A Novel Framework to Quantify Dynamic Convergence and Divergence of Overlapping Brain States characterizing Four Psychiatric Disorders

Abstract Brain function is inherently dynamic, characterized by transient, overlapping functional states rather than static connectivity patterns. Current clustering-based dynamic functional network connectivity methods often fail to capture overlapping states; meanwhile, independent component analysis (ICA)-based methods typically rely on group-level analysis, limiting subject-specific accuracy. To address this gap, we introduce a novel analytical framework estimating individualized dynamic double functional independent primitives (ddFIP)-based states. Our methodological innovation includes: (1) a two-stage ICA combining spatially constrained ICA to define group-level intrinsic connectivity networks (ICNs), followed by constrained ICA to estimate subject-specific states and timecourses; (2) calibration ensuring derived states preserve original correlation scales, enabling meaningful cross-subject and group-level comparisons; and (3) novel metrics leveraging this calibrated representation, including amplitude convergence (uniformity of simultaneous state contributions), amplitude divergence (variability of states independent of state dominance), and dynamic state density (number of concurrently active states at any given time). These methodological advances enhance our ability to characterize subtle differences in brain connectivity dynamics, offering deeper insight into healthy and disrupted connectivity patterns. Validating our framework on an extensive resting-state fMRI dataset (N 5.5K) spanning four neuropsychiatric conditions revealed disorder-specific connectivity signatures: schizophrenia exhibited extensive variability (increased divergence), while autism displayed pronounced stability (increased convergence). In summary, our proposed method uniquely integrates subject-specific ICA estimation, unit-preserving calibration, and novel convergence-divergence metrics, providing data-driven biomarkers differentiating psychiatric disorders.