Why diffusion tensor MRI does well only some of the time: variance and covariance of white matter tissue microstructure attributes in the living human brain

Abstract

Fundamental to increasing our understanding of the role of white matter microstructure in normal/abnormal function in the living human is the development of MR-based metrics that provide increased specificity to distinct attributes of the white matter (e.g., local fibre architecture, axon morphology, and myelin content). In recent years, different approaches have been developed to enhance this specificity, and the Tractometry framework was introduced to combine the resulting multi-parametric data for a comprehensive assessment of white matter properties. The present work exploits that framework to characterise the statistical properties, specifically the variance and covariance, of these advanced microstructural indices across the major white matter pathways, with the aim of giving clear indications on the preferred metric(s) given the specific research question. A cohort of healthy subjects was scanned with a protocol that combined multi-component relaxometry with conventional and advanced diffusion MRI acquisitions to build the first comprehensive MRI atlas of white matter microstructure. The mean and standard deviation of the different metrics were analysed in order to understand how they vary across different brain regions/individuals and the correlation between them. Characterising the fibre architectural complexity (in terms of number of fibre populations in a voxel) provides clear insights into correlation/lack of correlation between the different metrics and explains why DT-MRI is a good model for white matter only some of the time. The study also identifies the metrics that account for the largest inter-subject variability and reports the minimal sample size required to detect differences in means, showing that, on the other hand, conventional DT-MRI indices might still be the safest choice in many contexts.

Keywords: CHARMED; Diffusion tensor MRI; Myelin; White matter microstructure.

Fig. 1

Tractography reconstruction superimposed on the normalised FA map for eight different tracts: the arcuate fasciculus (ARC) (1), the cingulum (CING) (2), the uncinate fasciculus (UNC) (3) the superior longitudinal fasciculus (SLF) (4), the inferior longitudinal fasciculus (ILF) (5), the inferior fronto-occipital fasciculus (IFOF) (6), the fornix (FX) (7) and the thalamo-cortical (TC) tract (8).

Fig. 2

FA, MD and AD maps projected onto the reconstructed left and right tracts, respectively.

Fig. 3

RD, MWF and T1 maps projected onto the reconstructed left and right tracts, respectively.

Fig. 4

T2, RF and IAD maps projected onto the reconstructed left and right tracts, respectively.

Fig. 5

Asymmetry profiles along the different tracts for the microstructural parameters.

Fig. 6

Correlation between the different microstructural metrics. Grey boxes indicates non-significant correlation. One asterisk indicates p < 0.05 while two asterisks indicate p < 0.001. The correlations are calculated on 42 ROIs obtained from the intersection of the FA-derived skeleton from the tract-based spatial statistics pipeline. Each dot is colour-coded according to the number of predominant orientations in the ROI: red indicates a single fibre orientation; blue indicates more than one predominant orientations.

Fig. 7

Correlation between FA and RF (panel a), FA and MWF (panel b), FR and MWF (panel c) (zoom of three elements of the matrix in Fig. 6). One asterisk indicates p < 0.05 while two asterisks indicate p < 0.001. The results are colour-coded according to the number of predominant orientations in the ROI: red indicates a single fibre orientation; blue indicates more than one predominant orientations.

Fig. 8

Principal component analysis of the inter-subject variability in different microstructural indices. Columns represent the principal components while rows correspond to the factor loadings of the different microstructural indices. Blue squares indicate that the factor contributes to the variability more than it would if each factor had equal weight.

Fig. 9

Tract-averaged sample size needed to obtain a statistical power of 0.9 at a relative effect size of 10%.