Learning evoked centrality dynamics in the schizophrenia brain: entropy, heterogeneity, and inflexibility of brain...

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 functional MRI (fMRI) data in SCZ and Healthy Controls (HC) acquired during associative learning.

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 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.

Clustering 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.

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.

The 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.