Parallel ICA identifies sub-components of resting state networks that covary with behavioral indices
- 1Neuroscience Training Program, University of Wisconsin, Madison, WI, USA
- 2Medical Scientist Training Program, University of Wisconsin, Madison, WI, USA
- 3The Mind Research Network, Albuquerque, NM, USA
- 4Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA
- 5Department of Radiology, University of Medicine and Dentistry of New Jersey, Newark, NJ, USA
- 6Department of Medical Physics, University of Wisconsin, Madison, WI, USA
- 7Department of Biomedical Engineering, University of Wisconsin, Madison, WI, USA
- 8Department of Psychiatry, University of Wisconsin, Madison, WI, USA
- 9Department of Radiology, University of Wisconsin, Madison, WI, USA
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.
Keywords: resting state fMRI, parallel ICA, resting state networks, behavior
Citation: Meier TB, Wildenberg JC, Liu J, Chen J, Calhoun VD, Biswal BB, Meyerand ME, Birn RM and Prabhakaran V (2012) Parallel ICA identifies sub-components of resting state networks that covary with behavioral indices. Front. Hum. Neurosci. 6:281. doi: 10.3389/fnhum.2012.00281
Received: 03 July 2012; Accepted: 25 September 2012;
Published online: 11 October 2012.
Copyright © 2012 Meier, Wildenberg, Liu, Chen, Calhoun, Biswal, Meyerand, Birn and Prabhakaran. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.
*Correspondence: Timothy B. Meier, Neuroscience Training Program, 1310d Wisconsin Institutes for Medical Research, University of Wisconsin-Madison, 1111 Highland Ave., Madison, 53705 WI, USA. e-mail: firstname.lastname@example.org