Measures of cortical microstructure are linked to amyloid pathology in Alzheimer's disease.

Markers of downstream events are a key component of clinical trials of disease-modifying therapies for Alzheimer's disease. Morphological metrics like cortical thickness are established measures of atrophy but are not sensitive enough to detect amyloid-beta (Aß)- related changes that occur before overt atrophy become visible. We aimed to investigate to what extent diffusion MRI can provide sensitive markers of cortical microstructural changes and to test their associations with multiple aspects of the Alzheimer's disease pathological cascade, including both Aß and tau accumulation, astrocytic activation and cognitive deficits. We applied the mean apparent diffusion propagator model to diffusion MRI data from 492 cognitively unimpaired elderly and patients with mild cognitive impairment from the Swedish BioFINDER-2 cohort. Participants were stratified in Aß-negative/tau-negative, Aß-positive/tau-negative and Aß-positive/tau-positive based on Aß- and tau-PET uptake. Cortical regional values of diffusion MRI metrics and cortical thickness were compared across groups. Associations between regional values of diffusion MRI metrics and both Aß- and tau-PET uptake were also investigated along with the association with plasma level of glial fibrillary acidic protein (GFAP), a marker of astrocyte activation (available in 292 participants). Mean squared displacement revealed widespread microstructural differences already between Aß-negative/tau-negative and Aß-positive/tau-negative participants with a spatial distribution that closely resembled the pattern of Aß accumulation. In contrast, differences in cortical thickness were clearly more limited. Mean squared displacement was also correlated with both Aß- and tau-PET uptake even independently from one another and from cortical thickness. Further, the same metric exhibited significantly stronger correlations with PET uptake than cortical thickness (P < 0.05). Mean squared displacement was also positively correlated with GFAP with a pattern that resembles Aß accumulation, and GFAP partially mediated the association between Aß accumulation and mean squared displacement. Further, impairments in executive functions were significantly more associated with mean squared displacement values extracted from a meta-region of interest encompassing regions accumulating Aß early in the disease process, than with cortical thickness (P < 0.05). Similarly, impairments in memory functions were significantly more associated with mean squared displacement values extracted from a temporal meta-region of interest than with cortical thickness (P < 0.05). Metrics of cortical microstructural alteration derived from diffusion MRI are highly sensitive to multiple aspects of the Alzheimer's disease pathological cascade. Of particular interest is the link with both Aß-PET and GFAP, suggesting diffusion MRI might reflects microstructural changes related to the astrocytic response to Aß aggregation. Therefore, metrics of cortical diffusion might be important outcome measures in anti-Aß treatments clinical trials for detecting drug-induced changes in cortical microstructure.

Accuracy and precision in super-resolution MRI: Enabling spherical tensor diffusion encoding at ultra-high b-values and high resolution.

Diffusion MRI (dMRI) can probe the tissue microstructure but suffers from low signal-to-noise ratio (SNR) whenever high resolution is combined with high diffusion encoding strengths. Low SNR leads to poor precision as well as poor accuracy of the diffusion-weighted signal; the latter is caused by the rectified noise floor and can be observed as a positive bias in magnitude signal. Super-resolution techniques may facilitate a beneficial tradeoff between bias and resolution by allowing acquisition at low spatial resolution and high SNR, whereafter high spatial resolution is recovered by image reconstruction. In this work, we describe a super-resolution reconstruction framework for dMRI and investigate its performance with respect to signal accuracy and precision. Using phantom experiments and numerical simulations, we show that the super-resolution approach improves accuracy by facilitating a more beneficial trade-off between spatial resolution and diffusion encoding strength before the noise floor affects the signal. By contrast, precision is shown to have a less straightforward dependency on acquisition, reconstruction, and intrinsic tissue parameters. Indeed, we find a gain in precision from super-resolution reconstruction is substantial only when some spatial resolution is sacrificed. Finally, we deployed super-resolution reconstruction in a healthy brain for the challenging combination of spherical b-tensor encoding at ultra-high b-values and high spatial resolution-a configuration that produces a unique contrast that emphasizes tissue in which diffusion is restricted in all directions. This demonstration showcased that super-resolution reconstruction enables a vastly superior image contrast compared to conventional imaging, facilitating investigations that would otherwise have prohibitively low SNR, resolution or require non-conventional MRI hardware.