Purpose The dependence of the direction-averaged diffusion-weighted imaging (DWI) signal in brain was studied as a function of b-value in order to help elucidate the relationship between diffusion weighting and brain microstructure. Methods High angular resolution diffusion imaging (HARDI) data were acquired from two human volunteers with 128 diffusion-encoding directions and six b-value shells ranging from 1000 to 6000 s/mm2 in increments of 1000 s/mm2. The direction-averaged signal was calculated for each shell by averaging over all diffusion-encoding directions, and the signal was plotted as a function of b-value for selected regions of interest. As a supplementary analysis, we also applied similar methods to retrospective DWI data obtained from the human connectome project (HCP), which includes b-values up to 10,000 s/mm2. Results For all regions of interest, a simple power law relationship accurately described the observed dependence of the direction-averaged signal as a function of the diffusion weighting. In white matter, the characteristic exponent was 0.56 ± 0.05, while in gray matter it was 0.88 ± 0.11. Similar results were obtained with the HCP data. Conclusion The direction-averaged DWI signal varies, to a good approximation, as a power of the b-value, for b-values between 1000 and 6000 s/mm2. The exponents characterizing this power law behavior were markedly different for white and gray matter, indicative of sharply contrasting microstructural environments. These results may inform the construction of microstructural models used to interpret the DWI signal.

Clinical studies have revealed a strong link between increased burden of cerebral microinfarcts and risk for cognitive impairment. Since the sum of tissue damage incurred by microinfarcts is a miniscule percentage of total brain volume, we hypothesized that microinfarcts disrupt brain function beyond the injury site visible to histological or radiological examination. We tested this idea using a mouse model of microinfarcts, where single penetrating vessels that supply mouse cortex were occluded by targeted photothrombosis. We found that in vivo structural and diffusion MRI reliably reported the acute microinfarct core, based on spatial co-registrations with post-mortem stains of neuronal viability. Consistent with our hypothesis, c-Fos assays for neuronal activity and in vivo imaging of single vessel hemodynamics both reported functional deficits in viable peri-lesional tissues beyond the microinfarct core. We estimated that the volume of tissue with functional deficit in cortex was at least 12-fold greater than the volume of the microinfarct core. Impaired hemodynamic responses in peri-lesional tissues persisted at least 14 days, and were attributed to lasting deficits in neuronal circuitry or neurovascular coupling. These data show how individually miniscule microinfarcts could contribute to broader brain dysfunction during vascular cognitive impairment and dementia.

Fiber ball imaging (FBI) provides a means of calculating the fiber orientation density function (fODF) in white matter from diffusion MRI (dMRI) data obtained over a spherical shell with a b-value of about 4000 s/mm or higher. By supplementing this FBI-derived fODF with dMRI data acquired for two lower b-value shells, it is shown that several microstructural parameters may be estimated, including the axonal water fraction (AWF) and the intrinsic intra-axonal diffusivity. This fiber ball white matter (FBWM) modeling method is demonstrated for dMRI data acquired from healthy volunteers, and the results are compared with those of the white matter tract integrity (WMTI) method. Both the AWF and the intra-axonal diffusivity obtained with FBWM are found to be significantly larger than for WMTI, with the FBWM values for the intra-axonal diffusivity being more consistent with recent results obtained using isotropic diffusion weighting. An important practical advantage of FBWM is that the only nonlinear fitting required is the minimization of a cost function with just a single free parameter, which facilitates the implementation of efficient and robust numerical routines.

Purpose To demonstrate how the T2 relaxation time of intra‐axonal water (T2a) in white matter can be measured with direction‐averaged diffusion MRI. Methods For b‐values larger than about 4000 s/mm2, the direction‐averaged diffusion MRI signal from white matter is dominated by the contribution from water within axons, which enables T2a to be estimated by acquiring data for multiple TE values and fitting a mono‐exponential decay curve. If given a value of the intra‐axonal diffusivity, an extension of the method allows the extra‐axonal relaxation time (T2e) to be calculated also. This approach was applied to estimate T2a in white matter for 3 healthy subjects at 3 T, as well as T2e for a selected set of assumed intra‐axonal diffusivities. Results The estimated T2a values ranged from about 50 ms to 110 ms, with considerable variation among white matter regions. For white matter tracts with primarily collinear fibers, T2a was found to depend on the angle of the tract relative to the main magnetic field, which is consistent with T2a being affected by magnetic field inhomogeneities arising from spatial differences in magnetic susceptibility. The T2e values were significantly smaller than the T2a values across white matter regions for several plausible choices of the intra‐axonal diffusivity. Conclusion The relaxation time for intra‐axonal water in white matter can be determined in a straightforward manner by measuring the direction‐averaged diffusion MRI signal with a large b‐value for multiple TEs. In healthy brain, T2a is greater than T2e and varies considerably with anatomical region.

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