Model and predict age and sex in healthy subjects using brain white matter features: A deep learning approach
This work addresses the problem of monitoring age-related changes and investigating sex differences in brain structure for neuroscience researchers, but it is incremental as it applies a novel ensembled neural network to an existing dataset.
The study tackled predicting age and sex from brain white matter features using diffusion MRI data, achieving 94.82% accuracy in sex prediction and a mean absolute error of 2.51 years in age prediction on the HCP dataset.
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.