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

• URL: http://arxiv.org/abs/2202.03595v1
• Date: Tue, 8 Feb 2022 01:50:12 GMT
• Title: Model and predict age and sex in healthy subjects using brain white matter features: A deep learning approach
• Authors: Hao He, Fan Zhang, Steve Pieper, Nikos Makris, Yogesh Rathi, William Wells III, Lauren J. O'Donnell
• Abstract summary: Diffusion MRI gives a powerful tool to describe the brain WM structure noninvasively. We extract fiber-cluster-based diffusion features and predict sex and age with a novel ensembled neural network. 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.
• Score: 8.202227194197066
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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