Frequency-specific directed interactions in the human brain network for language

The brain’s remarkable capacity for language requires bidirectional interactions between functionally specialized brain regions. We used magnetoencephalography to investigate interregional interactions in the brain network for language while 102 participants were reading sentences. Using Granger causality analysis, we identified inferior frontal cortex and anterior temporal regions to receive widespread input and middle temporal regions to send widespread output. This fits well with the notion that these regions play a central role in language processing. Characterization of the functional topology of this network, using data-driven matrix factorization, which allowed for partitioning into a set of subnetworks, revealed directed connections at distinct frequencies of interaction. Connections originating from temporal regions peaked at alpha frequency, whereas connections originating from frontal and parietal regions peaked at beta frequency. These findings indicate that the information flow between language-relevant brain areas, which is required for linguistic processing, may depend on the contributions of distinct brain rhythms.

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Frequency-specific directed interactions in the human brain network for language

10.1101/108753 ◽

2017 ◽

Author(s):

J. M. Schoffelen ◽

A. Hultén ◽

N. Lam ◽

A. Marquand ◽

J. Uddén ◽

...

Keyword(s):

Language Processing ◽

Brain Regions ◽

Brain Network ◽

Causality Analysis ◽

Functional Topology ◽

Using Data ◽

Beta Frequency ◽

Bidirectional Interactions ◽

The Brain

AbstractThe brain’s remarkable capacity for language requires bidirectional interactions between functionally specialized brain regions. We used magnetoencephalography to investigate interregional interactions in the brain network for language, while 102 participants were reading sentences. Using Granger causality analysis, we identified inferior frontal cortex and anterior temporal regions to receive widespread input, and middle temporal regions to send widespread output. This fits well with the notion that these regions play a central role in language processing. Characterization of the functional topology of this network, using data-driven matrix factorization, which allowed for partitioning into a set of subnetworks, revealed directed connections at distinct frequencies of interaction. Connections originating from temporal regions peaked at alpha frequency, whereas connections originating from frontal and parietal regions peaked at beta frequency. These findings indicate that processing different types of linguistic information may depend on the contributions of distinct brain rhythms.One Sentence SummaryCommunication between language relevant areas in the brain is supported by rhythmic synchronization, where different rhythms reflect the direction of information flow.

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A Graphlet-Based Topological Characterization of the Resting-State Network in Healthy People

Frontiers in Neuroscience ◽

10.3389/fnins.2021.665544 ◽

2021 ◽

Vol 15 ◽

Author(s):

Paolo Finotelli ◽

Carlo Piccardi ◽

Edie Miglio ◽

Paolo Dulio

Keyword(s):

Resting State ◽

Brain Regions ◽

Brain Network ◽

Induced Subgraphs ◽

Topological Characterization ◽

Network Information ◽

The Subject ◽

Euclidean Distances ◽

The Brain

In this paper, we propose a graphlet-based topological algorithm for the investigation of the brain network at resting state (RS). To this aim, we model the brain as a graph, where (labeled) nodes correspond to specific cerebral areas and links are weighted connections determined by the intensity of the functional magnetic resonance imaging (fMRI). Then, we select a number of working graphlets, namely, connected and non-isomorphic induced subgraphs. We compute, for each labeled node, its Graphlet Degree Vector (GDV), which allows us to associate a GDV matrix to each one of the 133 subjects of the considered sample, reporting how many times each node of the atlas “touches” the independent orbits defined by the graphlet set. We focus on the 56 independent columns (i.e., non-redundant orbits) of the GDV matrices. By aggregating their count all over the 133 subjects and then by sorting each column independently, we obtain a sorted node table, whose top-level entries highlight the nodes (i.e., brain regions) most frequently touching each of the 56 independent graphlet orbits. Then, by pairwise comparing the columns of the sorted node table in the top-k entries for various values of k, we identify sets of nodes that are consistently involved with high frequency in the 56 independent graphlet orbits all over the 133 subjects. It turns out that these sets consist of labeled nodes directly belonging to the default mode network (DMN) or strongly interacting with it at the RS, indicating that graphlet analysis provides a viable tool for the topological characterization of such brain regions. We finally provide a validation of the graphlet approach by testing its power in catching network differences. To this aim, we encode in a Graphlet Correlation Matrix (GCM) the network information associated with each subject then construct a subject-to-subject Graphlet Correlation Distance (GCD) matrix based on the Euclidean distances between all possible pairs of GCM. The analysis of the clusters induced by the GCD matrix shows a clear separation of the subjects in two groups, whose relationship with the subject characteristics is investigated.

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Functional Neuroanatomy of Meaning Acquisition from Context

Journal of Cognitive Neuroscience ◽

10.1162/jocn.2008.20150 ◽

2008 ◽

Vol 20 (12) ◽

pp. 2153-2166 ◽

Cited By ~ 68

Author(s):

Anna Mestres-Missé ◽

Estela Càmara ◽

Antoni Rodriguez-Fornells ◽

Michael Rotte ◽

Thomas F. Münte

Keyword(s):

Language Processing ◽

Inferior Frontal Gyrus ◽

Imaging Study ◽

Brain Regions ◽

Brain Network ◽

Parahippocampal Gyrus ◽

Middle Temporal Gyrus ◽

Functional Neuroanatomy ◽

Subcortical Structures ◽

The Brain

An important issue in language learning is how new words are integrated in the brain representations that sustain language processing. To identify the brain regions involved in meaning acquisition and word learning, we conducted a functional magnetic resonance imaging study. Young participants were required to deduce the meaning of a novel word presented within increasingly constrained sentence contexts that were read silently during the scanning session. Inconsistent contexts were also presented in which no meaning could be assigned to the novel word. Participants showed meaning acquisition in the consistent but not in the inconsistent condition. A distributed brain network was identified comprising the left anterior inferior frontal gyrus (BA 45), the middle temporal gyrus (BA 21), the parahippocampal gyrus, and several subcortical structures (the thalamus and the striatum). Drawing on previous neuroimaging evidence, we tentatively identify the roles of these brain areas in the retrieval, selection, and encoding of the meaning.

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Masked features of task states found in individual brain networks

10.1101/2021.06.12.448198 ◽

2021 ◽

Author(s):

Alexis Porter ◽

Ashley M. Nielsen ◽

Caterina Gratton

Keyword(s):

Individual Differences ◽

Brain Networks ◽

Brain Regions ◽

Brain Network ◽

Classification Performance ◽

Cognitive States ◽

Single Person ◽

Flexible Integration ◽

Brain States ◽

Using Data

Completing complex tasks requires flexible integration of functions across brain regions. While studies have shown that functional networks are altered across tasks, recent work highlights that brain networks exhibit substantial individual differences. Here we asked whether individual differences are important for predicting brain network interactions across cognitive states. We trained classifiers to decode state using data from single person "precision" fMRI datasets across 5 diverse cognitive states. Classifiers were then tested on either independent sessions from the same person or new individuals. Classifiers were able to decode task states in both the same and new participants above chance. However, classification performance was significantly higher within a person, a pattern consistent across model types, datasets, tasks, and feature subsets. This suggests that individualized approaches can uncover robust features of brain states, including features obscured in cross-subject analyses. Individualized approaches have potential to deepen our understanding of brain interactions during complex cognition.

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New Method for fMRI Investigations of Language: Defining ROIs Functionally in Individual Subjects

Journal of Neurophysiology ◽

10.1152/jn.00032.2010 ◽

2010 ◽

Vol 104 (2) ◽

pp. 1177-1194 ◽

Cited By ~ 255

Author(s):

Evelina Fedorenko ◽

Po-Jang Hsieh ◽

Alfonso Nieto-Castañón ◽

Susan Whitfield-Gabrieli ◽

Nancy Kanwisher

Keyword(s):

Language Processing ◽

A Priori ◽

Functional Characterization ◽

Brain Regions ◽

Future Application ◽

Standard Group ◽

Linguistic Processing ◽

Functional Specificity ◽

Functional Regions ◽

Within Subjects

Previous neuroimaging research has identified a number of brain regions sensitive to different aspects of linguistic processing, but precise functional characterization of these regions has proven challenging. We hypothesize that clearer functional specificity may emerge if candidate language-sensitive regions are identified functionally within each subject individually, a method that has revealed striking functional specificity in visual cortex but that has rarely been applied to neuroimaging studies of language. This method enables pooling of data from corresponding functional regions across subjects rather than from corresponding locations in stereotaxic space (which may differ functionally because of the anatomical variability across subjects). However, it is far from obvious a priori that this method will work as it requires that multiple stringent conditions be met. Specifically, candidate language-sensitive brain regions must be identifiable functionally within individual subjects in a short scan, must be replicable within subjects and have clear correspondence across subjects, and must manifest key signatures of language processing (e.g., a higher response to sentences than nonword strings, whether visual or auditory). We show here that this method does indeed work: we identify 13 candidate language-sensitive regions that meet these criteria, each present in ≥80% of subjects individually. The selectivity of these regions is stronger using our method than when standard group analyses are conducted on the same data, suggesting that the future application of this method may reveal clearer functional specificity than has been evident in prior neuroimaging research on language.

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Expanding the language network: Domain-specific hippocampal recruitment during high-level linguistic processing

10.1101/091900 ◽

2016 ◽

Cited By ~ 4

Author(s):

Idan A. Blank ◽

Melissa C. Duff ◽

Sarah Brown-Schmidt ◽

Evelina Fedorenko

Keyword(s):

Language Processing ◽

Language Comprehension ◽

Memory Task ◽

Brain Network ◽

Healthy Adults ◽

Hippocampal Damage ◽

Story Comprehension ◽

Linguistic Processing ◽

Language Network ◽

Relational Binding

AbstractLanguage processing requires us to encode linear relations between acoustic forms and map them onto hierarchical relations between meaning units. Such relational binding of linguistic elements might recruit the hippocampus given its engagement by similar operations in other cognitive domains. Historically, hippocampal engagement in online language use has received little attention because patients with hippocampal damage are not aphasic. However, recent studies have found that these patients exhibit language impairments when the demands on flexible relational binding are high, suggesting that the hippocampus does, in fact, contribute to linguistic processing. A fundamental question is thus whether language processing engages domain-general hippocampal mechanisms that are also recruited across other cognitive processes or whether, instead, it relies on certain language-selective areas within the hippocampus. To address this question, we conducted the first systematic analysis of hippocampal engagement during comprehension in healthy adults (n=150 across three experiments) using fMRI. Specifically, we functionally localized putative “language-regions” within the hippocampus using a language comprehension task, and found that these regions (i) were selectively engaged by language but not by six non-linguistic tasks; and (ii) were coupled in their activity with the cortical language network during both “rest” and especially story comprehension, but not with the domain-general “multiple-demand (MD)” network. This functional profile did not generalize to other hippocampal regions that were localized using a non-linguistic, working memory task. These findings suggest that some hippocampal mechanisms that maintain and integrate information during language comprehension are not domain-general but rather belong to the language-specific brain network.Significance statementAccording to popular views, language processing is exclusively supported by neocortical mechanisms. However, recent patient studies suggest that language processing may also require the hippocampus, especially when relations among linguistic elements have to be flexibly integrated and maintained. Here, we address a core question about the place of the hippocampus in the cognitive architecture of language: are certain hippocampal operations language-specific rather than domain-general? By extensively characterizing hippocampal recruitment during language comprehension in healthy adults using fMRI, we show that certain hippocampal subregions exhibit signatures of language specificity in both their response profiles and their patterns of activity synchronization with known functional regions in the neocortex. We thus suggest that the hippocampus is a satellite constituent of the language network.

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Average fractional polarization of extragalactic sources at Planck frequencies

Astronomy and Astrophysics ◽

10.1051/0004-6361/201732342 ◽

2018 ◽

Vol 618 ◽

pp. A29 ◽

Cited By ~ 6

Author(s):

T. Trombetti ◽

C. Burigana ◽

G. De Zotti ◽

V. Galluzzi ◽

M. Massardi

Keyword(s):

High Sensitivity ◽

Point Sources ◽

Distribution Analysis ◽

Microwave Background ◽

Residual Noise ◽

Upper Limits ◽

Dusty Galaxies ◽

Using Data ◽

Polarization Fraction

Recent detailed simulations have shown that an insufficiently accurate characterization of the contamination of unresolved polarized extragalactic sources can seriously bias measurements of the primordial cosmic microwave background (CMB) power spectrum if the tensor-to-scalar ratio r ∼ 0.001, as predicted by models currently of special interest (e.g., Starobinsky’s R2 and Higgs inflation). This has motivated a reanalysis of the median polarization fraction of extragalactic sources (radio-loud AGNs and dusty galaxies) using data from the Planck polarization maps. Our approach, exploiting the intensity distribution analysis, mitigates or overcomes the most delicate aspects of earlier analyses based on stacking techniques. By means of simulations, we have shown that the residual noise bias on the median polarization fraction, Πmedian, of extragalactic sources is generally ≲0.1%. For radio sources, we have found Πmedian ≃ 2.83%, with no significant dependence on either frequency or flux density, in good agreement with the earlier estimate and with high-sensitivity measurements in the frequency range 5–40 GHz. No polarization signal is detected in the case of dusty galaxies, implying 90% confidence upper limits of Πdusty ≲ 2.2% at 353 GHz and of ≲3.9% at 217 GHz. The contamination of CMB polarization maps by unresolved point sources is discussed.

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On the Role of Bilateral Brain Hypofunction and Abnormal Lateralization of Cortical Information Flow as Neural Underpinnings of Conventional Metaphor Processing Impairment in Schizophrenia: An fMRI and EEG Study

Brain Topography ◽

10.1007/s10548-021-00849-x ◽

2021 ◽

Author(s):

Przemysław Adamczyk ◽

Martin Jáni ◽

Tomasz S. Ligeza ◽

Olga Płonka ◽

Piotr Błądziński ◽

...

Keyword(s):

Information Flow ◽

Language Processing ◽

Time Course ◽

Figurative Language ◽

Neural Circuits ◽

Effective Connectivity ◽

Brain Regions ◽

Blood Oxygenation ◽

Control Group ◽

Metaphor Processing

AbstractFigurative language processing (e.g. metaphors) is commonly impaired in schizophrenia. In the present study, we investigated the neural activity and propagation of information within neural circuits related to the figurative speech, as a neural substrate of impaired conventional metaphor processing in schizophrenia. The study included 30 schizophrenia outpatients and 30 healthy controls, all of whom were assessed with a functional Magnetic Resonance Imaging (fMRI) and electroencephalography (EEG) punchline-based metaphor comprehension task including literal (neutral), figurative (metaphorical) and nonsense (absurd) endings. The blood oxygenation level-dependent signal was recorded with 3T MRI scanner and direction and strength of cortical information flow in the time course of task processing was estimated with a 64-channel EEG input for directed transfer function. The presented results revealed that the behavioral manifestation of impaired figurative language in schizophrenia is related to the hypofunction in the bilateral fronto-temporo-parietal brain regions (fMRI) and various differences in effective connectivity in the fronto-temporo-parietal circuit (EEG). Schizophrenia outpatients showed an abnormal pattern of connectivity during metaphor processing which was related to bilateral (but more pronounced at the left hemisphere) hypoactivation of the brain. Moreover, we found reversed lateralization patterns, i.e. a rightward-shifted pattern during metaphor processing in schizophrenia compared to the control group. In conclusion, the presented findings revealed that the impairment of the conventional metaphor processing in schizophrenia is related to the bilateral brain hypofunction, which supports the evidence on reversed lateralization of the language neural network and the existence of compensatory recruitment of alternative neural circuits in schizophrenia.

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Does higher education properly prepare graduates for the growing artificial intelligence market? Gaps identification using text mining

Human Systems Management ◽

10.3233/hsm-211179 ◽

2021 ◽

pp. 1-13

Author(s):

Lamiae Benhayoun ◽

Daniel Lang

Keyword(s):

Artificial Intelligence ◽

Natural Language Processing ◽

Text Mining ◽

Natural Language ◽

Language Processing ◽

Academic Training ◽

Market Requirements ◽

Job Advertisements ◽

The Individual

BACKGROUND: The renewed advent of Artificial Intelligence (AI) is inducing profound changes in the classic categories of technology professions and is creating the need for new specific skills. OBJECTIVE: Identify the gaps in terms of skills between academic training on AI in French engineering and Business Schools, and the requirements of the labour market. METHOD: Extraction of AI training contents from the schools’ websites and scraping of a job advertisements’ website. Then, analysis based on a text mining approach with a Python code for Natural Language Processing. RESULTS: Categorization of occupations related to AI. Characterization of three classes of skills for the AI market: Technical, Soft and Interdisciplinary. Skills’ gaps concern some professional certifications and the mastery of specific tools, research abilities, and awareness of ethical and regulatory dimensions of AI. CONCLUSIONS: A deep analysis using algorithms for Natural Language Processing. Results that provide a better understanding of the AI capability components at the individual and the organizational levels. A study that can help shape educational programs to respond to the AI market requirements.

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Fear-induced brain activations distinguish anxious and trauma-exposed brains

Translational Psychiatry ◽

10.1038/s41398-020-01193-7 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Zhenfu Wen ◽

Marie-France Marin ◽

Jennifer Urbano Blackford ◽

Zhe Sage Chen ◽

Mohammed R. Milad

Keyword(s):

Neural Network ◽

Fear Conditioning ◽

Psychiatric Diagnosis ◽

Data Analytics ◽

Conditioned Fear ◽

Brain Regions ◽

Brain Network ◽

Neuroimaging Data ◽

Task Irrelevant ◽

Translational Models

AbstractTranslational models of fear conditioning and extinction have elucidated a core neural network involved in the learning, consolidation, and expression of conditioned fear and its extinction. Anxious or trauma-exposed brains are characterized by dysregulated neural activations within regions of this fear network. In this study, we examined how the functional MRI activations of 10 brain regions commonly activated during fear conditioning and extinction might distinguish anxious or trauma-exposed brains from controls. To achieve this, activations during four phases of a fear conditioning and extinction paradigm in 304 participants with or without a psychiatric diagnosis were studied. By training convolutional neural networks (CNNs) using task-specific brain activations, we reliably distinguished the anxious and trauma-exposed brains from controls. The performance of models decreased significantly when we trained our CNN using activations from task-irrelevant brain regions or from a brain network that is irrelevant to fear. Our results suggest that neuroimaging data analytics of task-induced brain activations within the fear network might provide novel prospects for development of brain-based psychiatric diagnosis.

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