Background: While resting-state fMRI demonstrated that brain networks are spatially dynamic (expanding, shrinking, and changing complexity over time), understanding the transient spatial network interactions that remain poorly characterized is critical for revealing the mechanisms underlying brain disorders. Methods: We introduce DESINE (Dynamic Estimation of Spatially Interactive Networks), a novel framework using joint density distributions (2D histograms) of voxel-wise activity to quantify 4D spatial network interactions across sliding windows. We analysed transient deviations from the average functional state using root-mean-square error (RMSE) and mean absolute deviation (MAD), and characterized recurring interaction patterns using k-means clustering. We applied DESINE to 91 network pairs (14 networks) in a cohort of 508 subjects (315 healthy controls; 193 patients with schizophrenia, SZ). Results: SZ is characterized by a significantly constrained dynamic repertoire of network interactions. SZ patients showed markedly lower means and standard deviations for both RMSE and MAD metrics across network pairs, particularly in regions of high activity, indicating systematic rigidity. Cluster analysis revealed significant alterations in state affinity metrics, suggesting a global breakdown in the brain's capacity to preserve diverse, high-fidelity spatial configurations. Critically, these interaction metrics were associated with cognitive performance, symptom scores on the positive and negative syndrome scale, and chlorpromazine equivalent drug scores. Conclusions: This work introduces DESINE as a global, voxel-agnostic framework for characterizing time-varying spatial interactions. Our findings highlight spatial rigidity as a fundamental feature of psychopathology, suggesting that the inability to express a diverse range of spatial interactions is a factor underlying cognitive deficits in schizophrenia.
Pusuluri, K., Pearlson, G., Iraji, A., Calhoun, V. D.
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