Brain age prediction in schizophrenia: Does the choice of machine learning algorithm matter?

ABSTRACT: Brain-predicted age difference (brainPAD) has been used in schizophrenia to assess individual-level deviation in the biological age of the patients' brain (i.e., brain-age) from normative reference brain structural datasets. There is marked inter-study variation in brainPAD in schizophrenia which is commonly attributed to sample heterogeneity. However, the potential contribution of the different machine learning algorithms used for brain-age estimation has not been systematically evaluated. Here, we aimed to assess variation in brain-age estimated by six commonly used algorithms [ordinary least squares regression, ridge regression, least absolute shrinkage and selection operator regression, elastic-net regression, linear support vector regression, and relevance vector regression] when appl

SUBMITTER: Lee WH 

PROVIDER: S-EPMC8056405 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature