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White matter tract microstructure has been shown to influenceneuropsychological scores of cognitive performance. However, prediction ofthese scores from white matter tract data has not been attempted. In thispaper, we propose a deep-learning-based framework for neuropsychological scoreprediction using microstructure measurements estimated from diffusion magneticresonance imaging (dMRI) tractography, focusing on predicting performance on areceptive vocabulary assessment task based on a critical fiber tract forlanguage, the arcuate fasciculus (AF). We directly utilize information from allpoints in a fiber tract, without the need to average data along the fiber as istraditionally required by diffusion MRI tractometry methods. Specifically, werepresent the AF as a point cloud with microstructure measurements at eachpoint, enabling adoption of point-based neural networks. We improve predictionperformance with the proposed Paired-Siamese Loss that utilizes informationabout differences between continuous neuropsychological scores. Finally, wepropose a Critical Region Localization (CRL) algorithm to localize informativeanatomical regions containing points with strong contributions to theprediction results. Our method is evaluated on data from 806 subjects from theHuman Connectome Project dataset. Results demonstrate superiorneuropsychological score prediction performance compared to baseline methods.We discover that critical regions in the AF are strikingly consistent acrosssubjects, with the highest number of strongly contributing points located infrontal cortical regions (i.e., the rostral middle frontal, pars opercularis,and pars triangularis), which are strongly implicated as critical areas forlanguage processes.

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