  Shape plays an important role in computer graphics, offering informative
features to convey an object's morphology and functionality. Shape analysis in
brain imaging can help interpret structural and functionality correlations of
the human brain. In this work, we investigate the shape of the brain's 3D white
matter connections and its potential predictive relationship to human cognitive
function. We reconstruct brain connections as sequences of 3D points using
diffusion magnetic resonance imaging (dMRI) tractography. To describe each
connection, we extract 12 shape descriptors in addition to traditional dMRI
connectivity and tissue microstructure features. We introduce a novel
framework, Shape--fused Fiber Cluster Transformer (SFFormer), that leverages a
multi-head cross-attention feature fusion module to predict subject-specific
language performance based on dMRI tractography. We assess the performance of
the method on a large dataset including 1065 healthy young adults. The results
demonstrate that both the transformer-based SFFormer model and its inter/intra
feature fusion with shape, microstructure, and connectivity are informative,
and together, they improve the prediction of subject-specific language
performance scores. Overall, our results indicate that the shape of the brain's
connections is predictive of human language function.
