Spatial Dynamic Subspaces Encode Sex-Specific Schizophrenia Disruptions in Transient Network Overlap and Their Links to Genetic Risk

Armin Iraji; Jiayu Chen; Noah Lewis; Ashkan Faghiri; Zening Fu; Oktay Agcaoglu; Peter Kochunov; Bhim M Adhikari; Daniel H Mathalon; Godfrey D Pearlson; Fabio Macciardi; Adrian Preda; Theo G M van Erp; Juan R Bustillo; Covadonga M Díaz-Caneja; Pablo Andrés-Camazón; Mukesh Dhamala; Tulay Adali; Vince D Calhoun

doi:10.1016/j.biopsych.2023.12.002

Spatial Dynamic Subspaces Encode Sex-Specific Schizophrenia Disruptions in Transient Network Overlap and Their Links to Genetic Risk

Biol Psychiatry. 2023 Dec 7:S0006-3223(23)01756-0. doi: 10.1016/j.biopsych.2023.12.002. Online ahead of print.

Authors

Armin Iraji¹, Jiayu Chen², Noah Lewis³, Ashkan Faghiri², Zening Fu², Oktay Agcaoglu², Peter Kochunov⁴, Bhim M Adhikari⁴, Daniel H Mathalon⁵, Godfrey D Pearlson⁶, Fabio Macciardi⁷, Adrian Preda⁷, Theo G M van Erp⁸, Juan R Bustillo⁹, Covadonga M Díaz-Caneja¹⁰, Pablo Andrés-Camazón¹⁰, Mukesh Dhamala¹¹, Tulay Adali¹², Vince D Calhoun¹³

Affiliations

¹ Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Atlanta, Georgia; Department of Computer Science, Georgia State University, Atlanta, Georgia. Electronic address: armin.iraji@gmail.com.
² Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Atlanta, Georgia.
³ Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Atlanta, Georgia; Department of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia.
⁴ Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland.
⁵ Department of Psychiatry, University of California San Francisco, San Francisco, California; San Francisco Veteran Affairs Medical Center, San Francisco, California.
⁶ Departments of Psychiatry and Neuroscience, Yale University School of Medicine, New Haven, Connecticut.
⁷ Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, California.
⁸ Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, California.
⁹ Department of Psychiatry and Behavioral Sciences, University of New Mexico, Albuquerque, New Mexico.
¹⁰ Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain.
¹¹ Department of Physics and Astronomy, Georgia State University, Atlanta, Georgia.
¹² Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, Maryland.
¹³ Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Atlanta, Georgia; Department of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia. Electronic address: vcalhoun@gsu.edu.

PMID: 38070846
DOI: 10.1016/j.biopsych.2023.12.002

Abstract

Background: Schizophrenia research reveals sex differences in incidence, symptoms, genetic risk factors, and brain function. However, a knowledge gap remains regarding sex-specific schizophrenia alterations in brain function. Schizophrenia is considered a dysconnectivity syndrome, but the dynamic integration and segregation of brain networks are poorly understood. Recent advances in resting-state functional magnetic resonance imaging allow us to study spatial dynamics, the phenomenon of brain networks spatially evolving over time. Nevertheless, estimating time-resolved networks remains challenging due to low signal-to-noise ratio, limited short-time information, and uncertain network identification.

Methods: We adapted a reference-informed network estimation technique to capture time-resolved networks and their dynamic spatial integration and segregation for 193 individuals with schizophrenia and 315 control participants. We focused on time-resolved spatial functional network connectivity, an estimate of network spatial coupling, to study sex-specific alterations in schizophrenia and their links to genomic data.

Results: Our findings are consistent with the dysconnectivity and neurodevelopment hypotheses and with the cerebello-thalamo-cortical, triple-network, and frontoparietal dysconnectivity models, helping to unify them. The potential unification offers a new understanding of the underlying mechanisms. Notably, the posterior default mode/salience spatial functional network connectivity exhibits sex-specific schizophrenia alteration during the state with the highest global network integration and is correlated with genetic risk for schizophrenia. This dysfunction is reflected in regions with weak functional connectivity to corresponding networks.

Conclusions: Our method can effectively capture spatially dynamic networks, detect nuanced schizophrenia effects including sex-specific ones, and reveal the intricate relationship of dynamic information to genomic data. The results also underscore the clinical potential of dynamic spatial dependence and weak connectivity.

Keywords: Polygenic risk score; Schizophrenia (SZ); Sex differences; Single nucleotide polymorphism (SNP); Spatial dynamics; Spatially dynamic covariance.

Abstract

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