Systematic evaluation of machine learning algorithms for neuroanatomically-based age prediction in youth.

ABSTRACT: Application of machine learning (ML) algorithms to structural magnetic resonance imaging (sMRI) data has yielded behaviorally meaningful estimates of the biological age of the brain (brain-age). The choice of the ML approach in estimating brain-age in youth is important because age-related brain changes in this age-group are dynamic. However, the comparative performance of the available ML algorithms has not been systematically appraised. To address this gap, the present study evaluated the accuracy (mean absolute error [MAE]) and computational efficiency of 21 machine learning algorithms using sMRI data from 2105 typically developing individuals aged 5-22 years from five cohorts. The trained models were then tested in two independent holdout datasets, one comprising 4078 individuals aged

SUBMITTER: Modabbernia A 

PROVIDER: S-EPMC9812239 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature