Cross-domain Fiber Cluster Shape Analysis for Language Performance Cognitive Score Prediction
This work addresses the challenge of linking brain structure to cognitive function for neuroscience and medical applications, but it is incremental as it builds on existing shape analysis and transformer methods.
The researchers tackled the problem of predicting human language performance from brain imaging by analyzing the shape of 3D white matter connections, and they found that their novel SFFormer framework, which fuses shape, microstructure, and connectivity features, improved prediction on a dataset of 1065 healthy young adults.
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.