Abstract
Negative symptoms remain a major therapeutic challenge in schizophrenia, significantly impacting functional outcomes, yet their underlying neural mechanisms remain poorly understood. Traditional static functional connectivity analyses, which examine average correlations over time, may overlook critical temporal features of brain network organization and fail to capture dynamic shifts in connectivity patterns. Resting-state functional magnetic resonance imaging (rs-fMRI), particularly when analyzed using co-activation pattern analysis (CAP), provides a framework to study these dynamic network changes with greater temporal resolution. Using CAP analysis of rs-fMRI data, we investigated brain network dynamics in 31 schizophrenia patients with predominant negative symptoms, 31 patients without predominant negative symptoms, and 34 healthy controls. Eight distinct brain states were identified, characterized by antagonistic relationships between sensorimotor, default mode, and salience networks. Compared to healthy controls, the overall schizophrenia group showed altered temporal characteristics, including a reduced occurrence of a sensorimotor-dominant state and excessive transitions from this state to a control-salience network state. Notably, patients with predominant negative symptoms demonstrated distinct temporal characteristics, including reduced dwell time in sensorimotor-salience states and excessive transitions from sensorimotor to control-salience network states. In contrast, patients without predominant negative symptoms did not exhibit such excessive state transitions, while their symptom severity correlated with the occurrence of a cognitive-sensorimotor network state. Network alterations significantly correlated with symptom severity in both the overall schizophrenia group and the subgroup without predominant negative symptoms, while no significant correlations were observed in patients with predominant negative symptoms. These findings suggest that predominant negative symptoms are associated with stable trait-like network reorganization characterized by excessive state transitions rather than state-dependent dysregulation, providing potential neuroimaging markers for clinical assessment.