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

Polygenic Risk Score Schizophrenia (SZ) Sex Differences Single Nucleotide Polymorphism (SNP) Spatial Dynamics Spatially Dynamic Covariance Time-Resolved Referenced-Informed Network Estimation Techniques

Journal

Biological psychiatry
ISSN: 1873-2402
Titre abrégé: Biol Psychiatry
Pays: United States
ID NLM: 0213264

Informations de publication

Date de publication:
07 Dec 2023
Historique:
received: 10 07 2023
revised: 15 11 2023
accepted: 01 12 2023
medline: 10 12 2023
pubmed: 10 12 2023
entrez: 9 12 2023
Statut: aheadofprint

Résumé

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 fMRI 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, limited short-time information, and uncertain network identification. We adapt a reference-informed network estimation technique to capture time-resolved networks and their dynamic spatial integration and segregation for 315 controls and 193 schizophrenia individuals. We focus on time-resolved spatial functional network connectivity (spFNC), an estimate of network spatial coupling, to study sex-specific alterations in schizophrenia and their links to genomic data. Our findings are consistent with the dysconnectivity and neurodevelopment hypotheses and align 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 spFNC exhibits sex-specific schizophrenia alteration during the state with the highest global network integration and correlates with schizophrenia genetic risk. This dysfunction is reflected in regions with weak functional connectivity to corresponding networks. 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.

Sections du résumé

BACKGROUND 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 fMRI 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, limited short-time information, and uncertain network identification.
METHODS METHODS
We adapt a reference-informed network estimation technique to capture time-resolved networks and their dynamic spatial integration and segregation for 315 controls and 193 schizophrenia individuals. We focus on time-resolved spatial functional network connectivity (spFNC), an estimate of network spatial coupling, to study sex-specific alterations in schizophrenia and their links to genomic data.
RESULTS RESULTS
Our findings are consistent with the dysconnectivity and neurodevelopment hypotheses and align 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 spFNC exhibits sex-specific schizophrenia alteration during the state with the highest global network integration and correlates with schizophrenia genetic risk. This dysfunction is reflected in regions with weak functional connectivity to corresponding networks.
CONCLUSIONS 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.

Identifiants

DOI: 10.1016/j.biopsych.2023.12.002 PMID: 38070846
pubmed: 38070846
pii: S0006-3223(23)01756-0
doi: 10.1016/j.biopsych.2023.12.002
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Copyright © 2023. Published by Elsevier Inc.

Auteurs

Armin Iraji (A)

Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA; Department of Computer Science, Georgia State University, Atlanta, GA, USA. Electronic address: armin.iraji@gmail.com.

Jiayu Chen (J)

Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA.

Noah Lewis (N)

Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA; Department of CSE, Georgia Institute of Technology, Atlanta, Georgia.

Ashkan Faghiri (A)

Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA.

Zening Fu (Z)

Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA.

Oktay Agcaoglu (O)

Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA.

Peter Kochunov (P)

Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, MD, USA.

Bhim M Adhikari (BM)

Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, MD, USA.

Daniel H Mathalon (DH)

Department of Psychiatry, University of California San Francisco, San Francisco, CA, USA; San Francisco VA Medical Center, San Francisco, CA, USA.

Godfrey D Pearlson (GD)

Departments of Psychiatry and Neuroscience, Yale University School of Medicine, New Haven, CT, USA.

Fabio Macciardi (F)

Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA.

Adrian Preda (A)

Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA.

Theo G M van Erp (TGM)

Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA.

Juan R Bustillo (JR)

Department of Psychiatry and Behavioral Sciences, University of New Mexico, Albuquerque, NM, USA.

Covadonga M Díaz-Caneja (CM)

Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, IiSGM, Madrid, Spain.

Pablo Andrés-Camazón (P)

Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, IiSGM, Madrid, Spain.

Mukesh Dhamala (M)

Department of Physics and Astronomy, Georgia State University, Atlanta, GA, USA.

Tulay Adali (T)

Department of CSEE, University of Maryland, Baltimore County, Baltimore, Maryland.

Vince D Calhoun (VD)

Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA; Department of CSE, Georgia Institute of Technology, Atlanta, Georgia.

Classifications MeSH