# Fiber Tract Shape Measures Inform Prediction of Non-Imaging Phenotypes

arXiv: https://arxiv.org/abs/2303.09124 · HTML: https://mlsanity.com/paper/2303.09124/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 35 references that are in the map.
- First posted on arXiv 16 Mar 2023.

## Builds on (most cited first, 3 of 3)

- [Model and predict age and sex in healthy subjects using brain white matter features: A deep learning approach](https://mlsanity.com/paper/2202.03595/) (2022)
- [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)

## Built on by (most cited first, 5 of 5)

- [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)
- [Cross-domain Fiber Cluster Shape Analysis for Language Performance Cognitive Score Prediction](https://mlsanity.com/paper/2403.19001/) (2024)
- [The shape of the brain's connections is predictive of cognitive performance: an explainable machine learning study](https://mlsanity.com/paper/2410.15108/) (2024)
- [White Matter Geometry-Guided Score-Based Diffusion Model for Tissue Microstructure Imputation in Tractography Imaging](https://mlsanity.com/paper/2407.19460/) (2024)
- [DeepMultiConnectome: Deep Multi-Task Prediction of Structural Connectomes Directly from Diffusion MRI Tractography](https://mlsanity.com/paper/2505.22685/) (2025)

## Abstract (arXiv)

Neuroimaging measures of the brain's white matter connections can enable the prediction of non-imaging phenotypes, such as demographic and cognitive measures. Existing works have investigated traditional microstructure and connectivity measures from diffusion MRI tractography, without considering the shape of the connections reconstructed by tractography. In this paper, we investigate the potential of fiber tract shape features for predicting non-imaging phenotypes, both individually and in combination with traditional features. We focus on three basic shape features: length, diameter, and elongation. Two different prediction methods are used, including a traditional regression method and a deep-learning-based prediction method. Experiments use an efficient two-stage fusion strategy for prediction using microstructure, connectivity, and shape measures. To reduce predictive bias due to brain size, normalized shape features are also investigated. Experimental results on the Human Connectome Project (HCP) young adult dataset (n=1065) demonstrate that individual shape features are predictive of non-imaging phenotypes. When combined with microstructure and connectivity features, shape features significantly improve performance for predicting the cognitive score TPVT (NIH Toolbox picture vocabulary test). Overall, this study demonstrates that the shape of fiber tracts contains useful information for the description and study of the living human brain using machine learning.