Optimizing Within-Subject Experimental Designs for jICA of Multi-Channel ERP and fMRI
Joint independent component analysis (jICA) can be applied within subject for fusion of multi-channel event-related potentials (ERP) and functional magnetic resonance imaging (fMRI), to measure brain function at high spatiotemporal resolution (Mangalathu-Arumana et al., 2012). However, the impact of...
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doaj-6cffcc874f0b4b8585da0c802c380c322020-11-25T00:48:56ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2018-01-011210.3389/fnins.2018.00013294778Optimizing Within-Subject Experimental Designs for jICA of Multi-Channel ERP and fMRIJain Mangalathu-Arumana0Jain Mangalathu-Arumana1Einat Liebenthal2Einat Liebenthal3Einat Liebenthal4Einat Liebenthal5Scott A. Beardsley6Scott A. Beardsley7Department of Biomedical Engineering, Medical College of Wisconsin, Marquette University, Milwaukee, WI, United StatesDepartment of Neurology, Medical College of Wisconsin, Milwaukee, WI, United StatesDepartment of Biomedical Engineering, Medical College of Wisconsin, Marquette University, Milwaukee, WI, United StatesDepartment of Neurology, Medical College of Wisconsin, Milwaukee, WI, United StatesDepartment of Psychiatry, Brigham and Women's Hospital, Boston, MA, United StatesClinical Translational Science Institute, Medical College of Wisconsin, Milwaukee, WI, United StatesDepartment of Biomedical Engineering, Medical College of Wisconsin, Marquette University, Milwaukee, WI, United StatesClinical Translational Science Institute, Medical College of Wisconsin, Milwaukee, WI, United StatesJoint independent component analysis (jICA) can be applied within subject for fusion of multi-channel event-related potentials (ERP) and functional magnetic resonance imaging (fMRI), to measure brain function at high spatiotemporal resolution (Mangalathu-Arumana et al., 2012). However, the impact of experimental design choices on jICA performance has not been systematically studied. Here, the sensitivity of jICA for recovering neural sources in individual data was evaluated as a function of imaging SNR, number of independent representations of the ERP/fMRI data, relationship between instantiations of the joint ERP/fMRI activity (linear, non-linear, uncoupled), and type of sources (varying parametrically and non-parametrically across representations of the data), using computer simulations. Neural sources were simulated with spatiotemporal and noise attributes derived from experimental data. The best performance, maximizing both cross-modal data fusion and the separation of brain sources, occurred with a moderate number of representations of the ERP/fMRI data (10–30), as in a mixed block/event related experimental design. Importantly, the type of relationship between instantiations of the ERP/fMRI activity, whether linear, non-linear or uncoupled, did not in itself impact jICA performance, and was accurately recovered in the common profiles (i.e., mixing coefficients). Thus, jICA provides an unbiased way to characterize the relationship between ERP and fMRI activity across brain regions, in individual data, rendering it potentially useful for characterizing pathological conditions in which neurovascular coupling is adversely affected.http://journal.frontiersin.org/article/10.3389/fnins.2018.00013/fullfMRIERPEEGindependent component analysis (ICA)multimodal neuroimagingmodeling |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jain Mangalathu-Arumana Jain Mangalathu-Arumana Einat Liebenthal Einat Liebenthal Einat Liebenthal Einat Liebenthal Scott A. Beardsley Scott A. Beardsley |
spellingShingle |
Jain Mangalathu-Arumana Jain Mangalathu-Arumana Einat Liebenthal Einat Liebenthal Einat Liebenthal Einat Liebenthal Scott A. Beardsley Scott A. Beardsley Optimizing Within-Subject Experimental Designs for jICA of Multi-Channel ERP and fMRI Frontiers in Neuroscience fMRI ERP EEG independent component analysis (ICA) multimodal neuroimaging modeling |
author_facet |
Jain Mangalathu-Arumana Jain Mangalathu-Arumana Einat Liebenthal Einat Liebenthal Einat Liebenthal Einat Liebenthal Scott A. Beardsley Scott A. Beardsley |
author_sort |
Jain Mangalathu-Arumana |
title |
Optimizing Within-Subject Experimental Designs for jICA of Multi-Channel ERP and fMRI |
title_short |
Optimizing Within-Subject Experimental Designs for jICA of Multi-Channel ERP and fMRI |
title_full |
Optimizing Within-Subject Experimental Designs for jICA of Multi-Channel ERP and fMRI |
title_fullStr |
Optimizing Within-Subject Experimental Designs for jICA of Multi-Channel ERP and fMRI |
title_full_unstemmed |
Optimizing Within-Subject Experimental Designs for jICA of Multi-Channel ERP and fMRI |
title_sort |
optimizing within-subject experimental designs for jica of multi-channel erp and fmri |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Neuroscience |
issn |
1662-453X |
publishDate |
2018-01-01 |
description |
Joint independent component analysis (jICA) can be applied within subject for fusion of multi-channel event-related potentials (ERP) and functional magnetic resonance imaging (fMRI), to measure brain function at high spatiotemporal resolution (Mangalathu-Arumana et al., 2012). However, the impact of experimental design choices on jICA performance has not been systematically studied. Here, the sensitivity of jICA for recovering neural sources in individual data was evaluated as a function of imaging SNR, number of independent representations of the ERP/fMRI data, relationship between instantiations of the joint ERP/fMRI activity (linear, non-linear, uncoupled), and type of sources (varying parametrically and non-parametrically across representations of the data), using computer simulations. Neural sources were simulated with spatiotemporal and noise attributes derived from experimental data. The best performance, maximizing both cross-modal data fusion and the separation of brain sources, occurred with a moderate number of representations of the ERP/fMRI data (10–30), as in a mixed block/event related experimental design. Importantly, the type of relationship between instantiations of the ERP/fMRI activity, whether linear, non-linear or uncoupled, did not in itself impact jICA performance, and was accurately recovered in the common profiles (i.e., mixing coefficients). Thus, jICA provides an unbiased way to characterize the relationship between ERP and fMRI activity across brain regions, in individual data, rendering it potentially useful for characterizing pathological conditions in which neurovascular coupling is adversely affected. |
topic |
fMRI ERP EEG independent component analysis (ICA) multimodal neuroimaging modeling |
url |
http://journal.frontiersin.org/article/10.3389/fnins.2018.00013/full |
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