Functional network overlap as revealed by fmri using sica and its potential relationships with functional heterogeneity, balanced excitation and inhibition, and sparseness of neuron activity

Jiansong Xu, Vince Daniel Calhoun, Patrick D. Worhunsky, Hui Xiang, Jian Li, John T. Wall, Godfrey D. Pearlson, Marc N. Potenza

Research output: Contribution to journalArticle

Abstract

Functional magnetic resonance imaging (fMRI) studies traditionally use general linear modelbased analysis (GLM-BA) and regularly report task-related activation, deactivation, or no change in activation in separate brain regions. However, several recent fMRI studies using spatial independent component analysis (sICA) find extensive overlap of functional networks (FNs), each exhibiting different task-relatedmodulation (e.g., activation vs. deactivation), different from the dominant findings of GLM-BA. This study used sICA to assess overlap of FNs extracted from four datasets, each related to a different cognitive task. FNs extracted from each dataset overlapped with each other extensively acrossmost or all brain regions and showed task-related concurrent increases, decreases, or no changes in activity. These findings indicate that neural substrates showing task-related concurrent but different modulations in activity intermix with each other and distribute across most of the brain. Furthermore, spatial correlation analyses found that most FNs were highly consistent in spatial patterns across different datasets. This finding indicates that these FNs probably reflect large-scale patterns of task-related brain activity. We hypothesize that FN overlaps as revealed by sICAmight relate to functional heterogeneity, balanced excitation and inhibition, and population sparseness of neuron activity, three fundamental properties of the brain. These possibilities deserve further investigation.

Original languageEnglish (US)
Article numbere0117029
JournalPLoS One
Volume10
Issue number2
DOIs
StatePublished - Feb 25 2015
Externally publishedYes

Fingerprint

Neurons
Brain
neurons
brain
Chemical activation
Independent component analysis
magnetic resonance imaging
Magnetic Resonance Imaging
Spatial Analysis
Modulation
Substrates
Population
Datasets

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Medicine(all)

Cite this

Functional network overlap as revealed by fmri using sica and its potential relationships with functional heterogeneity, balanced excitation and inhibition, and sparseness of neuron activity. / Xu, Jiansong; Calhoun, Vince Daniel; Worhunsky, Patrick D.; Xiang, Hui; Li, Jian; Wall, John T.; Pearlson, Godfrey D.; Potenza, Marc N.

In: PLoS One, Vol. 10, No. 2, e0117029, 25.02.2015.

Research output: Contribution to journalArticle

Xu, Jiansong ; Calhoun, Vince Daniel ; Worhunsky, Patrick D. ; Xiang, Hui ; Li, Jian ; Wall, John T. ; Pearlson, Godfrey D. ; Potenza, Marc N. / Functional network overlap as revealed by fmri using sica and its potential relationships with functional heterogeneity, balanced excitation and inhibition, and sparseness of neuron activity. In: PLoS One. 2015 ; Vol. 10, No. 2.
@article{71e2f4c2bdd548c6986e7524261740a5,
title = "Functional network overlap as revealed by fmri using sica and its potential relationships with functional heterogeneity, balanced excitation and inhibition, and sparseness of neuron activity",
abstract = "Functional magnetic resonance imaging (fMRI) studies traditionally use general linear modelbased analysis (GLM-BA) and regularly report task-related activation, deactivation, or no change in activation in separate brain regions. However, several recent fMRI studies using spatial independent component analysis (sICA) find extensive overlap of functional networks (FNs), each exhibiting different task-relatedmodulation (e.g., activation vs. deactivation), different from the dominant findings of GLM-BA. This study used sICA to assess overlap of FNs extracted from four datasets, each related to a different cognitive task. FNs extracted from each dataset overlapped with each other extensively acrossmost or all brain regions and showed task-related concurrent increases, decreases, or no changes in activity. These findings indicate that neural substrates showing task-related concurrent but different modulations in activity intermix with each other and distribute across most of the brain. Furthermore, spatial correlation analyses found that most FNs were highly consistent in spatial patterns across different datasets. This finding indicates that these FNs probably reflect large-scale patterns of task-related brain activity. We hypothesize that FN overlaps as revealed by sICAmight relate to functional heterogeneity, balanced excitation and inhibition, and population sparseness of neuron activity, three fundamental properties of the brain. These possibilities deserve further investigation.",
author = "Jiansong Xu and Calhoun, {Vince Daniel} and Worhunsky, {Patrick D.} and Hui Xiang and Jian Li and Wall, {John T.} and Pearlson, {Godfrey D.} and Potenza, {Marc N.}",
year = "2015",
month = "2",
day = "25",
doi = "10.1371/journal.pone.0117029",
language = "English (US)",
volume = "10",
journal = "PLoS One",
issn = "1932-6203",
publisher = "Public Library of Science",
number = "2",

}

TY - JOUR

T1 - Functional network overlap as revealed by fmri using sica and its potential relationships with functional heterogeneity, balanced excitation and inhibition, and sparseness of neuron activity

AU - Xu, Jiansong

AU - Calhoun, Vince Daniel

AU - Worhunsky, Patrick D.

AU - Xiang, Hui

AU - Li, Jian

AU - Wall, John T.

AU - Pearlson, Godfrey D.

AU - Potenza, Marc N.

PY - 2015/2/25

Y1 - 2015/2/25

N2 - Functional magnetic resonance imaging (fMRI) studies traditionally use general linear modelbased analysis (GLM-BA) and regularly report task-related activation, deactivation, or no change in activation in separate brain regions. However, several recent fMRI studies using spatial independent component analysis (sICA) find extensive overlap of functional networks (FNs), each exhibiting different task-relatedmodulation (e.g., activation vs. deactivation), different from the dominant findings of GLM-BA. This study used sICA to assess overlap of FNs extracted from four datasets, each related to a different cognitive task. FNs extracted from each dataset overlapped with each other extensively acrossmost or all brain regions and showed task-related concurrent increases, decreases, or no changes in activity. These findings indicate that neural substrates showing task-related concurrent but different modulations in activity intermix with each other and distribute across most of the brain. Furthermore, spatial correlation analyses found that most FNs were highly consistent in spatial patterns across different datasets. This finding indicates that these FNs probably reflect large-scale patterns of task-related brain activity. We hypothesize that FN overlaps as revealed by sICAmight relate to functional heterogeneity, balanced excitation and inhibition, and population sparseness of neuron activity, three fundamental properties of the brain. These possibilities deserve further investigation.

AB - Functional magnetic resonance imaging (fMRI) studies traditionally use general linear modelbased analysis (GLM-BA) and regularly report task-related activation, deactivation, or no change in activation in separate brain regions. However, several recent fMRI studies using spatial independent component analysis (sICA) find extensive overlap of functional networks (FNs), each exhibiting different task-relatedmodulation (e.g., activation vs. deactivation), different from the dominant findings of GLM-BA. This study used sICA to assess overlap of FNs extracted from four datasets, each related to a different cognitive task. FNs extracted from each dataset overlapped with each other extensively acrossmost or all brain regions and showed task-related concurrent increases, decreases, or no changes in activity. These findings indicate that neural substrates showing task-related concurrent but different modulations in activity intermix with each other and distribute across most of the brain. Furthermore, spatial correlation analyses found that most FNs were highly consistent in spatial patterns across different datasets. This finding indicates that these FNs probably reflect large-scale patterns of task-related brain activity. We hypothesize that FN overlaps as revealed by sICAmight relate to functional heterogeneity, balanced excitation and inhibition, and population sparseness of neuron activity, three fundamental properties of the brain. These possibilities deserve further investigation.

UR - http://www.scopus.com/inward/record.url?scp=84923872979&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84923872979&partnerID=8YFLogxK

U2 - 10.1371/journal.pone.0117029

DO - 10.1371/journal.pone.0117029

M3 - Article

C2 - 25714362

AN - SCOPUS:84923872979

VL - 10

JO - PLoS One

JF - PLoS One

SN - 1932-6203

IS - 2

M1 - e0117029

ER -