Model and predict age and sex in healthy subjects using brain white matter features: A deep learning approach

TL;DR

This paper presents a deep learning model that uses diffusion MRI features to accurately predict age and sex in healthy individuals, aiding understanding of brain structure differences.

Contribution

It introduces a novel ensembled neural network classifier that combines fiber-cluster-based diffusion features for improved age and sex prediction from brain white matter.

Findings

Achieved 94.82% accuracy in sex prediction

Predicted age with 2.51 years MAE

Identified key features like FA and fiber count as most predictive

Abstract

The human brain's white matter (WM) structure is of immense interest to the scientific community. Diffusion MRI gives a powerful tool to describe the brain WM structure noninvasively. To potentially enable monitoring of age-related changes and investigation of sex-related brain structure differences on the mapping between the brain connectome and healthy subjects' age and sex, we extract fiber-cluster-based diffusion features and predict sex and age with a novel ensembled neural network classifier. We conduct experiments on the Human Connectome Project (HCP) young adult dataset and show that our model achieves 94.82% accuracy in sex prediction and 2.51 years MAE in age prediction. We also show that the fractional anisotropy (FA) is the most predictive of sex, while the number of fibers is the most predictive of age and the combination of different features can improve the model performance.