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 ventricles, white matter, cortical thickness, and functional connectivity‐based imaging phenotypes, every subject was parameterized with an outlier score per imaging phenotype. Outlier scores of these imaging phenotypes had good‐to‐excellent test–retest reliability, with the exception of resting‐state functional connectivity (RSFC). Due to the low reliability of RSFC outlier scores, RSFC outliers were excluded from further individual‐level outlier screening. In secondary screening, the extreme outliers (1,026 subjects) were examined individually, and those arising from data collection/processing errors were eliminated. A representative subgroup of 120 subjects from the remaining non‐artifactual outliers were radiologically reviewed, and radiological findings were identified in 97.5% of them. This study establishes an unsupervised framework for investigating rare individual imaging phenotypes within a large neuroimaging cohort.

Blockwise Robust Singular Value Decomposition for MRI Brain Image Denoising

“…MRI scan produces different slices of an image. However, MRI scan image often contain outliers [1] and the presence of noise/outliers hampers the disease diagnosis [2]. Therefore, image preprocessing as well as image filtering can help minimize the effects of these degradations [3].…”