Vadim Elisseev, Max Esposito, et al.
NeurIPS 2024
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.
Vadim Elisseev, Max Esposito, et al.
NeurIPS 2024
Bertram Poettering, Simon Rastikian
PKC 2024
Prateek Yadav, Derek Tam, et al.
NeurIPS 2023
Tao Cai, Yifei Li, et al.
MRS Fall Meeting 2024