# Microstructural damage and repair in the spinal cord of patients with early multiple sclerosis and association with disability at 5 years

Malo Gaubert, Benoit Combès, Elise Bannier, Arthur Masson, Vivien Caron, Gaëlle Baudron, Jean-Christophe Ferré, Laure Michel, Emmanuelle Le Page, Bruno Stankoff, et al.

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## To cite this version:

Malo Gaubert, Benoit Combès, Elise Bannier, Arthur Masson, Vivien Caron, et al. Microstructural damage and repair in the spinal cord of patients with early multiple sclerosis and association with disability at 5 years. Neurology Neuroimmunology & Neuroinflammation, 2025, 12 (1), pp.e200333. 10.1212/NXI.0000000000200333. hal-04782133

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# Microstructural damage and repair in the spinal cord of patients with early multiple sclerosis and association with disability at 5 years

## Running Title

Spinal cord damage and repair in MS

## Authors and affiliations

Malo Gaubert¹,², Benoit Combès², Elise Bannier¹,², Arthur Masson², Vivien Caron², Gaëlle

Baudron², Jean-Christophe Ferré¹,², Laure Michel³, Emmanuelle Le Page³, Bruno Stankoff⁴,⁵, Gilles Edan³, Benedetta Bodini⁴,⁵ and Anne Kerbrat²,³

1. Department of Neuroradiology, Rennes University Hospital, Rennes, France

2. Empenn, INRIA, Rennes University- CNRS- INSERM, Rennes, France

3. Department of Neurology, Rennes University Hospital, Rennes, France

4. Paris Brain Institute (ICM), Sorbonne University- CNRS- INSERM, Paris, France

5. Neurology Department, APHP St Antoine Hospital, Paris, France
# Abstract

## Background and objective

The dynamics of microstructural spinal cord (SC)

damage and repair in **people with multiple sclerosis (pwMS)** and their clinical relevance have
yet to be explored. We set out to describe patient-specific profiles of microstructural SC
damage and change during the first year after MS diagnosis, and to investigate their
associations with disability and SC atrophy at 5 years.

## Methods

We performed a longitudinal monocentric cohort study among **people** with relapsing-
remitting MS: first relapse < 1 year, no relapse < 1 month, and high initial severity on MRI (>
9 T2 lesions on brain MRI and/or initial myelitis). **PwMS** and age-matched healthy controls
(HC) underwent cervical SC magnetisation transfer (MT) imaging at baseline, and at 1 year
for **pwMS**. Based on HC data, SC MT ratio z-score maps were computed for each **pwMS**. An
index of microstructural damage was calculated as the proportion of voxels classified as
normal at baseline and identified as damaged after 1 year. Similarly, an index of repair was
also calculated (**voxels classified as damaged at baseline and as normal after 1 year**). Linear
models including these indices and disability or SC cross sectional area (CSA) change
between baseline and 5 years were calculated.

## Results

Thirty-seven patients and 19 HC were included. We observed considerable variability in the extent of microstructural SC damage at baseline (0-58% of SC voxels). We also observed
considerable variability in damage and repair indices over 1 year (0-31 % and 0-20%), with 18 patients showing predominance of damage, and 18 predominance of repair. The index of microstructural damage was associated positively with EDSS score (r = .504, p = .002) and negatively with CSA change (r = -.416, p = .02) at 5 years, independently of baseline SC lesion volume.

## Discussion

People with early RRMS exhibited heterogeneous profiles of microstructural SC damage and repair. Progression of microstructural damage was associated with disability progression and SC atrophy 5 years later. These results indicate a potential for microstructural repair in the SC to prevent disability progression in pwMS.

## Keywords

Multiple sclerosis, spinal cord, MRI, magnetisation transfer, remyelination, longitudinal
# Introduction

Spinal cord (SC) lesions frequently occur in people with MS (pwMS),¹ and have a prognostic value early on the disease for the development of subsequent ambulatory disability.² However, in clinical practice, some pwMS have a high SC lesion load and a low level of disability, and vice-versa. One hypothesis is that patients have a different potential to remyelinate/demyelinate, which has an impact on the severity of their lesions. In contrast to the brain, the ability of the SC to remyelinate/demyelinate over time remains largely unexplored,³,⁴ while degeneration of chronically demyelinated SC axons is considered to be the major pathological correlate of ambulatory disability.⁵,⁶

Complementing anatomopathological studies, the dynamic exploration of microstructural SC damage and repair in pwMS requires longitudinal studies using quantitative imaging. In particular, myelin PET imaging was used to describe the remyelination/demyelination profiles of pwMS over time.⁷ To date, no myelin-specific PET studies have been carried out in the SC of pwMS, mainly owing to limited spatial resolution. Alternative MRI-based approaches can also be considered, such as magnetisation transfer (MT) imaging. This technique is less specific to myelin content than PET, but results have been shown to correlate well with both myelin and axonal tissue content.⁸⁻¹⁰ In longitudinal brain MRI studies, it has been used to approximate microstructural variations at the voxel scale, both in white matter (WM)¹¹,¹² and grey matter (GM).¹³ To date, no such studies have been carried out at the SC level. More generally, studies involving longitudinal quantitative SC MRI acquisitions in pwMS remain scarce,¹⁴⁻¹⁹ mainly owing to the technical challenges.²⁰

Thus, the main objective of this pilot study was to describe patient-specific profiles of microstructural SC damage and repair during the first year after MS diagnosis. We investigated the associations between these profiles and disability and SC atrophy at 5 years.
# Materials and methods

## Standard Protocol Approvals, Registrations, and Patient

### Consents

All participants were enrolled in EMISEP, a multicentre longitudinal study approved by an institutional review board (NCT02117375)²¹ and gave their written informed consent.

## Study sample

The main inclusion criteria were i) diagnosis of relapsing-remitting MS (RRMS)²² ii) first relapse < 1 year, iii) Expanded Disability Status Scale (EDSS) score < 3,²³ iv) age > 18 years, v) no relapse in month before inclusion, and vi) high severity on MRI (> 9 T2 lesions on brain MRI and/or initial myelitis on SC MRI). A group of healthy controls (HC) was matched for age with the pwMS group. The main inclusion criteria for HC were no family history of MS, and no central nervous system disease.

In the present study, we only included participants from the main inclusion centre (Rennes), owing to scanner variability between centres for MT ratio (MTR) measurement and too few HC for the other centres ($n < 5$) to compute z-score maps. The original dataset comprised 44 pwMS and 20 HC. Of these, seven **pwMS** and one HC had to be excluded from analyses, owing to comorbidity incompatible with disability assessment (one pwMS), missing MRI acquisition (one pwMS), issues during the acquisition protocol (two pwMS), or poor image quality (three pwMS and one HC). The final dataset therefore comprised 37 **pwMS** and 19 HC.
## Clinical assessment

All participants were assessed by a senior neurologist every 6 months for 5 years, including the EDSS (and pyramidal subscale), Timed 25-Foot Walk (T25-FW), Six-Minutes Walk Test (6M-walk),²⁴,²⁵ Nine-Hole Peg Test (9HPT),²⁵ 12-Item MS Walking Scale (MSWS-12),²⁶ and French-language version of the urinary disorder-specific quality of life questionnaire (Qualiveen).²⁷

## MRI acquisition

All participants underwent a baseline 3T MRI scan (Magnetom Verio, Siemens Healthineers, Erlangen, Germany, VB17) between January 2014 and April 2017 for pwMS and between March 2016 and December 2017 for HC. All pwMS had a second scan 12 months later on the same machine, and 36 had an MRI scan at 5 years on a different scanner of the same manufacturer (3T Magnetom Prisma). The acquisition protocol (detailed in eTable 1) was the same for all timepoints and was split into two consecutive sessions. The first one included T1-weighted (T1w) 3D MPRAGE imaging comprising brain and upper cervical SC and 3D FLAIR imaging with a 32-channel (64 for Prisma) head coil. The second one included SC acquisition with neck and spine coils: two sagittal T2-weighted (T2w) images covering the whole SC, T2*-weighted MEDIC (T2*w) axial images covering C1-C3 and C4-C7 SC vertebra levels, and two 3D gradient echo sequences with (MT1) and without (MT0) prepulse covering cervical SC and centred on C5 (manufacturer's default magnetization transfer prepulse [Gaussian envelope, 1200 Hz off-resonance frequency]). Only baseline and 12-month follow-up MT scans were considered for primary measures. The acquisitions at 5 years were used solely for secondary measures (CSA). The impact of the change of MRI scanner on CSA is discussed in eAppendix 1.
# MRI processing

Processing was performed using the Spinal Cord Toolbox (SCT) v5.8 for the SC images,²⁸
FMRIB Software Library (FSL)²⁹ v6.0.5 and Anima v4.1³⁰ for brain and further processing.

Indices of microstructural change

Details of MTR computation are provided in eAppendix 2. Briefly, after rigid registration of
the MT0 map to MT1 space, MTR maps were computed as the ratio of (MT0 - MT1) to MT0
(expressed as percentage), normalised to PAM50 template space³¹ and smoothed with a ball
kernel with 1-mm diameter. The full pipeline of processing from the baseline and 12-month
follow-up MTR maps to the calculation of the indices is described in Figure 1. Cervical SC
MTR values were taken as a proxy of microstructural integrity. Voxel-based z-score maps of
MTR values at each timepoint (baseline and 12-month follow-up) for each pwMS were
computed based on mean and standard deviation values calculated in the HC group. A similar
voxel-based approach was previously used ⁷,¹² and had the advantage to tackle the variability
of the MTR measure associated with different SC locations.²¹ In each pwMS at each time
point, any given voxel with an MTR z-score more than 2.58 SDs below the mean
 cor responding to a p value of .01) of the corresponding voxel in HC, was classified as
having an abnormal microstructure. To confirm those results, all subsequent analyses were
repeated with a thresholding of the MTR z-score maps at 1.96 SDs (corresponding to a p
value of .05). Results are provided in eAppendix 3.

Three indices of microstructural content were then computed (Fig. 1C). First, an *index of dynamic microstructural damage* was defined as the percentage of voxels classified as damaged at 12-month follow-up but not at baseline. Second, an *index of dynamic microstructural repair* was defined as the percentage of voxels classified as damaged at baseline but not at 12-month follow-up. Finally, a *global index of microstructural change*
was defined as the difference between these indices with positive values indicating predominant microstructural damage over the follow-up period, and negative values indicating repair. All three indices were computed for cervical SC (mask eroded by 1 mm, comprising C2-C6 from PAM atlas).

##########

# Insert Figure 1

##########

## Lesion segmentation

Cervical SC lesions were manually segmented on axial T2\*w images as described elsewhere.³² Details are provided in supplementary material (eMethods).³²

## Cross-sectional areas

The mean CSAs across C2 and C3 levels were computed on the T1w images at baseline and 5-year follow-up using the steps described in the SCT tutorial. As two different MRI scanners were used at baseline and at 5-year follow-up, we corrected for the variability induced by the change of machine (see eAppendix 1). The relative change between baseline and 5-year follow-up was computed as in Eq. (1).

$$ \square \square \square \square \square \square \square \square \square \square \square \square = \frac{[\square \square \square \square 5 - \square \square \square \square \square \square \square \square \square \square \square] - [\square \square \square \square \square \square \square \square \square \square \square \square]}{[\square \square \square \square \square \square \square \square \square \square \square \square \square]} \quad (1) $$

## Statistical analysis

All statistical analyses were performed using R v4.3.0 and RStudio 2022.07.2+576 with ppcor package (v1.1) for partial correlations and khroma (v1.12.0) and ggplot2 (v3.5.1) packages for data visualisation. First, we focused our analyses on microstructural abnormalities at baseline and at 12-month follow-up at the group level, to compare HC and
patients with RRMS. The mean signals for these group analyses were extracted in the C2-C6 mask. Second, we ran individual analyses to capture the uniqueness of the microstructural damage and repair profiles in patients over the 12-month follow-up. Third, we looked for associations between the indices and disability and SC atrophy 5 years later.

For all tests unrelated to the three microstructure indices, the significance threshold for results was set at *p* < .05. When considering analyses with the three microstructure-related indices, we used a significance level of *p* < .0083 (.05/6), corresponding to a Bonferroni correction for multiple comparisons. Age at baseline and sex were included as covariates in multiple regression models.

## Baseline and 5-year follow-up characteristics

Differences in characteristics between the HC and pwMS groups were assessed using two-tailed two-sample *t* tests for continuous variables, and chi-squared (*χ*²) tests for categorical variables. Two-tailed paired *t* tests were performed to assess changes in clinical and anatomical measures between baseline and 5-year follow-up data.

### Microstructural damage at baseline

A two-tailed two-sample *t* test was computed to compare the pwMS and HC groups on MTR values for cervical SC at baseline. A partial linear correlation was computed with the percentage of abnormal voxels in cervical SC at baseline and lesion load at baseline (log-transformed in all subsequent analyses).

### Microstructural damage and repair at 12-month follow-up

A two-tailed paired *t* test was used to assess the mean difference in MTR values between the two timepoints in the pwMS group. Individual profiles were then defined, based on our three indices. Finally, multiple regressions were computed between the three indices and T2\*w
lesion load, percentage of microstructural abnormality at baseline, age at baseline, and sex at baseline, to assess the associations between these factors and microstructural change over time.

## Microstructural damage and lesion location

We evaluated the location of microstructural damage cross-sectionally and longitudinally and the location of lesions at baseline in three different ways. First, regarding lesion locations, and microstructural damage at baseline and repair over time in GM or WM, we took the number of voxels for each index in each compartment (GM or WM) and divided it by the sum of voxels in GM and WM. Second, we computed percentages for each compartment by dividing the number of voxels for each index by the number of voxels in the whole cervical SC. For these two calculations, GM and WM in cervical SC were retrieved using the PAM50 atlas.³¹ Third, the effect of distance from external cerebrospinal fluid (CSF) was assessed using a distance map computed with the *distancemap* tool provided by FSL and applied to the cervical SC mask (see above and illustration in Fig. 5). In each 0.5-mm thick ribbon, we computed the percentage of voxels in the ribbon with a lesion or microstructural damage at baseline or where microstructural damage or repair took place over time.

## Associations with clinical and anatomical findings at 5-year follow-up

We ran three multiple regressions, one for each of the three indices of microstructural change as independent variables, and EDSS at 5-year follow-up as the dependent variable as in Eq. 2.

$$ \text{EDSS at 5-year follow-up} = \beta_0 + \beta_1 * \text{Index} + \beta_2 * \text{Age} + \beta_3 * \text{Sex} + \varepsilon \quad (2) $$

With index = either Index of dynamic microstructural damage or Index of dynamic microstructural repair or Global index of microstructural change, β₀ the intercept, β₁, β₂, β₃ the regression coefficients and ε the error term.
The same model was applied with regards to CSA change between baseline and 5-year follow-up (Eq. (1)) as the dependent variable. Moreover, we conducted exploratory analyses with multiple regressions between the three indices as independent variables and the other clinical scores (i.e., pyramidal EDSS, T25-FW, 6M-walk, 9HPT, MSWS-12 and Qualiveen) as dependent variables (eTable 2). ANCOVAs were also computed to compare patients with versus without confirmed disability progression (defined as an increase in EDSS score of ≥1 point confirmed for at least 6 months and until the end of patient follow-up), with the three indices as independent variables. The T2*w lesion load at baseline and the presence (vs. absence; binary) of new lesions at 12-month follow-up (compared with baseline) were added as extra covariates for secondary analyses.

**Data availability statement**

Data supporting the findings of this study are available from the corresponding author, upon
reasonable request.
# Results

## Baseline and follow-up characteristics

There was no significant change in clinical scores between 0 and 5 years at group level, except on the 9HPT (all *p*-values > .05, but 9HPT: *p* < .001; Table 1). Twelve out of 36 patients had confirmed disability progression during follow-up. CSAs were comparable across the HC and pwMS groups at baseline (*p* = .166), but greater at baseline than at 12-month follow-up in the pwMS group (*p* < .001).

######

# Insert Table 1

######

## Microstructural damage at baseline

### Group-level analysis

MTR values in pwMS were lower than those of HC in both the whole cervical SC (HC: 35.672 ± 0.888 pu; pwMS: 34.649 ± 2.208 pu; *p* = .017; Cohen's *d* = 0.539) and, albeit to a lesser extent, in normal appearing cervical SC (pwMS: 34.938 ± 1.936 pu; *p* = .058; Cohen's *d* = 0.436).

### Individual-level analysis

The percentage of microstructural damage in cervical SC varied considerably, ranging from 0 to 58% (see Fig. 2 for examples of pwMS with different degrees of baseline microstructural SC damage). A moderate positive association was observed between the SC lesion load
delineated on axial T2*w and the percentage of microstructural damage in cervical SC on MTR maps (r = .405, p = .016). However, the mean volume of lesions on T2*w (191.902 ± 319.274 mm³) was much smaller than the volume categorised as damaged on MTR maps (357.983 ± 542.494 mm³). As depicted in Figure 2, most of the microstructural damage at baseline was found in the lesion area (82.4 %), but it was also present in regions not related to a lesion delineated on axial T2*w (17.6 %). Finally, 25% of SC lesions identified on axial T2*w images were not associated with significant damage in MTR maps.

## Location

The lesions at baseline were mainly found in WM (77.967 ± 15.234%) rather than GM. Microstructural damage in the cervical SC was also found mainly in WM regions (74.325 ± 17.853%) rather than GM. However, when lesional volume was related to GM and WM volumes, on average 5.630% (± 8.714%) of GM was affected by lesions, and 6.648% (± 8.601%) of WM. Similarly, on average, 9.824% (± 14.88%) of GM and 9.643% (± 13.13%) of WM was affected by microstructural damage. Finally, we observed a homogeneous distribution of both lesions (Fig. 5, top left) and microstructural damage (Fig. 5, top right), in terms of distance from external CSF.

##########

# Insert Figure 2

##########
# Microstructural damage and repair at 12-month follow-up

## Group-level analysis

No significant difference in MTR in cervical SC was observed in pwMS between baseline (34.649 ± 2.208 pu) and 12-month follow-up (34.466 ± 2.216 pu; *p* = .417; Cohen's *d* = 0.633).

## Individual-level analysis

Heterogeneity was observed in microstructural change in cervical SC over the 12-month follow-up, with values ranging from 0 to 31% for the index of dynamic microstructural damage, and 0 to 20% for the index of dynamic repair (Fig. 3, top row; see Fig. 4 for examples). The predominance profiles were also heterogeneous, with 18 patients exhibiting predominant repair, and 18 exhibiting predominant damage (no predominance in one patient) (Fig. 3, bottom left). Lesion load at baseline was positively associated with the index of dynamic damage (*r* = .43, *p* = .008), but not with either the index of dynamic repair (*r* = .223, *p* = .185) or the global index of microstructural change (*r* = .203, *p* = .228). The indices of dynamic damage (*r* = .506, *p* = .001) and repair (*r* = .713, *p* < .001) in the cervical SC were positively associated with the percentage of microstructural damage at baseline.

No association was found between any of the three indices and age or sex at baseline (all *p*-values > .1 after multiple comparison correction).

Five of the 37 pwMS had new lesions between C2 and C6 at 12-month follow-up. The global index of microstructural change showed predominant repair for one pwMS, and predominant damage for the other four pwMS. However, most of the microstructural damage identified at 12-month follow-up was outside the new lesions (66%).
At 12-month follow-up, 8 pwMS had a high efficacy therapy, of whom 4 had a predominance of microstructural repair, 3 of microstructural damage and 1 had no predominance.

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# Insert Figure 3

# Insert Figure 3

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# Insert Figure 4

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# Insert Figure 5

Location

There was more microstructural damage over time in WM than in GM (WM/GM: 75.076/24.924 ± 14.078%). There was also more microstructural repair over time in WM than in GM (WM: 73.589/26.411 ± 19.265%). The distribution of microstructural damage (Fig. 5, bottom left) and repair (Fig 5, bottom right) over time was homogenous in terms of distance from external CSF.

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Associations with disability and atrophy measures at 5 years

Baseline microstructural damage was associated with EDSS at 5-year follow-up (r = .420, p < .001), but not with CSA change between baseline and 5-year follow-up (r = -.221, p = .063).
Similarly, baseline T2* lesion volume was associated with EDSS at 5-year follow-up ($r = .340, p = .004$), but not with CSA change between baseline and 5-year follow-up ($r = -.144, p = .232$).

The associations between the three indices and EDSS/CSA are summarised in Table 2. Positive associations were found between the index of dynamic microstructural damage and EDSS score ($r = .632, p < .001$), and between the global index of microstructural change and EDSS score ($r = .452, p = .007$) at 5-year follow-up. Furthermore, a negative association was observed between the index of dynamic microstructural damage and CSA change ($r = -.416, p = .014$) at 5-year follow-up. Results remained significant when baseline SC lesional volume was added as an additional covariate, and when the appearance of new lesions or number of relapses between baseline and 5-year follow-up were added as a supplementary covariate (eTable 3).

No significant group differences were found on the three indices when we compared patients with or without confirmed disability progression during follow-up (all *p*-values > .1).

engoogh
# Insert Table 2

#####

e

# Insert Table 2
Discussion

We found considerable heterogeneity in the initial extent of SC microstructural damage
among pwMS. Subsequent damage/repair dynamics were also highly variable across pwMS,
even for an equivalent initial lesion load. A high index of dynamic microstructural damage
for the first year of the disease was associated with the development of SC atrophy at 5 years,
and with an increased EDSS score, irrespective of initial SC lesion load.

Spinal cord microstructural damage at baseline

While the majority of abnormal areas on MTR were consistent with lesions detected on T2*w
or on the periphery of these lesions, other areas that were farther away from the lesions only
appeared abnormal on MTR z-score maps (Figure 2). This result can be explained by the
imperfect specificity of MTR for quantifying demyelination.⁸ The presence of axonal loss can
also reduce MTR values,¹⁰ affecting the lesion areas, but also having repercussions at some
distance from them.⁶ Moreover, the detection of SC lesions is a difficult task subject to inter-
expert variability.³³ The axial T2*w sequence used in the present study had satisfactory in-
plane resolution (0.7 × 0.7 mm²), but we cannot rule out the possibility that some small SC
lesions may not have been detected. We did not find any association between distance from
CSF (i.e., SC periphery) and the presence of abnormal MTR areas at baseline. In the brain,
periventricular WM is known to be one of the main sites of MS lesions, as well as of lower
MTR, with a gradient of values.¹²,³⁴,³⁵ Explanatory factors include CSF proximity and the
presence of pro-inflammatory factors. One potential explanation for our results is that this
gradient is either weak or absent in the SC, as the latter's anatomical configuration means that
it is generally close to CSF.⁴
Microstructural damage and repair at 12-month follow-up

The indices of dynamic microstructural damage and repair during the first year of the disease varied considerably across pwMS. Interestingly, we observed not only a worsening of microstructural damage in some pwMS but also an improvement in 18 pwMS. Several factors may explain these results. First, some pwMS may be able to remyelinate their SC lesions within the first year of diagnosis. Our results are consistent with previous reports concerning the variability of dynamic demyelinating/remyelinating profiles observed for brain lesions.

⁷,¹² The limited number of pwMS included did not allow us to assess the impact of the treatments used on these profiles. Second, the MTR increase may also be due to a regression in inflammation and initial edema. However, pwMS were included more than 1 month after their most recent relapse. Third, there may have been an impact of MTR measurement variability,²⁰,²¹ given that the 19 HC were only scanned at inclusion. However, in a previous study, using the same acquisition protocol, we showed that the between-session coefficient of variation of cervical SC MTR measurement was quite small (2.0 %).²¹ We also computed the indices of dynamic microstructural damage or repair on a second dataset comprising 9 HC with longitudinal SC MTR measurements acquired on another 3T MRI scanner from the same manufacturer (Siemens Prisma) (details in eAppendix 4). We found indices of damage and repair below 5 % in this HC population. Thus, the considerable variability in the indices (0-31 % and 0-21 %) for the patients in our study is probably mainly due to the effect of the pathology during follow-up.

There are probably several factors that explain the MTR decrease observed in other pwMS. First, new SC lesions may have developed. However, in 14 of 18 pwMS with a predominant microstructural damage profile, no new lesions were identified. Second, some demyelinated areas may have undergone further demyelination, as previously described in
anatomopathological studies.⁴ Third, secondary axonal degeneration may have occurred. Few longitudinal studies have included quantitative SC MRI acquisitions outside acute relapse.¹⁴,¹⁶,³⁶ One previous study measured changes in SC MTR values at baseline, 2 years and 5 years in pwMS with more advanced disease. This study showed that a significant MTR decrease had already occurred between baseline and 2 years.¹⁴ In our study, we failed to find a similar result, probably owing to the different profile of our population (early RRMS) and our shorter time interval (1 year). However, the observation of different early damage/repair profiles is interesting, particularly in the context of treatment developments aimed at promoting remyelination/neuroprotection.³⁷

## Associations with disability and atrophy measures at 5 years

A high rate of microstructural damage during the first year of the disease was associated with the development of SC atrophy and with EDSS score at 5 years, independently of the initial SC lesion load. SC lesions, particularly at an early stage of MS, are known to have a long-term prognostic value.²,³⁸ However, even with equal lesion load, there is substantial inter-individual variability in the development of disability.³⁹ Thus, the capacity of the SC microstructure to damage/repair over time is potentially an additional key factor for the development of disability. Consistent with this idea, a previous cross-sectional study showed that in patients with similar SC lesion loads, those with a high EDSS score had lower SC MTR values than those with a low EDSS score.⁴⁰ Similarly, more rapid rates of SC MTR change between baseline, 2 years and 5 years in a population of pwMS with more advanced disease were associated with increased disability at follow-up.¹⁴ Interestingly, a previous anatomopathological study including both the brain and SC of pwMS reported a positive correlation between SC demyelination and the Multiple Sclerosis Severity Score (MSSS),⁴¹
as well as negative correlation between SC remyelination capacity and the MSSS.⁴ No such associations were demonstrated at the cerebral level, suggesting that the extent of SC demyelination and the patient's demyelination/remyelination capacity play a prime role in the development of disability. We found an association between EDSS score at 5 years and the index of dynamic microstructural damage in our study, but no significant difference in this index between patients with or without confirmed disability progression during follow-up. This can probably be explained in part by the low baseline EDSS scores in our population (median EDSS at 0) and the known variability of EDSS measurement for these low scores. In our study, CSA was not significantly lower in patients than in HC at baseline, but the index of microstructural damage in the first year of MS was associated with CSA loss at 5 years, suggesting that microstructural damage and changes in this damage are measurable using MTR at an early stage in the disease when the CSA difference is not yet measurable, and are associated with the development of the degenerative component of the disease at 5 years. Of note, the CSA in our cohort was low compared to a recent meta-analyse.⁴² Nevertheless, some studies⁴³,⁴⁴ also reported low values when using a combination of T1w images and SCT computation, especially on Siemens scanner.

## Strengths and limitations

The strengths of the present study were its prospective design, the inclusion of a homogeneous cohort of pwMS, the longitudinal MT acquisitions, and the clinical and MRI follow-up at 5 years. These features meant that we had a unique database to study the consequences of early microstructural SC damage beyond any focal lesions detected on conventional MRI. Our study nevertheless had several limitations. First, as discussed before, the variations in patients' MTR abnormalities measured between baseline and 1-year follow-up were probably linked not only to actual worsening/repair of SC microstructure, but also to
MTR measurement variability.²⁰,²¹ In particular, we did not have any reproducibility data for our MS population but believe the rather good reproducibility in HC is convincing (eAppendixes 4 and 5). Second, we used z-scores with a threshold of -2.58 SD to define an abnormal microstructure at the voxel scale in each pwMS compared with HC. To ensure that our results were not dependent on the chosen threshold, they were confirmed using a lower threshold (1.96 SD; eAppendix 3). Third, our study had a rather small sample size and confirmation with a larger cohort is needed. Fourth, as discussed above, MTR has limited specificity for myelin content or axonal loss. Our findings therefore need to be confirmed using other myelin-sensitive imaging techniques such as T1w ³⁵,⁴⁵ or T2w relaxometry,⁴⁶,⁴⁷ MTSat⁴⁸ or ihMT,⁴⁹ and other axon-sensitive diffusion models,⁵⁰,⁵¹ although none of these techniques has perfect specificity.⁵² Finally, it would be particularly interesting to replicate our study using sequences with shorter acquisition time and therefore compatible with clinical use, such as MP2RAGE.⁵³

## Conclusion and perspectives

Our study suggests that a high index of dynamic microstructural damage in the SC of people with early RRMS is associated with greater development of SC atrophy at 5 years, and an increased EDSS score, irrespective of initial SC lesion load. Further imaging studies that accurately describe the dynamics of SC demyelination and axonal loss in pwMS are needed to confirm our results, better understand the substrate of progressive ambulatory disability, and test the effects of new treatments on this damage.

## Acknowledgments

We are grateful for the tremendous efforts made by all EMISEP study teams across participating sites. Furthermore, we express our sincere gratitude to all the volunteers and
their family members who are participating in the EMISEP study. We gratefully
acknowledge all the administrative, medical and scientific staff members involved in data
acquisition, data management, and quality control. We would like to thank Elizabeth Portier
and Ricky Walsh for checking the English style. MRI data acquisition was supported by the
Neurinfo MRI research facility from the University of Rennes I, University Hospital of
Rennes, Inria, CNRS and the Rennes Cancer Center. Neurinfo was also supported by the
European Research Council (FEDER), the French State, the Brittany Council, Rennes
Metropole, Inserm and GIS IBISA

**Funding information**

This research was supported by the French Hospital Programme of Clinical Research
(PHRC).

Competing interests

The authors report no competing interests in relation to the work described here.

Author contributions

Conception of the study and acquisition of the data: BC, EB, JCF, LM, ELP, GE, AK

Analyses of the present data: MG, BC, EB, AM, VC, GB, BS, BB, AK

Interpretation of the present data: MG, BC, EB, BB, AK

Manuscript drafting and/or revision: MG, BC, EB, ELP, BB, AK

All authors contributed to the article and approved the final version.
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Tables

<table><caption>Table 1: Participant's Demographic and Clinical Characteristics.</caption><thead><tr><th rowspan="2"></th><th rowspan="2">HC<br>(n = 19)</th><th colspan="2">RRMS (n = 37)</th><th rowspan="2">HC vs.<br>RRMS at<br>baseline<br>p value</th><th rowspan="2">RRMS at<br>baseline vs.<br>5-year<br>follow-up<br>p value</th></tr><tr><th>Baseline<br>(n = 37)</th><th>5-year follow-up<br>(n = 36)</th></tr></thead><tbody><tr><td>Sex, % male</td><td>52.63</td><td>35.13</td><td></td><td>.208</td><td></td></tr><tr><td>Age at first MRI, years</td><td>32.58<br>(8.18)</td><td>32.24<br>(5.89)</td><td></td><td>.861</td><td></td></tr><tr><td>Age at RRMS diagnosis, years</td><td></td><td>31.60<br>(5.85)</td><td></td><td></td><td></td></tr><tr><td>Time between baseline MRI<br>and 12-month follow-up, years</td><td></td><td>0.90<br>(0.15)</td><td></td><td></td><td></td></tr><tr><td>Time between baseline MRI<br>and 5-year follow-up, years</td><td></td><td>5.06<br>(0.26)</td><td></td><td></td><td></td></tr><tr><td>Treatment at baseline, % (n)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Medium efficacy<br>therapy</td><td></td><td>95.6 (35)</td><td>48.6 (18)</td><td></td><td></td></tr><tr><td>High efficacy therapy</td><td></td><td>2.7 (1)</td><td>45.9 (17)</td><td></td><td></td></tr><tr><td>No treatment</td><td></td><td>2.7 (1)</td><td>2.7 (1)</td><td></td><td></td></tr><tr><td>EDSS</td><td></td><td>0.00<br>[0.00, 2.50]</td><td>1.00<br>[0.00, 7.50]</td><td></td><td>.193</td></tr><tr><td>Pyramidal EDSS</td><td></td><td>0.00<br>[0.00, 2.00]</td><td>0.00<br>[0.00, 4.00]</td><td></td><td>.109</td></tr><tr><td>T25-FW, minutes</td><td></td><td>4.60<br>[2.75, 6.30]</td><td>4.70<br>[3.30, 6.08]</td><td></td><td>.749</td></tr></tbody></table>
<table><thead><tr><th rowspan="2"></th><th rowspan="2">HC<br>(n = 19)</th><th colspan="2">RRMS (n = 37)</th><th rowspan="2">HC vs.<br>RRMS at<br>baseline<br>p value</th><th rowspan="2">RRMS at<br>baseline vs.<br>5-year<br>follow-up<br>p value</th></tr><tr><th>Baseline<br>(n = 37)</th><th>5-year follow-up<br>(n = 36)</th></tr></thead><tbody><tr><td>Sex, % male</td><td>52.63</td><td>35.13</td><td></td><td>.208</td><td></td></tr><tr><td>Age at first MRI, years</td><td>32.58<br>(8.18)</td><td>32.24<br>(5.89)</td><td></td><td>.861</td><td></td></tr><tr><td>Age at RRMS diagnosis, years</td><td></td><td>31.60<br>(5.85)</td><td></td><td></td><td></td></tr><tr><td>76MWT, metres</td><td></td><td>510.00<br>[396.00, 792.00]</td><td>504.50<br>[0.00, 715.00]</td><td></td><td>.416</td></tr><tr><td>1/9HPT</td><td></td><td>0.05<br>[0.03, 0.06]</td><td>0.06<br>[0.03, 0.07]</td><td></td><td>&lt;.001</td></tr><tr><td>MSWS-12</td><td></td><td>12.00<br>[12.00, 53.00]</td><td>12.00<br>[12.00, 60.00]</td><td></td><td>.368</td></tr><tr><td>Qualiveen</td><td></td><td>0.00<br>[0.00, 1.06]</td><td>0.00<br>[0.00, 2.85]</td><td></td><td>.372</td></tr><tr><td>CSA, mm²</td><td>59.72 (5.6)</td><td>62.917<br>(6.294)</td><td>58.832<br>(7.54)</td><td>.166</td><td>&lt;.001</td></tr></tbody></table>

Note: Data are shown as means (± standard deviation), or medians [range] for clinical scores, unless stated otherwise. Two-sample t tests were used for age and CSA comparisons between the healthy group (HC) and relapsing-remitting multiple sclerosis (RRMS) groups, and χ² for sex ratio comparisons. For clinical scores, paired t tests were computed to compare baseline and 5-year follow-up in patients with RRMS. EDSS: Expanded Disability Status Scale, T25-FW: 8-metres timed-walked, 6M-walk: 6-minutes walk, 9HPT: nine holes peg test, MSWS-12: twelve item MS walking scale, Qualiveen: urinary quality of life questionnaire, CSA: cross-sectional area. Data for the T25-FW test was missing for one RRMS patient at 5-year follow-up as this patient was no longer able to walk.
<table><caption>Table 2: Results of Multiple Regressions Between Expanded Disability Status Scale (EDSS) and Cross-Sectional Area (CSA) Change at 5-Year Follow-Up and the Three Indices of Dynamic Microstructural Change Between Baseline and 12-Month Follow-Up.</caption><thead><tr><th rowspan="2"></th><th colspan="2">Multiple regression with age and sex as covariates</th><th colspan="2">Multiple regression with age, sex, and T2*w spinal cord lesion load as covariates</th></tr><tr><th>r</th><th>p value</th><th>r</th><th>p value</th></tr></thead><tbody><tr><td colspan="5"><strong>EDSS at 5-year follow-up</strong></td></tr><tr><td>Index of dynamic microstructural damage</td><td>.632</td><td><u>&lt;.001*</u></td><td>.603</td><td><u>&lt;.001*</u></td></tr><tr><td>Index of dynamic microstructural repair</td><td>.051</td><td>.774</td><td>.018</td><td>.920</td></tr><tr><td>Global index of microstructural change</td><td>.452</td><td><u>.007*</u></td><td>.43</td><td>.0126*</td></tr><tr><td colspan="5"><strong>CSA change between baseline and 5-year follow-up</strong></td></tr><tr><td>Index of dynamic microstructural damage</td><td>-.416</td><td>.014*</td><td>-.399</td><td>.021*</td></tr><tr><td>Index of dynamic microstructural repair</td><td>-.176</td><td>.319</td><td>-.161</td><td>.372</td></tr><tr><td>Global index of microstructural change</td><td>-.202</td><td>.251</td><td>-.184</td><td>.305</td></tr></tbody></table>

Note. Results are displayed with age and sex (leftthand columns) and age, sex, and T2*w lesion load (righthand columns) as covariates. *: p < .05. P-values that survived Bonferroni correction are underlined.
**Figure legends**

Figure 1: Schematic overview of computation of the three dynamic indices. First, baseline (BL) and 12-month follow-up (12M-FU) magnetisation transfer ratios were normalised (and smoothed) to PAM50 space, and z-score maps were computed based on means and standard deviations (SD) from the HC group (A). Then, the two z-score maps were binarised at -2.58 SD and merged into a single map displaying damage at baseline only (blue), follow-up only (yellow), or both (purple) (B). Finally, the three indices used in the study were calculated (C).

Figure 2: Examples of five patients with relapsing-remitting multiple sclerosis with different degrees of lesion load (blue) and microstructural damage (orange), and the intersection between the two (green), in cervical spinal cord at baseline. The second line displays the raw z-score maps of the corresponding five patients. On top are displayed the distance from the centre of the spinal cord from left to right.

Figure 3. Changes in microstructural damage changes between baseline and 12-month follow-up in cervical SC of individual patients: index of dynamic microstructural damage (top left), index of dynamic microstructural repair (top right), global index of microstructural change (bottom left), and lesion load at baseline (bottom right). All graphs are ordered according to the global index of microstructural change values.

Figure 4: Examples of two patients with relapsing-remitting multiple sclerosis with a high T2*w lesion load and exhibited profiles of either predominant microstructural damage (left) or predominant microstructural repair (right) over time. For each patient, the T2*w lesions are displayed on the left (green), while microstructural damage and repair over time are
displayed on the right. The left part of the figure shows the sagittal view of the PAM50 T2
and the level of the different axial slices.

Figure 5: Relationships between baseline and follow-up indices with external cerebrospinal fluid (CSF). Lesion load (%) at baseline (graph top left), microstructural damage at baseline (graph top right), microstructural damage over time (graph bottom left), and microstructural repair over time (graph bottom right), according to distance from external CSF. Median, interquartile range, and overall range are represented in the boxplots, and means are shown as solid circles. Figures map the distance between external CSF (cool colours) and the centre of the spinal cord (hot colours).
<figure><img src="image_3.png" /><figcaption>**Figure 1: Schematic overview of computation of the three dynamic indices.** First, baseline (BL) and 12-month follow-up (12M-FU) magnetisation transfer ratios were normalised (and smoothed) to PAM50 space, and z-score maps were computed based on means and standard deviations (SD) from the HC group (A). Then, the two z-score maps were binarised at -2.58 SD and merged into a single map displaying damage at baseline only (blue), follow-up only (yellow), or both (purple) (B). Finally, the three indices used in the study were calculated (C).</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>Figure 2: Examples of five patients with relapsing-remitting multiple sclerosis with different degrees of lesion load (blue) and microstructural damage (orange), and the intersection between the two (green), in cervical spinal cord at baseline. The second line displays the raw z-score maps of the corresponding five patients. On top are displayed the distance from the centre of the spinal cord from left to right.</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>Figure 3. Changes in microstructural damage changes between baseline and 12-month follow-up in cervical SC of individual patients: index of dynamic microstructural damage</figcaption></figure>
(top left), index of dynamic microstructural repair (top right), global index of microstructural
change (bottom left), and lesion load at baseline (bottom right). **In each graph, each bar
represents one subject. All graphs are ordered according to the global index of
microstructural change values.**
<figure><img src="image_6.png" /><figcaption>Figure 4: Examples of two patients with relapsing-remitting multiple sclerosis with a high T2*w lesion load and exhibited profiles of either predominant microstructural damage (left) or predominant microstructural repair (right) over time. For each patient, the T2*w lesions are displayed on the left (green), while microstructural damage and repair over time are displayed on the right. The left part of the figure shows the sagittal view of the PAM50 T2 and the level of the different axial slices.</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>**Figure 5:** Relationships between baseline and follow-up indices with external cerebrospinal fluid (CSF). Lesion load (%) at baseline (graph top left), microstructural damage at baseline (graph top right), microstructural damage over time (graph bottom left), and microstructural repair over time (graph bottom right), according to distance from external CSF. Median, interquartile range, and overall range are represented in the boxplots, and means are shown as solid circles. Figures map the distance between external CSF (cool colours) and the centre of the spinal cord (hot colours).</figcaption></figure>