Kcc-ReHo and Cohe-ReHo in bipolar disorder: their associated genes and potential for diagnosis and treatment prediction

Neuropharmacology. 2025 Jun 25:110575. doi: 10.1016/j.neuropharm.2025.110575. Online ahead of print.

Abstract

The neural mechanisms underlying resting-state cerebral functional activity in bipolar disorder (BD) and the effects of pharmacotherapy on it remain unclear. This study investigated changes in local brain activity in BD patients (BDPs) following treatment, evaluated the diagnostic and prognostic potential of regional homogeneity (ReHo), and explored associated genes and biological processes. Resting-state fMRI data and clinical variables were collected from 68 BDPs (at baseline and after 3 months of pharmacotherapy) and 80 healthy controls (HCs). Local brain activity was assessed using Kendall's coefficient of concordance ReHo (KCC-ReHo) and Coherence ReHo (Cohe-ReHo). Support Vector Machine (SVM) and Support Vector Regression (SVR) were employed for classification and treatment response prediction. Neuroimaging-transcriptomic analysis was conducted to explore the relationship between altered ReHo and gene expression profiles from the Allen Human Brain Atlas. BDPs exhibited greater KCC-ReHo and Cohe-ReHo values in the striatum and cerebellum circuit, but lower values in the prefrontal cortex compared to HCs. Following pharmacotherapy, KCC-ReHo values in the cerebellum circuit decreased. Classification accuracy was 68% (AUC: 0.76 and 0.75 for KCC-ReHo and Cohe-ReHo, respectively), with predicted treatment response moderately correlating with actual outcomes (r = 0.34 and 0.31). Twenty-seven genes were found to be associated with ReHo group differences. Our findings underscore the dysfunction of the prefrontal-striatum and cerebellar circuits as key neuropathological mechanisms in BD. The observed reduction in cerebellar activity post-pharmacotherapy suggests a potential therapeutic mechanism, while neuroimaging-transcriptomic analysis highlights the genetic underpinnings of these alterations in BDPs.

Keywords: Bipolar disorder; Coherence regional homogeneity; Kendall coefficient consistency regional homogeneity; Machine learning; Neuroimaging-transcription association analysis.