Title: Cross-domain Fiber Cluster Shape Analysis for Language Performance Cognitive Score Prediction Show Chinese title

Title: 跨域纤维集群形状分析用于语言表现认知评分预测

Abstract: 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. Show Chinese abstract

Abstract: 形状在计算机图形学中扮演着重要的角色，提供了传达物体形态和功能的有用特征。在脑成像中的形状分析可以帮助解释人脑的结构与功能之间的相关性。 在这项工作中，我们研究了大脑三维白质连接的形状及其与人类认知功能潜在预测关系。 我们使用扩散磁共振成像（dMRI）纤维束成像技术重建大脑连接为三维点序列。 为了描述每条连接，除了传统的dMRI连通性和组织微结构特征外，我们还提取了12种形状描述符。 我们引入了一种新的框架，即形状融合纤维束聚类变换器（SFFormer），该框架利用多头交叉注意力特征融合模块，基于dMRI纤维束成像预测特定受试者的语言表现。 我们在一个包含1065名健康年轻成年人的大数据集上评估了该方法的性能。 结果显示，基于变压器的SFFormer模型以及与形状、微结构和连通性的特征融合都是信息丰富的，并且它们共同提高了特定受试者语言表现评分的预测。 总体而言，我们的结果表明，大脑连接的形状可以预测人类的语言功能。