Interpreting brain diffusion MRI measurements in terms of neuronal structure at a micrometer level is an exciting unresolved problem. Here we consider diffusion transverse to a bundle of fibers, and show theoretically, as well as using Monte Carlo simulations and measurements in a phantom made of parallel fibers mimicking axons, that the time dependent diffusion coefficient approaches its macroscopic limit slowly, in a (lnt)/t fashion. The logarithmic singularity arises due to short range disorder in the fiber packing. We identify short range disorder in axonal fibers based on histological data from the splenium, and argue that the time dependent contribution to the overall diffusion coefficient from the extra-axonal water dominates that of the intra-axonal water. This dominance may explain the bias in measuring axon diameters in clinical settings. The short range disorder is also reflected in the linear frequency dependence of the diffusion coefficient measured with oscillating gradients, in agreement with recent experiments. Our results relate the measured diffusion to the mesoscopic structure of neuronal tissue, uncovering the sensitivity of diffusion metrics to axonal arrangement within a fiber tract, and providing an alternative interpretation of axonal diameter mapping techniques.

The presence of micrometer-level restrictions leads to a decrease of diffusion coefficient with diffusion time. Here we investigate this effect in human white matter in vivo. We focus on a broad range of diffusion times, up to 600 ms, covering diffusion length scales up to about 30 microns. We perform stimulated echo diffusion tensor imaging on 5 healthy volunteers and observe a relatively weak time-dependence in diffusion transverse to major fiber tracts. Remarkably, we also find notable time-dependence in the longitudinal direction. Comparing models of diffusion in ordered, confined and disordered media, we argue that the time-dependence in both directions can arise due to structural disorder, such as axonal beads in the longitudinal direction, and the random packing geometry of fibers within a bundle in the transverse direction. These time-dependent effects extend beyond a simple picture of Gaussian compartments, and may lead to novel markers that are specific to neuronal fiber geometry at the micrometer scale.

No abstract

Natural fluids, such as crude oils, are often mixtures of a broad range of different molecules, and in situ measurement of their composition is highly desirable. Furthermore, the relationship between their composition and their physical properties has always been a challenge for such mixtures. We have analyzed diffusion in alkane mixtures to find a power law for the self-diffusion coefficient in terms of molecular sizes. We demonstrate that this power law can be used to obtain the molecular size distribution of crude oils using noninvasive measurements of diffusion distributions.

In experiments involving decaying signals, it is often desirable to analyze the data as a sum of exponential decays using the Laplace inversion method. However, Laplace inversion is an ill-conditioned problem, and it is difficult to ascertain the stability of the reconstruction method and resolution of the resulting spectrum. This paper provides an easily computed approximate bound of the resolution and offers guidelines on how to design experiments to improve the spectral resolution.