This study intended to examine network homogeneity (NH) alterations in drug-naive patients with panic disorder (PD) before and after treatment and whether NH could serve as a potential biomarker.
Methods:
Fifty-eight patients and 85 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging. Patients were rescanned following a 4-week course of paroxetine monotherapy. NH was computed to evaluate intra-network functional integration across the Yeo 7-Network. Machine learning (ML) was employed to assess the diagnostic and prognostic potential of NH metrics. Transcriptome-neuroimaging association analyses were conducted to explore the molecular correlates of NH alterations.
Results:
Compared with HCs, patients showed disrupted intra-network integration in the frontoparietal, default mode, sensorimotor, limbic, and ventral attention networks, with prominent NH alterations in the superior frontal gyrus (SFG), middle temporal gyrus (MTG), superior temporal gyrus (STG), somatosensory cortex, insular, and anterior cingulate cortex. Importantly, the SFG, MTG, and STG demonstrated cross-network abnormalities. After treatment, clinical improvement correlated with normalized NH in the SFG and additional changes in the inferior occipital gyrus and calcarine sulcus within the visual network. ML demonstrated the utility of NH for PD classification and treatment outcome prediction. Transcriptome-neuroimaging analysis identified specific gene profiles related to NH alterations.
Conclusions:
NH reflects both pathological features and treatment-related changes in PD, providing a measure of network dysfunction and therapeutic response. Cross-network NH disruptions in hub regions and visual processing may reflect core neuropharmacological mechanisms underlying PD. ML findings support the potential of NH as a neuroimaging biomarker for diagnosis and treatment monitoring in PD.
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