Altered brain dynamics across bipolar disorder and schizophrenia revealed by overlapping brain states

Aberrant brain dynamics putatively characterize bipolar disorder (BD) and schizophrenia (SCZ). Previous studies often adopted a state discretization approach when investigating how individuals recruited recurring brain states. Since multiple brain states are likely engaged simultaneously at any given moment, focusing on the dominant state can obscure changes in less prominent but critical brain states in clinical populations. To address this limitation, we introduced a novel framework to simultaneously assess brain state engagement for multiple brain states, and we examined how brain state engagement differs in patients with BD or SCZ compared to healthy controls (HC). Using task-based data from the Human Connectome Project, we applied nonlinear manifold learning and K-means clustering to identify four recurring brain states. We then examined how the engagement and transition variability of these four states differed between patients with BD, SCZ, and HC across two other international, open-source datasets. Comparing these measures across groups revealed significantly altered state transition variability, but not engagement, across all four states in individuals with BD and SCZ during both resting-state and task-based fMRI. In our post hoc and exploratory analysis, we also observed associations between state transition variability and age as well as avolition. Our results suggest that disrupted state transition variability affects multiple brain states in BD and SCZ. By studying several brain states simultaneously, our framework more comprehensively reveals how brain dynamics differ across individuals and in psychiatric disorders. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement JY was supported by the Gruber Science Fellowship from the Gruber Foundation through Yale University. MR was supported by the National Science Foundation Graduate Research Fellowship under Grant No. DGE-2139841. RXR was supported by the National Research Service Award (award number: 5T32GM100884-09) from the National Institute of General Medicine. SN was supported by the National Institute of Mental Health under award number K00MH122372. MLW was supported by the National Institute on Drug Abuse (T32DA022975). DS was supported by R01MH121095. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The Institutional Review Board of Yale University School of Medicine determined this research to be exempt. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes The HCP dataset is available at . The CNP dataset can be accessed through . The SPRBS dataset can be obtained through .