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

Motivation: 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 unclear. Goal(s): We aim to identify the specific neural fingerprints of negative symptoms in schizophrenia from resting-state fMRI data. Approach: Connectome-based predictive modeling (CPM) with cross-validation was applied to identify the specific neural fingerprints of negative symptoms in schizophrenia. The generalizability of identified networks was then validated in another independent sample Results: We identified the neural fingerprints of negative symptoms, which included connections within and between canonical networks implicated in motivation, cognition and error processing. Impact: Our findings provide fundamental insights into the neurobiological underpinnings of negative symptoms in schizophrenia, and the established brain-symptom models have the potential to further translate into clinical applications as targets for precise intervention such as brain stimulation.