In this work, we report a case study of a stroke model in animals using two methods of quantification of the deviations from Gaussian behaviour: diffusion kurtosis imaging (DKI) and log-normal distribution function imaging (LNDFI). The affected regions were predominantly in grey rather than in white matter. The parameter maps were constructed for metrics quantifying the apparent diffusivity (evaluated from conventional diffusion tensor imaging, DKI and LNDFI) and for those quantifying the degree of deviations (mean kurtosis and a parameter σ characterising the width of the distribution). We showed that both DKI and LNDFI were able to dramatically enhance the visualisation of ischaemic lesions in comparison with conventional methods. The largest relative change in the affected versus healthy regions was observed in the mean kurtosis values. The average changes in the mean kurtosis and σ values in the lesions were a factor of two to three larger than the relative changes observed in the mean diffusivity. In conclusion, the applied methods promise valuable perspectives in the assessment of stroke.

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Previous studies have reported substantial involvement of the noradrenergic system in Parkinson’s disease. Neuromelanin-sensitive MRI sequences and PET tracers have become available to visualize the cell bodies in the locus coeruleus and the density of noradrenergic terminal transporters. Combining these methods, we investigated the relationship of neurodegeneration in these distinct compartments in Parkinson’s disease. We examined 93 subjects (40 healthy controls and 53 Parkinson’s disease patients) with neuromelanin-sensitive turbo spin-echo MRI and calculated locus coeruleus-to-pons signal contrasts. Voxels with the highest intensities were extracted from published locus coeruleus coordinates transformed to individual MRI. To also investigate a potential spatial pattern of locus coeruleus degeneration, we extracted the highest signal intensities from the rostral, middle, and caudal third of the locus coeruleus. Additionally, a study-specific probabilistic map of the locus coeruleus was created and used to extract mean MRI contrast from the entire locus coeruleus and each rostro-caudal subdivision. Locus coeruleus volumes were measured using manual segmentations. A subset of 73 subjects had 11C-MeNER PET to determine noradrenaline transporter density, and distribution volume ratios of noradrenaline transporter-rich regions were computed. Parkinson’s disease patients showed reduced locus coeruleus MRI contrast independently of the selected method (voxel approaches: p < 0.0001, p < 0.001; probabilistic map: p < 0.05), specifically on the clinically-defined most affected side (p < 0.05), and reduced locus coeruleus volume (p < 0.0001). Reduced MRI contrast was confined to the middle and caudal locus coeruleus (voxel approach—rostral: p = 0.48, middle: p < 0.0001, and caudal: p < 0.05; probabilistic map—rostral: p = 0.90, middle: p < 0.01, and caudal: p < 0.05). The noradrenaline transporter density was lower in Parkinson’s disease patients in all examined regions (group effect p < 0.0001). No significant correlation was observed between locus coeruleus MRI contrast and noradrenaline transporter density. In contrast, the individual ratios of noradrenaline transporter density and locus coeruleus MRI contrast were lower in Parkinson’s disease patients in all examined regions (group effect p < 0.001). Our multimodal imaging approach revealed pronounced noradrenergic terminal loss relative to cellular locus coeruleus degeneration in Parkinson’s disease; the latter followed a distinct spatial pattern with the middle-caudal portion being more affected than the rostral part. The data shed first light on the interaction between the axonal and cell body compartments and their differential susceptibility to neurodegeneration in Parkinson’s disease, which may eventually direct research toward potential novel treatment approaches.

Recent diffusion MRI studies of stroke in humans and animals have shown that the quantitative parameters characterising the degree of non-Gaussianity of the diffusion process are much more sensitive to ischemic changes than the apparent diffusion coefficient (ADC) considered so far as the “gold standard”. The observed changes exceeded that of the ADC by a remarkable factor of 2 to 3. These studies were based on the novel non-Gaussian methods, such as diffusion kurtosis imaging (DKI) and log-normal distribution function imaging (LNDFI). As shown in our previous work investigating the animal stroke model, a combined analysis using two methods, DKI and LNDFI provides valuable complimentary information. In the present work, we report the application of three non-Gaussian diffusion models to quantify the deviations from the Gaussian behaviour in stroke induced by transient middle cerebral artery occlusion in rat brains: the gamma-distribution function (GDF), the stretched exponential model (SEM), and the biexponential model. The main goal was to compare the sensitivity of various non-Gaussian metrics to ischemic changes and to investigate if a combined application of several models will provide added value in the assessment of stroke. We have shown that two models, GDF and SEM, exhibit a better performance than the conventional method and allow for a significantly enhanced visualization of lesions. Furthermore, we showed that valuable information regarding spatial properties of stroke lesions can be obtained. In particular, we observed a stratified cortex structure in the lesions that were well visible in the maps of the GDF and SEM metrics, but poorly distinguishable in the ADC-maps. Our results provided evidence that cortical layers tend to be differently affected by ischemic processes.

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