Outlier detection in multimodal MRI identifies rare individual phenotypes among more than 15,000 brains.

ABSTRACT: Outliers in neuroimaging represent spurious data or the data of unusual phenotypes that deserve special attention such as clinical follow-up. Outliers have usually been detected in a supervised or semi-supervised manner for labeled neuroimaging cohorts. There has been much less work using unsupervised outlier detection on large unlabeled cohorts like the UK Biobank brain imaging dataset. Given its large sample size, rare imaging phenotypes within this unique cohort are of interest, as they are often clinically relevant and could be informative for discovering new processes. Here, we developed a two-level outlier detection and screening methodology to characterize individual outliers from the multimodal MRI dataset of more than 15,000 UK Biobank subjects. In primary screening, using brain v

SUBMITTER: Ma Z 

PROVIDER: S-EPMC8886649 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature