TractGeoNet: A geometric deep learning framework for pointwise analysis
  of tract microstructure to predict language assessment performance
Yuqian Chen Leo R. Zekelman Chaoyi Zhang Tengfei Xue Yang Song Nikos Makris Yogesh Rathi Alexandra J. Golby Weidong Cai Fan Zhang Lauren J. O'Donnell
We propose a geometric deep-learning-based framework, TractGeoNet, for
performing regression using diffusion magnetic resonance imaging (dMRI)
tractography and associated pointwise tissue microstructure measurements. By
employing a point cloud representation, TractGeoNet can directly utilize
pointwise tissue microstructure and positional information from all points
within a fiber tract. To improve regression performance, we propose a novel
loss function, the Paired-Siamese Regression loss, which encourages the model
to focus on accurately predicting the relative differences between regression
label scores rather than just their absolute values. In addition, we propose a
Critical Region Localization algorithm to identify highly predictive anatomical
regions within the white matter fiber tracts for the regression task. We
evaluate the effectiveness of the proposed method by predicting individual
performance on two neuropsychological assessments of language using a dataset
of 20 association white matter fiber tracts from 806 subjects from the Human
Connectome Project. The results demonstrate superior prediction performance of
TractGeoNet compared to several popular regression models. Of the twenty tracts
studied, we find that the left arcuate fasciculus tract is the most highly
predictive of the two studied language performance assessments. The localized
critical regions are widespread and distributed across both hemispheres and all
cerebral lobes, including areas of the brain considered important for language
function such as superior and anterior temporal regions, pars opercularis, and
precentral gyrus. Overall, TractGeoNet demonstrates the potential of geometric
deep learning to enhance the study of the brain's white matter fiber tracts and
to relate their structure to human traits such as language performance.