Water diffusion in q-space imaging as a probe of cell local viscosity and anomalous diffusion in grey and white matter

Extraction of accurate quantitative parameters to characterize water diffusion in complex porous media like brain tissue in neuroimaging is a challenging inverse problem, that depends on medium\''s structural and geometrical factors [1,3]. If the role of membranes is generally invoked, probe collisions with the insoluble cytoskeleton network and water hydrodynamic interactions with dissolved macromolecules and cytoskeleton occur as well [2]. The latter two interactions have been shown to determine the phenomenological “anomalous diffusion” of probes in the cytoplasm [4,5,6,7,8]. Additionally, the diffusion of small solutes in cytoplasm could be slowed by the local micro-viscosity of the aqueous phase, a phenomenon generally not taken into account in simulations. We suggest that the Grey and White Matter contrast in Diffusion Decay Imaging (DDI) could be caused by differences in cytoskeleton structures, composed respectively of actin and tubulin that could act by the elimination of one possible water diffusion pathlength by the volume occupied by the network phase. This could explain why anomalous DDI signal has been shown to be independent of membrane integrity [9]. Cytoplasm is able to rapidly shift from a sol (aqueous solutions embedded with insolubles particles) to a gel state (a colloidal solutions with a structured semi-solid and an aqueous fluid phase) or to a viscous solution when the insoluble particles become soluble. Does water have the ability of being a sensor of its local “self-viscosity” ? What is the length of the water diffusion\''s path compared to cells size ? Compared to this path length, how many cellular structures should be probed by water\''s translational diffusion ? We try to respond to these questions by investigating Diffusion Decay Imaging models and their effects on the hypothese-free q-space diffusion propagator shape [3], containing all informations concerning viscosity-slowed gaussian diffusion, structural informations [3] and restricted diffusion [1]