Deficits in neural information integration are hypothesized to underlie diverse psychiatric symptoms, yet the specific patterns of alteration across different disorders remain unclear. In this study, we decomposed information dynamics between brain regions into synergistic and redundant components using a recent information-theoretic approach based on Partial Information Decomposition and applied to resting-state fMRI data from individuals with schizophrenia (SZ), autism spectrum disorder (ASD), and attention-deficit/hyperactivity disorder (ADHD). Our analysis revealed distinct disorder-specific profiles: SZ and ASD exhibited a widespread reduction in synergy, whereas ADHD showed a contrasting increase. Furthermore, ASD was uniquely characterized by a significant reduction in redundancy. Meta-analytic functional annotation using NeuroSynth associated synergy with higher-order cognitive functions and redundancy with lower-level sensorimotor processing. To investigate multivariate organization of these patterns that distinguish psychiatric diagnoses, we employed Linear Discriminant Analysis (LDA). This analysis demonstrated that synergy and redundancy partially capture distinct dimensions of network variation, exhibiting substantial complementarity in their multivariate structure. While redundancy overlapped considerably with correlation-based connectivity, synergy reflected additional structure not fully represented by conventional measures. Together, these findings indicate that decomposing information dynamics provides complementary perspectives on large-scale network organization, offering a refined framework for characterizing psychiatric and neurodevelopmental disorders.
Nago et al. (Sat,) studied this question.
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