Group ICA for identifying biomarkers in schizophrenia: ‘Adaptive’ networks via spatially constrained ICA show more sensitivity to group differences than spatio-temporal regression

Mustafa S. Salman; Yuhui Du; Dongdong Lin; Zening Fu; Alex Fedorov; Eswar Damaraju; Jing Sui; Jiayu Chen; Andrew R. Mayer; Stefan Posse; Daniel H. Mathalon; Judith M. Ford; Theodorus Van Erp; Vince D. Calhoun

NeuroImage: Clinical (Jan 2019)

Group ICA for identifying biomarkers in schizophrenia: ‘Adaptive’ networks via spatially constrained ICA show more sensitivity to group differences than spatio-temporal regression

Mustafa S. Salman,
Yuhui Du,
Dongdong Lin,
Zening Fu,
Alex Fedorov,
Eswar Damaraju,
Jing Sui,
Jiayu Chen,
Andrew R. Mayer,
Stefan Posse,
Daniel H. Mathalon,
Judith M. Ford,
Theodorus Van Erp,
Vince D. Calhoun

Affiliations

Mustafa S. Salman: Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA; The Mind Research Network, Albuquerque, NM, USA
Yuhui Du: The Mind Research Network, Albuquerque, NM, USA; School of Computer & Information Technology, Shanxi University, Taiyuan, China; Corresponding author at: The Mind Research Network, 1101 Yale Blvd NE, Albuquerque, NM 87131, USA.
Dongdong Lin: The Mind Research Network, Albuquerque, NM, USA
Zening Fu: The Mind Research Network, Albuquerque, NM, USA
Alex Fedorov: Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA; The Mind Research Network, Albuquerque, NM, USA
Eswar Damaraju: Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA; The Mind Research Network, Albuquerque, NM, USA
Jing Sui: The Mind Research Network, Albuquerque, NM, USA; Brainnetome Center and National Laboratory of Pattern Recognition, University of Chinese Academy of Sciences, Beijing, China
Jiayu Chen: The Mind Research Network, Albuquerque, NM, USA
Andrew R. Mayer: The Mind Research Network, Albuquerque, NM, USA
Stefan Posse: Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA; Department of Neurology, University of New Mexico, Albuquerque, NM, USA; Department of Physics and Astronomy, University of New Mexico, Albuquerque, NM, USA
Daniel H. Mathalon: Department of Psychiatry, University of California, San Francisco, CA, USA; San Francisco VA Medical Center, San Francisco, CA, USA
Judith M. Ford: Department of Psychiatry, University of California, San Francisco, CA, USA
Theodorus Van Erp: Department of Psychiatry and Human Behavior, University of California Irvine, CA, USA
Vince D. Calhoun: Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA; The Mind Research Network, Albuquerque, NM, USA

Journal volume & issue: Vol. 22

Abstract

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Brain functional networks identified from fMRI data can provide potential biomarkers for brain disorders. Group independent component analysis (GICA) is popular for extracting brain functional networks from multiple subjects. In GICA, different strategies exist for reconstructing subject-specific networks from the group-level networks. However, it is unknown whether these strategies have different sensitivities to group differences and abilities in distinguishing patients. Among GICA, spatio-temporal regression (STR) and spatially constrained ICA approaches such as group information guided ICA (GIG-ICA) can be used to propagate components (indicating networks) to a new subject that is not included in the original subjects. In this study, based on the same a priori network maps, we reconstructed subject-specific networks using these two methods separately from resting-state fMRI data of 151 schizophrenia patients (SZs) and 163 healthy controls (HCs). We investigated group differences in the estimated functional networks and the functional network connectivity (FNC) obtained by each method. The networks were also used as features in a cross-validated support vector machine (SVM) for classifying SZs and HCs. We selected features using different strategies to provide a comprehensive comparison between the two methods. GIG-ICA generally showed greater sensitivity in statistical analysis and better classification performance (accuracy 76.45 ± 8.9%, sensitivity 0.74 ± 0.11, specificity 0.79 ± 0.11) than STR (accuracy 67.45 ± 8.13%, sensitivity 0.65 ± 0.11, specificity 0.71 ± 0.11). Importantly, results were also consistent when applied to an independent dataset including 82 HCs and 82 SZs. Our work suggests that the functional networks estimated by GIG-ICA are more sensitive to group differences, and GIG-ICA is promising for identifying image-derived biomarkers of brain disease. Keywords: fMRI, Functional network, ICA, Classification, Schizophrenia, GIG-ICA, Spatio-temporal regression

Published in NeuroImage: Clinical

ISSN: 2213-1582 (Online)
Publisher: Elsevier
Country of publisher: Netherlands
LCC subjects: Medicine: Medicine (General): Computer applications to medicine. Medical informatics; Medicine: Internal medicine: Neurosciences. Biological psychiatry. Neuropsychiatry: Neurology. Diseases of the nervous system
Website: http://www.journals.elsevier.com/neuroimage-clinical/

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