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

arXiv: https://arxiv.org/abs/2202.03595 · HTML: https://mlsanity.com/paper/2202.03595/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 21 references that are in the map.
- First posted on arXiv 8 Feb 2022.

## Built on by (most cited first, 17 of 17)

- [TractGeoNet: A geometric deep learning framework for pointwise analysis of tract microstructure to predict language assessment performance](https://mlsanity.com/paper/2307.03982/) (2023)
- [White Matter Tracts are Point Clouds: Neuropsychological Score Prediction and Critical Region Localization via Geometric Deep Learning](https://mlsanity.com/paper/2207.02402/) (2022)
- [TractGraphCNN: anatomically informed graph CNN for classification using diffusion MRI tractography](https://mlsanity.com/paper/2301.01911/) (2023)
- [TractoSCR: A Novel Supervised Contrastive Regression Framework for Prediction of Neurocognitive Measures Using Multi-Site Harmonized Diffusion MRI Tractography](https://mlsanity.com/paper/2210.07411/) (2022)
- [TractoFormer: A Novel Fiber-level Whole Brain Tractography Analysis Framework Using Spectral Embedding and Vision Transformers](https://mlsanity.com/paper/2207.02327/) (2022)
- [TractGraphFormer: Anatomically Informed Hybrid Graph CNN-Transformer Network for Interpretable Sex and Age Prediction from Diffusion MRI Tractography](https://mlsanity.com/paper/2407.08883/) (2024)
- [Fiber Tract Shape Measures Inform Prediction of Non-Imaging Phenotypes](https://mlsanity.com/paper/2303.09124/) (2023)
- [The shape of the brain's connections is predictive of cognitive performance: an explainable machine learning study](https://mlsanity.com/paper/2410.15108/) (2024)
- [Age Prediction Performance Varies Across Deep, Superficial, and Cerebellar White Matter Connections](https://mlsanity.com/paper/2211.07398/) (2022)
- [White Matter Geometry-Guided Score-Based Diffusion Model for Tissue Microstructure Imputation in Tractography Imaging](https://mlsanity.com/paper/2407.19460/) (2024)
- [Brain age identification from diffusion MRI synergistically predicts neurodegenerative disease](https://mlsanity.com/paper/2410.22454/) (2024)
- [Deep multimodal saliency parcellation of cerebellar pathways: linking microstructure and individual function through explainable multitask learning](https://mlsanity.com/paper/2407.15132/) (2024)
- [DeepMultiConnectome: Deep Multi-Task Prediction of Structural Connectomes Directly from Diffusion MRI Tractography](https://mlsanity.com/paper/2505.22685/) (2025)
- [Age Sensitive Hippocampal Functional Connectivity: New Insights from 3D CNNs and Saliency Mapping](https://mlsanity.com/paper/2507.01411/) (2025)
- [Latent Representation Learning from 3D Brain MRI for Interpretable Prediction in Multiple Sclerosis](https://mlsanity.com/paper/2510.00051/) (2025)
- [Tractogram foundation model](https://mlsanity.com/paper/2606.09893/) (2026)
- [Contrastive Joint-Embedding Prediction for Representation Learning in Structural MRI](https://mlsanity.com/paper/2607.11962/) (2026)

## Abstract (arXiv)

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.