Elsevier

NeuroImage

Volume 241, 1 November 2021, 118417
NeuroImage

Fixel-based Analysis of Diffusion MRI: Methods, Applications, Challenges and Opportunities

https://doi.org/10.1016/j.neuroimage.2021.118417Get rights and content
Under a Creative Commons license
open access

Highlights

  • The fixel-based analysis framework was proposed for fibre-specific statistical analysis of diffusion MRI data.

  • A “fixel” represents an individual fibre population in a voxel, allowing for increased specificity over voxel-wise measures.

  • A state-of-the-art fixel-based analysis pipeline consists of several bespoke steps, but is conceptually similar to a voxel-based analysis.

  • Fixel-based analysis has seen increased adoption recently, with 75 published studies to date.

  • The framework has unique benefits and future opportunities, but specific challenges and limitations exist as well.

Abstract

Diffusion MRI has provided the neuroimaging community with a powerful tool to acquire in-vivo data sensitive to microstructural features of white matter, up to 3 orders of magnitude smaller than typical voxel sizes. The key to extracting such valuable information lies in complex modelling techniques, which form the link between the rich diffusion MRI data and various metrics related to the microstructural organization. Over time, increasingly advanced techniques have been developed, up to the point where some diffusion MRI models can now provide access to properties specific to individual fibre populations in each voxel in the presence of multiple “crossing” fibre pathways. While highly valuable, such fibre-specific information poses unique challenges for typical image processing pipelines and statistical analysis. In this work, we review the “Fixel-Based Analysis” (FBA) framework, which implements bespoke solutions to this end. It has recently seen a stark increase in adoption for studies of both typical (healthy) populations as well as a wide range of clinical populations. We describe the main concepts related to Fixel-Based Analyses, as well as the methods and specific steps involved in a state-of-the-art FBA pipeline, with a focus on providing researchers with practical advice on how to interpret results. We also include an overview of the scope of all current FBA studies, categorized across a broad range of neuro-scientific domains, listing key design choices and summarizing their main results and conclusions. Finally, we critically discuss several aspects and challenges involved with the FBA framework, and outline some directions and future opportunities.

Keywords

Fixel-Based Analysis
Diffusion MRI
Fixel
White matter
Microstructure
Fibre density
Fibre-bundle cross-section
Statistical analysis

Abbreviations

AFD
apparent fibre density
BEDPOSTX
Bayesian estimation of diffusion parameters obtained using sampling techniques with modelling of crossing fibres
CFE
connectivity-based fixel enhancement
CHARMED
composite hindered and restricted model of diffusion
CSD
constrained spherical deconvolution
CSF
cerebrospinal fluid
dMRI
diffusion magnetic resonance imaging
DTI
diffusion tensor imaging
EPI
echo-planar imaging
FA
fractional anisotropy
FC
fibre-bundle cross-section
FD
fibre density
FDC
fibre density and cross-section
Fixel
a specific fibre population within a voxel
FBA
fixel-based analysis
FBM
fixel-based morphometry
FLAIR
fluid-attenuated inversion recovery
FOD
fibre orientation distribution
FWE
family-wise error
FWHM
full width at half maximum
GM
grey matter
HARDI
high angular resolution diffusion imaging
MD
mean diffusivity
MRI
magnetic resonance imaging
MSMT-CSD
multi-shell multi-tissue constrained spherical deconvolution
NODDI
neurite orientation dispersion and density imaging
ROI
region-of-interest
SNR
signal-to-noise ratio
SS3T-CSD
single-shell 3-tissue constrained spherical deconvolution
SWI
susceptibility-weighted imaging
TBM
tensor-based morphometry
TBSS
tract-based spatial statistics
TFCE
threshold-free cluster enhancement
VBA
voxel-based analysis
VBM
voxel-based morphometry
WM
white matter

Cited by (0)

Adam Clemente and Mervyn Singh contributed equally to this work.