Multimodal Brain Graph Analysis for Uncovering Path-Based Biomarkers in Schizophrenia: Integrating Static and Dynamic Approaches.
11 affiliations
- 1School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA.
- 2Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA.
- 3Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, San Francisco, California, USA.
- 4San Francisco VA Medical Center, San Francisco, California, USA.
- 5Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, Minnesota, USA.
- 6Department of Psychiatry and Human Behavior, University of California, Irvine, Irvine, California, USA.
- 7Department of Psychiatry and Behavioral Health, The Ohio State University Wexner Medical Center, Columbus, Ohio, USA.
- 8Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA.
- 9Olin Neuropsychiatry Research Center, Hartford Hospital, Hartford, Connecticut, USA.
- 10Department of Psychology, Georgia State University, Atlanta, Georgia, USA.
- 11Department Computer Science, Georgia State University, Atlanta, Georgia, USA.
Abstract
Analyzing multimodal brain networks offers new insights into the mechanisms of brain disorders. However, most studies focus on unimodal networks or use global/local graph metrics, often missing critical information about disrupted pathways. In this study, we propose a novel framework for estimating static and dynamic multimodal brain graphs by integrating time-resolved functional network connectivity (FNC) and gray matter features using a Gaussian graphical model. We then apply a path-based graph theory analysis to identify connectors and disconnectors, edges that respectively introduce or eliminate paths in the patient graph compared to controls. Our findings in schizophrenia reveal several edges linked to missing or additional paths, indicating network disruptions that are undetectable using unimodal or pairwise analyses. These disrupted pathways span both within- and cross-modal connections, particularly involving regions like the middle temporal gyrus and cerebellum. This work demonstrates the value of multimodal integration and emphasizes the importance of moving beyond individual edge-level analysis when characterizing disease-related network alterations. Our path-centric approach provides a complementary view of brain connectivity disruption and identifies candidate path-based markers of schizophrenia that warrant validation in independent datasets.
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BibTeX
@article{falakshahi2026multimodal,
title = {{Multimodal Brain Graph Analysis for Uncovering Path-Based Biomarkers in Schizophrenia: Integrating Static and Dynamic Approaches.}},
author = {Haleh Falakshahi and Hooman Rokham and Daniel H Mathalon and Judith M Ford and Bryon A Mueller and Adrian Preda and Theo G M van Erp and Jessica A Turner and Godfrey Pearlson and Vince D Calhoun},
journal = {Human brain mapping},
year = {2026},
month = oct,
doi = {10.1002/hbm.70654},
url = {https://europepmc.org/article/MED/42908307},
note = {CC BY},
}
RIS
TY - JOUR
TI - Multimodal Brain Graph Analysis for Uncovering Path-Based Biomarkers in Schizophrenia: Integrating Static and Dynamic Approaches.
AU - Haleh Falakshahi
AU - Hooman Rokham
AU - Daniel H Mathalon
AU - Judith M Ford
AU - Bryon A Mueller
AU - Adrian Preda
AU - Theo G M van Erp
AU - Jessica A Turner
AU - Godfrey Pearlson
AU - Vince D Calhoun
PY - 2026
DA - 2026/10/01
JO - Human brain mapping
DO - 10.1002/hbm.70654
UR - https://europepmc.org/article/MED/42908307
AB - Analyzing multimodal brain networks offers new insights into the mechanisms of brain disorders. However, most studies focus on unimodal networks or use global/local graph metrics, often missing critical information about disrupted pathways. In this study, we propose a novel framework for estimating static and dynamic multimodal brain graphs by integrating time-resolved functional network connectivity (FNC) and gray matter features using a Gaussian graphical model. We then apply a path-based graph theory analysis to identify connectors and disconnectors, edges that respectively introduce or eliminate paths in the patient graph compared to controls. Our findings in schizophrenia reveal several edges linked to missing or additional paths, indicating network disruptions that are undetectable using unimodal or pairwise analyses. These disrupted pathways span both within- and cross-modal connections, particularly involving regions like the middle temporal gyrus and cerebellum. This work demonstrates the value of multimodal integration and emphasizes the importance of moving beyond individual edge-level analysis when characterizing disease-related network alterations. Our path-centric approach provides a complementary view of brain connectivity disruption and identifies candidate path-based markers of schizophrenia that warrant validation in independent datasets.
SN - 1065-9471
SN - 1097-0193
ER -