There is a paucity of graph theoretic methods applied to task-based data in schizophrenia (SCZ). Tasks are useful for modulating brain network dynamics, and topology. Understanding how changes in task conditions impact inter-group differences in topology can elucidate unstable network characteristics in SCZ. Here, in a group of patients and healthy controls (n = 59 total, 32 SCZ), we used an associative learning task with four distinct conditions (Memory Formation, Post-Encoding Consolidation, Memory Retrieval, and Post-Retrieval Consolidation) to induce network dynamics. From the acquired fMRI time series data, betweenness centrality (BC), a metric of a node’s integrative value was used to summarize network topology in each condition. Patients showed (a) differences in BC across multiple nodes and conditions; (b) decreased BC in more integrative nodes, but increased BC in less integrative nodes; (c) discordant node ranks in each of the conditions; and (d) complex patterns of stability and instability of node ranks across conditions. These analyses reveal that task conditions induce highly variegated patterns of network dys-organization in SCZ. We suggest that the dys-connection syndrome that is schizophrenia, is a contextually evoked process, and that the tools of network neuroscience should be oriented toward elucidating the limits of this dys-connection.

This paper demonstrates disordered network repertoires in schizophrenia during learning and memory. Graph theoretic analysis was applied to fMRI data collected during four distinct phases of a learning paradigm, to capture network repertoires. We show that the network topology changes across conditions, and that brain nodes peripheral to learning assumed greater importance in schizophrenia. Moreover, nodes with highly stable contributions to network topology were aberrantly localized to early sensory cortex in schizophrenia, but distributed across the brain in healthy participants. These results highlight the dynamic nature of the dys-connection syndrome, emphasizing the value of studying task-induced network topology in schizophrenia in conjunction with graph theoretic metrics.

This content is only available as a PDF.

Author notes

Competing Interests: The authors have declared that no competing interests exist.

Handling Editor: Alex Fornito

This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. For a full description of the license, please visit

Supplementary data