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Gray matter pathology changes in multiple sclerosis: a comparison of diffusion kurtosis and volumetrics.

Lily Q. Cao¹, Benjamin Ades-Aron¹, Katarina Yaros¹, Nicolas Gillingham¹, Dmitry S. Novikov¹, Yvonne W. Lui¹, Ilya Kister¹, Timothy K. Shepherd¹, and Els Fieremans¹

¹NYU School of Medicine, New York, NY, United States

Synopsis

Along with well-characterized abnormalities in normal appearing and lesional white matter, gray matter (GM) pathology has been observed in multiple sclerosis (MS) both with diffusion tensor imaging (DTI) and structural MRI. We used diffusion kurtosis imaging (DKI), a clinically feasible extension of DTI, to characterize pathology in cortical and subcortical GM in MS and uncovered correlations with disease severity, quantified in terms of the Patient Determined Disease Score (PDDS). Our results suggest that DKI metrics are sensitive to changes in GM and could be helpful to use alongside standard markers of disease progression, such as GM volume atrophy.

Introduction

While multiple sclerosis (MS) has traditionally been considered a progressive disorder of white matter¹, recent studies^2,3suggest that pathology of gray matter (GM) is also an important contributor to the disease process. This study aimed to characterize GM pathological changes and its association with clinical manifestation in MS using diffusion kurtosis imaging (DKI), a clinically feasible extension of diffusion tensor imaging (DTI), and compare against conventional GM volumetrics⁴.

Methods

Subjects: This retrospective analysis was approved by an IRB and included 36 patients with relapsing-remitting MS and 24 age and gender-matched healthy controls who received neurological examinations, underwent MRI on a 3T MR Scanner (Siemens Skyra or Prisma), and completed a Patient Determined Disease Steps (PDDS) score, a validated patient-reported metric of disease severity. Table 1 lists demographics of the cohort.

MRI: Imaging included whole brain 3D magnetization-prepared rapid gradient-echo (MPRAGE) (1 mm³ isotropic resolution) for extracting volumetrics and monopolar diffusion-weighted EPI (voxel size = 1.7 x 1.7 x 3 mm³, b=0, 250, 1000, and 2000 s/m² along 84 directions, TE/TR = 100/3500 ms, GRAPPA with acceleration 2, and multiband 2) for deriving diffusion metrics.

Statistical Analysis: For each subject, regions of interest (ROI) were automatically segmented via anatomical MPRAGE using Freesurfer, then combined and narrowed down into 7 subcortical and 5 cortical GM regions. For this study, total subcortical regions were defined as the non-cortical GM regions of the prosencephalon. With the addition of total subcortical and cortical GM, we had a total of 14 ROIs. The corresponding volume metrics for each ROI were extracted. Diffusion MRI data was processed using DESIGNER⁵ to derive parametric maps for the mean diffusivity (MD), mean kurtosis (MK) and fractional anisotropy (FA). An affine transform from a b=0 diffusion image to the MPRAGE was computed using bbregister to propagate Deskan/Killiany atlas⁶ labels to diffusion space. The diffusion and volume metrics gathered were compared between MS patients and controls using analysis of covariance with age as the covariate. We analyzed the correlation of these metrics with disease severity as characterized by the PDDS score covarying with disease duration, gender, and age.

Results

Figure 1 and 2 show box plots of the various metrics (volume, MD, MK, and FA) for control and MS patients for cortical and subcortical ROIs, respectively. Significantly (p<0.05) decreased volumes were found in MS for several subcortical ROIs, with even more pronounced differences in terms of the diffusion metrics. In particular, both MD and MK were significantly increased in all ROIs (except MK in the pallidum) in patients compared to controls, while FA was either increased in the subcortical GM ROIs, or decreased in the cortical GM (except for the occipital cortex).

Correlations between the various metrics and PDDS are displayed for subcortical and cortical regions in Table 2 and 3 respectively. The GM volumes were negatively correlated with increasing PDSS, reaching significance in the amygdala. Several trends (p<0.1) were observed for increasing MD, decreasing MK, and decreasing FA with increasing PDDS, reaching significance for the FA in the cingulate cortex.

Discussion

This study characterized GM pathology in MS patients using DKI and volumetry in several GM ROIs constituting to the total cortical and subcortical GM. While our results confirmed previous reports of decreased GM volumes in MS patients compared to controls^7-10, we also found differences in diffusion metrics with larger effect sizes. Interestingly, the observed increase in MK may be indicative of increased restriction due to microgliosis and astrocytosis in GM¹¹.

In terms of correlation with disease manifestation, our results showed a negative correlation in volumes of certain GM ROIs with increasing disease severity, which agrees with previous studies^12,13, though contrasts others studies^14-16. Similarly, the positive correlations found between MD and PDDS in various GM ROIs are consistent with previous work¹⁷. Here, we also found a negative correlation between MK in various GM regions and increasing PDDS, which is in line with one previous study¹⁸ reporting correlation between cortical GM MK and worse outcome in cognitive test (but not with EDSS). The observed negative correlation (as opposed to the observed increase in MK between MS and controls) may suggest that neurodegenerative processes such as demyelination and/or neuronal loss more so than inflammation (potentially causing an increase in MK) are contributing in GM to increasing MS disease severity.

Conclusions

Our results suggest that DKI metrics are sensitive to changes in GM and could be helpful to use alongside standard markers of disease progression, such as GM volume atrophy. Future longitudinal studies may yield further insight into the underlying GM pathology in MS.

Acknowledgements

This study was supported by the National Institute of Neurological Disorders and Stroke of the National Institutes of Health under award number R01NS088040, and performed at the Center of Advanced Imaging Innovation and Research CAI2R, www.cai2r.net) and NIBIB Biomedical Technology Resource Center P41 EB017183.

References

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Figures

Table 1. Demographic and clinical characteristics of MS subjects and controls. All measures are reported as mean ± standard deviation, unless specified otherwise. No statistical difference in age or gender were observed between groups.

Figure 1: Group differences (p<0.05 highlighted by *) in volume and diffusion metrics between MS subjects and controls for subcortical gray matter regions which included the thalamus, hippocampus, amygdala, caudate, putamen, pallidum, accumbens area, and total subcortical gray regions. Significant decrease in volume were seen in the amygdala, caudate, putamen, and accumbens area in MS subjects versus controls, while significant increases in FA were seen in all subcortical regions studied except the thalamus. In addition, significant increases in MD were observed in all subcortical regions, as well as in MK in all subcortical regions except for the pallidum.

Figure 2: Group differences in volume and diffusion metrics between MS subjects and controls for cortical gray matter regions including the frontal, parietal, temporal, occipital, cingulate, and total cortices. A trend of decreased volumes of cortical regions in MS subjects versus controls were observed, but these results did not reach significance. On the other hand, significant decreases in FA in MS subjects versus controls were observed for the frontal, temporal, and total cortices while significant increases in FA in MS subjects versus controls were observed for the occipital cortex. In addition, the MD and MK of all cortical regions were increased in MS subjects compared to controls.

Table 2. Two-sided Spearman rank correlations between diffusion or volumetric parameters and PDDS corrected for age, sex, and duration in subcortical gray matter regions. Significant correlations (p< 0.05) are marked in bold underline, while trends (p< 0.01) are marked in bold.

Table 3: Two-sided Spearman rank correlations between diffusion or volumetric parameters and PDDS corrected for age, sex, and duration in cortical gray matter regions. Significant correlations (p< 0.05) are marked in bold underline, while trends (p< 0.01) are marked in bold.

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

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