Parallel ICA identifies sub-components of resting state networks that covary with behavioral indices

Timothy B. Meier, Joseph C. Wildenberg, Jingyu Liu, Jiayu Chen, Vince Daniel Calhoun, Bharat B. Biswal, Mary E. Meyerand, Rasmus M. Birn, Vivek Prabhakaran

Research output: Contribution to journalArticle

Abstract

Parallel Independent Component Analysis (para-ICA) is a multivariate method that can identify complex relationships between different data modalities by simultaneously performing Independent Component Analysis on each data set while finding mutual information between the two data sets. We use para-ICA to test the hypothesis that spatial sub-components of common resting state networks (RSNs) covary with specific behavioral measures. Resting state scans and a battery of behavioral indices were collected from 24 younger adults. Group ICA was performed and common RSNs were identified by spatial correlation to publically available templates. Nine RSNs were identified and para-ICA was run on each network with a matrix of behavioral measures serving as the second data type. Five networks had spatial sub-components that significantly correlated with behavioral components. These included a sub-component of the temporo-parietal attention network that differentially covaried with different trial-types of a sustained attention task, sub-components of default mode networks that covaried with attention and working memory tasks, and a sub-component of the bilateral frontal network that split the left inferior frontal gyrus into three clusters according to its cytoarchitecture that differentially covaried with working memory performance. Additionally, we demonstrate the validity of para- ICA in cases with unbalanced dimensions using simulated data.

Original languageEnglish (US)
JournalFrontiers in Human Neuroscience
Issue numberSEPTEMBER
DOIs
StatePublished - Sep 25 2012
Externally publishedYes

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Short-Term Memory
Prefrontal Cortex
Young Adult
Datasets

Keywords

  • Behavior
  • Parallel ICA
  • Resting state fMRI
  • Resting state networks

ASJC Scopus subject areas

  • Psychiatry and Mental health
  • Neurology
  • Biological Psychiatry
  • Behavioral Neuroscience
  • Neuropsychology and Physiological Psychology

Cite this

Parallel ICA identifies sub-components of resting state networks that covary with behavioral indices. / Meier, Timothy B.; Wildenberg, Joseph C.; Liu, Jingyu; Chen, Jiayu; Calhoun, Vince Daniel; Biswal, Bharat B.; Meyerand, Mary E.; Birn, Rasmus M.; Prabhakaran, Vivek.

In: Frontiers in Human Neuroscience, No. SEPTEMBER, 25.09.2012.

Research output: Contribution to journalArticle

Meier, TB, Wildenberg, JC, Liu, J, Chen, J, Calhoun, VD, Biswal, BB, Meyerand, ME, Birn, RM & Prabhakaran, V 2012, 'Parallel ICA identifies sub-components of resting state networks that covary with behavioral indices', Frontiers in Human Neuroscience, no. SEPTEMBER. https://doi.org/10.3389/fnhum.2012.00281
Meier, Timothy B. ; Wildenberg, Joseph C. ; Liu, Jingyu ; Chen, Jiayu ; Calhoun, Vince Daniel ; Biswal, Bharat B. ; Meyerand, Mary E. ; Birn, Rasmus M. ; Prabhakaran, Vivek. / Parallel ICA identifies sub-components of resting state networks that covary with behavioral indices. In: Frontiers in Human Neuroscience. 2012 ; No. SEPTEMBER.
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