Diagnostically distinct resting state fMRI energy distributions: A subject-specific maximum entropy modeling study

Abstract

Objective: Existing neuroimaging studies of psychotic and mood disorders have reported regional brain activation differences (first-order properties) and alterations in functional connectivity based on pairwise correlations in activation (second-order properties). This study used a generalized Ising model, also called a pairwise maximum entropy model (MEM), to integrate first- and second-order properties to provide a comprehensive picture of BOLD patterns and a system-wide summary measure called energy. This study examines the usefulness of individual level MEMs, attempts to identify image-derived counterparts of the model, and explores potential applications to psychiatry. Method: MEMs are fit to resting state fMRI data of each individual of a sample of 132 participants consisting of schizophrenia/schizoaffective disorder, bipolar disorder, and major depression, and a demographically matched 132 participants without these diagnoses from the UK Biobank to examine the default mode network (DMN). Results: The model explained observed brain state occurrence probabilities well across all participants, and model parameters were highly correlated to image-derived parameters for all groups. Within clinical groups, schizophrenia/schizoaffective disorder and bipolar disorder patients showed significant differences in averaged energy distribution compared to controls for all sub-systems of the DMN except for depression, where differences in the energy distributions were only detected in the DMN of the regions from the right hemisphere. Conclusions: Subject-specific Ising modeling may offer an improved measure of biological functional correlates relative to traditional approaches. The observation of distinct patterns of energy distribution among the three clinical groups compared to controls suggests relative diagnostic specificity and potential for clinical translation.

Energy in functional brain states correlates with cognition in adolescent-onset schizophrenia and healthy persons

“…More specifically, the schizophrenia group showed higher energy states while the bipolar disorder group showed significantly lower energy states in both unilateral and bilateral DMN compared to controls while persons with major depression showed higher energy in the right DMN only compared to controls. These findings support that high energy states may be associated with schizophrenia even among adults and resting state and may be a distinct marker of schizophrenia (Theis et al, 2024). Replication of these findings can enhance our understanding of the dynamically changing neurobiology during cognitive processing.…”

Classification of psychosis spectrum disorders using graph convolutional networks with structurally constrained functional connectomes

“…The dataset examined here is thus arguably more challenging, so we did not necessarily expect the classification accuracy to be higher than prior studies classifying samples of clinically diagnosed subjects, but PLE are likely to still be distinguishable from controls. Similarly, we expected modest classification accuracy with the transdiagnostic ICD sample as well because of mixed network features (Theis, et al, 2024). If these classification methods can overcome or equal the accuracy of using official classificatory system for diagnosing, then machine learning methods could improve PLE clinical accuracy and prognosis.…”