A Novel Deep Learning Tractography Fiber Clustering Framework for Functionally Consistent White Matter Parcellation Using Multimodal Diffusion MRI and Functional MRI

• URL: http://arxiv.org/abs/2411.01859v1
• Date: Mon, 04 Nov 2024 07:21:06 GMT
• Title: A Novel Deep Learning Tractography Fiber Clustering Framework for Functionally Consistent White Matter Parcellation Using Multimodal Diffusion MRI and Functional MRI
• Authors: Jin Wang, Bocheng Guo, Yijie Li, Junyi Wang, Yuqian Chen, Jarrett Rushmore, Nikos Makris, Yogesh Rathi, Lauren J O'Donnell, Fan Zhang,
• Abstract summary: Deep Multi-view Fiber Clustering (DMVFC) uses joint dMRI and fMRI data to enable functionally consistent white matter parcellation. DMVFC can effectively integrate the geometric characteristics of the WM fibers with the fMRI BOLD signals along the fiber tracts.
• Score: 9.958759471577068
• License:
• Abstract: 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.

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