Multimodal data fusion of cortical-subcortical morphology and functional network connectivity in psychotic spectrum disorder

T.P. DeRamus; L. Wu; S. Qi; A. Iraji; R. Silva; Y. Du; G. Pearlson; A. Mayer; J.R. Bustillo; S.F. Stromberg; V.D. Calhoun

NeuroImage: Clinical (Jan 2022)

Multimodal data fusion of cortical-subcortical morphology and functional network connectivity in psychotic spectrum disorder

T.P. DeRamus,
L. Wu,
S. Qi,
A. Iraji,
R. Silva,
Y. Du,
G. Pearlson,
A. Mayer,
J.R. Bustillo,
S.F. Stromberg,
V.D. Calhoun

Affiliations

T.P. DeRamus: Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) - Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA; Corresponding author.
L. Wu: Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) - Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
S. Qi: College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
A. Iraji: Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) - Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
R. Silva: Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) - Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
Y. Du: Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) - Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA; School of Computer and Information Technology, Shanxi University, Taiyuan, China
G. Pearlson: Olin Neuropsychiatry Research Center, Institute of Living at Hartford Hospital, Hartford, CT, USA; Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA; Department of Neuroscience, Yale University School of Medicine, New Haven, CT, USA
A. Mayer: The Mind Research Network, Lovelace Biomedical and Environmental Research Institute, Albuquerque, USA
J.R. Bustillo: Department of Psychiatry, University of New Mexico School of Medicine, Albuquerque, NM, USA
S.F. Stromberg: Psychiatry and Behavioral Health Clinical Program, Presbyterian Healthcare System, Albuquerque, NM, USA
V.D. Calhoun: Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) - Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA; The Mind Research Network, Lovelace Biomedical and Environmental Research Institute, Albuquerque, USA; Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, USA; Department of Computer Science, Georgia State University, Atlanta, USA; Department of Psychology, Georgia State University, Atlanta, USA

Journal volume & issue: Vol. 35
p. 103056

Abstract

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Multiple authors have noted overlapping symptoms and alterations across clinical, anatomical, and functional brain features in schizophrenia (SZ), schizoaffective disorder (SZA), and bipolar disorder (BPI). However, regarding brain features, few studies have approached this line of inquiry using analytical techniques optimally designed to extract the shared features across anatomical and functional information in a simultaneous manner. Univariate studies of anatomical or functional alterations across these disorders can be limited and run the risk of omitting small but potentially crucial overlapping or joint neuroanatomical (e.g., structural images) and functional features (e.g., fMRI-based features) which may serve as informative clinical indicators of across multiple diagnostic categories. To address this limitation, we paired an unsupervised multimodal canonical correlation analysis (mCCA) together with joint independent component analysis (jICA) to identify linked spatial gray matter (GM), resting-state functional network connectivity (FNC), and white matter fractional anisotropy (FA) features across these diagnostic categories. We then calculated associations between the identified linked features and trans-diagnostic behavioral measures (MATRICs Consensus Cognitive Battery, MCCB).Component number 4 of the 13 identified displayed a statistically significant relationship with overall MCCB scores across GM, resting-state FNC, and FA. These linked modalities of component 4 consisted primarily of positive correlations within subcortical structures including the caudate and putamen in the GM maps with overall MCCB, sparse negative correlations within subcortical and cortical connection tracts (e.g., corticospinal tract, superior longitudinal fasciculus) in the FA maps with overall MCCB, and negative relationships with MCCB values and loading parameters with FNC matrices displaying increased FNC in subcortical-cortical regions with auditory, somatomotor, and visual regions.

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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