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The most relevant human brain regions for functional connectivity: Evidence for a dynamical workspace of binding nodes from whole-brain computational modelling

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The most relevant human brain regions for functional connectivity : Evidence for a dynamical workspace of binding nodes from whole-brain computational modelling. / Deco, Gustavo; Van Hartevelt, Tim J; Fernandes, Henrique M; Stevner, Angus; Kringelbach, Morten L.

In: NeuroImage, Vol. 146, 05.11.2016, p. 197-210.

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@article{6a7212bbb9874f66b3b650d9de5137e9,
title = "The most relevant human brain regions for functional connectivity: Evidence for a dynamical workspace of binding nodes from whole-brain computational modelling",
abstract = "In order to promote survival through flexible cognition and goal-directed behaviour, the brain has to optimize segregation and integration of information into coherent, distributed dynamical states. Certain organizational features of the brain have been proposed to be essential to facilitate cognitive flexibility, especially hub regions in the so-called rich club with shows dense interconnectivity. These structural hubs have been suggested to be vital for integration and segregation of information. Yet, this has not been evaluated in terms of resulting functional temporal dynamics. A complementary measure covering the temporal aspects of functional connectivity could thus bring new insights into a more complete picture of the integrative nature of brain networks. Here, we use causal whole-brain computational modelling to determine the functional dynamical significance of the rich club and compare this to a new measure of the most functionally relevant brain regions for binding information over time ({"}dynamical workspace of binding nodes{"}). We found that removal of the iteratively generated workspace of binding nodes impacts significantly more on measures of integration and encoding of information capability than the removal of the rich club regions. While the rich club procedure produced almost half of the binding nodes, the remaining nodes have low degree yet still play a significant role in the workspace essential for binding information over time and as such goes beyond a description of the structural backbone.",
author = "Gustavo Deco and {Van Hartevelt}, {Tim J} and Fernandes, {Henrique M} and Angus Stevner and Kringelbach, {Morten L}",
note = "Copyright {\textcopyright} 2016. Published by Elsevier Inc.",
year = "2016",
month = nov,
day = "5",
doi = "10.1016/j.neuroimage.2016.10.047",
language = "English",
volume = "146",
pages = "197--210",
journal = "NeuroImage",
issn = "1053-8119",
publisher = "Elsevier BV",

}

RIS

TY - JOUR

T1 - The most relevant human brain regions for functional connectivity

T2 - Evidence for a dynamical workspace of binding nodes from whole-brain computational modelling

AU - Deco, Gustavo

AU - Van Hartevelt, Tim J

AU - Fernandes, Henrique M

AU - Stevner, Angus

AU - Kringelbach, Morten L

N1 - Copyright © 2016. Published by Elsevier Inc.

PY - 2016/11/5

Y1 - 2016/11/5

N2 - In order to promote survival through flexible cognition and goal-directed behaviour, the brain has to optimize segregation and integration of information into coherent, distributed dynamical states. Certain organizational features of the brain have been proposed to be essential to facilitate cognitive flexibility, especially hub regions in the so-called rich club with shows dense interconnectivity. These structural hubs have been suggested to be vital for integration and segregation of information. Yet, this has not been evaluated in terms of resulting functional temporal dynamics. A complementary measure covering the temporal aspects of functional connectivity could thus bring new insights into a more complete picture of the integrative nature of brain networks. Here, we use causal whole-brain computational modelling to determine the functional dynamical significance of the rich club and compare this to a new measure of the most functionally relevant brain regions for binding information over time ("dynamical workspace of binding nodes"). We found that removal of the iteratively generated workspace of binding nodes impacts significantly more on measures of integration and encoding of information capability than the removal of the rich club regions. While the rich club procedure produced almost half of the binding nodes, the remaining nodes have low degree yet still play a significant role in the workspace essential for binding information over time and as such goes beyond a description of the structural backbone.

AB - In order to promote survival through flexible cognition and goal-directed behaviour, the brain has to optimize segregation and integration of information into coherent, distributed dynamical states. Certain organizational features of the brain have been proposed to be essential to facilitate cognitive flexibility, especially hub regions in the so-called rich club with shows dense interconnectivity. These structural hubs have been suggested to be vital for integration and segregation of information. Yet, this has not been evaluated in terms of resulting functional temporal dynamics. A complementary measure covering the temporal aspects of functional connectivity could thus bring new insights into a more complete picture of the integrative nature of brain networks. Here, we use causal whole-brain computational modelling to determine the functional dynamical significance of the rich club and compare this to a new measure of the most functionally relevant brain regions for binding information over time ("dynamical workspace of binding nodes"). We found that removal of the iteratively generated workspace of binding nodes impacts significantly more on measures of integration and encoding of information capability than the removal of the rich club regions. While the rich club procedure produced almost half of the binding nodes, the remaining nodes have low degree yet still play a significant role in the workspace essential for binding information over time and as such goes beyond a description of the structural backbone.

U2 - 10.1016/j.neuroimage.2016.10.047

DO - 10.1016/j.neuroimage.2016.10.047

M3 - Journal article

C2 - 27825955

VL - 146

SP - 197

EP - 210

JO - NeuroImage

JF - NeuroImage

SN - 1053-8119

ER -