Neurobiological fingerprints of negative symptoms in schizophrenia identified by connectome-based modeling.

Abstract

Aim: As a central component of schizophrenia psychopathology, negative symptoms result in detrimental effects on long-term functional prognosis. However, the neurobiological mechanism underlying negative symptoms remains poorly understood, which limits the development of novel treatment interventions. This study aimed to identify the specific neural fingerprints of negative symptoms in schizophrenia.

Methods: Based on resting-state functional connectivity data obtained in a large sample (n = 132) of first-episode drug-naïve schizophrenia patients (DN-FES), connectome-based predictive modeling (CPM) with cross-validation was applied to identify functional networks that predict the severity of negative symptoms. The generalizability of identified networks was then validated in an independent sample of n = 40 DN-FES.

Results: A connectivity pattern significantly driving the prediction of negative symptoms (ρ = 0.28, MSE = 81.04, P = 0.012) was identified within and between networks implicated in motivation (medial frontal, subcortical, sensorimotor), cognition (default mode, frontoparietal, medial frontal) and error processing (medial frontal and cerebellum). The identified networks also predicted negative symptoms in the independent validation sample (ρ = 0.37, P = 0.018). Importantly, the predictive model was symptom-specific and robust considering the potential effects of demographic characteristics and validation strategies.

Conclusions: Our study discovers and validates a comprehensive network model as the unique neural substrates of negative symptoms in schizophrenia, which provides a novel and comprehensive perspective to the development of target treatment strategies for negative symptoms.