Learning evoked centrality dynamics in the schizophrenia brain: Entropy, heterogeneity and inflexibility of brain networks
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Background
Brain 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.
Methods
fMRI (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 node’s Betweenness Centrality (BC) before building 246 unique time series (representing a node’s CD) from a node’s BC in successive connectomes. Next, in each group nodes were clustered based on similarities in CD.
Results
Clustering 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.
Limitations
Centrality 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.
Conclusion
The brain’s 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 brain’s connectome and shows that in the schizophrenia brain, learning-evoked sub-network dynamics were less responsive to learning evoked changes and showed greater stochasticity.
