Purpose

Multiple sclerosis (MS) is a chronic inflammatory and degenerative disease of the central nervous system, characterized by the presence of focal and diffuse demyelination, gliosis and axonal damage in both white matter (WM) and grey matter (GM)¹. Neurite orientation dispersion and density imaging (NODDI) is a recent diffusion imaging technique that uses diffusion gradients of different strengths to provide novel metrics of axonal and dendrites integrity². Two previous preliminary works^3,4 showed that NODDI is sensitive to detect brain structural damage in MS patients compared to controls. In this study, we explore the value of NODDI to investigate the longitudinal remodeling of axons and neurites in a population of early MS patients.

Methods

Thirty-five RRMS patients with less than 5 years disease duration and 20 healthy controls (HC) were enrolled in the study (Table 1); 87% of patients were on treatment (high-dosage interferon beta and fingolimod) at study entry (time-point 1, tp1) and 97% at 2 years follow-up (time-point 2, tp2). All subjects underwent disability, motor and cognitive assessment (Table 2) and Magnetic Resonance Imaging (MRI) at both time-points (Table 3). We performed: (i) tissue segmentation and lobar parcellation on MPRAGE images using Morphobox^5,6; (ii) manual lesion segmentation on 3DFLAIR, DIR and MP2RAGE images by two raters by consensus; (iii) MP2RAGE registration to the diffusion space and application of the resulting transformation matrix to all segmented volumes, masks and diffusion weighted images (DWI) volumes using Statistical Parametric Mapping toolbox⁷. To minimize partial volume (PV) effects, PV was estimated in each DWI by applying an iterative PV estimation algorithm to the b0 image⁸. DWIs were fitted to the NODDI model using the AMICO framework⁹ to extract the intra-cellular volume fraction (ICVF), neurite dispersion index (Orientation Dispersion Index (ODI)) and isotropic volume fraction (isoVF) in normal appearing WM (NAWM) and in lesions. To study the evolution of lesion properties, the lesion mask obtained at tp1 was registered to NODDI maps at tp2 and only lesions > 10 voxels were considered to minimize PV effects. At tp1, NODDI metrics in NAWM of each lobe were compared between RRMS patients and HC using the Hotelling two-sided permutation test (n=10000 permutations), with age and gender as covariates and correction for family-wise error rate. The same statistical analysis was used to compare mean NODDI metrics in lesions with mean NODDI metrics in the WM of HC. For longitudinal analysis we performed a t-test to assess differences between tp1 and tp2 in mean ICVF and ODI metrics in NAWM and lesions. At both time points, a GLM model was performed with mean ICVF and ODI measures at tp1 as predictors and clinical scores at tp1 and tp2 as random variables, with age, gender, educational years, anxiety and depression scores as covariates. Clinical scores were adapted using a box-cox transformation to satisfy the model assumption for normality. Bonferroni correction was applied to account for multiple comparisons.

Results

Results for the cross-sectional comparison between patients and controls at tp1 are summarized in Table 4. The GLM analysis showed that (i) NAWM-ODI in the occipital lobe of tp1 was correlated to the SRT-LTS score (p=0.02, Adjusted-R²=0.32, Leave-one-Out Spearman (LOO-S)=0.43) and to the MSFC score (p=0.05, Adjusted-R2=0.3, LOO-S=0.37) at tp1; and (ii) NAWM ODI in the parietal and occipital lobes was correlated with the SRT-LTR score at tp2 (p=0.0018, Adjusted-R2=0.44, LOO-S=0.59). The combined analysis of ODI and ICVF variations in lesions showed a decreased dispersion and an increased neurite density at 2 years follow-up (Figure 1, p < 0,05).

Discussion

NODDI appeared to be sensitive to detect cross-sectional differences between early RRMS patients and HC and longitudinal changes in patients. Cross-sectionally, MS patients exhibited an increase in orientation dispersion and a decrease in neurite density in NAWM and lesions compared to HC. The significant decrease of the ICVF may be due to axonal loss or damage, whilst the increase of the neural dispersion may result from cell and axonal loss resulting in increased extracellular space¹⁰. Longitudinal analysis showed subtle changes indicating an increase of neurite dispersion and of intraneurite volume in lesions, which suggest repair mechanisms in areas of focal damage¹¹. Future work will extend the current findings to other MS subtypes and explore the value of NODDI in monitoring disease progression.

Acknowledgements

No acknowledgement found.

References

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