Learning evoked centrality dynamics in the schizophrenia brain: Entropy, heterogeneity and inflexibility of brain networks

Bhatt, D.; Kopchick, J.; Abel, C. C.; Khatib, D.; Thomas, P.; Rajan, U.; Zajac-Benitez, C.; Haddad, L.; Amirsadri, A.; Stanley, J.; Diwadkar, V.

doi:10.1101/2025.04.05.647398

bioRxiv · 10.1101/2025.04.05.647398

Learning evoked centrality dynamics in the schizophrenia brain: Entropy, heterogeneity and inflexibility of brain networks

Bhatt, D.·Kopchick, J.·Abel, C. C.·Khatib, D.·Thomas, P.·Rajan, U.·Zajac-Benitez, C.·Haddad, L.·Amirsadri, A.·Stanley, J.·Diwadkar, V.

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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 graph theoretic measures of centrality. We applied CD to fMRI data in SCZ and Healthy Controls (HC) acquired during a learning paradigm. 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 widowing technique to estimate 280 partially overlapping connectomes (30,135 region-pairs in each connectome). In each connectome we calculated every nodes Betweenness Centrality (BC) before building 246 unique time series (representing a nodes CD) from a nodes BC in successive connectomes. Next, in each group nodes were clustered based on similarities in CD. ResultsClustering gave rise to fewer sub-networks in SCZ, and these were formed by nodes with greater functional heterogeneity. These sub-networks also showed greater ApEn (indicating greater stochasticity) but lower amplitude variability (suggesting less adaptability to task-induced dynamics). Higher ApEn was associated with worse clinical symptoms. LimitationsCentrality Dynamics is a new method for network discovery in health and schizophrenia but will need further extension to other tasks and psychiatric conditions, before we achieve a fuller understanding of its promise. ConclusionThe brains functional connectome is not static under task-driven conditions, and characterizing the dynamics of the connectome will provide new insight on the dysconnection syndrome that is schizophrenia. Centrality Dynamics provides novel characterization of task-induced changes in the brains connectome and shows that in the schizophrenia brain, learning-evoked sub-network dynamics were less responsive to learning evoked changes and showed greater stochasticity.

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Bhatt, D., Kopchick, J., Abel, C. C., Khatib, D., Thomas, P., Rajan, U., Zajac-Benitez, C., Haddad, L., Amirsadri, A., Stanley, J., Diwadkar, V.. 2025-04-07. Learning evoked centrality dynamics in the schizophrenia brain: Entropy, heterogeneity and inflexibility of brain networks. https://doi.org/10.1101/2025.04.05.647398

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