Temporally intermittent communities in brain fMRI correlation networks

Ulf Aslak*, Søren F.V. Nielsen, Morten Mørup, Sune Lehmann

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

It is widely agreed that the human brain is organized as a system of segregated modules that reside in separate regions and, through coordinated integration, support different cognitive functions. Through recent breakthroughs in modeling the activity of the brain, it has been demonstrated that each such module can participate in multiple so-called functional networks – networks of brain regions that activate in synchrony during specific types of cognition. If we model the brain as a temporal network, by representing brain regions as nodes and correlations within activity-windows at different times as links, we can formulate the task of finding functional networks as a community detection problem. In spite of this, however, relatively little attention has been given to solving this problem using recently developed techniques for temporal community detection. In this paper, as a proof-of-concept, we apply a novel technique for community detection in temporal networks to a dataset of fMRI measurements from 100 healthy subjects undertaking a working memory task with intermittent fixation (or resting-state) periods. We show that this method recovers two distinct communities that are shared between subjects: one that activates during the fixation period, and another that activates during a period associated with high cognitive load.

Original languageEnglish
Article number65
JournalApplied Network Science
Volume4
Issue number1
DOIs
Publication statusPublished - 1 Dec 2019

Keywords

  • Community detection
  • fMRI
  • Network neuroscience
  • Temporal networks

Cite this

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title = "Temporally intermittent communities in brain fMRI correlation networks",
abstract = "It is widely agreed that the human brain is organized as a system of segregated modules that reside in separate regions and, through coordinated integration, support different cognitive functions. Through recent breakthroughs in modeling the activity of the brain, it has been demonstrated that each such module can participate in multiple so-called functional networks – networks of brain regions that activate in synchrony during specific types of cognition. If we model the brain as a temporal network, by representing brain regions as nodes and correlations within activity-windows at different times as links, we can formulate the task of finding functional networks as a community detection problem. In spite of this, however, relatively little attention has been given to solving this problem using recently developed techniques for temporal community detection. In this paper, as a proof-of-concept, we apply a novel technique for community detection in temporal networks to a dataset of fMRI measurements from 100 healthy subjects undertaking a working memory task with intermittent fixation (or resting-state) periods. We show that this method recovers two distinct communities that are shared between subjects: one that activates during the fixation period, and another that activates during a period associated with high cognitive load.",
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Temporally intermittent communities in brain fMRI correlation networks. / Aslak, Ulf; Nielsen, Søren F.V.; Mørup, Morten; Lehmann, Sune.

In: Applied Network Science, Vol. 4, No. 1, 65, 01.12.2019.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

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AU - Aslak, Ulf

AU - Nielsen, Søren F.V.

AU - Mørup, Morten

AU - Lehmann, Sune

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Y1 - 2019/12/1

N2 - It is widely agreed that the human brain is organized as a system of segregated modules that reside in separate regions and, through coordinated integration, support different cognitive functions. Through recent breakthroughs in modeling the activity of the brain, it has been demonstrated that each such module can participate in multiple so-called functional networks – networks of brain regions that activate in synchrony during specific types of cognition. If we model the brain as a temporal network, by representing brain regions as nodes and correlations within activity-windows at different times as links, we can formulate the task of finding functional networks as a community detection problem. In spite of this, however, relatively little attention has been given to solving this problem using recently developed techniques for temporal community detection. In this paper, as a proof-of-concept, we apply a novel technique for community detection in temporal networks to a dataset of fMRI measurements from 100 healthy subjects undertaking a working memory task with intermittent fixation (or resting-state) periods. We show that this method recovers two distinct communities that are shared between subjects: one that activates during the fixation period, and another that activates during a period associated with high cognitive load.

AB - It is widely agreed that the human brain is organized as a system of segregated modules that reside in separate regions and, through coordinated integration, support different cognitive functions. Through recent breakthroughs in modeling the activity of the brain, it has been demonstrated that each such module can participate in multiple so-called functional networks – networks of brain regions that activate in synchrony during specific types of cognition. If we model the brain as a temporal network, by representing brain regions as nodes and correlations within activity-windows at different times as links, we can formulate the task of finding functional networks as a community detection problem. In spite of this, however, relatively little attention has been given to solving this problem using recently developed techniques for temporal community detection. In this paper, as a proof-of-concept, we apply a novel technique for community detection in temporal networks to a dataset of fMRI measurements from 100 healthy subjects undertaking a working memory task with intermittent fixation (or resting-state) periods. We show that this method recovers two distinct communities that are shared between subjects: one that activates during the fixation period, and another that activates during a period associated with high cognitive load.

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