Outlier detection in multimodal MRI identifies rare individual phenotypes among 20,000 brains
Zhiwei Ma, Daniel S. Reich, Sarah J Dembling, Jeff H. Duyn, Alan P. Koretsky
This study uses unsupervised outlier detection in multimodal MRI data from 20,000 UK Biobank subjects to identify rare individual phenotypes, finding high reliability for some measures and confirming many outliers through radiological review.
Human Brain Mapping · Dec 26, 2021Peer-reviewedNeuroscience