Machine learning-based estimation of cognitive performance using regional brain MRI markers: the Northern Manhattan Study.

ABSTRACT: High dimensional neuroimaging datasets and machine learning have been used to estimate and predict domain-specific cognition, but comparisons with simpler models composed of easy-to-measure variables are limited. Regularization methods in particular may help identify regions-of-interest related to domain-specific cognition. Using data from the Northern Manhattan Study, a cohort study of mostly Hispanic older adults, we compared three models estimating domain-specific cognitive performance: sociodemographics and APOE ε4 allele status (basic model), the basic model and MRI markers, and a model with only MRI markers. We used several machine learning methods to fit our regression models: elastic net, support vector regression, random forest, and principal components regression. Model performan

SUBMITTER: Caunca MR 

PROVIDER: S-EPMC7854979 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature