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.

Structural Variability within Frontoparietal Networks and Individual Differences in Attentional Functions: An Approach Using the Theory of Visual Attention

“…Whereas HMOA represents a surrogate of tissue density (tissue microstructure) (Dell'Acqua et al, 2013), the track volume, which is calculated as the number of voxels intersected by the reconstructed streamlines, denotes the space occupied by the tract (macrostructure) and its relationship to axonal number, axonal diameter, and density has yet to be established (Beaulieu, 2002). The findings that HMOA and tract volume differentially contribute to the anatomical lateralization of frontoparietal attentional networks are in agreement with previous studies showing that the asymmetry of WM pathways varies across different measures derived from diffusion MRI (De Santis et al, 2014). A remaining question, which cannot be answered by the current study, is whether variability in WM organization and in structural lateralization is reflected in individual differences in functional connectivity and/or preferential right hemisphere activation during visuospatial attention tasks.…”

Abnormal White Matter Properties in Adolescent Girls With Anorexia Nervosa

“…Group differences in FA occurred in a smaller subset of white matter tracts. This pattern of findings is generally consistent with empirical evidence demonstrating that larger sample sizes may be required for observing significant group differences with FA than with quantitative T1 (R1 = 1/T1) measures (De Santis et al, 2014). In the same study, De Santis et al (2014) also found that FA, T1, and a separate measure of myelin water fraction (MWF), were most strongly correlated in tract regions comprised of single fiber populations but were not correlated in regions comprised of multiple fiber populations.…”

“…The lack of significant FA differences within these pathways and other tracts may be explained by limitations of diffusion-based methods for identifying group differences in FA in small sample sizes or by possible differences between adolescent and adult study populations. Empirical work performed by De Santis and colleagues has estimated that group sizes involving 20 to 30 participants are necessary for observing group FA differences within inferior fronto-occipital fasciculus or cingulate pathways (De Santis et al, 2014). These estimates are comparable to a previous dMRI study of AN in which significant FA differences were observed for these pathways (Frieling et al, 2012).…”

“…In white matter regions in which fibers are coherently organized in a single direction, higher FA is typically associated with favorable neurobiological factors, such as increased myelination, greater axonal count, and higher axonal density (Basser and Pierpaoli, 1996;Beaulieu, 2002). However, in white matter regions of multiple fiber directions, FA in isolation is highly challenging to interpret (De Santis et al, 2014;Jeurissen et al, 2013;Jones and Cercignani, 2010).…”

“…However, not all studies of AN observe changes in FA (Yau et al, 2013). Failure to find group differences may be related to the biology of AN, such as stage of recovery following treatment (Yau et al, 2013), but may also reflect limitations in voxel-based methods for analyzing dMRI data (Jones and Cercignani, 2010), or difficulty detecting group FA differences with small sample sizes (De Santis et al, 2014). To our knowledge, only a single study has examined diffusion property changes associated with AN during adolescence (Frank et al, 2013).…”