Learning without contingencies induces higher order asynchrony in brain networks in schizophrenia

Schizophrenia (SCZ) is characterized by both cognitive and reward impairments. A recent study suggests that SCZ is associated with a loss of synchrony between learning and reward circuits (Robison et al., 2019) and higher levels of dis-organization of functional brain networks may underpin failures in learning that characterize SCZ (Hütt et al., 2014). Therefore, here we examined inter-group (HC ≠ SCZ) 4th order differences in statistical regularity across a connectome of cognition and reward brain circuits. The analyses were conducted on fMRI time series data from a previous learning paradigm (Stanley et al., 2017) with periods of Encoding and Retrieval. 75 participants (46 SCZ, 18 Age 50) consented for fMRI. Time Series were extracted from eight bilateral a priori nodes (across learning and reward sub-networks). 2nd order undirectional functional connectivity was characterized across all nodal pairs during Encoding, Retrieval, and their subsequent rest periods. From this, a 4th order cross-correlation matrix was produced within each group and condition. Significant 4th order differences were projected to chords in the 4th order connectomic rings for Encoding and Retrieval (Figure 1). Two effects are evident: 1) SCZ are characterized by a massive loss of 4th order synchrony during both Encoding and Retrieval and 2) Retrieval evokes a greater loss in 4th order statistical synchrony than does Encoding. These results appear to validate the idea the SCZ is characterized by a loss of synergy between cognition and reward circuits, and that this loss of synergy is evident at higher order scales

The topology, stability, and instability of learning-induced brain network repertoires in schizophrenia

AbstractThere 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