Schizophrenia has been increasingly conceptualized as a neurodevelopmental disorder with potential neurodegenerative components, yet evidence for progressive brain changes remains controversial. We investigated longitudinal trajectories of structural, functional, and cognitive biomarkers to characterize neurodegenerative patterns in schizophrenia using machine learning approaches. We analysed longitudinal neuroimaging and cognitive data from 150 participants (75 schizophrenia patients, 75 healthy controls) across three timepoints (baseline, 2-year, and 4-year follow-up). Multimodal biomarkers included frontal and temporal cortical thickness, hippocampal volume, default mode network (DMN) functional connectivity, and cognitive performance measures. Annualized rates of change were calculated for each participant. Random Forest classification was employed to identify the neurodegenerative signature distinguishing schizophrenia from controls. Schizophrenia patients exhibited significantly accelerated decline across all biomarkers compared to controls. Frontal cortical thickness declined 4.2× faster (p < 0.001), hippocampal volume atrophied 3.8× faster (p < 0.001), and cognitive measures deteriorated 5 - 6× faster (p < 0.001) in patients. Correlation network analysis revealed disrupted connectivity patterns in schizophrenia, particularly between structural and functional measures. Machine learning classification achieved 86.7% accuracy (AUC = 0.917) with hippocampal volume, DMN connectivity, and executive function as top predictive features. Our findings provide robust machine learning evidence for a distinct neurodegenerative signature in schizophrenia characterized by accelerated multimodal decline. These results support the integration of neurodegenerative models into schizophrenia pathophysiology and highlight the potential for composite biomarker panels in early identification and monitoring of disease progression.Subject AreasPsychiatry
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