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Abstract #2485

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

Ziyang Gao1, Yuan Xiao1, Qiyong Gong1, and Su Lui1
1West China Hospital of Sichuan University, Chengdu, China

Synopsis

Keywords: Functional Connectivity, Psychiatric Disorders, schizophrenia, connectome, negative symptom, predictive model, neural fingerprint

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.

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Keywords