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

TL;DR

This paper introduces DMVFC, a deep learning framework that combines diffusion MRI and functional MRI data to improve white matter fiber clustering and parcellation by incorporating both geometric and functional information.

Contribution

The novel DMVFC framework effectively integrates multimodal MRI data for functionally consistent white matter fiber clustering, surpassing existing methods.

Findings

DMVFC outperforms state-of-the-art methods in WM parcellation accuracy.

The framework successfully integrates geometric and functional signals.

Results demonstrate improved functional consistency in fiber clustering.

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.