From Snapshots to Stable Outcomes: Resting-State Functional Magnetic Resonance Imaging-Based Prognosis of Functioning in Patients With Psychosis Risk or Recent-Onset Depression

Clinical Snapshot

Key Findings

• P Value: p < 0.001

• Effect Size: Balanced accuracy 67% for transdiagnostic functional impairment prediction

• Primary Outcome: Stable good functioning across 9- and 18-month follow-ups

• Nnt Or Sensitivity: Sensitivity 65%, Specificity 70% for functional impairment prediction

• Confidence Interval: Not explicitly reported for primary outcome

Clinical Application

Limited by requirement for specialized fMRI facilities and machine learning expertise Relevant to Australian early psychosis services and headspace centers, though implementation would require significant infrastructure investment and training Patients at clinical high risk for psychosis or with recent-onset depression in specialized mental health services

Abstract

BACKGROUND: Early recovery of functioning is critical for favorable outcomes in psychotic and affective disorders. Transdiagnostic brain activity patterns may capture pathways for poor outcomes before clinical manifestation, thereby supporting timely prevention and intervention. METHODS: Using machine learning, we evaluated the transdiagnostic prognostic value of resting-state functional magnetic resonance imaging fractional amplitude of low-frequency fluctuations (fALFF) (slow-5 and slow-4 sub-bands) for functional outcomes in patients at clinical high risk for psychosis (n = 217) or with recent-onset depression (n = 198) from the multisite PRONIA (Prognostic Tools for Early Psychosis Management) study. Leave-site-out cross-validation assessed the geographic generalizability of models across disability and symptom domains, with outcomes defined as snapshots at 9- or 18-month follow-up or across both time points. We examined diagnosis-specific performance, generalization to recent-onset psychosis (n = 140), and negative symptoms and the added value of fALFF over clinical prognostication. RESULTS: Transdiagnostic models predicting stable good functioning across follow-ups showed up to 10% higher balanced accuracy (BAC) than snapshot models. Decreased slow-5 fALFFs in the default mode network, executive control network (ECN), and dorsal attentional network (DAN) and increased fALFF in the salience network, ECN, and DAN predicted impairment with BAC = 67% (sensitivity = 65%, specificity = 70%, p < .001). This model generalized to recent-onset psychosis (BAC = 62%, sensitivity = 64%, specificity = 59%, p < .001) and predicted (BAC = 65%, sensitivity = 66%, specificity = 65%, p < .001) and was mediated by negative symptoms. Slow-5-based models improved prognostic accuracy over expert ratings in disability (BACraters = 66%, BACraters+slow-5 = 75%, W = 1680, p < .001) and symptom (BACraters = 61%, BACraters+slow-5 = 71%, W = 1444, p < .001) domains. CONCLUSIONS: We highlighted the prognostic value of fALFF for functional impairment in psychosis risk and early depression. Leveraging trajectorial information, we identified candidate imaging biomarkers to improve prognostication, thereby supporting personalized prevention and recovery strategies.

References

1. 1.Buciuman, M. O., Haas, S. S., Antonucci, L. A., Sarisik, E., Khuntia, A., Lichtenstein, T., Rosen, M., Kambeitz, J., Pantelis, C., Lencer, R., Bertolino, A., Brambilla, P., Upthegrove, R., Wood, S. J., Falkai, P., Riecher-Rössler, A., Ruhrmann, S., Schultze-Lutter, F., Meisenzahl, E., Hietala, J., Salokangas, R. K. R., Borgwardt, S., Dwyer, D. B., Kambeitz-Ilankovic, L., & Koutsouleris, N. (2026). From snapshots to stable outcomes: Resting-state functional magnetic resonance imaging-based prognosis of functioning in patients with psychosis risk or recent-onset depression. Biological Psychiatry. https://doi.org/10.1016/j.biopsych.2025.07.003