Resting-State fMRI Co-Activation Patterns Reveal Multiscale Brain Functional Alterations in Primary Angle-Closure Glaucoma: Transcriptomic, Cellular, Neurochemical, and Machine-Learning Signatures.

Background: Primary angle-closure glaucoma (PACG) is increasingly recognized as involving brain alterations beyond the visual pathway, but the dynamic organization of large-scale brain activity and its biological context remain unclear. Co-activation pattern (CAP) analysis can characterize transient brain states and may provide insight into state-specific functional reorganization in PACG. Methods: Resting-state fMRI data were collected from 44 PACG patients and 57 healthy controls. CAP analysis was performed across multiple frequency bands, and six CAP states were identified. Group differences in CAP temporal dynamics and transition profiles were examined. Spatial associations between CAP-related alterations and normative transcriptomic, cell-type, and neurotransmitter receptor maps were assessed using Allen Human Brain Atlas data, enrichment analyses, cell-type-specific profiling, and receptor/transporter density maps. CAP-derived features were further evaluated using support vector machine-recursive feature elimination and multiple machine-learning classifiers. Results: PACG patients showed state-specific alterations in CAP dynamics, with increased occurrence, dwell time, fractional occupancy, and self-transition probability of selected CAP states, alongside reduced engagement of complementary states. These altered states were organized into limbic-centered, spatially antithetical configurations involving attention, sensorimotor, and control networks. Imaging-transcriptomic analysis identified a dominant normative transcriptional gradient spatially associated with CAP alterations, involving genes enriched for neuronal excitability and synaptic regulation. Cell-type analyses indicated preferential enrichment in excitatory neurons, inhibitory neurons, and endothelial cells. CAP-related alterations also showed spatial associations with cholinergic, serotonergic, glutamatergic, and synaptic vesicle-related receptor systems. Machine-learning analyses demonstrated modest discriminative performance of CAP-derived features, with relatively high specificity but limited sensitivity. Conclusion: PACG is associated with state-specific alterations in intrinsic brain dynamics that spatially align with normative molecular, cellular, and neuromodulatory architectures. These findings provide a multiscale, hypothesis-generating framework for understanding brain functional alterations in PACG.