Fiber Tract Shape Measures Inform Prediction of Non-Imaging Phenotypes

TL;DR

This study explores how fiber tract shape features from diffusion MRI can enhance the prediction of non-imaging phenotypes, showing that shape information improves predictive accuracy when combined with traditional microstructure and connectivity measures.

Contribution

It introduces the use of fiber tract shape features like length, diameter, and elongation for phenotype prediction, demonstrating their added value over traditional measures.

Findings

Shape features are individually predictive of phenotypes.

Combining shape with microstructure and connectivity improves prediction accuracy.

Normalized shape features help reduce brain size bias.

Abstract

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.