Visual Target Modulation of Functional Connectivity Networks Revealed by Self-Organizing Group ICA

V.G. van de Ven, C. Bledowski, D. Prvulovic, R.W. Goebel, E. Formisano, F. Di Salle, D. Linden, F. Esposito

Research output: Contribution to journalArticleAcademicpeer-review

32 Citations (Scopus)

Abstract

We applied a data-driven analysis based on self-organizing group independent component analysis (sogICA) to fMRI data from a three-stimulus visual oddball task. SogICA is particularly suited to the investigation of the underlying functional connectivity and does not rely on a predefined model of the experiment, which overcomes some of the limitations of hypothesis-driven analysis. Unlike most previous applications of ICA in functional imaging, our approach allows the analysis of the data at the group level, which is of particular interest in high order cognitive studies. SogICA is based on the hierarchical clustering of spatially similar independent components, derived from single subject decompositions. We identified four main clusters of components, centered on the posterior cingulate, bilateral insula, bilateral prefrontal cortex, and right posterior parietal and prefrontal cortex, consistently across all participants. Post hoc comparison of time courses revealed that insula, prefrontal cortex and right fronto-parietal components showed higher activity for targets than for distractors. Activation for distractors was higher in the posterior cingulate cortex, where deactivation was observed for targets. While our results conform to previous neuroimaging studies, they also complement conventional results by showing functional connectivity networks with unique contributions to the task that were consistent across subjects. SogICA can thus be used to probe functional networks of active cognitive tasks at the group-level and can provide additional insights to generate new hypotheses for further study. Hum Brain Mapp 29:1450-1461, 2008.
Original languageEnglish
Pages (from-to)1450-1461
JournalHuman Brain Mapping
Volume29
Issue number12
DOIs
Publication statusPublished - 1 Jan 2008

Cite this

@article{920a7dbbc0ab442eaab122f004ba8149,
title = "Visual Target Modulation of Functional Connectivity Networks Revealed by Self-Organizing Group ICA",
abstract = "We applied a data-driven analysis based on self-organizing group independent component analysis (sogICA) to fMRI data from a three-stimulus visual oddball task. SogICA is particularly suited to the investigation of the underlying functional connectivity and does not rely on a predefined model of the experiment, which overcomes some of the limitations of hypothesis-driven analysis. Unlike most previous applications of ICA in functional imaging, our approach allows the analysis of the data at the group level, which is of particular interest in high order cognitive studies. SogICA is based on the hierarchical clustering of spatially similar independent components, derived from single subject decompositions. We identified four main clusters of components, centered on the posterior cingulate, bilateral insula, bilateral prefrontal cortex, and right posterior parietal and prefrontal cortex, consistently across all participants. Post hoc comparison of time courses revealed that insula, prefrontal cortex and right fronto-parietal components showed higher activity for targets than for distractors. Activation for distractors was higher in the posterior cingulate cortex, where deactivation was observed for targets. While our results conform to previous neuroimaging studies, they also complement conventional results by showing functional connectivity networks with unique contributions to the task that were consistent across subjects. SogICA can thus be used to probe functional networks of active cognitive tasks at the group-level and can provide additional insights to generate new hypotheses for further study. Hum Brain Mapp 29:1450-1461, 2008.",
author = "{van de Ven}, V.G. and C. Bledowski and D. Prvulovic and R.W. Goebel and E. Formisano and {Di Salle}, F. and D. Linden and F. Esposito",
year = "2008",
month = "1",
day = "1",
doi = "10.1002/hbm.20479",
language = "English",
volume = "29",
pages = "1450--1461",
journal = "Human Brain Mapping",
issn = "1065-9471",
publisher = "Wiley",
number = "12",

}

Visual Target Modulation of Functional Connectivity Networks Revealed by Self-Organizing Group ICA. / van de Ven, V.G.; Bledowski, C.; Prvulovic, D.; Goebel, R.W.; Formisano, E.; Di Salle, F.; Linden, D.; Esposito, F.

In: Human Brain Mapping, Vol. 29, No. 12, 01.01.2008, p. 1450-1461.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Visual Target Modulation of Functional Connectivity Networks Revealed by Self-Organizing Group ICA

AU - van de Ven, V.G.

AU - Bledowski, C.

AU - Prvulovic, D.

AU - Goebel, R.W.

AU - Formisano, E.

AU - Di Salle, F.

AU - Linden, D.

AU - Esposito, F.

PY - 2008/1/1

Y1 - 2008/1/1

N2 - We applied a data-driven analysis based on self-organizing group independent component analysis (sogICA) to fMRI data from a three-stimulus visual oddball task. SogICA is particularly suited to the investigation of the underlying functional connectivity and does not rely on a predefined model of the experiment, which overcomes some of the limitations of hypothesis-driven analysis. Unlike most previous applications of ICA in functional imaging, our approach allows the analysis of the data at the group level, which is of particular interest in high order cognitive studies. SogICA is based on the hierarchical clustering of spatially similar independent components, derived from single subject decompositions. We identified four main clusters of components, centered on the posterior cingulate, bilateral insula, bilateral prefrontal cortex, and right posterior parietal and prefrontal cortex, consistently across all participants. Post hoc comparison of time courses revealed that insula, prefrontal cortex and right fronto-parietal components showed higher activity for targets than for distractors. Activation for distractors was higher in the posterior cingulate cortex, where deactivation was observed for targets. While our results conform to previous neuroimaging studies, they also complement conventional results by showing functional connectivity networks with unique contributions to the task that were consistent across subjects. SogICA can thus be used to probe functional networks of active cognitive tasks at the group-level and can provide additional insights to generate new hypotheses for further study. Hum Brain Mapp 29:1450-1461, 2008.

AB - We applied a data-driven analysis based on self-organizing group independent component analysis (sogICA) to fMRI data from a three-stimulus visual oddball task. SogICA is particularly suited to the investigation of the underlying functional connectivity and does not rely on a predefined model of the experiment, which overcomes some of the limitations of hypothesis-driven analysis. Unlike most previous applications of ICA in functional imaging, our approach allows the analysis of the data at the group level, which is of particular interest in high order cognitive studies. SogICA is based on the hierarchical clustering of spatially similar independent components, derived from single subject decompositions. We identified four main clusters of components, centered on the posterior cingulate, bilateral insula, bilateral prefrontal cortex, and right posterior parietal and prefrontal cortex, consistently across all participants. Post hoc comparison of time courses revealed that insula, prefrontal cortex and right fronto-parietal components showed higher activity for targets than for distractors. Activation for distractors was higher in the posterior cingulate cortex, where deactivation was observed for targets. While our results conform to previous neuroimaging studies, they also complement conventional results by showing functional connectivity networks with unique contributions to the task that were consistent across subjects. SogICA can thus be used to probe functional networks of active cognitive tasks at the group-level and can provide additional insights to generate new hypotheses for further study. Hum Brain Mapp 29:1450-1461, 2008.

U2 - 10.1002/hbm.20479

DO - 10.1002/hbm.20479

M3 - Article

VL - 29

SP - 1450

EP - 1461

JO - Human Brain Mapping

JF - Human Brain Mapping

SN - 1065-9471

IS - 12

ER -