Home Computer Science Computational Diffusion MRI: MICCAI Workshop, Athens, Greece, October 2016
In this work, we have shown that different metrics of DTI, NODDI and MAP-MRI appear to be sensitive to different processes as age-dependent cerebral amyloidosis manifests in both grey and white matter in the Alzheimer rats.
Fig. 4 DTI, NODDI and MAP-MRI metrics for the same time points in the hippocampus (red), corpus callosum (blue) and cingulate cortex (green)
DTI findings: We find a significant drop in FA in all ROIs from 10 to 15 months and a small increase from 15 to 24 months. This corresponds with previous findings in the hippocampus using data up to b = 1000s/mm2 . While a comparison of using different b-values in the DTI estimation was outside of the scope of this study, it was shown that when higher b-values are included, the FA trend consistently decreases over time . Nonetheless, it has been argued that compared to FA, MD lends itself better to the assessment of cortical and subcortical grey matter, where net diffusion may not be expected to conform to any one specific direction . When we assess MD, we consistently find an increase from 10 to 15 months and a decrease from 15 to 24 months. This may suggest that FA and MD are sensitive to different processes taking place in AD.
NODDI findings: Several studies have suggested that NODDI metrics, in particular ODI, have better AD classifying potential due to NODDI’s ability to delineate signal contributions from different tissue compartments [4,7]. While we cannot do a classification study using our data, we find that ODI consistently increases in areas where tau pathology increases in our rat model ; the hippocampus, cingulate cortex and corpus callosum. We also find that IsoVF shows an increase from 10 to 15 months and a decrease from 15 to 24 months in all areas, following the same trend as DTI’s MD. Though, it should be mentioned that fitting NODDI requires presetting the intra-cellular and isotropic diffusivity, which influences obtained metric values. Fitting NODDI on the selected bmax = 3000 s/mm2 or the full data does not significantly impact our findings.
MAP-MRI findings: To the best of our knowledge, this is the first study that estimates MAP-MRI metrics on data from an AD model. We find that all metrics
Table 1 Mean and standard deviation of DTI, NODDI and MAP-MRI metrics for the three time points in each region of interest
Fig. 5 Scatter plots of FA, ODI and PA for the rats of ages 10 months (blue), 15 months (green) and 24 months (red) in the hippocampus. It can be seen that ODI is negatively correlated with both FA and PA
except PA follow a two-stage progression pattern similar to DTI’s MD. The decrease-increase of return-to-origin, return-to-axis and return-to-plane probability (RTOP, RTAP and RTPP) makes sense with the increase-decrease of MD, as an increased diffusivity means that spins are able to move away farther, reducing the chance they return to their origin, axis or plane. Interestingly, this does not make the signal more Gaussian, as the Non-Gaussianity follows an increase-decrease pattern in all ROIs. The exception to this trend is the RTPP in the corpus callosum, which increases monotonically, indicating a steady increase in restriction parallel to the axon direction. Finally, PA consistently decreases in all areas except the cortex, where a small increase is found, followed by a larger decrease. This decreasing trend in anisotropy measures when using higher gradients strengths was also reported with DTI’s FA or HYDI’s NQA . We note that while we fitted MAP-MRI to the full data with 300 DWIs, it was shown that its metrics are stable under subsampling to
Fig. 6 Scatter plots of MD, IsoVF and RTOP for the rats of ages 10 months (blue), 15 months (green) and 24 months (red) in the hippocampus. It can be seen that IsoVF is positively correlated with MD and negatively with RTOP
less than 100 DWIs  or could even be fitted directly on a NODDI acquisition scheme.
Biological explanation for biomarker trends: The trends of all derived metrics can be divided into two groups: those that consistently decrease or increase and those that show a ‘decrease-increase’ or ‘increase-decrease’ pattern.
The first group could point towards the accelerating cerebral amyloidosis as age increases in these rats . Over time, this “amyloid burden” results in age- dependent neuronal demise that is likely owed to oligomeric A^ accumulation. In turn, this neuronal demise could result in a more dispersed, less anisotropic diffusion signal. This corresponds with the observed correlations between dispersion and anisotropy measures in Fig. 5.
The second group may indicate an inflammatory response to amyloid accumulation, occurring prior to (or coincident with and obscuring) the onset of microstruc?tural breakdown and macrostructural atrophy . At 15 months TgF344-AD rats have heavy plaque burden and strong neuroinflammation, whereas by 24 months most of the inflammatory reaction to the plaques has passed. This corresponds to what we see when MD and IsoVF increase-decrease and RTOP, RTAP and RTPP decrease-increase (except RTPP at corpus callosum). The correlations between MD, IsoVF and RTOP in Fig. 6 therefore makes sense. Though, the increase-decrease in NG indicates that while the inflammatory response increases diffusivity, it also increases the non-Gaussian portion of the signal at higher b-values.
Difficulties of comparing our findings with previous animal studies: There have been several previous dMRI studies using Alzheimer animal models. However, different species and disease expressions make comparisons of dMRI metrics difficult. For instance, our TgF344-AD rat model was made to drive cerebral amyloid and downstream tauopathy and neuronal loss, also known as the “amyloid cascade hypothesis” of John Hardy . In contrast, the Tg4510 mouse model used by Colgan et al.  was developed to only assess tauopathy; and not the amyloid cascade hypothesis. For this reason, it is hard to make claims about differences in biomarker trends found between this study and theirs.
Limitations of the study: As we did not have healthy rats to statistically test for changes with disease progression—which means there is room for improvement— we used the youngest rat (10 months old) as a control subject to compare against suggestive changes at later time points. Another limitation is the low number of experimental subjects that also prevents us from statistically differentiating between the disease stages of the transgenic Alzheimer rat model.
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