Dynamic Convergence of Multiple Overlapping Brain States as a Biomarker for Mental Disorders

Exploring the biomarkers linked to various psychiatric disorders is essential for improving diagnostic accuracy and monitoring disease progression. Recent developments in neuroimaging techniques have shifted the emphasis from conventional univariate approaches, which evaluate isolated brain areas, to sophisticated multivariate models that examine intricate interactions across multiple brain networks. Dynamic functional network connectivity (dFNC), the coupling among independent component fluctuations, has become an important method in this landscape, providing a sophisticated understanding of how connectivity fluctuates over time between brain regions. In this research, we aim to uncover unique connectivity profiles and their transitions that are characteristic of disorders such as schizophrenia (SCZ), autism spectrum disorder(ASD), bipolar disorder (BPD) and major depressive disorder (MDD) by leveraging FNC-based biomarkers. The results reveal distinct patterns of convergence and divergence in functional connectivity across clinical groups, highlighting increased connectivity variability in ASD and SCZ, stable but rigid patterns in MDD, and moderate divergence in BPD, which together provide insights into disorder-specific neurobiological signatures.