Motivation and pleasure deficits reflect altered network embedding of normal brain deviations in schizophrenia

Negative symptoms in schizophrenia are associated with significantly worse outcomes, and their factor structure (i.e., diminished motivation and pleasure [MAP] and diminished expressivity [EXP]) has only recently been clarified. Studies investigating structural imaging correlates of negative symptoms have largely focused on cortical regions involved in motivation, reward processing, affect expression, and language. Structural covariance network modeling provides a framework for examining covariance among these cortical regions in a network-like manner.

Clinical and imaging data from 203 individuals with schizophrenia were obtained from three independent data sites. Cortical thickness measures based on the Desikan-Killiany atlas were standardized using a normative modeling algorithm developed by the ENIGMA Lifespan Working Group. Structural covariance networks were constructed using a priori cortical regions of interest related to MAP and EXP. Linear regression models tested whether MAP and EXP severity predicted network embeddedness.

Both the MAP-related (F_8,177 = 22.76, p < 0.001, adjusted R² = 0.4848) and EXP-related networks (F_8,1778 = 14.43, p = 0.001, adjusted R² = 0.3673) were significant. Howerver, only MAP severity predicted network embeddedness within the MAP network (β = 0.085, p = 0.03, 95% CI [0.009, 0.162]).

Results suggest that MAP is selectively associated with network-level over-coupling of cortical morphology, whereas EXP may reflect more distributed network properties. These findings underscore the potential role of cortical network rigidity in the pathophysiology of negative symptoms.