Tractography fiber clustering using diffusion MRI (dMRI) is a crucial strategy for white matter (WM) parcellation. Current methods primarily use the geometric information of fibers (i.e., the spatial trajectories) to group similar fibers into clusters, overlooking the important functional signals present along the fiber tracts. There is increasing evidence that neural activity in the WM can be measured using functional MRI (fMRI), offering potentially valuable multimodal information for fiber clustering. In this paper, we develop a novel deep learning fiber clustering framework, namely Deep Multi-view Fiber Clustering (DMVFC), that uses joint dMRI and fMRI data to enable functionally consistent WM parcellation. DMVFC can effectively integrate the geometric characteristics of the WM fibers with the fMRI BOLD signals along the fiber tracts. It includes two major components: 1) a multi-view pretraining module to compute embedding features from fiber geometric information and functional signals separately, and 2) a collaborative fine-tuning module to simultaneously refine the two kinds of embeddings. In the experiments, we compare DMVFC with two state-of-the-art fiber clustering methods and demonstrate superior performance in achieving functionally meaningful and consistent WM parcellation results.

翻译：利用扩散磁共振（dMRI）进行纤维束追踪聚类是实现白质（WM）分区的重要策略。现有方法主要依据纤维的几何信息（即空间轨迹）将相似纤维分组为簇，却忽略了沿纤维束存在的重要功能信号。越来越多的证据表明，白质中的神经活动可通过功能磁共振（fMRI）进行测量，这为纤维聚类提供了潜在有价值的跨模态信息。本文提出了一种新颖的深度学习纤维聚类框架——深度多视角纤维聚类（DMVFC），该框架联合利用dMRI与fMRI数据，实现功能一致的白质分区。DMVFC能够有效整合白质纤维的几何特征与沿纤维束的fMRI BOLD信号。其包含两个核心模块：1）多视角预训练模块，分别从纤维几何信息与功能信号中计算嵌入特征；2）协同微调模块，同时对两类嵌入表示进行联合优化。在实验中，我们将DMVFC与两种先进的纤维聚类方法进行比较，结果表明该方法在获得功能意义明确且一致的白质分区结果方面具有优越性能。