Predicting sex, age, general cognition and mental health with machine learning on brain structural connectomes.

ABSTRACT: There is an increasing expectation that advanced, computationally expensive machine learning (ML) techniques, when applied to large population-wide neuroimaging datasets, will help to uncover key differences in the human brain in health and disease. We take a comprehensive approach to explore how multiple aspects of brain structural connectivity can predict sex, age, general cognitive function and general psychopathology, testing different ML algorithms from deep learning (DL) model (BrainNetCNN) to classical ML methods. We modelled N = 8183 structural connectomes from UK Biobank using six different structural network weightings obtained from diffusion MRI. Streamline count generally provided the highest prediction accuracies in all prediction tasks. DL did not improve on prediction accura

SUBMITTER: Yeung HW 

PROVIDER: S-EPMC9980898 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature