We examined whether quantitative density measures of cerebral tissue consistent with histology can be obtained from diffusion magnetic resonance imaging (MRI). By incorporating prior knowledge of myelin and cell membrane densities, absolute tissue density values were estimated from relative intracellular and intraneurite density values obtained from diffusion MRI. The NODDI (neurite orientation distribution and density imaging) technique, which can be applied clinically, was used. Myelin density estimates were compared with the results of electron and light microscopy in ex vivo mouse brain and with published density estimates in a healthy human brain. In ex vivo mouse brain, estimated myelin densities in different subregions of the mouse corpus callosum were almost identical to values obtained from electron microscopy (diffusion MRI: 42 ± 6%, 36 ± 4%, and 43 ± 5%; electron microscopy: 41 ± 10%, 36 ± 8%, and 44 ± 12% in genu, body and splenium, respectively). In the human brain, good agreement was observed between estimated fiber density measurements and previously reported values based on electron microscopy. Estimated density values were unaffected by crossing fibers. Hum Brain Mapp 36:3687–3702, 2015. © 2015 Wiley Periodicals, Inc.

Developments in microscopy have been instrumental to progress in the life sciences, and many new techniques have been introduced and led to new discoveries throughout the last century. A wide and diverse range of methodologies is now available, including electron microscopy, atomic force microscopy, magnetic resonance imaging, small-angle x-ray scattering and multiple super-resolution fluorescence techniques, and each of these methods provides valuable read-outs to meet the demands set by the samples under study. Yet, the investigation of cell development requires a multi-parametric approach to address both the structure and spatio-temporal organization of organelles, and also the transduction of chemical signals and forces involved in cell–cell interactions. Although the microscopy technologies for observing each of these characteristics are well developed, none of them can offer read-out of all characteristics simultaneously, which limits the information content of a measurement. For example, while electron microscopy is able to disclose the structural layout of cells and the macromolecular arrangement of proteins, it cannot directly follow dynamics in living cells. The latter can be achieved with fluorescence microscopy which, however, requires labelling and lacks spatial resolution. A remedy is to combine and correlate different readouts from the same specimen, which opens new avenues to understand structure–function relations in biomedical research. At the same time, such correlative approaches pose new challenges concerning sample preparation, instrument stability, region of interest retrieval, and data analysis. Because the field of correlative microscopy is relatively young, the capabilities of the various approaches have yet to be fully explored, and uncertainties remain when considering the best choice of strategy and workflow for the correlative experiment. With this in mind, the Journal of Physics D: Applied Physics presents a special roadmap on the correlative microscopy techniques, giving a comprehensive overview from various leading scientists in this field, via a collection of multiple short viewpoints.