Longitudinal microstructural MRI markers of demyelination and neurodegeneration in early relapsing-remitting multiple sclerosis: Magnetisation transfer, water diffusion and g-ratio

Graphical abstract


The need for longitudinal microstructural markers in multiple sclerosis
Multiple sclerosis (MS) is a chronic, immune-mediated neurodegenerative disease of the central nervous system (CNS) with heterogeneous symptomology, including motor impairment, fatigue, visual disturbances, and variable disease course Filippi et al., 2018;Gelfand, 2014). Inflammation-associated demyelination is believed to result in axonal degeneration that ultimately causes disability (Bitsch et al., 2000;Frischer et al., 2009;Lassmann, 2018). Relapsing-remitting MS (RRMS) is characterised by clinical relapses interspersed with periods of remission, and it is difficult to predict at disease onset which patients will subsequently experience a more aggressive disease course (Scalfari et al., 2010).
Magnetic resonance imaging (MRI) is widely used in clinical practice for diagnosis of and tracking MS activity over time. Conventional structural MRI sequences demonstrate white matter lesions (WML) although provide limited specificity for characterising damage within these, and are insensitive to abnormalities such as decreased fibre density (Kutzelnigg et al., 2005) and subtle demyelination (Allen and McKeown, 1979) in normal-appearing white matter (NAWM). Longitudinal measurements of brain volume provide downstream indicators of neurodegeneration but lack specificity for these disease-relevant processes and are confounded by other factors (Wattjes et al., 2021). Candidate in vivo microstructural MRI markers, that are sensitive and specific to early changes in myelin and axonal integrity, are thus needed for tracking and predicting disease trajectory, and evaluating response to disease-modifying therapies (DMTs), putative remyelinating and neuroprotective treatments.

Magnetisation transfer imaging
Magnetisation transfer imaging (MTI) derives signal indirectly from protons 'bound' to macromolecules, which tend to be myelin-associated in the CNS. The T2 of 'bound' protons is shorter than normal echo times (~10 μs), and thus typically not MRI-visible. The derived magnetisation transfer ratio (MTR) has been extensively applied in cohorts of RRMS , but its use as a surrogate endpoint in large, multicentre clinical trials is limited by lack of sensitivity to subtle demyelination in NAWM (Bonnier et al., 2014;Cercignani et al., 2009), poor reproducibility and sensitivity to variation in scanning acquisition parameters (Helms et al., 2010a;Tofts et al., 2006).
Measures such as magnetisation transfer saturation (MTsat) (Helms et al., 2008a) and inhomogeneous MTR (ihMTR) (Varma et al., 2015) present clinically feasible alternatives to time-consuming fully quantitative MTI approaches while overcoming some of the limitations of MTR. MTsat inherently corrects for T1 relaxation and B1 inhomogeneities (Helms et al., 2008a). In MS, MTsat is lower in NAWM in MS than healthy control white matter (Lommers et al., 2019), and lower NAWM and WML MTsat in the brain and cervical spinal cord are associated with worse clinical disability (Lema et al., 2017). MTsat may therefore be more specific to changes in myelin integrity than MTR, but has not previously been studied longitudinally in recently diagnosed RRMS.

Diffusion-weighted imaging
Diffusion-weighted MRI (dMRI) is sensitive to neuroaxonal structures, but relatively insensitive to myelin. In the presence of highly structured white matter tracts, the measured water diffusion is anisotropic but becomes increasingly isotropic with neuronal degeneration. Glial cell infiltration (Yi et al., 2019) and crossing fibres may, however, complicate biological interpretation (Jones et al., 2013).
Modelling the dMRI signal from multi-shell acquisition protocols may help to resolve structural uncertainty. Neurite Orientation Dispersion and Density Imaging (NODDI) (Zhang et al., 2012), for example, considers the diffusion signal as isotropic (ISOVF), restricted (ICVF) and hindered diffusion volume fractions, plus an orientation dispersion index (ODI) (Zhang et al., 2012). The NODDI model is based on a number of assumptions (Zhang et al., 2012), including a fixed intrinsic diffusivity rate, but has previously been applied in studies of MS (Alotaibi et al., 2021;Schneider et al., 2017). ICVF may be useful as a marker of neurite (axon and dendrite) density and is reduced in WML in RRMS compared with healthy control white matter, while results for NAWM are mixed (Collorone et al., 2020;Hagiwara et al., 2019;Rahmanzadeh et al., 2021). NODDI metrics may provide useful early markers of neuroaxonal degeneration, however NODDI changes with time in RRMS are largely unexplored (Alotaibi et al., 2021).

Aggregate MRI g-ratio
Measures which combine microstructural MRI methods, such as the MRI aggregate g-ratio, may also better capture the net effects of disease and/or treatment than an individual imaging biomarker alone (Stikov et al., 2015;York et al., 2021). The g-ratio is a measure of myelin thickness, defined as the ratio of the diameter of the neuronal axon to the diameter of the myelinated axon (Rushton, 1951). Originally a neuropathological measure, a theoretical optimal g-ratio of 0.6 for maximum neuronal transduction was proposed (Rushton, 1951), although later work suggests a higher value (0.72-0.81 in the CNS) is more realistic (Chomiak and Hu, 2009). Abnormally high g-ratios are indicative of myelin disruption (Ellerbrock and Mohammadi, 2018;Kamagata et al., 2019).
G-ratio parametric maps may be derived by combining MTsat and NODDI data on a voxel-by-voxel basis (Campbell et al., 2018;Hori et al., 2018;Kamagata et al., 2019;Stikov et al., 2015;York et al., 2021). Although dependent on a number of prior assumptions that have been extensively reviewed elsewhere (Campbell et al., 2018;Mohammadi and Callaghan, 2021), the MRI g-ratio has been validated against ex vivo electron microscopy in the macaque (Stikov et al., 2015). In RRMS, increased g-ratios have shown an association with elevated plasma neurofilament, a blood marker of active axonal damage (York et al., 2021), and g-ratio structural connectome disruption has been related to disease severity . Again, the limited published studies of g-ratios in MS have been cross-sectional, rather than measuring neurodegeneration with time.

Rationale and aims
This study aims to evaluate in vivo markers of microstructural integrity for early disease stratification and as surrogate endpoints for future therapeutic trials. To this end, the sensitivity of MTR, MTsat, NODDI and g-ratio measures for detecting pathological change with time were compared in WML and NAWM in recently diagnosed RRMS; and with whole brain atrophy, an established general marker of neurodegeneration. To establish the applicability of these parametric changes to individual patients, the magnitude of these changes was compared with technique test-retest agreement in a group of healthy controls at both the individual-and group-level.

Patients with relapsing-remitting multiple sclerosis
Seventy-nine people with recently diagnosed RRMS were recruited sequentially, beginning in November 2017, to a longitudinal singlecentre sub-study of FutureMS at the Anne Rowling Regenerative Neurology Centre (Edinburgh, Scotland). FutureMS (Kearns et al., 2022;Meijboom et al., 2022) is a multicentre, prospective, longitudinal cohort study of 440 people with RRMS, who were diagnosed within the previous six months, according to 2010 McDonald criteria (Polman et al., 2011). Individuals with MS were required to be over 18 years of age and the baseline assessment was prior to initiation of any DMT. Visits at baseline (M0) and one-year follow-up (M12) included MRI and clinical assessment.

Healthy controls
Twelve healthy volunteers were additionally imaged with the same MRI protocol, which was repeated within two weeks to determine test-retest agreement.

Ethical Approval
Approval for the sub-study was obtained from the local Research Ethics Committee (reference REC 15/SS/0233). The study conformed to the Declaration of Helsinki 2000 (amendments in 2002 and 2004) and Good Clinical Practice ICH guidelines. All participants provided written informed consent.

MRI acquisition
All images were acquired on a 3.0T Prisma MRI system (Siemens, Erlangen, DE) at the Edinburgh Imaging Facility (Royal Infirmary of Edinburgh) with a 32 channel head coil.
Structural images included a 3D T1-weighted MPRAGE, 2D and 3D FLAIR, and 2D T2-weighted dual echo sequences (see Table 1 for full MRI acquisition parameters). MTI consisted of three consecutive 3D gradient-echo FLASH sequences: two proton density images with and without a Gaussian off-resonance MT saturation pulse (MT on and MT off , respectively), and an additional T1-weighted image. Multi-shell diffusion-weighted 2D spin-echo echo-planar imaging was also performed with 151 diffusion directions and three reverse phase encoding volumes.

Brain tissue segmentation
Structural MRI data processing is described in detail elsewhere . Briefly, all images were first converted from DICOM to NIfTI format (dcm2niix v1.0.20190410 (Li et al., 2016)). For people with RRMS, WML were defined as hyperintensities on T2 FLAIR at M0 and segmented automatically using an in-house thresholding approach , with manual correction where necessary (ITK-SNAP v3.6, https://www.itksnap.org). At follow-up, baseline WML masks were registered to follow-up FLAIR images and manually edited for changes.

NODDI parametric maps
dMRI processing included brain extraction and removal of bulk motion and eddy-current-induced distortions with FSL (v6.0.1). All dMRI volumes were registered to the first b0 diffusion volume before processing with the NODDI toolbox (Zhang et al., 2012 to produce ICVF and ISOVF parametric maps ( Fig. 1) .

g-ratio parametric maps
MTsat maps were registered to dMRI b0 reference volumes before calculating g-ratio maps (FSL epi_reg). Creation of g-ratio maps ( Fig. 2) followed methodology detailed previously (York et al., 2021;Zhang et al., 2012), using the equation from Stikov et al., (2015): MVF is the myelin volume fraction derived from linearly-scaled MTsat maps (Campbell et al., 2018); AVF is the axonal volume fraction derived from NODDI dMRI data, calibrated in healthy control subjects (York et al., 2021). The cerebellum was not included in dMRI and g-ratio analyses due to technical inaccuracies.

Mask-to-map registration
For each time-point, tissue segmentations were registered to either the MT off image (FSL FLIRT (Jenkinson et al., 2002)) for MT maps or the first b0 volume of diffusion data for g-ratio maps. To minimise partial volume effects, erosion by one voxel was applied to NAWM masks.

Descriptive statistics
Tissue masks were applied to parametric maps (in-house code with RNifti package v1.3.0) to output summary statistics (e.g. mean, median).

Test-retest agreement of microstructural metrics in healthy white matter
Bland-Altman plots (Bland and Altman, 1986) (BlandAltmanLeh R package) were used to assess test-retest agreement from healthy control data. Here, the difference in mean values between time points is plotted against the average value over time points for each subject and each microstructural metric. The limits of agreement show the range within which 95 % of subjects would be expected to fall if measures were repeated in healthy control white matter, and thus establishes reference levels for pathological change. Sign tests were used to determine whether the difference between time points was significantly different from zero (significance level, α = 0.05).

Longitudinal change
Longitudinal changes in MTsat, MTR, ISOVF, ICVF and g-ratio across NAWM and WML in RRMS patients were first assessed using paired ttests (α = 0.05). When significant, follow-up linear mixed modelling (maximum likelihood) was performed to account for potential confounding variables (age, sex, lesion load [as a percentage of intracranial volume] and initiation of DMTs; R packages lme4 and lmerTest (Bates et al., 2015;Kuznetsova et al., 2017)). Whole brain atrophy (i.e. change in BPF) was also examined with a linear mixed model. Interaction terms were included in models where appropriate. Post-hoc false discovery rate (FDR) correction for multiple comparisons was performed for linear mixed models. Goodness-of-fit was assessed with Nakagawa's marginal R 2 for mixed models (Nakagawa et al., 2017) (R package performance (Lüdecke et al., 2021)). Estimated marginal mean differences, averaged over model covariate levels, were calculated with the Satterthwaite method (R package emmeans).
The relationship between longitudinal change across microstructural metrics, and with whole brain atrophy was examined with Pearson's correlation coefficients. Post-hoc comparisons of longitudinal changes between patients with and without new lesions at M12, identified by an experienced neuroradiologist, was performed with Welch's t-tests.

Comparison with healthy control test-retest agreement
To compare longitudinal change over one year with test-retest agreement, the mean difference and limits of agreement from NAWM Bland-Altman plots from test-retest data acquired in healthy controls were superimposed on boxplots of longitudinal change.

Simulating pathological change
To understand how individual changes in myelin and/or axonal volume fractions would affect the g-ratio, in a post hoc simulation, biologically realistic parametric values for MTsat and NODDI measures were substituted into equations to calculate MVF, AVF and g-ratio, and plotted in R (ggplot package).

Healthy controls
One healthy control was excluded from all analyses due to an unexpected incidental imaging finding. Eleven healthy controls (7 females, mean age 44 years [range 27-58 years]) contributed to MTI test-retest analyses. Because of dMRI processing errors at the second time-point, two of those eleven controls were excluded from NODDI analyses and therefore also g-ratio cross-time-point test-retest comparisons.

People with RRMS
Seventy-nine patients with RRMS underwent imaging at M0. Two were excluded from all analyses as more than six months had passed between diagnosis and imaging. Fifteen datasets were excluded from longitudinal analyses due to poor FreeSurfer tissue segmentation (n = 5), missing M12 MRI data (n = 8), and failure to complete the full MRI protocol at M0 (n = 1) or M12 (n = 1). In addition, dMRI processing failed for two subjects. Longitudinal MTI data were hence available for 62 patients, and dMRI data were available for 60 patients (see Table 2 for demographics).

Test-retest agreement
Descriptive statistics and Bland-Altman limits of agreement for healthy white matter are reported in Table 3. Sign tests and Bland-Altman plots (Appendix Figs. A1 and A2) show that the mean difference did not differ from zero between time points for any microstructural metric (all p > 0.05, Table 3).

Longitudinal change in 'normal-appearing' white matter
In NAWM, paired t-tests show a significant decrease in MTsat and a significant increase in NODDI ICVF and g-ratio over one year (Table 4). Group mean changes over time (Table 4) were lower than the limits of agreement established from healthy control data (Table 3), although a small number of subjects exceeded these limits (Appendix Fig. A4). MTR and NODDI ISOVF did not change significantly over one year.
In NAWM, one year change in g-ratio was strongly associated with change in MTsat (Pearson's R 2 = 0.98, p < 0.001, Appendix Fig. A5). Correlations between change in NODDI metrics and g-ratio were weaker but statistically significant (Pearson's R 2 = 0.18 and 0.11, p < 0.001 and p = 0.011 for NODDI ICVF and ISOVF, respectively), as were associations between longitudinal change in MTsat and NODDI metrics (Pearson's R 2 = 0.11 and p = 0.009 for both ICVF and ISOVF).
Group mean changes in MTsat and MTR lay within limits of agreement established in healthy control white matter (Table 3). Group-wise  Table 3 Mean (standard deviation) MTI, NODDI and g-ratio values in healthy control white matter (n = 11, except where indicated *n = 9). P-values are given for two-sided sign tests for matched pairs. $ excludes cerebellum.  Table 4 Descriptive statistics and paired t-tests for MTI (n = 62), g-ratio and NODDI (n = 60) data in 'normal-appearing' white matter. MTsat: magnetisation transfer saturation; MTR: magnetisation transfer ratio; ICVF: intracellular volume fraction; ISOVF: isotropic volume fraction; SD: standard deviation; M0: baseline; M12: one year follow-up. *excludes cerebellum for g-ratio and NODDI metrics.  Table 5 Descriptive statistics and paired t-tests for MTI (n = 62), g-ratio and NODDI (n = 60) data in T2 FLAIR white matter lesions. MTsat: magnetisation transfer saturation; MTR: magnetisation transfer ratio; ICVF: intracellular volume fraction; ISOVF: isotropic volume fraction; SD: standard deviation; M0: baseline; M12: one year follow-up. *excludes cerebellum for g-ratio and NODDI metrics. increases in NODDI ISOVF and ICVF were greater than the limits of agreement and a large number of individual subjects exceeded both positive and negative limits of agreement for both metrics (Appendix Fig. A4).

Whole brain atrophy
After adjusting for covariates, there was no significant decrease in BPF over one year (Appendix Table A8) and no relationship was found between whole brain atrophy and any of the microstructural measures that showed significant change over time (Appendix Fig. A7).

Presence of new lesions
There were no significant differences in mean change over time between patients who did or did not have new lesions at M12 (FDR-corrected p > 0.05 for all metrics).

Simulating pathological change
Simulated data points (Appendix Fig. A3) show the competing effects of changes in MTsat and NODDI metrics on g-ratio. As MTsat decreases, g-ratio increases, when NODDI metrics are held constant. An increase in ICVF, however, may also increase the g-ratio while an increase in ISOVF lowers the g-ratio.

Summary of results
In this study, changes in microstructural MRI measures in WML and NAWM were assessed over the year following diagnosis of RRMS. In NAWM, there was a decrease in MTsat and an increase in g-ratio and NODDI ICVF, but no change in MTR or NODDI ISOVF (see summary Table 6). In WML, a longitudinal increase in MTsat, NODDI ICVF and NODDI ISOVF but no change in g-ratio or MTR was demonstrated. Despite significant group-wise changes, the majority of individual patients remain within test-retest limits of agreement in healthy white matter, with the exception of NODDI measures in cerebral WML. No significant whole brain atrophy was detectable, nor did atrophy correlate with any microstructural measure.
Simulated data indicate that biological interpretation of longitudinal change in g-ratio is complicated due to its inverse relationship with MTsat, positive dependence on NODDI ICVF and a negative dependence on NODDI ISOVF.

MTI
The longitudinal decline in MTsat observed in NAWM suggests that subtle loss of myelin integrity occurs in recently diagnosed RRMS, which cannot be seen on conventional T2-weighted FLAIR. Longitudinal MTsat data in RRMS have not previously been reported, although MTsat is lower in NAWM in MS than healthy control white matter (Lommers et al., 2019). The reduction in MTsat in NAWM over one year in our study was small in comparison to variance in test-retest healthy control white matter. This suggests that, although there may be a weak groupwise longitudinal change in NAWM MTsat, measurement error may limit application on an individual patient level (e.g. for clinical decisionmaking). Patients in our study were recruited shortly after diagnosis, and resolving effects associated with the acute inflammatory episode that prompted diagnosis at baseline are potential confounds; heterogeneous demyelination and myelin repair across NAWM could therefore also contribute to the weak effect over a relatively short period early in disease.
Nevertheless, results indicate that MTsat is more sensitive to early RRMS pathology in NAWM than MTR, which shows no detectable change. While MTsat and MTR are both sensitive to protons 'bound' to macromolecules within the lipid bilayers of myelin, MTR signal also depends non-linearly on T1 recovery effects and B1 inhomogeneities, which are effectively corrected for in MTsat (Helms et al., 2008a). T1 prolongation, which accompanies myelin damage (Al-Radaideh et al., 2015), may systematically affect MTR in such a way as to render it less sensitive to demyelination than MTsat (Helms et al., 2008a). Previous case-control studies show that MTR in NAWM is typically only 1.25 percent units lower than control white matter  and the longitudinal decline in NAWM MTR has been estimated at 0.1 % per year (Davies et al., 2005), reiterating the subtlety of NAWM changes. Longer follow-up may therefore be required in order to detect changes in NAWM MTR in early RRMS.

NODDI
An unexpected increase in NODDI ICVF was seen within NAWM over one year. NODDI ICVF (the 'restricted' diffusion signal fraction) is typically lower in RRMS compared with healthy controls (Alotaibi et al., 2021;Collorone et al., 2020;Johnson et al., 2021a), although this is not a universal finding (Kato et al., 2022). The established literature on longitudinal NODDI measurements is sparse, but decreases in NAWM ICVF over time have previously been noted (Sacco et al., 2020). A 'borderline' significant increase in mean fractional anisotropy (FA) within 'normal-appearing' brain tissue over two years has been reported elsewhere (Rashid et al., 2008), but the annualised rate of change did not differ from healthy control subjects.
The biological mechanism underlying the observed increase in NODDI ICVF, with no concomitant change in ISOVF, is unclear, but could partly be attributed to axonal swelling (Moll et al., 2011), axonal bundling or changes in cytoskeleton composition following demyelination (Brady et al., 1999), or axonal repair. Axonal regeneration per se appears unlikely given the limited ability of the CNS to repair following axonal injury (Huebner and Strittmatter, 2009), and the lack of positive association with change in BPF, an established marker of neurodegeneration. A decrease in glial cell infiltration following resolution of acute inflammation could additionally explain an increase in ICVF relative to a resulting decrease in hindered diffusion. Previous evidence also suggests that the parallel diffusivity in supratentorial brain may increase over time in RRMS (Harrison et al., 2011), which would artificially increase ICVF (Guerrero et al., 2019). The possibility that the assumption of a fixed intrinsic parallel diffusivity does not hold in NAWM can also not be excluded here.

Table 6
Summary of linear mixed model results. MTsat: magnetisation transfer saturation; MTR: magnetisation transfer ratio; NODDI: neurite orientation dispersion and density index; ISOVF: isotropic volume fraction; ICVF: intraneurite volume fraction; NAWM: normal-appearing white matter; WML: white matter lesions; $ cerebellum excluded; *significant after False Detection Rate correction for multiple comparisons. York et al. The observed absence of longitudinal change in NODDI ISOVF was expected as significant increases in water content would likely be visible as hyperintense signal on conventional T2 FLAIR.

g-ratio
The longitudinal increase in g-ratio within NAWM observed is also consistent with subtle demyelination in early RRMS which is not otherwise visible on conventional MRI. The MRI g-ratio has been studied in cross-sectional studies of MS Maekawa et al., 2022;York et al., 2021;Yu et al., 2019), healthy cohorts (Mohammadi et al., 2015), other diseases (e.g. Moyamoya disease (Hara et al., 2020), Huntington's disease (Johnson et al., 2021b)), and childhood development (Geeraert et al., 2019); however longitudinal analysis of g-ratio in the adult brain has been notably absent. The data presented illustrate that g-ratio may be suited to assessing myelin integrity changes over time. Conversely, an increase in g-ratio could also result from the observed increase in NODDI ICVF. Nevertheless, the strong relationship in NAWM between longitudinal change in g-ratio and MTsat but little association with NODDI measures suggests that myelin loss is driving the longitudinal increase in NAWM g-ratio.
The majority of patient data points for g-ratio in NAWM fell within limits of agreement established from healthy control test-retest measures. While the significant group-wise longitudinal change in g-ratio indicates a biological change in NAWM over one year post-diagnosis in RRMS, such change may not exceed measurement error for an individual patient.

MTI
In WML, MTsat increased over one year, suggestive of myelin repair. As far as these authors are aware, this is the first study to examine longitudinal evolution of MTsat in MS lesions. The findings presented are, however, in line with previous post-mortem evidence showing that the percentage of remyelinated tissue in WML may be as high as 85 % in RRMS, although highly heterogeneous across patients (Patrikios et al., 2006). Furthermore, WML MTsat at follow-up remained lower than NAWM values, in keeping with reports that myelin sheaths of remyelinated axons within T2 WML are abnormally thin (Barkhof et al., 2003) and remyelination is patchy (Patrikios et al., 2006). Although a decrease in water content could also account for a lesion-specific increase in MTsat, the increase in NODDI ISOVF within lesions, and the previously reported lack of change in total water content in existing WML (Vavasour et al., 2021), suggests this is not the case. Moreover, the effect size was large with several patients exceeding limits of agreement established in control white matter. Some recovery of myelin following acute inflammation therefore appears to be a plausible explanation.
Early treatment with DMTs, which target inflammation and reduce likelihood of progression (Brown et al., 2019), could also contribute to the increase in WML MTsat. Our cohort was treatment-naïve at baseline but nearly-two thirds of patients had commenced DMTs by one year follow-up. Initiation of DMTs was not a significant covariate in WML models, however, suggesting that spontaneous remyelination may be a more significant effect. Reclassification of NAWM tissue as WML at follow-up is a potential additional explanation for an increase in MTsat over time, as new lesions may not be as extensively damaged. Our comparison of patients with and without new lesions at follow-up, however, showed a similar effect in both groups.
Unlike MTsat, MTR in WML did not change significantly postdiagnosis, after accounting for confounding variables including lesion load and age. The relative stability of MTR within WML has been noted previously , although MTR may fluctuate with time and lesion type (e.g. contrast-enhancing versus non-enhancing lesions) (Brown et al., 2014;York et al., 2022). Here, we did not investigate lesion sub-types; nonetheless, the discrepancy between results for MTsat and MTR suggests that T1 may be a contributing factor. The effect of T1 is likely to be greater in WML compared to NAWM, where marked myelin loss is associated with varying degrees of T1 prolongation, including visible 'black hole' lesions in more extreme cases. Taken together, results suggest MTsat may be more sensitive to alterations in myelin integrity than MTR, including spontaneous remyelination.

NODDI
The increase in NODDI ICVF within cerebral WML was greater than in NAWM and exceeded limits of agreement from healthy controls, although WML ICVF remained lower than in NAWM. Pathologically swollen axons are seen in secondary progressive MS (SPMS) lesions (Moll et al., 2011) and similar pathology occurring in early RRMS could explain our finding. A post-relapse reduction in glial cell presence, poor adaptability of the NODDI model to pathological tissue, residual sensitivity to remyelination, or alternatively axonal regeneration may be other explanations. The longitudinal increase in NODDI ISOVF within WML, however, does suggest ongoing progressive destruction of neuroaxonal architecture. This explanation would fit with the reduced axonal count typically seen in SPMS WML (Moll et al., 2011), but would seem to contradict myelin repair indicated by MTsat results. Heterogeneous tissue repair and destruction across/within lesions could perhaps consolidate these conflicting theories.

g-ratio
The lack of change in g-ratio within WML may be due to competing effects; increase in MTsat, suggestive of remyelination, combined with significant increases in NODDI ICVF and NODDI ISOVF may mitigate each other, as discussed below. Intra-patient heterogeneity across lesions may also explain the negative result, and g-ratio changes on a lesion-by-lesion basis cannot be excluded.

g-ratio dependence on MTI and NODDI parameters
Simulation of the impact of concurrent changes in microstructural metrics on the g-ratio was performed post hoc to understand better the results obtained, and sheds light on the complexity of interpreting longitudinal change in MRI markers. As expected with demyelination in RRMS, decreasing MTsat leads to an increase in MRI g-ratio. Only a weak correlation between longitudinal changes in MTsat and g-ratio in WML is, however, seen, despite a strong correlation in NAWM. While MTsat may be sensitive to myelinated axonal integrity, there may be a "floor" effect in focal regions of low myelin density which may limit the usefulness of the MTsat signal within WML.
Moreover, significant changes in NODDI measures are problematic for interpretation of the g-ratio. In RRMS, axonal density, measured here with NODDI ICVF, is expected to decrease with neurodegenerative processes, and free water (i.e. ISOVF) is expected to increase as tissue destruction becomes more pronounced. Simulations suggest, however, that a large increase in ISOVF or a large decrease in ICVF, without a concomitant increase in MTsat, would lead to a decrease in g-ratio (see Appendix Fig. A3). While the latter scenario would not be expected in MS pathology given the proximity in time of demyelination and axonal degeneration, increases in ISOVF may be more common, particularly in WML; thus rendering the g-ratio model flawed in severely damaged WML. Nonetheless, the g-ratio may remain relevant in NAWM, where large changes in ISOVF and ICVF are not expected.
Although the g-ratio is clearly attractive as a parameter with a specific histopathological correlate and provides a mechanistic link between white matter integrity and neuronal conductivity in RRMS, these data suggest that g-ratio may not provide significant additional information to MTsat.

Limitations
There are a number of limitations in the present study. NODDI, MTsat and g-ratio analyses are heavily model-dependent and based on assumptions from healthy brain tissue, some of which may break down where there is marked loss of normal microstructural integrity. For example, the g-ratio model applied here does not account for the impact of varying neurite orientations on a sub-voxel level. NODDI ODI did not change significantly over one year, however, and was not associated with change in g-ratio (data not shown). Moreover, true evaluation of these techniques' sensitivity to change in myelin and axonal integrity would require comparison with 'ground truth' brain tissue examination, which is not available in early RRMS, and a limitation common to the majority of imaging biomarker studies.
The cohort characteristics also impose limitations on analysis; over one year, a substantial number of patients will be recovering from an acute inflammatory episode, and progression in disability measures was minimal (Kearns et al., 2022), limiting the opportunity for correlating microstructural imaging measures with clinical progression. Future planned follow-up at five years will help to mitigate this limitation. Although all participants were diagnosed within six months prior to baseline, the time duration between first symptom onset and diagnosis was variable, and not corrected for here due to an association with age (Spearman's rho = 0.48, p < 0.001, data not shown). Nevertheless, longitudinal data from a sizable, comparatively homogeneous RRMS cohort who were recruited at a similar disease stage (Kearns et al., 2022) is a strength of this study.
Test-retest measures are important to establish sensitivity to detection of pathological change, however these were calculated from white matter in a small number of healthy control subjects and thus have wide confidence intervals, and may not be representative of WML. Moreover, repeat measures on control subjects were made over a short time interval, and technique reproducibility over a one year period was not assessed. Additional sources of variance such as MRI system drift may therefore be underestimated, and longitudinal microstructural changes seen in MS patients over a year should therefore be interpreted with caution.
Finally, loss of subject data to drop-out and technical inaccuracies may introduce bias to the analyses.

Conclusion
Measures specific to microstructural integrity show change in early RRMS where there is no detectable atrophy. MTsat is a promising in vivo biomarker of myelin integrity, which appears more sensitive than MTR to demyelination and spontaneous remyelination in early RRMS. Gratio, despite its sensitivity to changes in NAWM and specific histopathological correlate with myelin thickness, is difficult to interpret biologically due to a complex dependence on NODDI parameters. Independent consideration of myelin-sensitive and axonal neuroimaging markers may ultimately be more informative for longitudinal tracking of neuropathology in RRMS than combining such measures. For clinical application, further research is required to improve technique reproducibility, broaden the applicability of dMRI and MTI models, and validate these against heterogeneous tissue pathology in MS.

Declaration of Competing Interest
The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: FutureMS, hosted by Precision Medicine Scotland Innovation Centre (PMS-IC) reports financial support was provided by Biogen UK Ltd.

Data availability
Data are available after approval of a research proposal via an established subcommittee.