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

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-I...

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Autores principales: Meier, Timothy B., Wildenberg, Joseph C., Liu, Jingyu, Chen, Jiayu, Calhoun, Vince D., Biswal, Bharat B., Meyerand, Mary E., Birn, Rasmus M., Prabhakaran, Vivek
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3468957/
https://www.ncbi.nlm.nih.gov/pubmed/23087635
http://dx.doi.org/10.3389/fnhum.2012.00281
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author Meier, Timothy B.
Wildenberg, Joseph C.
Liu, Jingyu
Chen, Jiayu
Calhoun, Vince D.
Biswal, Bharat B.
Meyerand, Mary E.
Birn, Rasmus M.
Prabhakaran, Vivek
author_facet Meier, Timothy B.
Wildenberg, Joseph C.
Liu, Jingyu
Chen, Jiayu
Calhoun, Vince D.
Biswal, Bharat B.
Meyerand, Mary E.
Birn, Rasmus M.
Prabhakaran, Vivek
author_sort Meier, Timothy B.
collection PubMed
description 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.
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spelling pubmed-34689572012-10-19 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 D. Biswal, Bharat B. Meyerand, Mary E. Birn, Rasmus M. Prabhakaran, Vivek Front Hum Neurosci Neuroscience 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. Frontiers Media S.A. 2012-10-11 /pmc/articles/PMC3468957/ /pubmed/23087635 http://dx.doi.org/10.3389/fnhum.2012.00281 Text en Copyright © 2012 Meier, Wildenberg, Liu, Chen, Calhoun, Biswal, Meyerand, Birn and Prabhakaran. http://www.frontiersin.org/licenseagreement 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.
spellingShingle Neuroscience
Meier, Timothy B.
Wildenberg, Joseph C.
Liu, Jingyu
Chen, Jiayu
Calhoun, Vince D.
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
title Parallel ICA identifies sub-components of resting state networks that covary with behavioral indices
title_full Parallel ICA identifies sub-components of resting state networks that covary with behavioral indices
title_fullStr Parallel ICA identifies sub-components of resting state networks that covary with behavioral indices
title_full_unstemmed Parallel ICA identifies sub-components of resting state networks that covary with behavioral indices
title_short Parallel ICA identifies sub-components of resting state networks that covary with behavioral indices
title_sort parallel ica identifies sub-components of resting state networks that covary with behavioral indices
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3468957/
https://www.ncbi.nlm.nih.gov/pubmed/23087635
http://dx.doi.org/10.3389/fnhum.2012.00281
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