Mind the gap: Performance metric evaluation in brain-age prediction.

ABSTRACT: Estimating age based on neuroimaging-derived data has become a popular approach to developing markers for brain integrity and health. While a variety of machine-learning algorithms can provide accurate predictions of age based on brain characteristics, there is significant variation in model accuracy reported across studies. We predicted age in two population-based datasets, and assessed the effects of age range, sample size and age-bias correction on the model performance metrics Pearson's correlation coefficient (r), the coefficient of determination (R² ), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The results showed that these metrics vary considerably depending on cohort age range; r and R² values are lower when measured in samples with a narrow

SUBMITTER: de Lange AG 

PROVIDER: S-EPMC9188975 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature