Kahn Rhrissorrakrai, Filippo Utro, et al.
Briefings in Bioinformatics
The prospective study of youths at clinical high risk (CHR) for psychosis, including neuroimaging, can identify neural signatures predictive of psychosis outcomes using algorithms that integrate complex information. Here, to identify risk and psychosis conversion, we implemented multiple kernel learning (MKL), a multimodal machine learning approach allowing patterns from each modality to inform each other. Baseline multimodal scans (n = 74, 11 converters) included structural, resting-state functional imaging, and diffusion-weighted data. Multimodal MKL outperformed unimodal models (AUC = 0.73 vs. 0.66 in predicting conversion). Moreover, patterns learned by MKL were robust to training set variations, suggesting it can identify cross-modality redundancies and synergies to stabilize the predictive pattern. We identified many predictors consistent with the literature, including frontal cortices, cingulate, thalamus, and striatum. This highlights the advantage of methods that leverage the complex pathophysiology of psychosis.
Kahn Rhrissorrakrai, Filippo Utro, et al.
Briefings in Bioinformatics
Gang Liu, Michael Sun, et al.
ICLR 2025
Jolie McDonnell, William Hord, et al.
SPIE Medical Imaging 2019
Jennifer Kelly, Ashley Evans, et al.
ISMB 2025