Histological validation of high-resolution DTI in human post mortem tissue

Abstract

Diffusion tensor imaging (DTI) is amongst the simplest mathematical models available for diffusion magnetic resonance imaging, yet still by far the most used one. Despite the success of DTI as an imaging tool for white matter fibers, its anatomical underpinnings on a microstructural basis remain unclear. In this study, we used 65 myelin-stained sections of human premotor cortex to validate modeled fiber orientations and oft used microstructure-sensitive scalar measures of DTI on the level of individual voxels. We performed this validation on high spatial resolution diffusion MRI acquisitions investigating both white and gray matter. We found a very good agreement between DTI and myelin orientations with the majority of voxels showing angular differences less than 10°. The agreement was strongest in white matter, particularly in unidirectional fiber pathways. In gray matter, the agreement was good in the deeper layers highlighting radial fiber directions even at lower fractional anisotropy (FA) compared to white matter. This result has potentially important implications for tractography algorithms applied to high resolution diffusion MRI data if the aim is to move across the gray/white matter boundary. We found strong relationships between myelin microstructure and DTI-based microstructure-sensitive measures. High FA values were linked to high myelin density and a sharply tuned histological orientation profile. Conversely, high values of mean diffusivity (MD) were linked to bimodal or diffuse orientation distributions and low myelin density. At high spatial resolution, DTI-based measures can be highly sensitive to white and gray matter microstructure despite being relatively unspecific to concrete microarchitectural aspects.

A watershed model of individual differences in fluid intelligence

“…A model focused on the contribution of the constituent physiological characteristics of white matter would be ideal for future applications of the watershed model, e.g. to test the complementary roles of axonal structure vs. myelin fraction (Caspers et al, 2015, Seehaus et al, 2015). Because our model does not make specific predictions for specific cellular constituents (e.g., water fraction, axonal diameter, myelin density), we favour the use of a simple tensor measure of diffusion organisation, fractional anisotropy (FA).…”

The role of myelination in measures of white matter integrity: Combination of diffusion tensor imaging and two-photon microscopy of CLARITY intact brains

“…Nonetheless, DTI remains a powerful method for noninvasively estimating WM structure in the brain with high sensitivity, but low specificity. A number of DTI validation studies have examined the potential anatomical underpinnings of the diffusion signal in animal and human post-mortem tissue (Budde et al, 2007; Leergaard et al, 2010; Seehaus et al, 2015), however, these studies have relied on traditional 2D slide-based immunohistochemistry which have major limitations for examining complex 3D structures. Using 3D-immunohistochemistry (3D-IHC), we show that WM regions with highly coherent fiber orientations, such as subregions of the mouse stria medullaris and the corpus collosum (Fig.…”

“…For example, it is possible to generate structural tensors from histological sections (Budde and Frank, 2012; Seehaus et al, 2015), and structure tensor informed fiber tractography (STIFT) has already been reported using high field gradient echo MRI (Kleinnijenhuis et al, 2012). A similar structural tensor approach could be applied to CLARITY-cleared tissue volumes (Ye et al, 2016), including blocks of human brain, for direct within-sample comparison to diffusion tractography.…”

Universal and stable medical image generation for tissue segmentation \newline(The unistable method)

“…It was reported that thresholds of normalized MD images with values around 0.3 can efficiently separate the images into CSF/non-CSF clustering maps [7]. Furthermore, thresholds of normalized FA images with values between 0.25 and 0.45 can separate images into WM/non-WM clustering maps [35]. By finding the maximum match (minimum difference) between clustering maps generated by using a range of thresholds around these values and different cluster maps of the selected SIs and unistable images generated by clustering algorithms, the unistable results point to similar threshold levels, but other SIs have a range of values (Figures 7 and 8).…”