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Diffusion Kurtosis Imaging (DKI) Biomarkers correlate with dysfunction in the EAE model of MS

Andrey Chuhutin¹, Brian Hansen¹, Agnieszka Wlodarczyk², Trevor Owens², Noam Shemesh³, and Sune Nørhøj Jespersen^1,4

¹CFIN, Aarhus University, Aarhus, Denmark, ²Department of Neurobiology Research, Institute for Molecular Medicine,University of South Denmark, Odense, Denmark, ³Champalimaud Center for Unknown, Lisbon, Portugal, ⁴Dept. of Physics and Astronomy, Aarhus University, Aarhus, Denmark

Synopsis

To study the relationship between microstructure and disability, 18 ex-vivo spinal cords from a mouse model of MS (EAE) were investigated using DKI and a biophysical model of diffusion. Diffusion data were acquired together with T2* images to delineate lesions. Kurtosis tensors and microstructural parameters were used for statistical analysis using a LME model. The results show a strong relation between disability and kurtosis tensor parameters similar to observations in other hypomyelinating MS models and in patients. Conversely, changes in model parameters, such as extra-axonal axial diffusivity, are clearly different from previous studies using other animal models of MS.

Introduction

Due to its noninvasiveness and ability to contrast soft tissues, MRI is among the most prominent multiple sclerosis (MS) diagnosis techniques^1,2. Conventional T1-or T2-weighted images can reveal brain atrophy and lesions, which are areas harboring demyelination, inflammation, gliosis and axonal injury^3,4. Recent studies^5–7 show that diffuse damage in normal appearing white matter (NAWM) and gray matter (GM) contributes to disability accumulation and chronic disease progression while lesions are mainly responsible for reversible impairment. Diffusion kurtosis imaging, DKI^8,9 is a technique, which utilizes deviations from Gaussian diffusion to yield biomarkers and provide access to microstructural parameters when combined with tissue modeling. DKI biomarkers have been associated with cognitive impairment in MS¹⁰, chronic injury in MS animal models^11–13 and neurite myelin content¹⁴. Experimental autoimmune encephalomyelitis (EAE) is one of the most compelling MS animal models. However, neither DKI metrics nor white matter models have been used to investigate EAE-induced disability. In this study, the ability of DKI to inform diagnostics for MS was tested by exploring the relationship between said metrics and EAE-disability.

Methods

18 (MOG)_p35–55-treated mice (experiment ethically approved) were monitored daily and EAE-severity was graded on a 6-point scale, prior to spinal cord extraction.

Imaging of spinal cords (SC, segments T8-L6) was performed with a 16.4T Bruker Aeon Ascend scanner using a diffusion weighted fast spin echo sequence(credit to Dr.KD Harkins and Prof.MD Does, NIH-EB019980)^14–16ETL=8,TE/ESP=15/4.23ms,TR=2000ms,$$$\delta/\Delta=$$$1.5/10ms and b=0.2,0.3,0.5,0.6,0.9,1,1.2,1.5,1.8,2.1,2.5ms/µm², voxel size 0.5x0.035x0.035mm³.

T₂^*-weighted images for manual WM, GM and lesion delineation¹⁷, were acquired using FLASH pulse sequence with TE=5ms.

Images were denoised¹⁸ and corrected for Gibbs ringing¹⁹. In WM, shells up to $$$b_\mathrm{max}=2.5$$$msµm^-2 and GM up to $$$b_\mathrm{max}=1.2$$$msµm^-2 were fit²⁰ with Weighted Linear Least Squares²¹, to yield diffusion and kurtosis tensors⁸. Tensor parameters were calculated according to^8,9, which yielded parameters of Watson Standard Model(WSM), assuming Watson distribution of neurites^22,23. Only the ‘plus’ branch^22,24,25($$$D_\mathrm{a}>D_{\mathrm{e},\parallel}$$$) was considered.

10 WM parameters (DKI:$$$D_\parallel$$$, FA,$$$K_\parallel$$$,$$$K_\perp$$$, and WSM:$$$D_{\mathrm{e},\perp}$$$,$$$D_{\mathrm{e},\parallel}$$$,$$$D_\mathrm{a}$$$,$$$f$$$,$$$\kappa$$$) and 2 GM parameters $$$\bar{D}$$$ and $$$\bar{W}=\frac{\mathrm{Tr}\left(\mathbf{W}\right)}{5}$$$ ^9,26,27 were estimated.

The voxels from all spinal cords were analyzed with linear mixed effects model^28,29(Wilkinson notation³⁰: $$$p_i \sim g\cdot s +l +\left(s\cdot g | a\right)+\left(l|a\right)$$$, where diffusion parameters are $$$p_i$$$, grade $$$g$$$, segment $$$s$$$, lesion $$$l$$$, animal $$$a$$$). For each of the ‘fixed’ effects, ANOVA p-values were calculated post-hoc.

Results

Lesion load was not significantly different between EAE-disabilities groups(Figure 1). In the parameter maps we observed an increased asymmetry in animals with a higher disability grade(Figures 2,3). Grade had a significant effect on 7 out of 12 parameters after FDR: $$$\bar{W}$$$ in GM, $$$K_\perp$$$,$$$D_\perp$$$,$$$D_{\mathrm{e},\parallel}$$$,$$$D_{\mathrm{e},\perp}$$$, $$$\kappa$$$ and $$$f$$$ in WM (Figure 4). Finally, post-hoc analysis investigated the group-wise behavior of the segment-wise means for GM and NAWM (Figure 5). In NAWM, 5 out of the 6 biomarkers surviving FDR demonstrated significant differences between the grades in the lumbar and 3 ($$$K_\perp$$$,$$$f$$$,$$$D_{\mathrm{e},\parallel}$$$) in lower thoracic and mid-thoracic SC segments. 2 DKI ($$$K_{\perp}$$$,$$$D_\perp$$$), and 2 WSM parameters ($$$f$$$,$$$D_{\mathrm{e},\parallel}$$$) depended significantly on EAE grade. $$$D_\perp$$$,$$$K_\perp$$$,$$$f$$$ and $$$D_{\mathrm{e},\parallel}$$$ were significant with no distinction between the segments. The same type of post-hoc analysis investigated the tissue inside lesions and did not show any significant differences between EAE-grades.

Discussion

In GM, $$$\bar{W}$$$ depended significantly on disability grade, in line with human studies^10,31–33 and the cuprizone model¹². Changes in $$$\bar{W}$$$ indicate GM pathology, that could be due to neuronal degeneration and myelin loss.

In NAWM, $$$K_\perp$$$ and $$$D_\perp$$$ were most robust parameters distinguishing between disability grades, which has been observed in other MS models^11,13,14. An increase in $$$D_\perp$$$ agrees with chronic demyelination studies^11,13,14. Human studies associated it with demyelination³⁴ and axonal loss³⁵.

Among WSM parameters, $$$D_{\mathrm{e},\parallel}$$$ and axonal water fraction($$$f$$$, a biomarker of axonal loss³⁶) affected the EAE-grade most. This is in contrast with hypomyelination models^11,12,37, where effects on $$$D\mathrm{a}$$$ and $$$D_{\mathrm{e},\perp}$$$ were the strongest. Technical differences (i.e. using SC or particular ‘branch’ of the WSM model^22,37,38) aside, the prominent role of $$$D_{\mathrm{e},\parallel}$$$ may result from different mechanisms underlying tissue degeneration in hypomyelination models compared to EAE. The increase in extra-axonal diffusivities can be explained by axonal damage, glial cells structure changes, and myelin loss, that cause lowered tortuosity in the extra-axonal space. This novel observation indicates $$$D_{\mathrm{e},\parallel}$$$ as a key parameter that may prove important for MS and EAE disability characterization

Conclusion

Inside WM lesions, no biomarker was found to correlate with disability.
In NAWM and GM, the relationship between disability and DKI/DTI metrics was similar to other hypomyelinating MS models and ex-vivo MS tissue.
A combination of strong increase in $$$D_{\mathrm{e},\parallel}$$$ and a potentially-verifiable change in $$$f$$$ is clearly distinct in comparison to other MS animal models.

Acknowledgements

The authors are grateful for financial support of this project by Lundbeck Foundation Grant R83‐A7548 and Simon Fougner Hartmanns Familiefond. AC and BH acknowledge support from NIH1R01EB012874‐01. The laboratory was made possible by funding from the Infrastructure program of the Danish Research Council, the Velux Foundations, and the Department of Clinical Medicine, AU. The authors thank Dr Kevin D Harkins and Prof. Mark D Does from Vanderbilt University for the REMMI pulse sequence and reconstruction toolbox used in this study, which were supported through grant number NIH EB019980. NS was supported in part by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 679058 – DIRECT-fMRI). The authors also thank Dina Arengoth and Pia Nyborg Nielsen for expert technical assistance. AW and TO acknowledge financial support from Lundbeck Foundation, Danish Multiple Sclerosis Society, Independent Research Fund Denmark The authors would also like to offer special thanks to Shemesh Lab members in Champalimaud Center for Unknown that provided their extensive help during the acquisition stages and, in particular, to Teresa Serrades Duarte, Daniel Nunes, Rui Simões and Cristina Chavarría.

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Figures

Figure 1. Animal disability grade against lesion load relative volume (number of voxels in lesions per voxels number in the corresponding segment WM) averaged per sample in SC WM. Bar plots correspond to different segments of spinal cord. One barplot represents average over all the segments. Voxel size is $$$6.1\cdot10^-4\mathrm{mm}^3$$$, an average segment volume is ~3000 voxels for mid thoracic, ~5000 for lower thoracic and ~8000 for lumbar segments. Data consists of 5 samples (low grade), 3 (intermediate grade), 5 (high grade), 5 (control). Error bars depict standard error within samples. There was no significant differences between the EAE-disability groups.

Figure 2. Examples of parameter maps for the diffusion and kurtosis tensors parameters in mid-thoracic segments of spinal cord. Each of the columns (from left to right) corresponds to different grades of EAE disability: control animal, low grade, intermediate grade and high grade of EAE. Rows correspond to different measured parameters (from top to bottom): mean diffusivity, mean kurtosis, FA, axial diffusivity, radial diffusivity, radial kurtosis, parallel kurtosis, the upper row depicts the delineation of spinal cord on the background of FA map.

Figure 3. Examples of parameter maps for the modeling parameters in mid-thoracic segments of spinal cord. Each of the columns (from left to right) corresponds to different grades of EAE disability: control animal, low grade, intermediate grade and high grade of EAE. Rows correspond to different measured parameters (from top to bottom): axonal water fraction, axonal diffusivity, axial extra-axonal diffusivity, radial extra-axonal diffusivity and concentration parameter of Watson distribution, the upper row depicts the delineation of spinal cord on the background of FA map.

Figure 4. The results of the fit of the linear mixed effects model. For each of the studied parameters (in rows), the following are presented in columns: p-values for coefficients of grade, lesion, segment and segment*lesion, grade fixed effect estimates and the results of the FDR multiple comparison test. Since lesions were registered only in WM, the coefficients of lesion are absent in GM (NaN). The details of the analysis see in ³⁹.

Figure 5. Post-hoc analysis of parameters in GM and NAWM. For each of the FDR significant parameters in the rows a post-hoc analysis (2 sample t-test, corrected for multiple comparison) was carried out to identify significantly different groups. The results are encoded in the table with the notation: 1 control-low, 2 control-intermediate, 3 control-high, 4 low-intermediate, 5 low-high, 6 intermediate-high. Only parameters that feature significant results in both linear mixed model and post-hoc analysis are presented. The details of the analysis see in ³⁹.

Proc. Intl. Soc. Mag. Reson. Med. 27 (2019)

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