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Brain activation mapping using functional MRI (fMRI) based on bloodoxygenation level-dependent (BOLD) contrast has been conventionally focused onprobing gray matter, the BOLD contrast in white matter having been generallydisregarded. Recent results have provided evidence of the functionalsignificance of the white matter BOLD signal, showing at the same time that itscorrelation structure is highly anisotropic, and related to the diffusiontensor in shape and orientation. This evidence suggests that conventionalisotropic Gaussian filters are inadequate for denoising white matter fMRI data,since they are incapable of adapting to the complex anisotropic domain of whitematter axonal connections. In this paper we explore a graph-based descriptionof the white matter developed from diffusion MRI data, which is capable ofencoding the anisotropy of the domain. Based on this representation we designlocalized spatial filters that adapt to white matter structure by leveraginggraph signal processing principles. The performance of the proposed filteringtechnique is evaluated on semi-synthetic data, where it shows potential forgreater sensitivity and specificity in white matter activation mapping,compared to isotropic filtering.

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