  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.
