Population heterogeneity in clinical cohorts affects the predictive accuracy of brain imaging.

ABSTRACT: Brain imaging research enjoys increasing adoption of supervised machine learning for single-participant disease classification. Yet, the success of these algorithms likely depends on population diversity, including demographic differences and other factors that may be outside of primary scientific interest. Here, we capitalize on propensity scores as a composite confound index to quantify diversity due to major sources of population variation. We delineate the impact of population heterogeneity on the predictive accuracy and pattern stability in 2 separate clinical cohorts: the Autism Brain Imaging Data Exchange (ABIDE, n = 297) and the Healthy Brain Network (HBN, n = 551). Across various analysis scenarios, our results uncover the extent to which cross-validated prediction performances ar

SUBMITTER: Benkarim O 

PROVIDER: S-EPMC9094526 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature