Abstract #3926
Bridging Structural MRI with Cognitive Function for Individual Level Classification of Early Psychosis via Deep Learning
Yang Wen1,2,3, Chuan Zhou2, Leiting Chen2, Yu Deng4, Martine Cleusix5, Raoul Jenni5, Philippe Conus6, Kim Q. Do5, and Lijing Xin1,3
1Animal imaging and technology core (AIT), Center for Biomedical Imaging (CIBM), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, 2University of Electronic Science and Technology of China, Chengdu, China, 3Laboratory of Functional and Metabolic Imaging, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, 4Department of Biomedical Engineering, King's College London, London, United Kingdom, 5Center for Psychiatric Neuroscience, Department of Psychiatry, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland, 6Service of General Psychiatry, Department of Psychiatry, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
Synopsis
The aims of this study were: (1) to test the feasibility of using a deep learning model with 7T sMRI as an input to predict cognition levels (CLs) at the single-subject level, and (2) to investigate whether the inclusion of CLs estimation could facilitate the classification for early psychosis (EP) patients and healthy controls (HCs). Promising accuracy was achieved in estimating CLs and the inclusion provides considerable classification improvement. Fivefold cross-validating experiments demonstrated higher classification AUC-ROC scores over published methods. Therefore, deep learning can be used to estimate CLs and CL estimation improves the classification performance of EP.
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