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Parallel ICA identifies sub-components of resting state networks that covary with behavioral indices. Front Hum Neurosci 2012;6:281

Date

10/23/2012

Pubmed ID

23087635

Pubmed Central ID

PMC3468957

DOI

10.3389/fnhum.2012.00281

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.

Author List

Meier TB, Wildenberg JC, Liu J, Chen J, Calhoun VD, Biswal BB, Meyerand ME, Birn RM, Prabhakaran V

Author

Timothy B. Meier PhD Associate Professor in the Neurosurgery department at Medical College of Wisconsin




jenkins-FCD Prod-403 0f9a74600e4e79798f8fa6f545ea115f3dd948b2