Modular architecture and resilience of structural covariance networks in first-episode antipsychotic-naive psychoses

Abstract

Abstract Structural covariance network (SCN) studies on first-episode antipsychotic-naïve psychosis (FEAP) have examined less granular parcellations on one morphometric feature reporting lower network resilience among other findings. We examined SCNs of volumes, cortical thickness, and surface area using the Human Connectome Project atlas-based parcellation of 358 regions from 79 FEAP and 68 controls to comprehensively characterize the networks using descriptive and perturbational network neuroscience approach. Using graph theoretic methods, we examined network integration, segregation, centrality, community structure, and hub distribution across small-worldness threshold range and correlated them with psychopathology severity. We used simulated nodal “attacks” (removal of nodes and all their edges) to investigate network resilience, and calculated DeltaCon similarity scores and contrasted the removed nodes to characterize the impact of simulated attacks. Compared to controls, FEAP SCN showed higher betweenness centrality (BC) and lower degree in all three morphometric features and disintegrated with fewer attacks with no change in global efficiency. SCNs showed higher similarity score at the first point of disintegration with ≈54% top-ranked BC nodes attacked. FEAP communities consisted of fewer prefrontal, auditory and visual regions. Lower BC, and higher clustering and degree were associated with greater positive and negative symptom severity. Negative symptoms required twice the changes in these metrics. Globally sparse but locally dense network with more higher-importance nodes in FEAP could result in higher communication cost compared to controls. FEAP network disintegration with fewer attacks suggests lower resilience without altering efficiency measure. Greater network disarray underlying negative symptom severity possibly explains the therapeutic challenge.