Pointwise analysis of tract microstructure using geometric deep learning for language performance prediction

Motivation: The prediction of cognitive performance scores using diffusion MRI tractography enables the study of relationships between brain structure and function. Goal(s): Our goal is to achieve accurate prediction of cognition and identify critical brain regions for prediction. Approach: We propose a geometric deep-learning framework for language score prediction. It utilizes a point cloud representation of fiber tracts for detailed spatial and microstructure information and incorporates a novel regression loss to utilize the continuity of language scores. Results: Our method outperforms comparison methods with state-of-the-art representations of fiber tracts and identifies predictive language-related brain regions. Impact: Our proposed novel geometric deep learning framework using a point cloud representation of fiber tracts can be applied to various tractography-based prediction tasks to improve performance and provide a probe to explore relationships between brain structure and function.