Abstract #4521
Meta-matching to translate phenotypic predictive models from big to small data on structural MRI
Naren Wulan1,2,3, Lijun An1,2,3, Chen Zhang1,2,3, Ru Kong1,2,3, Pansheng Chen1,2,3, Danilo Bzdok4,5, Simon Eickhoff6,7, Avram Holmes8, and B. T. Thomas Yeo1,2,3,9,10
1Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore, 2Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore, 3N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore, Singapore, 4Department of Biomedical Engineering,McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), Faculty of Medicine, School of Computer Science, McGill University, Montreal, QC, Canada, 5Mila – Quebec Artificial Intelligence Institute, Montreal, QC, Canada, 6Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany, 7Institute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Center Jülich, Jülich, Germany, 8Department of Psychiatry, Brain Health Institute, Rutgers University, Piscataway, NJ, United States, 9Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore, 10Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
Synopsis
Keywords: Diagnosis/Prediction, Brain, Phenotypic prediction, structural MRI, transfer learning
Motivation: Small sample size on structural MRI is evitable in reality and significantly limits phenotypic prediction performance.
Goal(s): Our goal was to improve prediction performance on small datasets for structural MRI brain imaging.
Approach: We adapted the meta-matching framework from functional to structural MRI, and compared it with baseline methods (Elastic net and direct transfer learning).
Results: Our meta-matching-based approaches can greatly boost behavioral prediction performance for different small-scale structural MRI datasets.
Impact: Our meta-matching-based methods should be able to make good predictions for a variety of neurological and psychiatric disorders even if the availability of structural MRI brain imaging is quite small.
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