Biological psychiatry · 2026
Machine Learning-Based Discrimination of Treatment-Resistant Schizophrenia Using Structural Brain Imaging: A Multi-Site Proof-of-Concept Study.
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Torres-Carmona Edgardo, Graff-Guerrero Mario, Nakajima Shinichiro, Iwata Yusuke, Ueno Fumihiko, Koizumi Teruki, Honda Shiori, Tsugawa Sakiko, Homma Saki, Ogyu Kamiyu, Tarumi Ryosuke, Song Jianmeng, Deluca Vincenzo, Remington Gary, Gerretsen Philip, Graff-Guerrero Ariel
Аннотация
BACKGROUND: Treatment-resistant schizophrenia (TRS) affects 20-30% of individuals with schizophrenia, with persistent symptoms, functional impairment, and reduced quality of life. Clinical identification remains dependent on sequential antipsychotic trials despite reported structural brain differences between TRS and treatment-responsive schizophrenia (TxR). This study evaluated whether structural MRI features could discriminate clinically defined TRS from TxR using machine learning.
METHODS: A total of 225 participants (122 TRS, 103 TxR) from multi-site studies in Canada and Japan were included. Cortical thickness, brain volume, surface area, and intrinsic curvature were derived from T1-weighted MRI using FreeSurfer, with feature engineering generating volumetric-cortical thickness interaction terms. A voting ensemble was evaluated under a primary leakage-controlled NeuroComBat harmonization and a secondary exploratory full-dataset harmonization. Performance was assessed using ROC-AUC, F1 score, precision, and recall.
RESULTS: In the leakage-controlled analysis, the voting ensemble achieved a held-out ROC-AUC of 0.57 and macro F1 of 0.56; training cross-validation yielded ROC-AUC of 0.715. The full-dataset harmonization analysis yielded ROC-AUC of 0.60 and macro F1 of 0.62, interpreted cautiously due to leakage. Temporal-occipital cortical thickness and choroid plexus volume interaction terms contributed most to model performance.
CONCLUSION: Structural MRI features may support cross-sectional discrimination of TRS from TxR. The divergence between harmonization strategies highlights the importance of leakage-aware preprocessing in neuroimaging. Validation in independent cohorts is required to establish whether these features contribute to earlier identification of treatment resistance.
METHODS: A total of 225 participants (122 TRS, 103 TxR) from multi-site studies in Canada and Japan were included. Cortical thickness, brain volume, surface area, and intrinsic curvature were derived from T1-weighted MRI using FreeSurfer, with feature engineering generating volumetric-cortical thickness interaction terms. A voting ensemble was evaluated under a primary leakage-controlled NeuroComBat harmonization and a secondary exploratory full-dataset harmonization. Performance was assessed using ROC-AUC, F1 score, precision, and recall.
RESULTS: In the leakage-controlled analysis, the voting ensemble achieved a held-out ROC-AUC of 0.57 and macro F1 of 0.56; training cross-validation yielded ROC-AUC of 0.715. The full-dataset harmonization analysis yielded ROC-AUC of 0.60 and macro F1 of 0.62, interpreted cautiously due to leakage. Temporal-occipital cortical thickness and choroid plexus volume interaction terms contributed most to model performance.
CONCLUSION: Structural MRI features may support cross-sectional discrimination of TRS from TxR. The divergence between harmonization strategies highlights the importance of leakage-aware preprocessing in neuroimaging. Validation in independent cohorts is required to establish whether these features contribute to earlier identification of treatment resistance.