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

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.

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

2017 ◽  
Vol 114 (30) ◽  
pp. 8083-8088 ◽  
Author(s):  
Jan-Mathijs Schoffelen ◽  
Annika Hultén ◽  
Nietzsche Lam ◽  
André F. Marquand ◽  
Julia Uddén ◽  
...  
Keyword(s):  
Language Processing ◽  
Brain Regions ◽  
Brain Network ◽  
Causality Analysis ◽  
Linguistic Processing ◽  
Functional Topology ◽  
Using Data ◽  
Beta Frequency ◽  
Bidirectional Interactions

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.

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

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.

scholarly journals Functional Neuroanatomy of Meaning Acquisition from Context

2008 ◽  
Vol 20 (12) ◽  
pp. 2153-2166 ◽  
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.

scholarly journals A Computational Network Control Theory Analysis of Depression Symptoms

2018 ◽  
Vol 1 ◽  
Author(s):  
Yoed N. Kenett ◽  
Roger E. Beaty ◽  
John D. Medaglia
Keyword(s):  
Control Theory ◽  
Diffusion Tensor ◽  
Brain Regions ◽  
Brain Network ◽  
Network Control ◽  
Depression Symptoms ◽  
Theory Approach ◽  
Imaging Data ◽  
The Brain ◽  
Subclinical Depression

AbstractRumination and impaired inhibition are considered core characteristics of depression. However, the neurocognitive mechanisms that contribute to these atypical cognitive processes remain unclear. To address this question, we apply a computational network control theory approach to structural brain imaging data acquired via diffusion tensor imaging in a large sample of participants, to examine how network control theory relates to individual differences in subclinical depression. Recent application of this theory at the neural level is built on a model of brain dynamics, which mathematically models patterns of inter-region activity propagated along the structure of an underlying network. The strength of this approach is its ability to characterize the potential role of each brain region in regulating whole-brain network function based on its anatomical fingerprint and a simplified model of node dynamics. We find that subclinical depression is negatively related to higher integration abilities in the right anterior insula, replicating and extending previous studies implicating atypical switching between the default mode and Executive Control Networks in depression. We also find that subclinical depression is related to the ability to “drive” the brain system into easy to reach neural states in several brain regions, including the bilateral lingual gyrus and lateral occipital gyrus. These findings highlight brain regions less known in their role in depression, and clarify their roles in driving the brain into different neural states related to depression symptoms.

Graph Theory-Based Brain Network Connectivity Analysis and Classification of Alzheimer’s Disease

2021 ◽  
Author(s):  
A. Thushara ◽  
C. Ushadevi Amma ◽  
Ansamma John
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Graph Theory ◽  
Neurodegenerative Disorder ◽  
Brain Networks ◽  
Network Connectivity ◽  
Brain Regions ◽  
Brain Network ◽  
Fiber Tracts ◽  
The Brain

Alzheimer’s Disease (AD) is basically a progressive neurodegenerative disorder associated with abnormal brain networks that affect millions of elderly people and degrades their quality of life. The abnormalities in brain networks are due to the disruption of White Matter (WM) fiber tracts that connect the brain regions. Diffusion-Weighted Imaging (DWI) captures the brain’s WM integrity. Here, the correlation betwixt the WM degeneration and also AD is investigated by utilizing graph theory as well as Machine Learning (ML) algorithms. By using the DW image obtained from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, the brain graph of each subject is constructed. The features extracted from the brain graph form the basis to differentiate between Mild Cognitive Impairment (MCI), Control Normal (CN) and AD subjects. Performance evaluation is done using binary and multiclass classification algorithms and obtained an accuracy that outperforms the current top-notch DWI-based studies.

scholarly journals Mindfulness Meditation Is Related to Long-Lasting Changes in Hippocampal Functional Topology during Resting State: A Magnetoencephalography Study

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Anna Lardone ◽  
Marianna Liparoti ◽  
Pierpaolo Sorrentino ◽  
Rosaria Rucco ◽  
Francesca Jacini ◽  
...  
Keyword(s):  
Resting State ◽  
Mindfulness Meditation ◽  
Brain Connectivity ◽  
Brain Networks ◽  
Brain Network ◽  
Long Term Effects ◽  
Age Related ◽  
Functional Topology ◽  
The Right ◽  
The Brain

It has been suggested that the practice of meditation is associated to neuroplasticity phenomena, reducing age-related brain degeneration and improving cognitive functions. Neuroimaging studies have shown that the brain connectivity changes in meditators. In the present work, we aim to describe the possible long-term effects of meditation on the brain networks. To this aim, we used magnetoencephalography to study functional resting-state brain networks in Vipassana meditators. We observed topological modifications in the brain network in meditators compared to controls. More specifically, in the theta band, the meditators showed statistically significant (p corrected = 0.009) higher degree (a centrality index that represents the number of connections incident upon a given node) in the right hippocampus as compared to controls. Taking into account the role of the hippocampus in memory processes, and in the pathophysiology of Alzheimer’s disease, meditation might have a potential role in a panel of preventive strategies.

Assessment of Graph Metrics and Lateralization of Brain Connectivity in Progression of Alzheimer's Disease Using fMRI

2017 ◽  
Vol 9 (4) ◽  
pp. 46-66 ◽  
Author(s):  
Bhuvaneshwari Bhaskaran ◽  
Kavitha Anandan
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Default Mode Network ◽  
Brain Connectivity ◽  
Brain Network ◽  
Brain Disorder ◽  
Default Mode ◽  
Graph Metrics ◽  
Functional Topology ◽  
The Brain

Alzheimer's disease (AD) is a progressive brain disorder which has a long preclinical phase. The beta-amyloid plaques and tangles in the brain are considered as the main pathological causes. Functional connectivity is typically examined in capturing brain network dynamics in AD. A definitive underconnectivity is observed in patients through the progressive stages of AD. Graph theoretic modeling approaches have been effective in understanding the brain dynamics. In this article, the brain connectivity patterns and the functional topology through the progression of Alzheimer's disease are analysed using resting state fMRI. The altered network topology is analysed by graphed theoretical measures and explains cognitive deficits caused by the progression of this disease. Results show that the functional topology is disrupted in the default mode network regions as the disease progresses in patients. Further, it is observed that there is a lack of left lateralization involving default mode network regions as the severity in AD increases.

scholarly journals Transient brain networks underlying interpersonal strategies during synchronized action

2020 ◽  
Author(s):  
Ole Adrian Heggli ◽  
Ivana Konvalinka ◽  
Joana Cabral ◽  
Elvira Brattico ◽  
Morten L Kringelbach ◽  
...  
Keyword(s):  
Computational Models ◽  
Brain Activity ◽  
Brain Regions ◽  
Brain Network ◽  
Human Interaction ◽  
Action Perception ◽  
Network Information ◽  
Mutual Adaptation ◽  
Underlying Mechanisms ◽  
The Brain

Abstract Interpersonal coordination is a core part of human interaction, and its underlying mechanisms have been extensively studied using social paradigms such as joint finger-tapping. Here, individual and dyadic differences have been found to yield a range of dyadic synchronization strategies, such as mutual adaptation, leading–leading, and leading–following behaviour, but the brain mechanisms that underlie these strategies remain poorly understood. To identify individual brain mechanisms underlying emergence of these minimal social interaction strategies, we contrasted EEG-recorded brain activity in two groups of musicians exhibiting the mutual adaptation and leading–leading strategies. We found that the individuals coordinating via mutual adaptation exhibited a more frequent occurrence of phase-locked activity within a transient action–perception-related brain network in the alpha range, as compared to the leading–leading group. Furthermore, we identified parietal and temporal brain regions that changed significantly in the directionality of their within-network information flow. Our results suggest that the stronger weight on extrinsic coupling observed in computational models of mutual adaptation as compared to leading–leading might be facilitated by a higher degree of action–perception network coupling in the brain.

Oral reading in bilingual aphasia: Evidence from Mongolian and Chinese

2007 ◽  
Vol 10 (2) ◽  
pp. 201-210 ◽  
Author(s):  
BRENDAN STUART WEEKES ◽  
I FAN SU ◽  
WENGANG YIN ◽  
XIHONG ZHANG
Keyword(s):  
Language Processing ◽  
Oral Reading ◽  
Word Processing ◽  
Brain Regions ◽  
Writing Systems ◽  
Word Comprehension ◽  
Bilingual Aphasia ◽  
Written Word ◽  
Cognitive Neuropsychological ◽  
The Brain

Cognitive neuropsychological studies of bilingual patients with aphasia have contributed to our understanding of how the brain processes different languages. The question we asked is whether differences in script have any impact on language processing in bilingual aphasic patients who speak languages with different writing systems: Chinese and Mongolian. We observed a pattern of greater impairment to written word comprehension and oral reading in L2 (Chinese) than in L1 (Mongolian) for two patients. We argue that differences in script have only a minimal effect on written word processing in bilingual aphasia when the age of acquisition, word frequency and imageability of lexical items is controlled. Our conclusion is that reading of familiar words in Mongolian and Chinese might not require independent cognitive systems or brain regions.

scholarly journals The network properties of the brain at the time of normal birth support the acquisition of language processing

2018 ◽  
Author(s):  
Piergiorgio Salvan ◽  
Tomoki Arichi ◽  
Diego Vidaurre ◽  
J Donald Tournier ◽  
Shona Falconer ◽  
...  
Keyword(s):  
Language Acquisition ◽  
Language Processing ◽  
Developmental Trajectories ◽  
Brain Structures ◽  
Brain Network ◽  
Language Abilities ◽  
Normal Birth ◽  
Neural Substrate ◽  
Network Properties ◽  
The Brain

AbstractLanguage acquisition appears to rely at least in part on recruiting pre-existing brain structures. We hypothesized that the neural substrate for language can be characterized by distinct, non-trivial network properties of the brain, that modulate language acquisition early in development. We tested whether these brain network properties present at the normal age of birth predicted later language abilities, and whether these were robust against perturbation by studying infants exposed to the extreme environmental stress of preterm birth.We found that brain network controllability and integration predicted respectively phonological, ‘bottom-up’ and syntactical, ‘top-down’ language skills at 20 months, and that syntactical but not phonological functions were modulated by premature extrauterine life. These data show that the neural substrate for language acquisition is a network property present at term corrected age. These distinct developmental trajectories may be relevant to the emergence of social interaction after birth.

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