# White Matter Tracts are Point Clouds: Neuropsychological Score Prediction and Critical Region Localization via Geometric Deep Learning

arXiv: https://arxiv.org/abs/2207.02402 · HTML: https://mlsanity.com/paper/2207.02402/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 34 references that are in the map.
- First posted on arXiv 6 Jul 2022.

## Builds on (most cited first, 11 of 11)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [Multi-View 3D Object Detection Network for Autonomous Driving](https://mlsanity.com/paper/1611.07759/) (2016)
- [Exploiting Edge-Oriented Reasoning for 3D Point-based Scene Graph Analysis](https://mlsanity.com/paper/2103.05558/) (2021)
- [Domain Adaptive Relational Reasoning for 3D Multi-Organ Segmentation](https://mlsanity.com/paper/2005.09120/) (2020)
- [3D Medical Point Transformer: Introducing Convolution to Attention Networks for Medical Point Cloud Analysis](https://mlsanity.com/paper/2112.04863/) (2021)
- [Tractogram filtering of anatomically non-plausible fibers with geometric deep learning](https://mlsanity.com/paper/2003.11013/) (2020)
- [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)
- [SupWMA: Consistent and Efficient Tractography Parcellation of Superficial White Matter with Deep Learning](https://mlsanity.com/paper/2201.12528/) (2022)

## Built on by (most cited first, 10 of 10)

- [TractGeoNet: A geometric deep learning framework for pointwise analysis of tract microstructure to predict language assessment performance](https://mlsanity.com/paper/2307.03982/) (2023)
- [TractCloud: Registration-free tractography parcellation with a novel local-global streamline point cloud representation](https://mlsanity.com/paper/2307.09000/) (2023)
- [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)
- [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)
- [TractCloud-FOV: Deep Learning-based Robust Tractography Parcellation in Diffusion MRI with Incomplete Field of View](https://mlsanity.com/paper/2502.20637/) (2025)
- [DeepMultiConnectome: Deep Multi-Task Prediction of Structural Connectomes Directly from Diffusion MRI Tractography](https://mlsanity.com/paper/2505.22685/) (2025)
- [FiberGeoText: A Vision-Language Model for Population- Level Organization of Superficial White Matter](https://mlsanity.com/paper/2610.02755/) (2026)

## Abstract (arXiv)

White matter tract microstructure has been shown to influence neuropsychological scores of cognitive performance. However, prediction of these scores from white matter tract data has not been attempted. In this paper, we propose a deep-learning-based framework for neuropsychological score prediction using microstructure measurements estimated from diffusion magnetic resonance imaging (dMRI) tractography, focusing on predicting performance on a receptive vocabulary assessment task based on a critical fiber tract for language, the arcuate fasciculus (AF). We directly utilize information from all points in a fiber tract, without the need to average data along the fiber as is traditionally required by diffusion MRI tractometry methods. Specifically, we represent the AF as a point cloud with microstructure measurements at each point, enabling adoption of point-based neural networks. We improve prediction performance with the proposed Paired-Siamese Loss that utilizes information about differences between continuous neuropsychological scores. Finally, we propose a Critical Region Localization (CRL) algorithm to localize informative anatomical regions containing points with strong contributions to the prediction results. Our method is evaluated on data from 806 subjects from the Human Connectome Project dataset. Results demonstrate superior neuropsychological score prediction performance compared to baseline methods. We discover that critical regions in the AF are strikingly consistent across subjects, with the highest number of strongly contributing points located in frontal cortical regions (i.e., the rostral middle frontal, pars opercularis, and pars triangularis), which are strongly implicated as critical areas for language processes.