Myelin damage in radiologically isolated syndrome is associated with decreased processing speed

Abstract

Background

Cognitive impairment (CI) is a common feature of multiple sclerosis (MS). Radiologically isolated syndrome (RIS) can demonstrate CI patterns similar to MS, providing an opportunity to explore the associations between CI and early stage disease pathology.

Objectives

To investigate myelin damage in RIS normal appearing white matter (NAWM) using the myelin heterogeneity index (MHI) and to determine the relationship between MHI and processing speed.

Methods

A total of 28 people with RIS and 22 controls completed an MRI, including multi-component T₂ mapping to calculate MHI for whole brain (WB), corpus callosum (CC), superior longitudinal fasciculus (SLF), and cingulum (CING) NAWM. The RIS cohort additionally completed the Processing Speed Test (PST), a measure of processing speed.

Results

CC NAWM MHI was higher in RIS versus controls (indicative of myelin damage, p = 0.0140), with similar trends seen in WB, SLF, and CING (p > 0.05). A moderate correlation was found between CING NAWM MHI and PST scores (ρ = –0.39, p = 0.040), with similar trends seen in WB, CC, and SLF (p > 0.05).

Conclusion

Diffuse myelin damage was detected in RIS NAWM, along with associations between increased damage and slower processing speed, suggesting a potential pathological mechanism for RIS-related CI.

Keywords

Myelin magnetic resonance imaging cognition radiologically isolated syndrome

Introduction

Up to 70% of people with multiple sclerosis (MS) experience cognitive impairment (CI), with processing speed being one of the most commonly affected abilities.¹ Radiologically isolated syndrome (RIS) refers to individuals with magnetic resonance imaging (MRI) findings indicative of MS, without apparent clinical symptoms.² Interestingly, people with RIS can present with a similar CI profile as MS, showing comparable deficits across several cognitive domains, including processing speed.^3–6 Over time, RIS may evolve into MS,⁵ with previous studies indicating that approximately one-third of people with RIS experience a clinical event within 5 years,⁷ and that the probability of a clinical event is ∼50% at 10 years.⁸ As RIS can convert to MS,⁵ it represents an early opportunity to explore the evolving disease mechanisms of MS and their associations with CI.

Advanced quantitative MRI techniques^9,10 and post-mortem¹¹ studies have indicated that white matter tissue appearing unaffected on conventional MRI (normal appearing white matter, NAWM)¹² is damaged in MS.¹³ Myelin water imaging (MWI) is a quantitative MRI technique capable of assessing myelin content.^14,15 MWI involves identifying distinct water pools through differences in T₂ times: the intra/extracellular water, the cerebrospinal fluid, and water between the myelin layers.^13,14 The ratio of myelin water to total water within a voxel is defined as the myelin water fraction (MWF).^12,14 MWF has been histologically validated as a marker of myelin content in post-mortem studies,^14,15 and has been shown to be substantially reduced in MS NAWM compared to controls.^14,16

While reductions in the mean MWF within a region of interest (ROI) reflect overall decreases in myelin,¹⁴ inter-group differences can be masked by the variation naturally found between various brain regions¹⁷ and between individuals, due to factors such as age.^18,19 The myelin heterogeneity index (MHI), defined as the standard deviation (SD) divided by the mean MWF within an ROI, reflects both overall myelin content and variability between voxels,^20,21 with increasing MHI implying greater myelin damage.²⁰ A higher MHI may be caused by a decrease in mean MWF (suggesting a loss of myelin) or by an increase in SD (suggesting a less uniform myelin distribution). Compared to mean MWF, MHI has been shown to better distinguish between control white matter and MS NAWM,²¹ suggesting that MHI may be more sensitive to subtle changes. Higher MHI in the corpus callosum (CC), superior longitudinal fasciculus (SLF), and cingulum (CING) has been associated with worse cognitive function in MS.²⁰ To our knowledge, no studies have investigated MHI and its relationship with cognition in RIS.

The objectives of this study were to (1) compare MHI between RIS NAWM and control normal white matter (NWM) for whole brain (WB), as well as CC, SLF, and CING (three ROIs previously implicated in MS-related cognition²⁰), and (2) determine the relationship between NAWM MHI and cognition in RIS using the Processing Speed Test (PST), a measure of processing speed.^22,23 In addition to our main objectives, we explored differences in mean MWF between RIS and controls and the relationship between mean MWF and PST scores for each ROI (see the Supplemental Material).

Materials and methods

Participants

This research is a part of the Canadian Prospective Cohort (CanProCo) study to understand progression in multiple sclerosis.²⁴ The present study involved two sites: the Djavad Mowafaghian Centre for Brain Health at the University of British Columbia (Vancouver, British Columbia) and the Centre hospitalier de l’Université de Montréal (Montreal, Quebec). This study was approved by the Clinical Research Ethics Board, University of British Columbia (H18-03047) and the Comité d’éthique de la recherche du Centre hospitalier de l’Université de Montréal (18–293).

Detailed recruitment methods, inclusion, and exclusion criteria are reported elsewhere.²⁴ Briefly, participants were recruited by patient clinical care teams and through advertisements, such as waiting room flyers. All participants provided written informed consent, and confidentiality was maintained through data de-identification. To be included in the CanProCo RIS cohort, participants had to be between 18 and 60 years old, have a Kurtzke Expanded Disability Status Scale (EDSS)²⁵ ≤ 6.5 and have an RIS diagnosis meeting the Okuda criteria² (all participants had lesions, but there was no requirement for spinal cord lesions). Participants were included in the control cohort if they were between 18 and 60 years old and did not meet the previously reported exclusion criteria (such as having a neurological disorder causing functional limitations).²⁴ Only participants who completed the research grade MWI MRI were included in the present analysis. A total of 28 participants were included in the RIS cohort, and 22 participants were included in the control cohort.

Clinical assessments

Participants in the RIS cohort were assessed for overall disability using the EDSS,²⁵ and completed the Multiple Sclerosis Performance Test (MSPT),^23,26,27 an electronically administered modified version of the Multiple Sclerosis Functional Composite (MSFC).²⁸ As part of the MSPT, participants completed the PST,^22,23 a test of processing speed based on the Symbol Digit Modalities Test (SDMT).²⁹ Briefly, participants were shown a screen with a reference key at the top displaying a series of symbols and associated numbers, followed by a series of symbols with empty boxes. Participants were tasked with using the reference key to input the appropriate numbers into the empty boxes. Participants had 120 seconds to complete as many boxes as possible. The total score was determined by the number of correct responses.²² The PST and SDMT have been shown to be well correlated.²² The PST is 30 seconds longer than the traditional SDMT (oral) to account for the increased time associated with manual responses.²² Four participants with RIS completed a paper version of the assessments (SDMT oral).

MRI data acquisition

At both the Vancouver and Montreal sites, MRI scans were conducted on a 3-Tesla scanner (Philips Medical Systems, Best, The Netherlands). Scan sequences included: -

MWI: Three-dimensional (3D) gradient and spin-echo (GRASE)³⁰ T₂ relaxation sequence, 48 echoes, acquired at 0.99 × 1.99 × 5 mm³, reconstructed to 0.96 × 0.96 × 2.5 mm³. Repetition time (TR) = 1073 ms, echo time (TE) = n*8.0 ms. Used to determine MWF and MHI.

3D-T₁-weighted anatomical scan (3D-T₁): Acquired at 1 × 1 × 1 mm³. TR = 8 ms, inversion time (TI) = 1052 ms, TE = 3.7 ms (Vancouver). TR = 8.3 ms, TI = 1052 ms, and TE = 3.8 ms (Montreal). Used for registration, lesion mask generation, and segmentation of white matter.

3D fluid attenuated inversion recovery (FLAIR): Acquired at 1 × 1 × 1 mm³, reconstructed to 0.67 × 0.67 × 0.67 mm³ (Vancouver) or 0.63 × 0.63 × 0.67 mm³ (Montreal). TR = 8000 ms, TI = 2400 ms, TE = 310 ms. Used for lesion mask generation.

3D-T₂-weighted scan: Acquired at 0.60 × 0.60 × 4 mm³ (Montreal) or 1.21 × 1.21 × 1.20 mm³ (Vancouver), reconstructed to 0.45 × 0.45 × 4 mm³ (Montreal) or 0.67 × 0.67 × 0.67 mm³ (Vancouver). TR = 2500 ms, TE = 376 ms (Vancouver). TR = 4000 ms, TE = 108 ms (Montreal). Used for lesion mask generation.

For additional details, see the Supplemental Material.

MRI image registration and analysis

MWF maps were generated using the DEcomposition and Component Analysis of Exponential Signals (DECAES) software.³¹ Briefly, voxel-wise T₂ decay curves were analyzed from the GRASE sequence using a regularized non-negative least squares algorithm and stimulated echo corrections.³¹ Voxel-wise MWF maps were defined as the area under the T₂ relaxation distribution with T₂ times < 40 ms relative to the total area under the distribution.¹⁴

Image processing was done using the Advanced Normalization Tools (ANTs)³² software (https://github.com/ANTsX/ANTs/) and the Analysis Group at the Oxford Centre for Functional MRI Brain Software Library (FSL).³³ 3D-T₁ scans were registered to the first echo of the GRASE sequence using ANTs registration tools. Whole-brain white matter masks were generated from the GRASE-registered 3D-T₁ scans using the FMRIB's Automated Segmentation Tool (FAST),³⁴ and thresholded to 99%. Lesions were identified for the RIS cohort using a fully automated segmentation algorithm known as a Method for Inter-Modal Segmentation Analysis (MIMoSA)³⁵ in the Montreal Neurological Institute (MNI) standard template space and were manually checked. The MNI template was registered to the 3D-T₁ scan using ANTs registration tools, and the transformation matrix was used to move lesion masks into participant space. For the RIS group, lesion masks were subtracted from the white matter masks to create the NAWM mask. For controls, the white matter mask served as the NWM mask. The CC, SLF, and CING ROI masks were created in MNI space using the Johns Hopkins University white matter atlas,³⁶ moved into participant space and thresholded to 50%. ROIs were multiplied by the NAWM/NWM masks to ensure only white matter was included. The masks were then overlayed onto the MWF maps. MWF mean and SD were extracted, and MHI (SD/mean MWF) was computed for CC, SLF, CING, and WB NAWM/NWM for the RIS and control cohorts. A summary schematic is shown in Figure 1.

Figure 1.

Myelin heterogeneity index analysis pipeline for a representative RIS participant: (a) ROIs created in MNI space: CC in blue, CING in pink, and SLF in red, and (b) ROIs transformed into the participant space and multiplied by the WB NAWM (yellow). The ROI masks were then overlaid onto the MWF maps, and the MWF mean and standard deviation were extracted to determine the myelin heterogeneity index.

Statistical analysis

Statistical procedures were performed using R-studio (R version 4.3.0). Normality was assessed using the Shapiro-Wilk test, and outliers were assessed using boxplots. Age was compared between the control and RIS cohorts using the Welch Two Sample t-test, as both groups of data were normally distributed with no outliers. The Wilcoxon rank sum test with continuity correction was used to compare differences in education due to non-normal data in both groups. Pearson's chi-square test (with Yates's continuity correction) was used to assess sex differences between groups. Differences in MHI values between groups were assessed using the Wilcoxon rank sum exact test due to non-normal data and/or outliers in one or both groups. Differences in mean MWF values between groups were assessed using the Wilcoxon rank sum exact test when outliers were present in one or both groups or the Welch Two Sample t-test when the data were normally distributed and had no outliers in both groups (Supplemental Material). Associations between MHI and PST scores for the different ROIs were assessed using Spearman's rank correlation rho (ρ), as MHI was either not normally distributed or had outliers for all ROIs. Associations between mean MWF and PST scores were assessed using Pearson's product-moment correlation when the ROI data were normally distributed with no outliers or using Spearman's rank correlation ρ when outliers were present (Supplemental Material). When rank ties were present, “exact” was set to “FALSE.” Linear regression lines are displayed on correlation plots for each ROI. As this analysis was exploratory, no corrections for multiple comparisons were made.

Results

Participant characteristics

One participant with RIS was excluded due to an incomplete MRI visit, resulting in a study population of 28 RIS and 22 controls. Demographic information is reported in Table 1. The two cohorts were similar in terms of age (p = 0.0945), sex (p = 0.139), and years of education (p = 0.164). Participants in the RIS cohort had a median EDSS of 0 (range 0.0–3.5) and a mean (SD) PST score of 53 (11), with values ranging from 33 to 76 (median = 54). A full description of the cohorts has been published elsewhere.³⁷

Table 1.

Demographic characteristics for the control and RIS cohorts.

	Controls (N = 22)	RIS (N = 28)	p-value
Age (years)
Mean	39	44	0.0945^a
Median (range)	41 (20–54)	44 (20–63)	0.0945^a
Sex
Female	14 (64%)	24 (86%)	0.139^b
Male	8 (36%)	4 (14%)	0.139^b
Education (years)
Mean	16	15	0.164^c
Median (range)	16 (8–20)	16 (12–20)	0.164^c
PST score
Mean	N/A	53	N/A
Median (range)	N/A	54 (33–76)	N/A
EDSS
Median (range)	N/A	0 (0.0–3.5)	N/A
EDSS frequency
0	N/A	17	N/A
0.5	N/A	0
1	N/A	3
1.5	N/A	0
2	N/A	5
2.5	N/A	1
3	N/A	0
3.5	N/A	2

RIS: radiologically isolated syndrome; PST: Processing Speed Test; EDSS: Expanded Disability Status Scale; N/A: not available.

Welch two-sample t-test.

Pearson's chi-squared test with Yates's continuity correction.

Wilcoxon rank sum test with continuity correction.

Myelin heterogeneity

Differences in MHI between the control and RIS cohorts for the 4 ROIs (CC, SLF, CING, and WB) are shown in Table 2 and Figure 2. In all ROIs, median MHI was numerically higher for the RIS group compared to the control group (although not always reaching significance), with the largest difference being in the CC (CC: +9.09%, p = 0.0140, SLF: +8.96%, p = 0.136; CING +4.26%, p = 0.377; WB: +3.85%, p = 0.239).

Figure 2.

Comparison of MHI values between the control and RIS cohorts. Boxplots of NWM and NAWM MHI values for the four different regions of interest are shown. Corresponding values can be found in Table 2. The Wilcoxon rank sum exact test was used to explore differences between groups. p-values are displayed on the graph. (a) Corpus callosum, (b) superior longitudinal fasciculus, (c) cingulum, and (d) whole brain. Controls are in green and RIS is in blue.

Table 2.

Myelin heterogeneity index for control NWM and RIS NAWM.

	Control NWM (N = 22)	RIS NAWM (N = 28)	\|% difference\|	p-value
Corpus callosum
Mean	0.42	0.46	9.09	0.0140^a
Median (range)	0.42 (0.35–0.52)	0.46 (0.35–0.59)	9.09	0.0140^a
Superior longitudinal fasciculus
Mean	0.33	0.35	5.88	0.136^a
Median (range)	0.32 (0.27–0.42)	0.35 (0.28–0.47)	8.96	0.136^a
Cingulum
Mean	0.47	0.50	6.19	0.377^a
Median (range)	0.46 (0.36–0.61)	0.48 (0.37–0.76)	4.26	0.377^a
Whole brain
Mean	0.52	0.55	5.61	0.239^a
Median (range)	0.51 (0.45–0.66)	0.53 (0.43–0.73)	3.85	0.239^a

NWM: normal white matter; RIS: radiologically isolated syndrome; NAWM: normal appearing white matter.

Wilcoxon rank sum exact test.

Relationship between MHI and PST scores in the RIS cohort

Figure 3 illustrates the negative relationship between NAWM MHI in the four ROIs (CC, SLF, CING, and WB) and PST scores for the RIS cohort. A moderate association was found between higher CING NAWM MHI and decreasing PST scores (ρ = –0.39, p = 0.040). For the remaining ROIs, similar patterns were observed, although with weaker correlations not reaching statistical significance (CC: ρ = –0.27, p = 0.16; SLF: ρ = –0.15, p = 0.44; WB: ρ = −0.19, p = 0.35).

Figure 3.

Correlation between NAWM MHI and PST scores in the RIS cohort. Spearman’s rank correlation rho (ρ) tests were performed for NAWM MHI and PST scores for each region of interest. (a) Corpus callosum, (b) superior longitudinal fasciculus, (c) cingulum, and (d) whole brain. Linear regression lines, ρ values and p-values are displayed on graphs.

Mean myelin water fraction

We compared mean MWF between RIS and controls and found consistent results with our MHI findings (Supplemental Table 1 and Supplemental Figure 1). No strong relationships were found between mean MWF and PST scores (Supplemental Figure 2).

Discussion

RIS refers to incidental MRI findings suggestive of MS, without evident symptoms.² Interestingly, people with RIS can have CI similar to MS.^3–5 Whether or not RIS CI is reflected by NAWM pathology has not been explored using MWI MHI. This study demonstrated diffuse myelin damage in CC NAWM in RIS compared to controls, and that higher CING NAWM MHI was associated with slower processing speed in RIS.

Previous work by Rudick et al.²³ reported a mean PST score of 58.7 for controls (mean age 46 years), compared to 50.8 for MS (mean age 46 years). Similarly, Rao et al.²² reported a mean PST score of 61.2 for controls (mean age 43 years) and 52.7 for MS (mean age 45 years), and a high convergent validity with the SDMT. To our knowledge, no studies have investigated RIS-related CI using the PST. In our study, the mean PST score for the RIS cohort was found to be 53.1 (mean age 44 years), which is comparable to previously reported scores for MS.^22,23 Our findings suggest that people with RIS do experience processing speed decline, in agreement with previous studies indicating that RIS-related CI presents with a similar profile to MS-related CI, and that people with RIS and MS experience comparable deficits in processing speed.^3–5

We found higher MHI in RIS CC NAWM compared to controls (p = 0.0140), with similar but less pronounced patterns in the SLF, CING, and WB (p = 0.136–0.377). Since higher MHI is suggestive of more myelin damage,²⁰ these results suggest that people with RIS have diffuse NAWM damage in the CC. The corpus callosum is a large white matter tract commonly affected in MS,³⁸ which may explain why the damage was particularly apparent in this region. However, as patterns were similar in other regions, the study may simply have been underpowered to reach significance. To date, only a handful of studies have investigated RIS using advanced MRI techniques. In contrast to our findings, a diffusion tensor imaging (DTI)-based study by Giorgio et al.³⁹ reported that microstructural changes were confined to lesional CC tissue in people with RIS (n = 18), whereas people with relapsing remitting MS had detectable changes in both lesional and NAWM CC tissue (n = 20). Other studies (with similar group sizes to our current study) using DTI⁴⁰ and magnetization transfer (MT)⁴¹ techniques also reported no differences between RIS and controls in terms of white matter tracts⁴⁰ and NAWM.⁴¹ However, both DTI⁴² and MT⁴³ are influenced by several other tissue properties in addition to myelin content.^42,43 MWI provides a highly specific, validated marker of myelin.^13,15 Our results are complemented by an MWI study showing decreased MWF in the splenium of the CC NAWM in MS,¹⁰ suggesting that RIS may have NAWM damage in the CC similar to MS.

We found a moderate correlation between increasing RIS CING NAWM MHI and lower PST scores (ρ = –0.39, p = 0.040). Similar but weaker trends were seen in the CC, SLF, and WB (ρ = –0.27 to −0.15, p = 0.16–0.44). The cingulum is a white matter tract with both cortical and subcortical connections.^44,45 It has been shown to play a role in multiple cognitive processes and has been implicated in several neurological and psychiatric disorders.⁴⁴ Our results showing the strongest relationship between cingulum MHI and processing speed support the role of the cingulum in cognition. However, the non-significant but directionally consistent findings for the relationship between MHI in the CC, SLF, and WB with PST scores suggest that the study is underpowered, which could have limited the ability to detect statistically significant relationships. The non-significant findings may be contributed to in part by the observation that not all people with RIS are expected to experience a clinical event within 10 years,⁸ and thus may not have the early MS pathology.

Our findings suggest that NAWM myelin integrity plays a role in processing speed deficits caused by early diffuse and subtle demyelination. Previous work has shown that the number of failed cognitive tests correlates with higher T₁ lesion volume and lower normalized cortical volume in RIS.³ Similarly, another study demonstrated that normalized brain volume is decreased in people with RIS experiencing CI compared to people with RIS with preserved cognition and controls.⁴⁶ Furthermore, the authors indicated that three of the four people who converted from RIS to MS had CI at baseline, and suggest that CI may be a risk factor for a future MS diagnosis.⁴⁶ Our results expand these findings by showing that damage in NAWM also contributes to CI in RIS, providing a potential myelin-specific pathological mechanism. Moreover, our results are similar to previous studies, indicating that increased MHI in MS NAWM is correlated with worse processing speed,²⁰ suggesting that RIS- and MS-related CI may be driven by the same mechanism. The specific biological processes underlying these myelin changes (i.e. demyelination vs. incomplete remyelination) could not be determined with MWI and should be investigated in future studies.

Consistent with our MHI results, we found a decreased mean MWF in the CC NAWM for people with RIS compared to controls (p = 0.0207), with similar, but less pronounced, trends in the other ROIs (p = 0.0663–0.474). These MWF results further support our finding that people with RIS have diffuse NAWM myelin damage, especially in the CC. Although small trends were emerging between higher mean MWF and better PST scores for the SLF, CING, WB NAWM (R = 0.11–0.23, p = 0.25–0.58), the correlations were weak at best, supporting that MHI may be more sensitive to early or subtle changes in NAWM than mean MWF. By using MHI, information is captured on both the myelin density and uniformity within a region of interest, providing additional information on myelin composition.

Myelin abnormalities in NAWM are found in MS,^14,16 are progressive,⁴⁷ and DTI studies support the presence of myelin damage in individuals with progression independent of relapse activity (PIRA).⁴⁸ Thus, NAWM myelin damage may represent injury due to smoldering MS, in which people experience PIRA.⁴⁹ Paramagnetic rim lesions (PRLs) have been associated with PIRA in MS,⁵⁰ and are present in RIS,⁶ suggesting that the biology of PIRA may exist as early as RIS for some individuals. Our results support this idea by showing NAWM myelin damage in RIS, which could contribute to PIRA in the form of CI. This hypothesis requires confirmation with longitudinal follow-up studies.

Of note, although people with RIS do not meet diagnostic criteria for MS,^2,51 they do not always have an EDSS of 0, as seen in previous work.⁷ This finding is consistent with our RIS cohort (EDSS scores ranging from 0 to 3.5).

There are several limitations to this study that should be considered. First, this study only used the PST as a measure of cognition. Previous work has shown that increased MHI correlates with worse performance on the Symbol Digit Modalities Test, the Selective Reminding Task, and the Controlled Oral Word Association Test for people with MS.²⁰ Although processing speed is an important and common cognitive deficit in MS¹ and RIS,^4,6 the inclusion of other cognitive tests would provide a more comprehensive understanding of the RIS cognitive profile and the relationship between MHI and cognition.

Another limitation is that the control cohort did not complete the PST. Therefore, we could not perform a direct comparison of PST performance between the RIS and control cohorts. However, several studies have reported consistent PST scores for controls,^22,23 which allowed us to make inferences about the cognitive state of our RIS cohort.

Due to the small sample size and exploratory nature of this analysis, we used correlation rather than linear regression, and raw scores rather than z-scores. These methods preclude the ability to adjust for potential confounders such as age, sex, years of education, and lesions. Future studies with larger sample sizes may wish to use linear regression encompassing covariates or adjusted z-scores. As an exploratory, confirmatory analysis for the cingulum, we considered the covariates age, education, and sex, and just age alone. The estimates (β) and p-value for the cingulum NAWM MHI were found to be β = –37.92, p = 0.065 and β = –39.49, p = 0.040, respectively. An exploratory analysis of lesions suggested a similar relationship to published literature,³ but was not included in the current work due to the limited scope and sample size. A future study utilizing more in-depth lesion analyses could provide clarity on the relative contributions of both lesions and NAWM to processing speed in RIS.

To increase statistical power, future studies should include a larger sample size. To increase generalizability, follow-up studies should use a battery of cognitive tests and collect control cognitive scores.

Overall, this is the first study to examine RIS-related CI using the PST and investigate RIS NAWM MHI. We demonstrated myelin damage in RIS NAWM, particularly in the corpus callosum, and that greater NAWM damage, particularly in the cingulum, was associated with decreased processing speed. These findings help to increase knowledge about the evolution of early disease processes involving myelin abnormalities in RIS/MS relevant to clinical disability and provide a potential biomarker of cognitive decline.

Supplemental Material

sj-docx-1-mso-10.1177_20552173261446217 - Supplemental material for Myelin damage in radiologically isolated syndrome is associated with decreased processing speed

Supplemental material, sj-docx-1-mso-10.1177_20552173261446217 for Myelin damage in radiologically isolated syndrome is associated with decreased processing speed by Olivia Kalau, Poljanka Johnson, , Jiwon Oh, Alexandre Prat, Irene M Vavasour, Cornelia Laule, Roger Tam, Connor Keane, Sarah A Morrow, Jeffrey Wilken, Alice Schabas, Larry D Lynd, Scott B Patten, Alan H Wilman, Yunyan Zhang, V Wee Yong, Shannon H Kolind and Anthony L Traboulsee in Multiple Sclerosis Journal – Experimental, Translational and Clinical

Supplemental Material

sj-pdf-2-mso-10.1177_20552173261446217 - Supplemental material for Myelin damage in radiologically isolated syndrome is associated with decreased processing speed

Supplemental material, sj-pdf-2-mso-10.1177_20552173261446217 for Myelin damage in radiologically isolated syndrome is associated with decreased processing speed by Olivia Kalau, Poljanka Johnson, , Jiwon Oh, Alexandre Prat, Irene M Vavasour, Cornelia Laule, Roger Tam, Connor Keane, Sarah A Morrow, Jeffrey Wilken, Alice Schabas, Larry D Lynd, Scott B Patten, Alan H Wilman, Yunyan Zhang, V Wee Yong, Shannon H Kolind and Anthony L Traboulsee in Multiple Sclerosis Journal – Experimental, Translational and Clinical

Footnotes

Acknowledgements

We would like to sincerely thank all the CanProCo volunteers and their families, as well as the entire CanProCo study team and collaborators, particularly Guillaume Gilbert. We would like to thank all the MR staff and study coordinators for their dedication and hard work. This work was performed with the support of the UBC MRI Research Centre (RRID: SCR_025374), a core facility within the Department of Radiology in the Faculty of Medicine at the University of British Columbia, which was established with funding from the Canadian Foundation for Innovation. We thank the UBC Statistical Opportunity for Students (SOS) program for their guidance.

Consent to participate

All participants provided written informed consent.

Data availability statement

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Declaration of conflicting interests

The authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: -

JO has received grant funding from Biogen-Idec, Roche, and EMD-Serono and has received personal compensation for consulting or speaking from: Biogen-Idec, BMS, EMD-Serono, Eli-Lilly, Horizon Therapeutics, Novartis, Roche, and Sanofi-Genzyme.

AP has received grant research funding from MS Canada, the Canadian Institutes of Health Research, the International Progressive MS Alliance, Biogen, Roche, and EMD-Serono, and has received personal compensation for consulting or speaking from Biogen, EMD-Serono, Novartis, Roche, and Sanofi-Genzyme.

SAM in the past 3 years, has served on advisory boards for: Biogen Idec, Celgene, EMD Serono, Novartis, Roche, Sanofi Genzyme, and Teva Neurosciences; has received Investigator Initiated Grant Funds from: Biogen Idec, Novartis, Roche, and Sanofi Genzyme and has acted as site PI for multi-center trials funded by: AbbVie, Celgene, EMDSerono, Novartis, Roche, and Sanofi Genzyme.

JW is a paid speaker for Biogen, EMD Serono, and Sanofi, received research funding from Biogen, is a consultant for BMS on research, and is the president-elect of the CMSC.

AS has received personal fees for consulting for Biogen-Idec, EMD-Serono, Novartis, Roche, and Sanofi-Genzyme.

VWY is funded by research grants from MS Canada, the Canadian Institutes of Health Research, the USA Department of Defense Multiple Sclerosis Research Program, Genentech, and Novartis. He has received speaker honoraria from Biogen, EMD Serono, Novartis, Roche, Sanofi-Genzyme, and Teva Canada. He is the recipient of unrestricted educational grants from Biogen, EMD Serono, Novartis, Roche, Sanofi-Genzyme, and Teva Canada to support educational activities of the Alberta MS Network, which he directs.

SHK has received grant funding from Biogen-Idec, Roche, and Sanofi-Genzyme and has received personal compensation for consulting or speaking from Roche.

ALT receives grant funding from Roche and Sanofi-Genzyme, personal compensation for consulting or speaking from Roche, Sanofi-Genzyme, and EMD Serono. He holds the MS Canada Research Chair supported by the UBC MSMRI Research Group.

OK, PJ, IMV, CL, CK, LDL, SBP, RT, AHW, and YZ declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: -

CanProCo is funded by MS Canada, Brain Canada, Biogen-Idec, Roche, and the Government of Alberta.

OK received funding in the form of a Canadian Graduate Scholarship – Master's from the Canadian Institute for Health Research.

PJ receives funding in the form of an endMS Doctoral Studentship Award from MS Canada.

JO receives support from the Waugh Family Chair in MS Research at the University of Toronto.

AP holds the Senior Canada Research Chair in multiple sclerosis and the Power Corporation Chair in Neurosciences.

CK was funded by the University of British Columbia, Faculty of Medicine Summer Student Research Program.

SBP is supported by the Cuthbertson & Fischer Chair in Pediatric Mental Health at the University of Calgary.

SHK receives support from Michael Smith Health Research BC, MS Canada, Government of Alberta, Biogen-Idec, Roche, and Brain Canada.

Ethical considerations

This study was approved by the Clinical Research Ethics Board, University of British Columbia (H18-03047) and the Comité d’éthique de la recherche du Centre hospitalier de l’Université de Montréal (18–293).

ORCID iDs

Olivia Kalau

Poljanka Johnson

Irene M Vavasour

Cornelia Laule

Sarah A Morrow

Shannon H Kolind

Supplemental material

Supplemental material for this article is available online.

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Supplementary Material

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