Leshem Choshen, Elad Venezian, et al.
EMNLP 2023
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.
Leshem Choshen, Elad Venezian, et al.
EMNLP 2023
Oscar Sainz, Iker García-ferrero, et al.
ACL 2024
Elliot Nelson, Dmitry Zubarev
MRS Spring Meeting 2023
Thanh Lam Hoang, Marco Luca Sbodio, et al.
AAAI 2024