ArticleJournal of psychiatry & neuroscience : JPN2025
Learning evoked centrality dynamics in the schizophrenia brain: entropy, heterogeneity, and inflexibility of brain networks.
Article in Journal of psychiatry & neuroscience : JPN, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Monotonicity in graph theoretic summaries of fMRI data acquired during human learning.Frontiers in human neuroscience · 2025Article
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11 authors.
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Abstract
backgroundBrain network dynamics are responsive to task induced fluctuations, but such responsivity may not hold in schizophrenia (SCZ). We introduce and implement Centrality Dynamics (CD), a method developed specifically to capture task-driven dynamic changes in
methodsfMRI (3T Siemens Verio) was acquired in 88 participants (49 SCZ). Time series were extracted from 246 functionally defined cerebral nodes. We applied a dynamic windowing technique to estimate 280 partially overlapping connectomes (with 30 135 unique region-pairs per connectome). In each connectome, we calculated every node's Betweenness Centrality (BC) following which we built 246 unique time series from a node's BC in successive connectomes (where each such time series represents a node's CD). Next, in each group similarities in CD were used to cluster nodes.
resultsClustering revealed fewer sub-networks in SCZ, and these sub-networks were formed by nodes with greater functional heterogeneity. The averaged CD of nodes in these sub-networks also showed greater Approximate Entropy (ApEn) (indicating greater stochasticity) but lower amplitude variability (suggesting less adaptability to task-induced dynamics). Finally, higher ApEn was associated with worse clinical symptoms and poorer task performance. LIMITATIONS: Centrality Dynamics is a new method for network discovery in health and schizophrenia. Further extensions to other task-driven and resting data in other psychiatric conditions will provide fuller understanding of its promise.
conclusionThe brain's functional connectome under task-driven conditions is not static. Characterizing these task-driven dynamics will provide new insight on the dysconnection syndrome that is schizophrenia. Centrality Dynamics provides novel characterization of task-induced changes in the brain's connectome and shows that in the schizophrenia brain, learning-evoked sub-network dynamics were (a) less responsive to learning evoked changes and (b) showed greater stochasticity.
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