No abstract

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 Aβ-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 Aβ 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 Aβ-negative/tau-negative, Aβ-positive/tau-negative, and Aβ-positive/tau-positive based on Aβ- 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 Aβ- and tau-PET uptake were also investigated along with the association with plasma level of glial fibrillary acidic protein (GFAP), a marker of astrocytes activation (available in 292 participants). Mean squared displacement revealed widespread microstructural differences already between Aβ-negative/tau-negative and Aβ-positive/tau-negative participants with a spatial distribution that closely resembled the pattern of Aβ accumulation. In contrast, differences in cortical thickness were clearly more limited. Mean squared displacement was also correlated with both Aβ- 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 resemble Aβ accumulation, and GFAP partially mediated the association between Aβ accumulation and mean squared displacement. Further, impairments in executive functions were significantly more associated with mean squared displacement values extracted from a meta-ROI encompassing regions accumulating Aβ 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-ROI, 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 Aβ-PET and GFAP suggesting diffusion-MRI might reflects microstructural changes related to the astrocytic response to Aβ aggregation. Therefore, metrics of cortical diffusion might be important outcome measures in anti-Aβ treatments clinical trials for detecting drug-induced changes in cortical microstructure.

Diffusion MRI (dMRI) is a useful probe of tissue microstructure but suffers from low signal-to-noise ratio (SNR) whenever high resolution and/or high diffusion encoding strengths are used. Low SNR leads not only to poor precision but also poor accuracy of the diffusion-weighted signal, as the rectified noise floor gives rise to a positive signal bias. Recently, super-resolution techniques have been proposed for signal acquisition at a low spatial resolution but high SNR, whereafter a higher 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 strictly controlled phantom experiments, 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. Moreover, precision is shown to have a less straightforward dependency on acquisition, reconstruction, and intrinsic tissue parameters. Indeed, we find that a gain in precision from super-resolution reconstruction (SRR) is substantial only when some spatial resolution is sacrificed. We also demonstrated the value of SRR in the challenging combination of high resolution and spherical b-tensor encoding at ultrahigh b-values—a configuration that produces a unique contrast that emphasizes tissue in which diffusion is restricted in all directions. We conclude that SRR is most valuable in low-SNR conditions, where it can suppress rectified noise floor effects and recover signal with high accuracy. The in vivo application showcases a vastly superior image contrast when using SRR compared to conventional imaging, facilitating investigations of brain tissue that would otherwise have prohibitively low SNR, resolution or required non-conventional MRI hardware.

BackgroundMarkers of downstream events are a key component of clinical trials of disease‐modifying therapies for Alzheimer’s disease (AD). Morphometric metrics like cortical thickness are established measures of atrophy but are not sensitive enough to detect Aβ‐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 that could complement morphometric macrostructural measures.MethodWe applied the mean apparent diffusion propagator model (MAP‐MRI) to diffusion MRI data from 492 cognitively unimpaired elderly and patients with mild cognitive impairment from the Swedish BioFINDER‐2 cohort. MAP‐MRI extends diffusion tensor imaging and provides metrics sensitive to subtle changes in the cortex. Participants were stratified in Aβ‐negative/tau‐negative, Aβ‐positive/tau‐negative, and Aβ‐positive/tau‐positive based on Aβ‐ and tau‐PET uptake. Cortical regional values of both MAP‐MRI metrics and CT were compared across groups. Associations between regional values of MAP‐MRI metrics and both Aβ‐ and tau‐PET uptake were also investigated as well as the association between MAP‐MRI metrics and plasma level of GFAP, a marker of astroglial activation (available in 292 participants).ResultMean square displacement (MSD) from MAP‐MRI revealed widespread microstructural differences already between Aβ‐negative/tau‐negative and Aβ‐positive/tau‐negative participants with a spatial distribution that closely resembled the pattern of Aβ accumulation. In contrast, differences in cortical thickness appeared to be more limited (figure 1). MSD was also highly correlated with both Aβ‐ and tau‐PET uptake even independently from one another (figure 2). Regional MSD values were associated with GFAP with a pattern that resemble Aβ accumulation, and GFAP partially mediated the association between Aβ and MSD. A sensitivity analysis controlling for cortical thickness revealed that the associations between MSD and Aβ‐PET, tau‐PET and GFAP were largely independent from macrostructural changes (figures 2‐3).ConclusionMetrics of cortical microstructural alteration derived from MAP‐MRI are highly sensitive to multiple aspects of the AD pathological cascade. Of particular interest is the link between MSD, Aβ‐PET and GFAP which suggests MSD might reflects microstructural changes related to the astrocytic response to Aβ aggregation. Therefore, MSD could help monitoring the response to anti‐Aβ treatments in clinical trials.

Diffusion weighted imaging has been used both as a radiological tool that provides a simple biomarker without quantitative qualities, and more recent efforts have endeavored to make it quantitative by fitting of biophysical models or representations. In this work, we explore the novel radiological contrasts that can be generated by introducing tensor-valued diffusion encoding. Unlike most model-based approaches, these contrasts can be produced by rapid acquisition schemes and they produce novel contrasts that may contribute new diagnostic and radiological biomarkers.