rates rendered by this elaborated CNN architecture are 85.2%, 80% and 95.3% for classes of AD, lesion and normal respectively with an average of 87.6%. Additionally, this improved CNN network appears to outperform the others when in comparison with 2D version only of CNN network as well as a number of state of the art hand-crafted approaches. As a result, these approaches deliver accuracy rates in percentage of 86.3, 85.6 ± 1.10, 86.3 ± 1.04, 85.2 ± 1.60, 83.1 ± 0.35 for 2D CNN, 2D SIFT, 2D KAZE, 3D SIFT and 3D KAZE respectively. The two major contributions of the paper constitute a new 3-D approach while applying deep learning technique to extract signature information

  4. Dismissing attachment characteristics dynamically modulate brain networks subserving social aversion.

    Directory of Open Access Journals (Sweden)

    Anna Linda eKrause

    2016-03-01

    Full Text Available Attachment patterns influence actions, thoughts and feeling through a person’s ‘Inner Working Model’. Speech charged with attachment-dependent content was proposed to modulate the activation of cognitive-emotional schemata in listeners. We performed a 7 Tesla rest-task-rest fMRI-experiment, presenting auditory narratives prototypical of dismissing attachment representations to investigate their effect on 23 healthy males. We then examined effects of participants’ attachment style and childhood trauma on brain state changes using seed-based functional connectivity (FC analyses, and finally tested whether subjective differences in responsivity to narratives could be predicted by baseline network states. In comparison to a baseline state, we observed increased FC in a previously described ‘social aversion network’ including dorsal anterior cingulated cortex (dACC and left anterior middle temporal gyrus (aMTG specifically after exposure to insecure-dismissing attachment narratives. Increased dACC-seeded FC within the social aversion network was positively related to the participants’ avoidant attachment style and presence of a history of childhood trauma. Anxious attachment style on the other hand was positively correlated with FC between the dACC and a region outside of the ‘social aversion network’, namely the dorsolateral prefrontal cortex, which suggests decreased network segregation as a function of anxious attachment. Finally, the extent of subjective experience of friendliness towards the dismissing narrative was predicted by low baseline FC-values between hippocampus and inferior parietal lobule. Taken together, our study demonstrates an activation of networks related to social aversion in terms of increased connectivity after listening to insecure-dismissing attachment narratives. A causal interrelation of brain state changes and subsequent changes in social reactivity was further supported by our observation of direct

  5. On the same wavelength: predictable language enhances speaker-listener brain-to-brain synchrony in posterior superior temporal gyrus.

    Science.gov (United States)

    Dikker, Suzanne; Silbert, Lauren J; Hasson, Uri; Zevin, Jason D

    2014-04-30

    Recent research has shown that the degree to which speakers and listeners exhibit similar brain activity patterns during human linguistic interaction is correlated with communicative success. Here, we used an intersubject correlation approach in fMRI to test the hypothesis that a listener's ability to predict a speaker's utterance increases such neural coupling between speakers and listeners. Nine subjects listened to recordings of a speaker describing visual scenes that varied in the degree to which they permitted specific linguistic predictions. In line with our hypothesis, the temporal profile of listeners' brain activity was significantly more synchronous with the speaker's brain activity for highly predictive contexts in left posterior superior temporal gyrus (pSTG), an area previously associated with predictive auditory language processing. In this region, predictability differentially affected the temporal profiles of brain responses in the speaker and listeners respectively, in turn affecting correlated activity between the two: whereas pSTG activation increased with predictability in the speaker, listeners' pSTG activity instead decreased for more predictable sentences. Listeners additionally showed stronger BOLD responses for predictive images before sentence onset, suggesting that highly predictable contexts lead comprehenders to preactivate predicted words.

  6. Systems Nutrigenomics Reveals Brain Gene Networks Linking Metabolic and Brain Disorders.

    Science.gov (United States)

    Meng, Qingying; Ying, Zhe; Noble, Emily; Zhao, Yuqi; Agrawal, Rahul; Mikhail, Andrew; Zhuang, Yumei; Tyagi, Ethika; Zhang, Qing; Lee, Jae-Hyung; Morselli, Marco; Orozco, Luz; Guo, Weilong; Kilts, Tina M; Zhu, Jun; Zhang, Bin; Pellegrini, Matteo; Xiao, Xinshu; Young, Marian F; Gomez-Pinilla, Fernando; Yang, Xia

    2016-05-01

    Nutrition plays a significant role in the increasing prevalence of metabolic and brain disorders. Here we employ systems nutrigenomics to scrutinize the genomic bases of nutrient-host interaction underlying disease predisposition or therapeutic potential. We conducted transcriptome and epigenome sequencing of hypothalamus (metabolic control) and hippocampus (cognitive processing) from a rodent model of fructose consumption, and identified significant reprogramming of DNA methylation, transcript abundance, alternative splicing, and gene networks governing cell metabolism, cell communication, inflammation, and neuronal signaling. These signals converged with genetic causal risks of metabolic, neurological, and psychiatric disorders revealed in humans. Gene network modeling uncovered the extracellular matrix genes Bgn and Fmod as main orchestrators of the effects of fructose, as validated using two knockout mouse models. We further demonstrate that an omega-3 fatty acid, DHA, reverses the genomic and network perturbations elicited by fructose, providing molecular support for nutritional interventions to counteract diet-induced metabolic and brain disorders. Our integrative approach complementing rodent and human studies supports the applicability of nutrigenomics principles to predict disease susceptibility and to guide personalized medicine. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

  7. Partially Overlapping Brain Networks for Singing and Cello Playing.

    Science.gov (United States)

    Segado, Melanie; Hollinger, Avrum; Thibodeau, Joseph; Penhune, Virginia; Zatorre, Robert J

    2018-01-01

    This research uses an MR-Compatible cello to compare functional brain activation during singing and cello playing within the same individuals to determine the extent to which arbitrary auditory-motor associations, like those required to play the cello, co-opt functional brain networks that evolved for singing. Musical instrument playing and singing both require highly specific associations between sounds and movements. Because these are both used to produce musical sounds, it is often assumed in the literature that their neural underpinnings are highly similar. However, singing is an evolutionarily old human trait, and the auditory-motor associations used for singing are also used for speech and non-speech vocalizations. This sets it apart from the arbitrary auditory-motor associations required to play musical instruments. The pitch range of the cello is similar to that of the human voice, but cello playing is completely independent of the vocal apparatus, and can therefore be used to dissociate the auditory-vocal network from that of the auditory-motor network. While in the MR-Scanner, 11 expert cellists listened to and subsequently produced individual tones either by singing or cello playing. All participants were able to sing and play the target tones in tune (singing in many areas within the auditory-vocal network. These include primary motor, dorsal pre-motor, and supplementary motor cortices (M1, dPMC, SMA),the primary and periprimary auditory cortices within the superior temporal gyrus (STG) including Heschl's gyrus, anterior insula (aINS), anterior cingulate cortex (ACC), and intraparietal sulcus (IPS), and Cerebellum but, notably, exclude the periaqueductal gray (PAG) and basal ganglia (Putamen). Second, we found that activity within the overlapping areas is positively correlated with, and therefore likely contributing to, both singing and playing in tune determined with performance measures. Third, we found that activity in auditory areas is functionally

  8. A Bayesian network meta-analysis of whole brain radiotherapy and stereotactic radiotherapy for brain metastasis.

    Science.gov (United States)

    Yuan, Xi; Liu, Wen-Jie; Li, Bing; Shen, Ze-Tian; Shen, Jun-Shu; Zhu, Xi-Xu

    2017-08-01

    This study was conducted to compare the effects of whole brain radiotherapy (WBRT) and stereotactic radiotherapy (SRS) in treatment of brain metastasis.A systematical retrieval in PubMed and Embase databases was performed for relative literatures on the effects of WBRT and SRS in treatment of brain metastasis. A Bayesian network meta-analysis was performed by using the ADDIS software. The effect sizes included odds ratio (OR) and 95% confidence interval (CI). A random effects model was used for the pooled analysis for all the outcome measures, including 1-year distant control rate, 1-year local control rate, 1-year survival rate, and complication. The consistency was tested by using node-splitting analysis and inconsistency standard deviation. The convergence was estimated according to the Brooks-Gelman-Rubin method.A total of 12 literatures were included in this meta-analysis. WBRT + SRS showed higher 1-year distant control rate than SRS. WBRT + SRS was better for the 1-year local control rate than WBRT. SRS and WBRT + SRS had higher 1-year survival rate than the WBRT. In addition, there was no difference in complication among the three therapies.Comprehensively, WBRT + SRS might be the choice of treatment for brain metastasis.

  9. Language impairments in youths with traumatic brain injury: implications for participation in criminal proceedings.

    Science.gov (United States)

    Wszalek, Joseph A; Turkstra, Lyn S

    2015-01-01

    As many as 30% of incarcerated juveniles have a history of traumatic brain injury (TBI). Moderate or severe TBI is associated with a high risk of impairment in language comprehension and expression, which may have profound effects on juveniles' ability to understand and express themselves in criminal proceedings. In this article, we review common language impairments in youths with TBI and discuss potential effects of these impairments on 3 stages of US criminal proceedings: (1) initial encounter with law enforcement; (2) interrogation and Miranda rights; and (3) competence to undergo trial proceedings. We then describe language assessment tools and procedures that may be helpful in legal contexts. Our aim was to inform clinicians and legal staff working with juvenile defendants with TBI, with the long-term goal of developing empirically based guidelines to ensure that juvenile defendants with TBI can fully and effectively participate in criminal proceedings.

  10. Brain basis of phonological awareness for spoken language in children and its disruption in dyslexia.

    Science.gov (United States)

    Kovelman, Ioulia; Norton, Elizabeth S; Christodoulou, Joanna A; Gaab, Nadine; Lieberman, Daniel A; Triantafyllou, Christina; Wolf, Maryanne; Whitfield-Gabrieli, Susan; Gabrieli, John D E

    2012-04-01

    Phonological awareness, knowledge that speech is composed of syllables and phonemes, is critical for learning to read. Phonological awareness precedes and predicts successful transition from language to literacy, and weakness in phonological awareness is a leading cause of dyslexia, but the brain basis of phonological awareness for spoken language in children is unknown. We used functional magnetic resonance imaging to identify the neural correlates of phonological awareness using an auditory word-rhyming task in children who were typical readers or who had dyslexia (ages 7-13) and a younger group of kindergarteners (ages 5-6). Typically developing children, but not children with dyslexia, recruited left dorsolateral prefrontal cortex (DLPFC) when making explicit phonological judgments. Kindergarteners, who were matched to the older children with dyslexia on standardized tests of phonological awareness, also recruited left DLPFC. Left DLPFC may play a critical role in the development of phonological awareness for spoken language critical for reading and in the etiology of dyslexia.

  11. Bayesian exponential random graph modeling of whole-brain structural networks across lifespan

    OpenAIRE

    Sinke, Michel R T; Dijkhuizen, Rick M; Caimo, Alberto; Stam, Cornelis J; Otte, Wim

    2016-01-01

    Descriptive neural network analyses have provided important insights into the organization of structural and functional networks in the human brain. However, these analyses have limitations for inter-subject or between-group comparisons in which network sizes and edge densities may differ, such as in studies on neurodevelopment or brain diseases. Furthermore, descriptive neural network analyses lack an appropriate generic null model and a unifying framework. These issues may be solved with an...

  12. Can you hear me now? Musical training shapes functional brain networks for selective auditory attention and hearing speech in noise

    Directory of Open Access Journals (Sweden)

    Dana L Strait

    2011-06-01

    Full Text Available Even in the quietest of rooms, our senses are perpetually inundated by a barrage of sounds, requiring the auditory system to adapt to a variety of listening conditions in order to extract signals of interest (e.g., one speaker’s voice amidst others. Brain networks that promote selective attention are thought to sharpen the neural encoding of a target signal, suppressing competing sounds and enhancing perceptual performance. Here, we ask: does musical training benefit cortical mechanisms that underlie selective attention to speech? To answer this question, we assessed the impact of selective auditory attention on cortical auditory-evoked response variability in musicians and nonmusicians. Outcomes indicate strengthened brain networks for selective auditory attention in musicians in that musicians but not nonmusicians demonstrate decreased prefrontal response variability with auditory attention. Results are interpreted in the context of previous work from our laboratory documenting perceptual and subcortical advantages in musicians for the hearing and neural encoding of speech in background noise. Musicians’ neural proficiency for selectively engaging and sustaining auditory attention to language indicates a potential benefit of music for auditory training. Given the importance of auditory attention for the development of language-related skills, musical training may aid in the prevention, habilitation and remediation of children with a wide range of attention-based language and learning impairments.

  13. LANGUAGE COMPETENCE OF STUDENT TOWARD RIGHT HEMISPHER BRAIN FUNCTION : A Neuropragmatic Study

    Directory of Open Access Journals (Sweden)

    Handoko Handoko

    2015-04-01

    Full Text Available It has been known that the right hemisphere is contributed to language processing, especially in macro level, including macrostructure or discourse processing. This research is aimed at evaluating the students’ ability in language processing concerning macrostructure and the right hemispher brain function. This research is based on Dharmaperwira-prins method “Right Hemisphere Communication Assessment” (Pemeriksaan Komunikasi Hemisfer Kanan/PKHK. Research on students’ ability in macrostructure processing is important to conduct since students nowadays are regarded lack of ability in well being communication. The research is conducted toward 38 students of English Department of Andalas University. The data are taken by paper test which is designed to evaluate the students’ ability in macrostructure. The result of research shows that most students have problems in providing important information, adjective, and feeling. By this result, it can be assumed that the participants have problem in right hemisphere competence concerning to language processing. These problems evoke not by accident or lesion in right hemisphere, yet it is caused by brain development which is focused on left hemisphere only. Keyword: Right Hemisphere, Language Assessment, Lexical Semantic, Macrostructure, Pragmatic

  14. Co-Ethnic Network, Social Class, and Heritage Language Maintenance among Chinese Immigrant Families

    Science.gov (United States)

    Zhang, Donghui

    2012-01-01

    This ethnographic study investigated heritage language maintenance among two distinct groups of Chinese immigrant families (Mandarin and Fujianese) from the social network perspective. The results indicated that a co-ethnic network could be a double-edged sword, which works differently on children from different social classes. While the Mandarin…

  15. Modelling the phonotactic structure of natural language words with simple recurrent networks

    NARCIS (Netherlands)

    Stoianov, [No Value; Nerbonne, J; Bouma, H; Coppen, PA; vanHalteren, H; Teunissen, L

    1998-01-01

    Simple Recurrent Networks (SRN) are Neural Network (connectionist) models able to process natural language. Phonotactics concerns the order of symbols in words. We continued an earlier unsuccessful trial to model the phonotactics of Dutch words with SRNs. In order to overcome the previously reported

  16. The brain as a "hyper-network": the key role of neural networks as main producers of the integrated brain actions especially via the "broadcasted" neuroconnectomics.

    Science.gov (United States)

    Agnati, Luigi F; Marcoli, Manuela; Maura, Guido; Woods, Amina; Guidolin, Diego

    2018-06-01

    Investigations of brain complex integrative actions should consider beside neural networks, glial, extracellular molecular, and fluid channels networks. The present paper proposes that all these networks are assembled into the brain hyper-network that has as fundamental components, the tetra-partite synapses, formed by neural, glial, and extracellular molecular networks. Furthermore, peri-synaptic astrocytic processes by modulating the perviousness of extracellular fluid channels control the signals impinging on the tetra-partite synapses. It has also been surmised that global signalling via astrocytes networks and highly pervasive signals, such as electromagnetic fields (EMFs), allow the appropriate integration of the various networks especially at crucial nodes level, the tetra-partite synapses. As a matter of fact, it has been shown that astrocytes can form gap-junction-coupled syncytia allowing intercellular communication characterised by a rapid and possibly long-distance transfer of signals. As far as the EMFs are concerned, the concept of broadcasted neuroconnectomics (BNC) has been introduced to describe highly pervasive signals involved in resetting the information handling of brain networks at various miniaturisation levels. In other words, BNC creates, thanks to the EMFs, generated especially by neurons, different assemblages among the various networks forming the brain hyper-network. Thus, it is surmised that neuronal networks are the "core components" of the brain hyper-network that has as special "nodes" the multi-facet tetra-partite synapses. Furthermore, it is suggested that investigations on the functional plasticity of multi-partite synapses in response to BNC can be the background for a new understanding and perhaps a new modelling of brain morpho-functional organisation and integrative actions.

  17. A Study of Recurrent and Convolutional Neural Networks in the Native Language Identification Task

    KAUST Repository

    Werfelmann, Robert

    2018-05-24

    Native Language Identification (NLI) is the task of predicting the native language of an author from their text written in a second language. The idea is to find writing habits that transfer from an author’s native language to their second language. Many approaches to this task have been studied, from simple word frequency analysis, to analyzing grammatical and spelling mistakes to find patterns and traits that are common between different authors of the same native language. This can be a very complex task, depending on the native language and the proficiency of the author’s second language. The most common approach that has seen very good results is based on the usage of n-gram features of words and characters. In this thesis, we attempt to extract lexical, grammatical, and semantic features from the sentences of non-native English essays using neural networks. The training and testing data was obtained from a large corpus of publicly available essays written by authors of several countries around the world. The neural network models consisted of Long Short-Term Memory and Convolutional networks using the sentences of each document as the input. Additional statistical features were generated from the text to complement the predictions of the neural networks, which were then used as feature inputs to a Support Vector Machine, making the final prediction. Results show that Long Short-Term Memory neural network can improve performance over a naive bag of words approach, but with a much smaller feature set. With more fine-tuning of neural network hyperparameters, these results will likely improve significantly.

  18. Links that speak: the global language network and its association with global fame.

    Science.gov (United States)

    Ronen, Shahar; Gonçalves, Bruno; Hu, Kevin Z; Vespignani, Alessandro; Pinker, Steven; Hidalgo, César A

    2014-12-30

    Languages vary enormously in global importance because of historical, demographic, political, and technological forces. However, beyond simple measures of population and economic power, there has been no rigorous quantitative way to define the global influence of languages. Here we use the structure of the networks connecting multilingual speakers and translated texts, as expressed in book translations, multiple language editions of Wikipedia, and Twitter, to provide a concept of language importance that goes beyond simple economic or demographic measures. We find that the structure of these three global language networks (GLNs) is centered on English as a global hub and around a handful of intermediate hub languages, which include Spanish, German, French, Russian, Portuguese, and Chinese. We validate the measure of a language's centrality in the three GLNs by showing that it exhibits a strong correlation with two independent measures of the number of famous people born in the countries associated with that language. These results suggest that the position of a language in the GLN contributes to the visibility of its speakers and the global popularity of the cultural content they produce.

  19. Mood-dependent integration in discourse comprehension: happy and sad moods affect consistency processing via different brain networks.

    Science.gov (United States)

    Egidi, Giovanna; Caramazza, Alfonso

    2014-12-01

    According to recent research on language comprehension, the semantic features of a text are not the only determinants of whether incoming information is understood as consistent. Listeners' pre-existing affective states play a crucial role as well. The current fMRI experiment examines the effects of happy and sad moods during comprehension of consistent and inconsistent story endings, focusing on brain regions previously linked to two integration processes: inconsistency detection, evident in stronger responses to inconsistent endings, and fluent processing (accumulation), evident in stronger responses to consistent endings. The analysis evaluated whether differences in the BOLD response for consistent and inconsistent story endings correlated with self-reported mood scores after a mood induction procedure. Mood strongly affected regions previously associated with inconsistency detection. Happy mood increased sensitivity to inconsistency in regions specific for inconsistency detection (e.g., left IFG, left STS), whereas sad mood increased sensitivity to inconsistency in regions less specific for language processing (e.g., right med FG, right SFG). Mood affected more weakly regions involved in accumulation of information. These results show that mood can influence activity in areas mediating well-defined language processes, and highlight that integration is the result of context-dependent mechanisms. The finding that language comprehension can involve different networks depending on people's mood highlights the brain's ability to reorganize its functions. Copyright © 2014 Elsevier Inc. All rights reserved.

  20. Driving and driven architectures of directed small-world human brain functional networks.

    Directory of Open Access Journals (Sweden)

    Chaogan Yan

    Full Text Available Recently, increasing attention has been focused on the investigation of the human brain connectome that describes the patterns of structural and functional connectivity networks of the human brain. Many studies of the human connectome have demonstrated that the brain network follows a small-world topology with an intrinsically cohesive modular structure and includes several network hubs in the medial parietal regions. However, most of these studies have only focused on undirected connections between regions in which the directions of information flow are not taken into account. How the brain regions causally influence each other and how the directed network of human brain is topologically organized remain largely unknown. Here, we applied linear multivariate Granger causality analysis (GCA and graph theoretical approaches to a resting-state functional MRI dataset with a large cohort of young healthy participants (n = 86 to explore connectivity patterns of the population-based whole-brain functional directed network. This directed brain network exhibited prominent small-world properties, which obviously improved previous results of functional MRI studies showing weak small-world properties in the directed brain networks in terms of a kernel-based GCA and individual analysis. This brain network also showed significant modular structures associated with 5 well known subsystems: fronto-parietal, visual, paralimbic/limbic, subcortical and primary systems. Importantly, we identified several driving hubs predominantly located in the components of the attentional network (e.g., the inferior frontal gyrus, supplementary motor area, insula and fusiform gyrus and several driven hubs predominantly located in the components of the default mode network (e.g., the precuneus, posterior cingulate gyrus, medial prefrontal cortex and inferior parietal lobule. Further split-half analyses indicated that our results were highly reproducible between two

  1. Development of the brain's structural network efficiency in early adolescence : A longitudinal DTI twin study

    NARCIS (Netherlands)

    Koenis, Marinka M G; Brouwer, Rachel M.; van den Heuvel, Martijn P.; Mandl, René C W; van Soelen, Inge L C; Kahn, René S.; Boomsma, Dorret I.; Hulshoff Pol, Hilleke E.

    2015-01-01

    The brain is a network and our intelligence depends in part on the efficiency of this network. The network of adolescents differs from that of adults suggesting developmental changes. However, whether the network changes over time at the individual level and, if so, how this relates to intelligence,

  2. Hierarchical brain networks active in approach and avoidance goal pursuit

    Directory of Open Access Journals (Sweden)

    Jeffrey Martin Spielberg

    2013-06-01

    Full Text Available Effective approach/avoidance goal pursuit is critical for attaining long-term health and well-being. Research on the neural correlates of key goal pursuit processes (e.g., motivation has long been of interest, with lateralization in prefrontal cortex being a particularly fruitful target of investigation. However, this literature has often been limited by a lack of spatial specificity and has not delineated the precise aspects of approach/avoidance motivation involved. Additionally, the relationships among brain regions (i.e., network connectivity vital to goal pursuit remain largely unexplored. Specificity in location, process, and network relationship is vital for moving beyond gross characterizations of function and identifying the precise cortical mechanisms involved in motivation. The present paper integrates research using more spatially specific methodologies (e.g., functional magnetic resonance imaging with the rich psychological literature on approach/avoidance to propose an integrative network model that takes advantage of the strengths of each of these literatures.

  3. Sign Language Recognition System using Neural Network for Digital Hardware Implementation

    International Nuclear Information System (INIS)

    Vargas, Lorena P; Barba, Leiner; Torres, C O; Mattos, L

    2011-01-01

    This work presents an image pattern recognition system using neural network for the identification of sign language to deaf people. The system has several stored image that show the specific symbol in this kind of language, which is employed to teach a multilayer neural network using a back propagation algorithm. Initially, the images are processed to adapt them and to improve the performance of discriminating of the network, including in this process of filtering, reduction and elimination noise algorithms as well as edge detection. The system is evaluated using the signs without including movement in their representation.

  4. Network dynamics with BrainX(3): a large-scale simulation of the human brain network with real-time interaction.

    Science.gov (United States)

    Arsiwalla, Xerxes D; Zucca, Riccardo; Betella, Alberto; Martinez, Enrique; Dalmazzo, David; Omedas, Pedro; Deco, Gustavo; Verschure, Paul F M J

    2015-01-01

    BrainX(3) is a large-scale simulation of human brain activity with real-time interaction, rendered in 3D in a virtual reality environment, which combines computational power with human intuition for the exploration and analysis of complex dynamical networks. We ground this simulation on structural connectivity obtained from diffusion spectrum imaging data and model it on neuronal population dynamics. Users can interact with BrainX(3) in real-time by perturbing brain regions with transient stimulations to observe reverberating network activity, simulate lesion dynamics or implement network analysis functions from a library of graph theoretic measures. BrainX(3) can thus be used as a novel immersive platform for exploration and analysis of dynamical activity patterns in brain networks, both at rest or in a task-related state, for discovery of signaling pathways associated to brain function and/or dysfunction and as a tool for virtual neurosurgery. Our results demonstrate these functionalities and shed insight on the dynamics of the resting-state attractor. Specifically, we found that a noisy network seems to favor a low firing attractor state. We also found that the dynamics of a noisy network is less resilient to lesions. Our simulations on TMS perturbations show that even though TMS inhibits most of the network, it also sparsely excites a few regions. This is presumably due to anti-correlations in the dynamics and suggests that even a lesioned network can show sparsely distributed increased activity compared to healthy resting-state, over specific brain areas.

  5. Network dynamics with BrainX3: a large-scale simulation of the human brain network with real-time interaction

    Science.gov (United States)

    Arsiwalla, Xerxes D.; Zucca, Riccardo; Betella, Alberto; Martinez, Enrique; Dalmazzo, David; Omedas, Pedro; Deco, Gustavo; Verschure, Paul F. M. J.

    2015-01-01

    BrainX3 is a large-scale simulation of human brain activity with real-time interaction, rendered in 3D in a virtual reality environment, which combines computational power with human intuition for the exploration and analysis of complex dynamical networks. We ground this simulation on structural connectivity obtained from diffusion spectrum imaging data and model it on neuronal population dynamics. Users can interact with BrainX3 in real-time by perturbing brain regions with transient stimulations to observe reverberating network activity, simulate lesion dynamics or implement network analysis functions from a library of graph theoretic measures. BrainX3 can thus be used as a novel immersive platform for exploration and analysis of dynamical activity patterns in brain networks, both at rest or in a task-related state, for discovery of signaling pathways associated to brain function and/or dysfunction and as a tool for virtual neurosurgery. Our results demonstrate these functionalities and shed insight on the dynamics of the resting-state attractor. Specifically, we found that a noisy network seems to favor a low firing attractor state. We also found that the dynamics of a noisy network is less resilient to lesions. Our simulations on TMS perturbations show that even though TMS inhibits most of the network, it also sparsely excites a few regions. This is presumably due to anti-correlations in the dynamics and suggests that even a lesioned network can show sparsely distributed increased activity compared to healthy resting-state, over specific brain areas. PMID:25759649

  6. Network Dynamics with BrainX3: A Large-Scale Simulation of the Human Brain Network with Real-Time Interaction

    Directory of Open Access Journals (Sweden)

    Xerxes D. Arsiwalla

    2015-02-01

    Full Text Available BrainX3 is a large-scale simulation of human brain activity with real-time interaction, rendered in 3D in a virtual reality environment, which combines computational power with human intuition for the exploration and analysis of complex dynamical networks. We ground this simulation on structural connectivity obtained from diffusion spectrum imaging data and model it on neuronal population dynamics. Users can interact with BrainX3 in real-time by perturbing brain regions with transient stimulations to observe reverberating network activity, simulate lesion dynamics or implement network analysis functions from a library of graph theoretic measures. BrainX3 can thus be used as a novel immersive platform for real-time exploration and analysis of dynamical activity patterns in brain networks, both at rest or in a task-related state, for discovery of signaling pathways associated to brain function and/or dysfunction and as a tool for virtual neurosurgery. Our results demonstrate these functionalities and shed insight on the dynamics of the resting-state attractor. Specifically, we found that a noisy network seems to favor a low firing attractor state. We also found that the dynamics of a noisy network is less resilient to lesions. Our simulations on TMS perturbations show that even though TMS inhibits most of the network, it also sparsely excites a few regions. This is presumably, due to anti-correlations in the dynamics and suggests that even a lesioned network can show sparsely distributed increased activity compared to healthy resting-state, over specific brain areas.

  7. Graph theoretical analysis of functional network for comprehension of sign language.

    Science.gov (United States)

    Liu, Lanfang; Yan, Xin; Liu, Jin; Xia, Mingrui; Lu, Chunming; Emmorey, Karen; Chu, Mingyuan; Ding, Guosheng

    2017-09-15

    Signed languages are natural human languages using the visual-motor modality. Previous neuroimaging studies based on univariate activation analysis show that a widely overlapped cortical network is recruited regardless whether the sign language is comprehended (for signers) or not (for non-signers). Here we move beyond previous studies by examining whether the functional connectivity profiles and the underlying organizational structure of the overlapped neural network may differ between signers and non-signers when watching sign language. Using graph theoretical analysis (GTA) and fMRI, we compared the large-scale functional network organization in hearing signers with non-signers during the observation of sentences in Chinese Sign Language. We found that signed sentences elicited highly similar cortical activations in the two groups of participants, with slightly larger responses within the left frontal and left temporal gyrus in signers than in non-signers. Crucially, further GTA revealed substantial group differences in the topologies of this activation network. Globally, the network engaged by signers showed higher local efficiency (t (24) =2.379, p=0.026), small-worldness (t (24) =2.604, p=0.016) and modularity (t (24) =3.513, p=0.002), and exhibited different modular structures, compared to the network engaged by non-signers. Locally, the left ventral pars opercularis served as a network hub in the signer group but not in the non-signer group. These findings suggest that, despite overlap in cortical activation, the neural substrates underlying sign language comprehension are distinguishable at the network level from those for the processing of gestural action. Copyright © 2017 Elsevier B.V. All rights reserved.

  8. Selective vulnerability related to aging in large-scale resting brain networks.

    Science.gov (United States)

    Zhang, Hong-Ying; Chen, Wen-Xin; Jiao, Yun; Xu, Yao; Zhang, Xiang-Rong; Wu, Jing-Tao

    2014-01-01

    Normal aging is associated with cognitive decline. Evidence indicates that large-scale brain networks are affected by aging; however, it has not been established whether aging has equivalent effects on specific large-scale networks. In the present study, 40 healthy subjects including 22 older (aged 60-80 years) and 18 younger (aged 22-33 years) adults underwent resting-state functional MRI scanning. Four canonical resting-state networks, including the default mode network (DMN), executive control network (ECN), dorsal attention network (DAN) and salience network, were extracted, and the functional connectivities in these canonical networks were compared between the younger and older groups. We found distinct, disruptive alterations present in the large-scale aging-related resting brain networks: the ECN was affected the most, followed by the DAN. However, the DMN and salience networks showed limited functional connectivity disruption. The visual network served as a control and was similarly preserved in both groups. Our findings suggest that the aged brain is characterized by selective vulnerability in large-scale brain networks. These results could help improve our understanding of the mechanism of degeneration in the aging brain. Additional work is warranted to determine whether selective alterations in the intrinsic networks are related to impairments in behavioral performance.

  9. The structural and functional brain networks that support human social networks.

    Science.gov (United States)

    Noonan, M P; Mars, R B; Sallet, J; Dunbar, R I M; Fellows, L K

    2018-02-20

    Social skills rely on a specific set of cognitive processes, raising the possibility that individual differences in social networks are related to differences in specific brain structural and functional networks. Here, we tested this hypothesis with multimodality neuroimaging. With diffusion MRI (DMRI), we showed that differences in structural integrity of particular white matter (WM) tracts, including cingulum bundle, extreme capsule and arcuate fasciculus were associated with an individual's social network size (SNS). A voxel-based morphology analysis demonstrated correlations between gray matter (GM) volume and SNS in limbic and temporal lobe regions. These structural changes co-occured with functional network differences. As a function of SNS, dorsomedial and dorsolateral prefrontal cortex showed altered resting-state functional connectivity with the default mode network (DMN). Finally, we integrated these three complementary methods, interrogating the relationship between social GM clusters and specific WM and resting-state networks (RSNs). Probabilistic tractography seeded in these GM nodes utilized the SNS-related WM pathways. Further, the spatial and functional overlap between the social GM clusters and the DMN was significantly closer than other control RSNs. These integrative analyses provide convergent evidence of the role of specific circuits in SNS, likely supporting the adaptive behavior necessary for success in extensive social environments. Crown Copyright © 2018. Published by Elsevier B.V. All rights reserved.

  10. Brain-computer interface with language model-electroencephalography fusion for locked-in syndrome.

    Science.gov (United States)

    Oken, Barry S; Orhan, Umut; Roark, Brian; Erdogmus, Deniz; Fowler, Andrew; Mooney, Aimee; Peters, Betts; Miller, Meghan; Fried-Oken, Melanie B

    2014-05-01

    Some noninvasive brain-computer interface (BCI) systems are currently available for locked-in syndrome (LIS) but none have incorporated a statistical language model during text generation. To begin to address the communication needs of individuals with LIS using a noninvasive BCI that involves rapid serial visual presentation (RSVP) of symbols and a unique classifier with electroencephalography (EEG) and language model fusion. The RSVP Keyboard was developed with several unique features. Individual letters are presented at 2.5 per second. Computer classification of letters as targets or nontargets based on EEG is performed using machine learning that incorporates a language model for letter prediction via Bayesian fusion enabling targets to be presented only 1 to 4 times. Nine participants with LIS and 9 healthy controls were enrolled. After screening, subjects first calibrated the system, and then completed a series of balanced word generation mastery tasks that were designed with 5 incremental levels of difficulty, which increased by selecting phrases for which the utility of the language model decreased naturally. Six participants with LIS and 9 controls completed the experiment. All LIS participants successfully mastered spelling at level 1 and one subject achieved level 5. Six of 9 control participants achieved level 5. Individuals who have incomplete LIS may benefit from an EEG-based BCI system, which relies on EEG classification and a statistical language model. Steps to further improve the system are discussed.

  11. The application of graph theoretical analysis to complex networks in the brain.

    Science.gov (United States)

    Reijneveld, Jaap C; Ponten, Sophie C; Berendse, Henk W; Stam, Cornelis J

    2007-11-01

    Considering the brain as a complex network of interacting dynamical systems offers new insights into higher level brain processes such as memory, planning, and abstract reasoning as well as various types of brain pathophysiology. This viewpoint provides the opportunity to apply new insights in network sciences, such as the discovery of small world and scale free networks, to data on anatomical and functional connectivity in the brain. In this review we start with some background knowledge on the history and recent advances in network theories in general. We emphasize the correlation between the structural properties of networks and the dynamics of these networks. We subsequently demonstrate through evidence from computational studies, in vivo experiments, and functional MRI, EEG and MEG studies in humans, that both the functional and anatomical connectivity of the healthy brain have many features of a small world network, but only to a limited extent of a scale free network. The small world structure of neural networks is hypothesized to reflect an optimal configuration associated with rapid synchronization and information transfer, minimal wiring costs, resilience to certain types of damage, as well as a balance between local processing and global integration. Eventually, we review the current knowledge on the effects of focal and diffuse brain disease on neural network characteristics, and demonstrate increasing evidence that both cognitive and psychiatric disturbances, as well as risk of epileptic seizures, are correlated with (changes in) functional network architectural features.

  12. Hierarchical anatomical brain networks for MCI prediction: revisiting volumetric measures.

    Directory of Open Access Journals (Sweden)

    Luping Zhou

    Full Text Available Owning to its clinical accessibility, T1-weighted MRI (Magnetic Resonance Imaging has been extensively studied in the past decades for prediction of Alzheimer's disease (AD and mild cognitive impairment (MCI. The volumes of gray matter (GM, white matter (WM and cerebrospinal fluid (CSF are the most commonly used measurements, resulting in many successful applications. It has been widely observed that disease-induced structural changes may not occur at isolated spots, but in several inter-related regions. Therefore, for better characterization of brain pathology, we propose in this paper a means to extract inter-regional correlation based features from local volumetric measurements. Specifically, our approach involves constructing an anatomical brain network for each subject, with each node representing a Region of Interest (ROI and each edge representing Pearson correlation of tissue volumetric measurements between ROI pairs. As second order volumetric measurements, network features are more descriptive but also more sensitive to noise. To overcome this limitation, a hierarchy of ROIs is used to suppress noise at different scales. Pairwise interactions are considered not only for ROIs with the same scale in the same layer of the hierarchy, but also for ROIs across different scales in different layers. To address the high dimensionality problem resulting from the large number of network features, a supervised dimensionality reduction method is further employed to embed a selected subset of features into a low dimensional feature space, while at the same time preserving discriminative information. We demonstrate with experimental results the efficacy of this embedding strategy in comparison with some other commonly used approaches. In addition, although the proposed method can be easily generalized to incorporate other metrics of regional similarities, the benefits of using Pearson correlation in our application are reinforced by the experimental

  13. Severe Traumatic Brain Injury, Frontal Lesions, and Social Aspects of Language Use: A Study of French-Speaking Adults

    Science.gov (United States)

    Dardier, Virginie; Bernicot, Josie; Delanoe, Anaig; Vanberten, Melanie; Fayada, Catherine; Chevignard, Mathilde; Delaye, Corinne; Laurent-Vannier, Anne; Dubois, Bruno

    2011-01-01

    The purpose of this study was to gain insight into the social (pragmatic) aspects of language use by French-speaking individuals with frontal lesions following a severe traumatic brain injury. Eleven participants with traumatic brain injury performed tasks in three areas of communication: production (interview situation), comprehension (direct…

  14. The application of graph theoretical analysis to complex networks in the brain

    NARCIS (Netherlands)

    Reijneveld, Jaap C.; Ponten, Sophie C.; Berendse, Henk W.; Stam, Cornelis J.

    2007-01-01

    Considering the brain as a complex network of interacting dynamical systems offers new insights into higher level brain processes such as memory, planning, and abstract reasoning as well as various types of brain pathophysiology. This viewpoint provides the opportunity to apply new insights in

  15. Networked neuroscience : brain scans and visual knowing at the intersection of atlases and databases

    NARCIS (Netherlands)

    Beaulieu, Anne; de Rijcke, Sarah; Coopmans, Catelijne; Woolgar, Steve

    2014-01-01

    This chapter discusses the development of authoritative collections of brain scans known as “brain atlases”, focusing in particular on how such scans are constituted as authoritative visual objects. Three dimensions are identified: first, brain scans are parts of suites of networked technologies

  16. Links that speak: The global language network and its association with global fame

    Science.gov (United States)

    Ronen, Shahar; Gonçalves, Bruno; Hu, Kevin Z.; Vespignani, Alessandro; Pinker, Steven; Hidalgo, César A.

    2014-01-01

    Languages vary enormously in global importance because of historical, demographic, political, and technological forces. However, beyond simple measures of population and economic power, there has been no rigorous quantitative way to define the global influence of languages. Here we use the structure of the networks connecting multilingual speakers and translated texts, as expressed in book translations, multiple language editions of Wikipedia, and Twitter, to provide a concept of language importance that goes beyond simple economic or demographic measures. We find that the structure of these three global language networks (GLNs) is centered on English as a global hub and around a handful of intermediate hub languages, which include Spanish, German, French, Russian, Portuguese, and Chinese. We validate the measure of a language’s centrality in the three GLNs by showing that it exhibits a strong correlation with two independent measures of the number of famous people born in the countries associated with that language. These results suggest that the position of a language in the GLN contributes to the visibility of its speakers and the global popularity of the cultural content they produce. PMID:25512502

  17. MDD diagnosis based on partial-brain functional connection network

    Science.gov (United States)

    Yan, Gaoliang; Hu, Hailong; Zhao, Xiang; Zhang, Lin; Qu, Zehui; Li, Yantao

    2018-04-01

    Artificial intelligence (AI) is a hotspot in computer science research nowadays. To apply AI technology in all industries has been the developing direction for researchers. Major depressive disorder (MDD) is a common disease of serious mental disorders. The World Health Organization (WHO) reports that MDD is projected to become the second most common cause of death and disability by 2020. At present, the way of MDD diagnosis is single. Applying AI technology to MDD diagnosis and pathophysiological research will speed up the MDD research and improve the efficiency of MDD diagnosis. In this study, we select the higher degree of brain network functional connectivity by statistical methods. And our experiments show that the average accuracy of Logistic Regression (LR) classifier using feature filtering reaches 88.48%. Compared with other classification methods, both the efficiency and accuracy of this method are improved, which will greatly improve the process of MDD diagnose. In these experiments, we also define the brain regions associated with MDD, which plays a vital role in MDD pathophysiological research.

  18. Memory networks in tinnitus: a functional brain image study.

    Directory of Open Access Journals (Sweden)

    Maura Regina Laureano

    Full Text Available Tinnitus is characterized by the perception of sound in the absence of an external auditory stimulus. The network connectivity of auditory and non-auditory brain structures associated with emotion, memory and attention are functionally altered in debilitating tinnitus. Current studies suggest that tinnitus results from neuroplastic changes in the frontal and limbic temporal regions. The objective of this study was to use Single-Photon Emission Computed Tomography (SPECT to evaluate changes in the cerebral blood flow in tinnitus patients with normal hearing compared with healthy controls.Twenty tinnitus patients with normal hearing and 17 healthy controls, matched for sex, age and years of education, were subjected to Single Photon Emission Computed Tomography using the radiotracer ethylenedicysteine diethyl ester, labeled with Technetium 99 m (99 mTc-ECD SPECT. The severity of tinnitus was assessed using the "Tinnitus Handicap Inventory" (THI. The images were processed and analyzed using "Statistical Parametric Mapping" (SPM8.A significant increase in cerebral perfusion in the left parahippocampal gyrus (pFWE <0.05 was observed in patients with tinnitus compared with healthy controls. The average total THI score was 50.8+18.24, classified as moderate tinnitus.It was possible to identify significant changes in the limbic system of the brain perfusion in tinnitus patients with normal hearing, suggesting that central mechanisms, not specific to the auditory pathway, are involved in the pathophysiology of symptoms, even in the absence of clinically diagnosed peripheral changes.

  19. Brain network dynamics in the human articulatory loop.

    Science.gov (United States)

    Nishida, Masaaki; Korzeniewska, Anna; Crone, Nathan E; Toyoda, Goichiro; Nakai, Yasuo; Ofen, Noa; Brown, Erik C; Asano, Eishi

    2017-08-01

    The articulatory loop is a fundamental component of language function, involved in the short-term buffer of auditory information followed by its vocal reproduction. We characterized the network dynamics of the human articulatory loop, using invasive recording and stimulation. We measured high-gamma activity 70-110 Hz recorded intracranially when patients with epilepsy either only listened to, or listened to and then reproduced two successive tones by humming. We also conducted network analyses, and analyzed behavioral responses to cortical stimulation. Presentation of the initial tone elicited high-gamma augmentation bilaterally in the superior-temporal gyrus (STG) within 40ms, and in the precentral and inferior-frontal gyri (PCG and IFG) within 160ms after sound onset. During presentation of the second tone, high-gamma augmentation was reduced in STG but enhanced in IFG. The task requiring tone reproduction further enhanced high-gamma augmentation in PCG during and after sound presentation. Event-related causality (ERC) analysis revealed dominant flows within STG immediately after sound onset, followed by reciprocal interactions involving PCG and IFG. Measurement of cortico-cortical evoked-potentials (CCEPs) confirmed connectivity between distant high-gamma sites in the articulatory loop. High-frequency stimulation of precentral high-gamma sites in either hemisphere induced speech arrest, inability to control vocalization, or forced vocalization. Vocalization of tones was accompanied by high-gamma augmentation over larger extents of PCG. Bilateral PCG rapidly and directly receives feed-forward signals from STG, and may promptly initiate motor planning including sub-vocal rehearsal for short-term buffering of auditory stimuli. Enhanced high-gamma augmentation in IFG during presentation of the second tone may reflect high-order processing of the tone sequence. The articulatory loop employs sustained reciprocal propagation of neural activity across a network of

  20. Social networking sites use and the morphology of a social-semantic brain network.

    Science.gov (United States)

    Turel, Ofir; He, Qinghua; Brevers, Damien; Bechara, Antoine

    2017-09-30

    Social lives have shifted, at least in part, for large portions of the population to social networking sites. How such lifestyle changes may be associated with brain structures is still largely unknown. In this manuscript, we describe two preliminary studies aimed at exploring this issue. The first study (n = 276) showed that Facebook users reported on increased social-semantic and mentalizing demands, and that such increases were positively associated with people's level of Facebook use. The second study (n = 33) theorized on and examined likely anatomical correlates of such changes in demands on the brain. Findings indicated that the grey matter volumes of the posterior parts of the bilateral middle and superior temporal, and left fusiform gyri were positively associated with the level of Facebook use. These results provided preliminary evidence that grey matter volumes of brain structures involved in social-semantic and mentalizing tasks may be linked to the extent of social networking sites use.

  1. Time Series Neural Network Model for Part-of-Speech Tagging Indonesian Language

    Science.gov (United States)

    Tanadi, Theo

    2018-03-01

    Part-of-speech tagging (POS tagging) is an important part in natural language processing. Many methods have been used to do this task, including neural network. This paper models a neural network that attempts to do POS tagging. A time series neural network is modelled to solve the problems that a basic neural network faces when attempting to do POS tagging. In order to enable the neural network to have text data input, the text data will get clustered first using Brown Clustering, resulting a binary dictionary that the neural network can use. To further the accuracy of the neural network, other features such as the POS tag, suffix, and affix of previous words would also be fed to the neural network.

  2. Isointense infant brain MRI segmentation with a dilated convolutional neural network

    OpenAIRE

    Moeskops, Pim; Pluim, Josien P. W.

    2017-01-01

    Quantitative analysis of brain MRI at the age of 6 months is difficult because of the limited contrast between white matter and gray matter. In this study, we use a dilated triplanar convolutional neural network in combination with a non-dilated 3D convolutional neural network for the segmentation of white matter, gray matter and cerebrospinal fluid in infant brain MR images, as provided by the MICCAI grand challenge on 6-month infant brain MRI segmentation.

  3. Handedness- and Brain Size-Related Efficiency Differences in Small-World Brain Networks: A Resting-State Functional Magnetic Resonance Imaging Study

    OpenAIRE

    Li, Meiling; Wang, Junping; Liu, Feng; Chen, Heng; Lu, Fengmei; Wu, Guorong; Yu, Chunshui; Chen, Huafu

    2015-01-01

    The human brain has been described as a complex network, which integrates information with high efficiency. However, the relationships between the efficiency of human brain functional networks and handedness and brain size remain unclear. Twenty-one left-handed and 32 right-handed healthy subjects underwent a resting-state functional magnetic resonance imaging scan. The whole brain functional networks were constructed by thresholding Pearson correlation matrices of 90 cortical and subcortical...

  4. Correlation between language function and the left arcuate fasciculus detected by diffusion tensor imaging tractography after brain tumor surgery.

    Science.gov (United States)

    Hayashi, Yutaka; Kinoshita, Masashi; Nakada, Mitsutoshi; Hamada, Jun-ichiro

    2012-11-01

    Disturbance of the arcuate fasciculus in the dominant hemisphere is thought to be associated with language-processing disorders, including conduction aphasia. Although the arcuate fasciculus can be visualized in vivo with diffusion tensor imaging (DTI) tractography, its involvement in functional processes associated with language has not been shown dynamically using DTI tractography. In the present study, to clarify the participation of the arcuate fasciculus in language functions, postoperative changes in the arcuate fasciculus detected by DTI tractography were evaluated chronologically in relation to postoperative changes in language function after brain tumor surgery. Preoperative and postoperative arcuate fasciculus area and language function were examined in 7 right-handed patients with a brain tumor in the left hemisphere located in proximity to part of the arcuate fasciculus. The arcuate fasciculus was depicted, and its area was calculated using DTI tractography. Language functions were measured using the Western Aphasia Battery (WAB). After tumor resection, visualization of the arcuate fasciculus was increased in 5 of the 7 patients, and the total WAB score improved in 6 of the 7 patients. The relative ratio of postoperative visualized area of the arcuate fasciculus to preoperative visualized area of the arcuate fasciculus was increased in association with an improvement in postoperative language function (p = 0.0039). The role of the left arcuate fasciculus in language functions can be evaluated chronologically in vivo by DTI tractography after brain tumor surgery. Because increased postoperative visualization of the fasciculus was significantly associated with postoperative improvement in language functions, the arcuate fasciculus may play an important role in language function, as previously thought. In addition, postoperative changes in the arcuate fasciculus detected by DTI tractography could represent a predicting factor for postoperative language

  5. Intra-operative multi-site stimulation: Expanding methodology for cortical brain mapping of language functions.

    Science.gov (United States)

    Gonen, Tal; Gazit, Tomer; Korn, Akiva; Kirschner, Adi; Perry, Daniella; Hendler, Talma; Ram, Zvi

    2017-01-01

    Direct cortical stimulation (DCS) is considered the gold-standard for functional cortical mapping during awake surgery for brain tumor resection. DCS is performed by stimulating one local cortical area at a time. We present a feasibility study using an intra-operative technique aimed at improving our ability to map brain functions which rely on activity in distributed cortical regions. Following standard DCS, Multi-Site Stimulation (MSS) was performed in 15 patients by applying simultaneous cortical stimulations at multiple locations. Language functioning was chosen as a case-cognitive domain due to its relatively well-known cortical organization. MSS, performed at sites that did not produce disruption when applied in a single stimulation point, revealed additional language dysfunction in 73% of the patients. Functional regions identified by this technique were presumed to be significant to language circuitry and were spared during surgery. No new neurological deficits were observed in any of the patients following surgery. Though the neuro-electrical effects of MSS need further investigation, this feasibility study may provide a first step towards sophistication of intra-operative cortical mapping.

  6. fMRI for mapping language networks in neurosurgical cases

    International Nuclear Information System (INIS)

    Gupta, Santosh S

    2014-01-01

    Evaluating language has been a long-standing application in functional magnetic resonance imaging (fMRI) studies, both in research and clinical circumstances, and still provides challenges. Localization of eloquent areas is important in neurosurgical cases, so that there is least possible damage to these areas during surgery, maintaining their function postoperatively, therefore providing good quality of life to the patient. Preoperative fMRI study is a non-invasive tool to localize the eloquent areas, including language, with other traditional methods generally used being invasive and at times perilous. In this article, we describe methods and various paradigms to study the language areas, in clinical neurosurgical cases, along with illustrations of cases from our institute

  7. Aberrant functional connectivity between motor and language networks in rolandic epilepsy.

    Science.gov (United States)

    Besseling, René M H; Overvliet, Geke M; Jansen, Jacobus F A; van der Kruijs, Sylvie J M; Vles, Johannes S H; Ebus, Saskia C M; Hofman, Paul A M; de Louw, Anton J A; Aldenkamp, Albert P; Backes, Walter H

    2013-12-01

    Rolandic epilepsy (RE) is an idiopathic focal childhood epilepsy with a well-established neuropsychological profile of language impairment. The aim of this study is to provide a functional correlate that links rolandic (sensorimotor) pathology to language problems using functional MRI. Twenty-three children with RE (8-14 years old) and 21 matched controls underwent extensive language assessment (Clinical Evaluation of Language Fundamentals). fMRI was performed at rest and using word generation, reading, and finger tapping paradigms. Since no activation group differences were found, regions of interest (ROIs) were defined at pooled (patients and controls combined) activation maxima and in contralateral homotopic cortex, and used to assess language lateralization as well as for a resting-state connectivity analysis. Furthermore, the association between connection strength and language performance was investigated. Reduced language performance was found in the children with RE. Bilateral activation was found for both language tasks with some predominance of the left hemisphere in both groups. Compared to controls, patient connectivity was decreased between the left sensorimotor area and right inferior frontal gyrus (planguage scores in the patient group (r=0.49, p=0.02), but not in the controls. Language laterality analysis revealed bilateral language representation in the age range under study (8-14 years). As a consequence, the connection of reduced functional connectivity we found represents an impaired interplay between motor and language networks, and aberrant functional connectivity associated with poorer language performance. These findings provide a first neuronal correlate in terms of aberrant resting-state functional connectivity for language impairment in RE. Copyright © 2013 Elsevier B.V. All rights reserved.

  8. The working memory networks of the human brain.

    Science.gov (United States)

    Linden, David E J

    2007-06-01

    Working memory and short-term memory are closely related in their cognitive architecture, capacity limitations, and functional neuroanatomy, which only partly overlap with those of long-term memory. The author reviews the functional neuroimaging literature on the commonalities and differences between working memory and short-term memory and the interplay of areas with modality-specific and supramodal representations in the brain networks supporting these fundamental cognitive processes. Sensory stores in the visual, auditory, and somatosensory cortex play a role in short-term memory, but supramodal parietal and frontal areas are often recruited as well. Classical working memory operations such as manipulation, protection against interference, or updating almost certainly require at least some degree of prefrontal support, but many pure maintenance tasks involve these areas as well. Although it seems that activity shifts from more posterior regions during encoding to more anterior regions during delay, some studies reported sustained delay activity in sensory areas as well. This spatiotemporal complexity of the short-term memory/working memory networks is mirrored in the activation patterns that may explain capacity constraints, which, although most prominent in the parietal cortex, seem to be pervasive across sensory and premotor areas. Finally, the author highlights open questions for cognitive neuroscience research of working memory, such as that of the mechanisms for integrating different types of content (binding) or those providing the link to long-term memory.

  9. A Language-Based Approach To Wireless Sensor Network Security

    Science.gov (United States)

    2014-03-06

    128 – RPC 119 7.0 Secure RPC 87 32.0 Figure 1: SpartanRPC Memory Overhead (L) and Impact on Messaging (R) Figure 2: Scalaness /nesT Compilation and...language for developing real WSN applica- tions. This language, called Scalaness /nesT, extends Scala with staging features for executing programs on hubs...particular note here is the fact that cross-stage type safety of Scalaness source code ensures that compiled bytecode can be deployed to, and run on

  10. Automatic recognition of holistic functional brain networks using iteratively optimized convolutional neural networks (IO-CNN) with weak label initialization.

    Science.gov (United States)

    Zhao, Yu; Ge, Fangfei; Liu, Tianming

    2018-07-01

    fMRI data decomposition techniques have advanced significantly from shallow models such as Independent Component Analysis (ICA) and Sparse Coding and Dictionary Learning (SCDL) to deep learning models such Deep Belief Networks (DBN) and Convolutional Autoencoder (DCAE). However, interpretations of those decomposed networks are still open questions due to the lack of functional brain atlases, no correspondence across decomposed or reconstructed networks across different subjects, and significant individual variabilities. Recent studies showed that deep learning, especially deep convolutional neural networks (CNN), has extraordinary ability of accommodating spatial object patterns, e.g., our recent works using 3D CNN for fMRI-derived network classifications achieved high accuracy with a remarkable tolerance for mistakenly labelled training brain networks. However, the training data preparation is one of the biggest obstacles in these supervised deep learning models for functional brain network map recognitions, since manual labelling requires tedious and time-consuming labours which will sometimes even introduce label mistakes. Especially for mapping functional networks in large scale datasets such as hundreds of thousands of brain networks used in this paper, the manual labelling method will become almost infeasible. In response, in this work, we tackled both the network recognition and training data labelling tasks by proposing a new iteratively optimized deep learning CNN (IO-CNN) framework with an automatic weak label initialization, which enables the functional brain networks recognition task to a fully automatic large-scale classification procedure. Our extensive experiments based on ABIDE-II 1099 brains' fMRI data showed the great promise of our IO-CNN framework. Copyright © 2018 Elsevier B.V. All rights reserved.

  11. The Segregation and Integration of Distinct Brain Networks and Their Relationship to Cognition.

    Science.gov (United States)

    Cohen, Jessica R; D'Esposito, Mark

    2016-11-30

    A critical feature of the human brain that gives rise to complex cognition is its ability to reconfigure its network structure dynamically and adaptively in response to the environment. Existing research probing task-related reconfiguration of brain network structure has concluded that, although there are many similarities in network structure during an intrinsic, resting state and during the performance of a variety of cognitive tasks, there are meaningful differences as well. In this study, we related intrinsic, resting state network organization to reconfigured network organization during the performance of two tasks: a sequence tapping task, which is thought to probe motor execution and likely engages a single brain network, and an n-back task, which is thought to probe working memory and likely requires coordination across multiple networks. We implemented graph theoretical analyses using functional connectivity data from fMRI scans to calculate whole-brain measures of network organization in healthy young adults. We focused on quantifying measures of network segregation (modularity, system segregation, local efficiency, number of provincial hub nodes) and measures of network integration (global efficiency, number of connector hub nodes). Using these measures, we found converging evidence that local, within-network communication is critical for motor execution, whereas integrative, between-network communication is critical for working memory. These results confirm that the human brain has the remarkable ability to reconfigure its large-scale organization dynamically in response to current cognitive demands and that interpreting reconfiguration in terms of network segregation and integration may shed light on the optimal network structures underlying successful cognition. The dynamic nature of the human brain gives rise to the wide range of behaviors and cognition of which humans are capable. We collected fMRI data from healthy young adults and measured large

  12. Is functional integration of resting state brain networks an unspecific biomarker for working memory performance?

    Science.gov (United States)

    Alavash, Mohsen; Doebler, Philipp; Holling, Heinz; Thiel, Christiane M; Gießing, Carsten

    2015-03-01

    Is there one optimal topology of functional brain networks at rest from which our cognitive performance would profit? Previous studies suggest that functional integration of resting state brain networks is an important biomarker for cognitive performance. However, it is still unknown whether higher network integration is an unspecific predictor for good cognitive performance or, alternatively, whether specific network organization during rest predicts only specific cognitive abilities. Here, we investigated the relationship between network integration at rest and cognitive performance using two tasks that measured different aspects of working memory; one task assessed visual-spatial and the other numerical working memory. Network clustering, modularity and efficiency were computed to capture network integration on different levels of network organization, and to statistically compare their correlations with the performance in each working memory test. The results revealed that each working memory aspect profits from a different resting state topology, and the tests showed significantly different correlations with each of the measures of network integration. While higher global network integration and modularity predicted significantly better performance in visual-spatial working memory, both measures showed no significant correlation with numerical working memory performance. In contrast, numerical working memory was superior in subjects with highly clustered brain networks, predominantly in the intraparietal sulcus, a core brain region of the working memory network. Our findings suggest that a specific balance between local and global functional integration of resting state brain networks facilitates special aspects of cognitive performance. In the context of working memory, while visual-spatial performance is facilitated by globally integrated functional resting state brain networks, numerical working memory profits from increased capacities for local processing

  13. A Comparative Study of Theoretical Graph Models for Characterizing Structural Networks of Human Brain

    Directory of Open Access Journals (Sweden)

    Xiaojin Li

    2013-01-01

    Full Text Available Previous studies have investigated both structural and functional brain networks via graph-theoretical methods. However, there is an important issue that has not been adequately discussed before: what is the optimal theoretical graph model for describing the structural networks of human brain? In this paper, we perform a comparative study to address this problem. Firstly, large-scale cortical regions of interest (ROIs are localized by recently developed and validated brain reference system named Dense Individualized Common Connectivity-based Cortical Landmarks (DICCCOL to address the limitations in the identification of the brain network ROIs in previous studies. Then, we construct structural brain networks based on diffusion tensor imaging (DTI data. Afterwards, the global and local graph properties of the constructed structural brain networks are measured using the state-of-the-art graph analysis algorithms and tools and are further compared with seven popular theoretical graph models. In addition, we compare the topological properties between two graph models, namely, stickiness-index-based model (STICKY and scale-free gene duplication model (SF-GD, that have higher similarity with the real structural brain networks in terms of global and local graph properties. Our experimental results suggest that among the seven theoretical graph models compared in this study, STICKY and SF-GD models have better performances in characterizing the structural human brain network.

  14. A study of brain networks associated with swallowing using graph-theoretical approaches.

    Directory of Open Access Journals (Sweden)

    Bo Luan

    Full Text Available Functional connectivity between brain regions during swallowing tasks is still not well understood. Understanding these complex interactions is of great interest from both a scientific and a clinical perspective. In this study, functional magnetic resonance imaging (fMRI was utilized to study brain functional networks during voluntary saliva swallowing in twenty-two adult healthy subjects (all females, [Formula: see text] years of age. To construct these functional connections, we computed mean partial correlation matrices over ninety brain regions for each participant. Two regions were determined to be functionally connected if their correlation was above a certain threshold. These correlation matrices were then analyzed using graph-theoretical approaches. In particular, we considered several network measures for the whole brain and for swallowing-related brain regions. The results have shown that significant pairwise functional connections were, mostly, either local and intra-hemispheric or symmetrically inter-hemispheric. Furthermore, we showed that all human brain functional network, although varying in some degree, had typical small-world properties as compared to regular networks and random networks. These properties allow information transfer within the network at a relatively high efficiency. Swallowing-related brain regions also had higher values for some of the network measures in comparison to when these measures were calculated for the whole brain. The current results warrant further investigation of graph-theoretical approaches as a potential tool for understanding the neural basis of dysphagia.

  15. On the edge of language acquisition: inherent constraints on encoding multisyllabic sequences in the neonate brain.

    Science.gov (United States)

    Ferry, Alissa L; Fló, Ana; Brusini, Perrine; Cattarossi, Luigi; Macagno, Francesco; Nespor, Marina; Mehler, Jacques

    2016-05-01

    To understand language, humans must encode information from rapid, sequential streams of syllables - tracking their order and organizing them into words, phrases, and sentences. We used Near-Infrared Spectroscopy (NIRS) to determine whether human neonates are born with the capacity to track the positions of syllables in multisyllabic sequences. After familiarization with a six-syllable sequence, the neonate brain responded to the change (as shown by an increase in oxy-hemoglobin) when the two edge syllables switched positions but not when two middle syllables switched positions (Experiment 1), indicating that they encoded the syllables at the edges of sequences better than those in the middle. Moreover, when a 25 ms pause was inserted between the middle syllables as a segmentation cue, neonates' brains were sensitive to the change (Experiment 2), indicating that subtle cues in speech can signal a boundary, with enhanced encoding of the syllables located at the edges of that boundary. These findings suggest that neonates' brains can encode information from multisyllabic sequences and that this encoding is constrained. Moreover, subtle segmentation cues in a sequence of syllables provide a mechanism with which to accurately encode positional information from longer sequences. Tracking the order of syllables is necessary to understand language and our results suggest that the foundations for this encoding are present at birth. © 2015 John Wiley & Sons Ltd.

  16. Statistical language learning in neonates revealed by event-related brain potentials

    Directory of Open Access Journals (Sweden)

    Näätänen Risto

    2009-03-01

    Full Text Available Abstract Background Statistical learning is a candidate for one of the basic prerequisites underlying the expeditious acquisition of spoken language. Infants from 8 months of age exhibit this form of learning to segment fluent speech into distinct words. To test the statistical learning skills at birth, we recorded event-related brain responses of sleeping neonates while they were listening to a stream of syllables containing statistical cues to word boundaries. Results We found evidence that sleeping neonates are able to automatically extract statistical properties of the speech input and thus detect the word boundaries in a continuous stream of syllables containing no morphological cues. Syllable-specific event-related brain responses found in two separate studies demonstrated that the neonatal brain treated the syllables differently according to their position within pseudowords. Conclusion These results demonstrate that neonates can efficiently learn transitional probabilities or frequencies of co-occurrence between different syllables, enabling them to detect word boundaries and in this way isolate single words out of fluent natural speech. The ability to adopt statistical structures from speech may play a fundamental role as one of the earliest prerequisites of language acquisition.

  17. Language-specific dysgraphia in Korean patients with right brain stroke: influence of unilateral spatial neglect.

    Science.gov (United States)

    Jang, Dae-Hyun; Kim, Min-Wook; Park, Kyoung Ha; Lee, Jae Woo

    2015-03-01

    The purpose of the present study was to investigate the relationship between Korean language-specific dysgraphia and unilateral spatial neglect in 31 right brain stroke patients. All patients were tested for writing errors in spontaneous writing, dictation, and copying tests. The dysgraphia was classified into visuospatial omission, visuospatial destruction, syllabic tilting, stroke omission, stroke addition, and stroke tilting. Twenty-three (77.4%) of the 31 patients made dysgraphia and 18 (58.1%) demonstrated unilateral spatial neglect. The visuospatial omission was the most common dysgraphia followed by stroke addition and omission errors. The highest number of errors was made in the copying and the least was in the spontaneous writing test. Patients with unilateral spatial neglect made a significantly higher number of dysgraphia in the copying test than those without. We identified specific dysgraphia features such as a right side space omission and a vertical stroke addition in Korean right brain stroke patients. In conclusion, unilateral spatial neglect influences copy writing system of Korean language in patients with right brain stroke.

  18. Dialogue, Language and Identity: Critical Issues for Networked Management Learning

    Science.gov (United States)

    Ferreday, Debra; Hodgson, Vivien; Jones, Chris

    2006-01-01

    This paper draws on the work of Mikhail Bakhtin and Norman Fairclough to show how dialogue is central to the construction of identity in networked management learning. The paper is based on a case study of a networked management learning course in higher education and attempts to illustrate how participants negotiate issues of difference,…