Keywords: Tractography, Tractography & Fibre Modelling, Point cloud, Deep learning
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.
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