Improved functional MRI activation mapping in white matter through diffusion-adapted spatial filtering

TL;DR

This paper introduces a novel graph-based spatial filtering method for fMRI white matter activation mapping, leveraging diffusion MRI data to improve sensitivity and specificity over traditional isotropic filters.

Contribution

It develops a diffusion-adapted graph filtering technique that encodes white matter anisotropy for enhanced fMRI signal denoising and activation detection.

Findings

Enhanced sensitivity in white matter activation detection

Improved specificity compared to isotropic filtering

Potential for better functional mapping in white matter

Abstract

Brain activation mapping using functional MRI (fMRI) based on blood oxygenation level-dependent (BOLD) contrast has been conventionally focused on probing gray matter, the BOLD contrast in white matter having been generally disregarded. Recent results have provided evidence of the functional significance of the white matter BOLD signal, showing at the same time that its correlation structure is highly anisotropic, and related to the diffusion tensor in shape and orientation. This evidence suggests that conventional isotropic Gaussian filters are inadequate for denoising white matter fMRI data, since they are incapable of adapting to the complex anisotropic domain of white matter axonal connections. In this paper we explore a graph-based description of the white matter developed from diffusion MRI data, which is capable of encoding the anisotropy of the domain. Based on this representation we design localized spatial filters that adapt to white matter structure by leveraging graph signal processing principles. The performance of the proposed filtering technique is evaluated on semi-synthetic data, where it shows potential for greater sensitivity and specificity in white matter activation mapping, compared to isotropic filtering.