Components of tissue, particularly cellular membranes, modulate the behavior of water diffusion. Through analysis of the three-dimensional diffusion profile of ensembles of water molecules, diffusion MRI (dMRI) is able to provide valuable information about tissue microstructure. In addition, long-range connections within the brain can be inferred by the integration of information across voxels through tractography. In the last two decades, dMRI has become a prominent tool for the study of several disorders of the brain and other organs. Yet, to accurately interpret dMRI, there needs to be a direct correlation between parameters obtained through analysis of dMRI and specific tissue characteristics. Interestingly, while early studies of diffusion anisotropy did provide evidence for the underlying tissue properties driving such a phenomenon (Beaulieu, 2002), as dMRI entered the mainstream clinical research arena there was a tendency to mis- or overinterpret diffusion changes through an overly-lenient extrapolation of previous descriptions (Jones and Cercignani, 2010). Fortunately, recent years have brought considerable advancements in our understanding of the reach of dMRI with direct histological confirmation through several methods. These will be summarized here, but the interested reader is directed to thorough reviews (Dyrby et al., 2018; Sierra, 2020).

Until recently, most validations were restricted to conventional two-dimensional visualization and analyses of histological slides. Depending on the structures of interest, researchers have employed several different techniques, such as light or electron microscopy, and immunohistochemistry or immunofluorescence. For example, one may choose the classic Nissl stain to visualize cell nuclei; myelin can be made evident through specific stains such as Black Gold, toluidine blue, or immunofluorescence of myelin basic protein (MBP); and glial cells can be stained generally with glial-fibrillary acid protein (GFAP), or targeting specific cell types with specific markers (e.g., Iba-1 for microglia). By studying the same specimens through dMRI and histological preparations, one can draw parallels between the two methods. This can be performed descriptively or through quantitative analyses. For example, cells can be counted by stereological methods or automatic segmentation, showing that axonal density correlates with diffusion anisotropy derived from DTI and multi-tensor approaches (Concha et al., 2010; Song et al., 2003), as well as apparent fiber density derived from CSD (Rojas-Vite et al., 2019). Stained structures can be quantified with optical density analyses and through mathematical analysis of the texture of the tissue. Using structure tensor analysis it has been possible to demonstrate a close correspondence of the orientation distribution of putative fibers obtained through dMRI and actual axons stained with lipophylic dyes (Budde and Frank, 2012). This method has been extended to three-dimensional histological acquisitions (Khan et al., 2015; Schilling et al., 2016). Similarly, Fourier analysis of histological slides is able to extract orientation of myelin-stained slides, showing a tight link to analogous metrics obtained through DTI (Salo et al., 2017).

Recent advances in three-dimensional electron microscopy have allowed for the acquisition of relatively large volumes of tissue (roughly the size of a preclinical imaging voxel) while showing exquisite cellular detail. Three-dimensional Fourier analysis showed good agreement between tissue structure obtained through EM and metrics derived from DTI (Salo et al., 2018). These highly-detailed volumes can be segmented using automated methods and have been useful to characterize the morphological variations of axons along their trajectories (Abdollahzadeh et al., 2021, 2019; Lee et al., 2019). Other high-resolution imaging methods, such as confocal light microscopy, polarized light imaging, serial optical coherence scanning and synchrotron X-ray micro-CT can also be used to create high resolution tissue volumes. Notably, these segmented detailed volumes provide an excellent opportunity to perform realistic simulations of diffusion to test virtually any acquisition or processing method for dMRI in a reusable fashion (Lee et al., 2020).

Even with high resolution attainable with ultra-high field preclinical scanners, dMRI conveys information about a multitude of cellular components that provide some texture to the tissue. On the other hand, histological methods directly visualize the individual actors that modulate diffusion. Such close inspections make evident that tissue is much more complex than the simple, albeit useful, caricatures that aid in the interpretation of dMRI. The advent of new biophysical models provide an increase in sensitivity to specific cellular components, therefore making it feasible to differentiate between different histopathological processes (Coronado-Leija et al., 2022b, 2022a; Ríos et al., 2021). Thorough validation of dMRI by means of correlations with histology is a complex topic that remains open yet moves forward rapidly. One of the ultimate goals of dMRI is to play the role of a virtual microscope with clinical applications, and current efforts to link dMRI to histology-derived gold-standards will ensure proper interpretations in the near future.

Acknowledgements

LC is supported in part by Conacyt 1782 and UNAM-DGAPA IG200117, IN204720.

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