Human brain hubs(provincial and connector) identification using centrality measures

GeethaRamani, R.; Sivaselvi, K.

doi:10.1109/icrtit.2014.6996144

Cited by 7 publications

(6 citation statements)

References 20 publications

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“…By examining the locations of those interacting nodes, we found that our proposed MCSE with C t-r approach identified subnetwork overlaps within pre-and postcentral gyri, medial superior frontal cortex, inferior frontal gyrus, superior parietal lobule, precuneous, lateral occipital cortex, occipital pole and frontal orbital cortex; which match well with functional hubs previously identified by graph-theoretical analysis based on the degree of the voxels [25]. Besides, brain regions of insula, putamen, thalamus, supramarginal gyrus have been found within subnetwork overlaps, which match well with the connector hubs identified using the centrality measures [26]. The results of using MCSE with C rest is very similar to C t-r , only that precuneous cortex was missed, and the temporal pole was misclassified into the subnetwork overlaps.…”

Section: Biological Meaning -Analyzing Function Integrationsupporting

confidence: 84%

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Coactivated Clique Based Multisource Overlapping Brain Subnetwork Extraction

Wang,

Abugharbieh

2018

Preprint

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Subnetwork extraction using community detection methods is commonly used to study the brain's modular structure. Recent studies indicated that certain brain regions are known to interact with multiple subnetworks. However, most existing methods are mainly for non-overlapping subnetwork extraction.In this paper, we present an approach for overlapping brain subnetwork extraction using cliques, which we defined as co-activated node groups performing multiple tasks. We proposed a multisource subnetwork extraction approach based on the co-activated clique, which (1) uses task co-activation and task connectivity strength information for clique identification, (2) automatically detects cliques of different sizes having more neuroscientific justifications, and(3) shares the subnetwork membership, derived from a fusion of rest and task data, among the nodes within a clique for overlapping subnetwork extraction.On real data, compared to the commonly used overlapping community detection techniques, we showed that our approach improved subnetwork extraction in terms of group-level and subject-wise reproducibility. We also showed that our multisource approach identified subnetwork overlaps within brain regions that matched well with hubs defined using functional and anatomical information, which enables us to study the interactions between the subnetworks and how hubs play their role in information flow across different subnetworks.We further demonstrated that the assignments of interacting/individual nodes using our approach correspond with the posterior probability derived independently from our multimodal random walker based approach.

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Section: Biological Meaning -Analyzing Function Integrationsupporting

confidence: 84%

“…supramarginal gyrus, insula [26], inferior temporal gyrus [29], and occipitotemporal cortex [28]. kclique failed to identify all the other aforementioned (connector) hubs which were found using our proposed methods.…”

Section: Biological Meaning -Analyzing Function Integrationmentioning

confidence: 81%

Coactivated Clique Based Multisource Overlapping Brain Subnetwork Extraction

Wang,

Abugharbieh

2018

Preprint

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“…Hub is a node with more edges than any other node [102] and indicates the important brain regions that interact with other regions [40]. Provincial hubs are those connected to other nodes in the same module, whereas connector hubs are connected to nodes in other modules [152].…”

Section: Degree Centrality (K)mentioning

confidence: 99%

A Graph Theory-Based Modeling of Functional Brain Connectivity Based on EEG: A Systematic Review in the Context of Neuroergonomics

Ismail

Karwowski

2020

IEEE Access

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Graph theory analysis, a mathematical approach, has been applied in brain connectivity studies to explore the organization of network patterns. The computation of graph theory metrics enables the characterization of the stationary behavior of electroencephalogram (EEG) signals that cannot be explained by simple linear methods. The main purpose of this study was to systematically review the graph theory applications for mapping the functional connectivity of the EEG data in neuroergonomics. Moreover, this article proposes a pipeline for constructing an unweighted functional brain network from EEG data using both source and sensor methods. Out of 57 articles, our results show that graph theory metrics used to characterize EEG data have attracted increasing attention since 2006, with the highest frequency of publications in 2018. Most studies have focused on cognitive tasks in comparison with motor tasks. The mean phase coherence method, based on the "phase-locking value," was the most frequently used functional estimation technique in the reviewed studies. Furthermore, the unweighted functional brain network has received substantially more attention in the literature than the weighted network. The global clustering coefficient and characteristic path length were the most prevalent metrics for differentiating between global integration and local segregation, and the small-worldness property emerged as a compelling metric for the characterization of information processing. This review provides insight into the use of graph theory metrics to model functional brain connectivity in the context of neuroergonomics research.

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“…In addition to MGB, the pulvinar has been reported to respond to visual, auditory, and somatosensory stimuli [78,79], and retrograde tracer studies identified it as the region with the most extensive overlap between auditory, somatosensory and premotor afferents [80]. The thalamus shows high network centrality and connector properties in resting state data [81,82]. Furthermore, eigenvector centrality of the thalamus is selectively decreased in participants sedated with propofol [83].…”

Section: Discussionmentioning

confidence: 99%

Angular gyrus responses show joint statistical dependence with brain regions selective for different categories

Fang¹,

Aglinskas²,

Y³

et al. 2019

Preprint

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Human visual cortex is organized into regions that respond preferentially to different categories of objects (i.e. faces, bodies, artifacts, scenes). However, often people need to integrate information about objects from different categories to make inferences about the world. How does the brain integrate information represented in different category-selective regions? In this work, we investigated this question taking advantage of a new analysis approach. Using artificial neural networks, we modeled the multivariate statistical dependence between fMRI responses in different brain regions. Regions whose responses were predicted significantly better by a combination of multiple category-selective regions than by the best-predicting category-selective region taken individually were identified as integration hubs. We used this approach to analyze fMRI responses to complex dynamic stimuli (the movie Forrest Gump), and identified five integration hubs: 1) the posterior medial thalamus, 2) the middle cingulate gyrus, 3) the posterior cingulate gyrus, 4) the angular gyrus, and 5) the cerebellum. Hubs were identified robustly across different artificial neural network architectures. Furthermore, representational similarity analysis revealed that, unlike in category-selective regions, representational geometry in integration hubs is not driven by the animate/inanimate distinction. These results indicate that a small set of localized regions integrates visual information about different object categories, and suggests that integration across multiple categories leads to a transformation of the similarity structure of neural representations.

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Human brain hubs(provincial and connector) identification using centrality measures

Cited by 7 publications

References 20 publications

Coactivated Clique Based Multisource Overlapping Brain Subnetwork Extraction

Coactivated Clique Based Multisource Overlapping Brain Subnetwork Extraction

A Graph Theory-Based Modeling of Functional Brain Connectivity Based on EEG: A Systematic Review in the Context of Neuroergonomics

Angular gyrus responses show joint statistical dependence with brain regions selective for different categories

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