Cross-domain Fiber Cluster Shape Analysis for Language Performance Cognitive Score Prediction

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.

@article{lo2025_2403.19001,
  title={ Cross-domain Fiber Cluster Shape Analysis for Language Performance Cognitive Score Prediction },
  author={ Yui Lo and Yuqian Chen and Dongnan Liu and Wan Liu and Leo Zekelman and Fan Zhang and Yogesh Rathi and Nikos Makris and Alexandra J. Golby and Weidong Cai and Lauren J. O'Donnell },
  journal={arXiv preprint arXiv:2403.19001},
  year={ 2025 }
}