
==== Front
Neurol Neuroimmunol Neuroinflamm
Neurol Neuroimmunol Neuroinflamm
nnn
NEURIMMINFL
Neurology® Neuroimmunology & Neuroinflammation
2332-7812
Lippincott Williams & Wilkins Hagerstown, MD

39270143
NXI-2024-100114
10.1212/NXI.0000000000200299
00011
3
120
41
Research Article
Advanced Quantitative MRI Unveils Microstructural Thalamic Changes Reflecting Disease Progression in Multiple Sclerosis
https://orcid.org/0000-0002-7703-0553
Cagol Alessandro MD
https://orcid.org/0000-0003-2239-7355
Ocampo-Pineda Mario PhD
https://orcid.org/0000-0001-6031-6865
Lu Po-Jui PhD
https://orcid.org/0000-0002-4910-1414
Weigel Matthias PhD
https://orcid.org/0000-0001-8557-9223
Barakovic Muhamed PhD
https://orcid.org/0000-0001-5602-8916
Melie-Garcia Lester PhD
https://orcid.org/0000-0002-3683-6489
Chen Xinjie MD
https://orcid.org/0000-0003-3281-5477
Lutti Antoine PhD
https://orcid.org/0000-0003-3347-5187
Calabrese Pasquale PhD
https://orcid.org/0000-0002-6963-8892
Kuhle Jens MD, PhD
https://orcid.org/0000-0003-4175-5509
Kappos Ludwig MD
https://orcid.org/0000-0001-6892-104X
Sormani Maria Pia PhD
https://orcid.org/0000-0002-4917-8761
Granziera Cristina MD, PhD
From the Translational Imaging in Neurology (ThINk) Basel (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., L.K., C.G.), Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel; Department of Neurology (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., J.K., L.K., C.G.), University Hospital Basel; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB) (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., J.K., L.K., C.G.), University Hospital Basel and University of Basel, Switzerland; Dipartimento di Scienze della Salute, (A.C., M.P.S.), Università degli Studi di Genova, Italy; Division of Radiological Physics (M.W.), Department of Radiology, University Hospital Basel; Laboratory for Research in Neuroimaging (A.L.), Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne; Neuropsychology and Behavioral Neurology Unit (P.C.), Division of Cognitive and Molecular Neuroscience, University of Basel, Switzerland; and IRCCS Ospedale Policlinico San Martino (M.P.S.), Genova, Italy.
Correspondence Prof. Granziera cristina.granziera@usb.ch
Go to Neurology.org/NN for full disclosures. Funding information is provided at the end of the article.

Submitted and externally peer reviewed. The handling editor was Deputy Editor Scott S. Zamvil, MD, PhD, FAAN.

The Article Processing Charge was funded by Swiss National Fund.

11 2024
13 9 2024
13 9 2024
11 6 e20029914 2 2024
02 7 2024
Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology.
2024
American Academy of Neurology
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND), which permits downloading and sharing the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.

Background and Objectives

In patients with multiple sclerosis (PwMS), thalamic atrophy occurs during the disease course. However, there is little understanding of the mechanisms leading to volume loss and of the relationship between microstructural thalamic pathology and disease progression. This cross-sectional and longitudinal study aimed to comprehensively characterize in vivo pathologic changes within thalamic microstructure in PwMS using advanced multiparametric quantitative MRI (qMRI).

Methods

Thalamic microstructural integrity was evaluated using quantitative T1, magnetization transfer saturation, multishell diffusion, and quantitative susceptibility mapping (QSM) in 183 PwMS and 105 healthy controls (HCs). The same qMRI protocol was available for 127 PwMS and 73 HCs after a 2-year follow-up period. Inclusion criteria for PwMS encompassed either an active relapsing-remitting MS (RRMS) or inactive progressive MS (PMS) disease course. Thalamic alterations were compared between PwMS and HCs and among disease phenotypes. In addition, the study investigated the relationship between thalamic damage and clinical and conventional MRI measures of disease severity.

Results

Compared with HCs, PwMS exhibited substantial thalamic alterations, indicative of microstructural and macrostructural damage, demyelination, and disruption in iron homeostasis. These alterations extended beyond focal thalamic lesions, affecting normal-appearing thalamic tissue diffusely. Over the follow-up period, PwMS displayed an accelerated decrease in myelin volume fraction [mean difference in annualized percentage change (MD-ApC) = −1.50; p = 0.041] and increase in quantitative T1 (MD-ApC = 0.92; p < 0.0001) values, indicating heightened demyelinating and neurodegenerative processes. The observed differences between PwMS and HCs were substantially driven by the subgroup with PMS, wherein thalamic degeneration was significantly accelerated, even in comparison with patients with RRMS. Thalamic qMRI alterations showed extensive correlations with conventional MRI, clinical, and cognitive disease burden measures. Disability progression over follow-up was associated with accelerated thalamic degeneration, as reflected by enhanced diffusion (β = −0.067; p = 0.039) and QSM (β = −0.077; p = 0.027) changes. Thalamic qMRI metrics emerged as significant predictors of neurologic and cognitive disability even when accounting for other established markers including white matter lesion load and brain and thalamic atrophy.

Discussion

These findings offer deeper insights into thalamic pathology in PwMS, emphasizing the clinical relevance of thalamic damage and its link to disease progression. Advanced qMRI biomarkers show promising potential in guiding interventions aimed at mitigating thalamic neurodegenerative processes.

OPEN-ACCESSTRUE
==== Body
pmcIntroduction

Gray matter pathology is prominent in patients with multiple sclerosis (PwMS), with degenerative processes initiating early in the disease course and correlating with clinical disability and cognitive performance.1,2 Among gray matter structures, the thalamus plays a pivotal role, serving as a relay and integration center within the CNS. In PwMS, the thalamus is distinctively affected, because of the complex interplay between heterogeneous pathophysiologic mechanisms.3 Inflammatory processes can directly cause thalamic damage, leading to the development of focal demyelinating lesions.3,4 Thalamic damage can also occur indirectly due to pathologic processes involving the white matter, where axonal transection in white matter lesions can trigger retrograde, anterograde, and trans-synaptic degeneration, ultimately resulting in thalamic neuronal loss.3,5 Besides, an increasingly recognized mechanism contributing to thalamic pathology is the damage following a distinctive “surface-in” pattern, hypothesized to be triggered by soluble inflammatory factors contained in the CSF.6-9 Finally, oxidative stress, mitochondrial dysfunction, and disturbances in iron homeostasis contribute to thalamic degeneration.3,10,11

Quantification of thalamic damage in multiple sclerosis has been the focus of extensive research, primarily through the measurement of thalamic volume, which exhibits consistent loss across disease phases and closely correlates with neurologic disability and cognitive impairment.3,12-14 While other neuroimaging techniques such as MR spectroscopy and quantitative MRI (qMRI) approaches have also been used,3,15-17 our understanding of the microstructural alterations accumulating in the thalamus of PwMS, particularly in relation to disease progression, remains limited. To comprehensively characterize thalamic tissue damage in PwMS and its accumulation across different disease phases, we conducted a multiparametric qMRI study. Our investigation leveraged quantitative T1-relaxometry (qT1) for a comprehensive assessment of tissue microstructural and macrostructural integrity,18,19 myelin volume fraction (MVF) as a proxy measure of tissue myelin and macromolecular content,18,20 neurite density index (NDI) as a surrogate measure of axon and dendrite density,21 and quantitative susceptibility mapping (QSM) to characterize changes in iron homeostasis and myelin damage.18 Thalamic atrophy, serving as a marker of the overall accumulation of thalamic neurodegenerative processes, was also measured.

We investigated thalamic microstructural damage and its accumulation over time, by performing a prospective, cross-sectional, and longitudinal study. Pathologic changes were investigated in both focal lesions and the “normal-appearing” tissue, by comparing PwMS with a reference group of healthy controls (HCs). Thalamic alterations associated with different disease phases were examined by comparing 2 subgroups of patients, one with active relapsing-remitting MS (RRMS) and another with inactive progressive MS (PMS), specifically selected to represent the opposing ends of the clinical spectrum. In addition, we investigated the relationship between qMRI measures of thalamic integrity and their rate of change over time with (1) conventional MRI markers of disease burden and (2) clinical measures of neurologic disability and cognitive impairment.

Methods

Participants

Participants were prospectively recruited at the University Hospital of Basel between 2018 and 2022, as part of the INsIDER study (NCT05177523). Inclusion criteria for patients were as follows: (1) age between 18 and 80 years; (2) diagnosis of MS fulfilling the 2017 revised McDonald criteria22; (3) disease course either active relapsing-remitting (with ≥1 relapse and/or ≥1 gadolinium-enhancing lesion during the year before enrollment) or nonactive primary/secondary progressive (without relapses and MRI activity during the year before enrollment); (4) absence of neurologic or psychiatric comorbidities. HCs were volunteers aged between 18 and 80 years, with a medical history negative for neurologic or psychiatric disorders. Exclusion criteria for patients and controls encompassed pregnancy, contraindication to MRI, and inability to provide consent. This study follows the STROBE guideline for reporting observational studies.23

A total of 183 PwMS and 105 HCs met the inclusion criteria. At baseline, all underwent a brain MRI scan, with additional neurologic examination for PwMS and cognitive assessment for a subset of 99 PwMS and 100 HCs. 127 PwMS and 73 HCs volunteered to undergo a second brain MRI scan after a period of 2 years (±3 months), with the same acquisition protocol. Concurrent neurologic follow-up was obtained for PwMS. The study design is presented in Figure 1.

Figure 1 Study Design

*Analyses performed in the group of patients with multiple sclerosis only. aAvailable in 99 patients with multiple sclerosis and 100 healthy controls. bAvailable in 98 patients with multiple sclerosis and 100 healthy controls. BPF = brain parenchymal fraction; EDSS = Expanded Disability Status Scale; MuSIC = Multiple Sclerosis Inventory Cognition; PMS = progressive multiple sclerosis; qMRI = quantitative MRI; RRMS = relapsing-remitting multiple sclerosis; SDMT = Symbol Digit Modalities Test; T2LV = T2-hyperintense lesion volume; VLMT = Verbal Learning and Memory Test.

Clinical Assessment

The neurologic examination for PwMS included the calculation of the Expanded Disability Status Scale (EDSS) score, by certified raters.24,25

Cognitive performance was assessed using (1) the oral version of the Symbol Digit Modalities Test (SDMT),26 (2) the Verbal Learning and Memory Test (VLMT),27 and (3) the Multiple Sclerosis Inventory Cognition (MuSIC)28 test. SDMT and VLMT scores were converted to z-scores,26,27 and the MuSIC score was corrected for age and sex.28

MRI Acquisition

All brain MRI scans were obtained with the same acquisition protocol on a 3T whole-body MR system (Magnetom Prisma, Siemens Healthineers), using a 64-channel phased-array head and neck coil for radio-frequency reception. The MRI protocol included the following: (1) 3D fluid-attenuated inversion recovery (FLAIR) [TR/TE/TI = 5,000/386/1,800 ms; resolution = 1 × 1 × 1 mm3]; (2) 3D magnetization-prepared 2 rapid gradient-echo (MP2RAGE) [TR/TI1/TI2 = 5,000/700/2,500 ms; resolution = 1 × 1 × 1 mm3]; (3) multishell diffusion (TR/TE/δ/Δ = 4,500/75/19/36 ms; resolution = 1.8 × 1.8 × 1.8 mm3; b-values 0/700/1,000/2,000/3,000 s/mm2 with 12/6/20/45/66 measurements, respectively, per shell; diffusion acquisition with 12 measurements of b-value 0 s/mm2 with reversed phase encoding); (4) 3D segmented echo planar imaging (EPI) [TR/TE = 64/35 ms; resolution = 0.67 × 0.67 × 0.67 mm3]; (5) three 3D radio-frequency spoiled gradient-echo acquisitions with predominantly magnetization transfer–weighted (TR/α = 25 ms/5°), proton density–weighted (TR/α = 25 ms/5°), and T1-weighted (TR/α = 11 ms/15°) contrasts, used to obtain magnetization transfer saturation (MTsat) maps (resolution = 1.33 × 1.33 × 1.33 mm3).29

MRI Processing

qT1 maps were obtained from MP2RAGE images as previously described.29,30 MTsat maps were computed as previously proposed,31,32 and MVF was then estimated by multiplying the MTsat signal by a calibration constant (α).33,34 Diffusion images were denoised and corrected for motion, eddy currents, and susceptibility-induced distortions; microscopic diffusion processes were then modeled using the spherical mean technique (SMT) to obtain NDI, a proxy measure of the signal originating from axons and dendrites.21 SMT was preferred over alternative approaches such as neurite orientation dispersion and density imaging (NODDI), given its effectiveness in addressing challenges posed by fiber crossing populations and orientation dispersion, which are particularly prominent within gray matter regions.21 QSM was reconstructed from 3D EPI images using the morphology enabled dipole inversion algorithm to compute the susceptibility from the local field, with ventricular CSF used as the zero reference.35

MRI Analysis

In PwMS, T2-hyperintense lesions were detected with a deep learning–based tool36; the resulting masks were manually corrected and used to estimate total T2-hyperintense lesion volume (T2LV).

FIRST37 was applied on MP2RAGE images to obtain an initial reference for thalamic segmentation, and the automatic output was manually refined following the previously proposed protocol for the manual segmentation of the thalamus.38 In PwMS, a mask of T2-hyperintense lesions contained within the thalamus was obtained by intersecting the lesion mask (registered to the MP2RAGE space) with the thalamic mask. Thalamic lesions were then subtracted from thalamic masks to obtain a segmentation of normal-appearing thalamic tissue. For each thalamic lesion, we investigated the morphology and the presence of a central vein or a paramagnetic rim (eMethods).

Thalamus optimized multi atlas segmentation (THOMAS) was used to segment the thalamus into 12 nuclei.39 In addition, the thalamus was partitioned into 3 bands having increasing distance from the CSF, to explore the “ependymal-in” gradient of damage. The 3 bands were identified by performing progressive expansions of a CSF mask (after a 1-mm erosion at the thalamus-CSF and thalamus-internal capsule boundaries, to reduce partial volume effect).

MVF, NDI, and QSM maps were linearly registered to the MP2RAGE space using FLIRT, and the results were visually inspected. The transformation matrices obtained in the process were then used to move the regions of interest (i.e., thalamic lesions and normal-appearing tissue, segmented on the MP2RAGE space) back to the original MVF, NDI, and QSM spaces. For all regions and contrasts of interest, the mean intensity values were extracted in the original space using fslstats. For thalamic lesions, qMRI metrics were extracted exclusively in lesions with volume ≥10 mm3.

Total brain volume and total intracranial volume (TIV) were measured with SAMSEG.40 Brain parenchymal fraction (BPF) was calculated as the ratio between total brain volume and TIV; similarly, normalized thalamic volume was estimated as the ratio between thalamic volume and TIV.

Statistical Analysis

All statistical analyses were performed with R (version 4.2.1; R Core Team, 2022).41 The threshold of statistical significance was set at p < 0.05.

Demographic, clinical, and MRI variables were compared between groups using the Welch t-test, Mann-Whitney U test, Pearson χ2 test, and linear regression models, as appropriate.

qMRI measures in the normal-appearing thalamic tissue were compared cross-sectionally, at baseline, between (1) PwMS and HCs and (2) patients with inactive PMS and patients with active RRMS. Comparisons were performed with general linear models, adjusting for age and sex. Differences in qMRI measures were also explored regionally, both in thalamic nuclei and in the 3 bands having increasing distance from the CSF; the results were corrected for multiple comparisons with the false discovery rate approach, using the Benjamini-Hochberg method.

To characterize the microstructural damage within T2-hyperintense thalamic lesions, we compared the qMRI metrics measured within the lesions with those obtained in a 2-voxel area of perilesional normal-appearing tissue. The comparison was performed using the Wilcoxon signed-rank test.

In PwMS, general linear models were used to explore the association of qMRI measures in the normal-appearing thalamic tissue with clinical and conventional MRI features, including (1) neurologic disability, as assessed with the EDSS; (2) cognitive impairment, as assessed with the SDMT, VLMT, and MuSIC test; (3) burden of T2-hyperintense lesion volume (T2LV); and (4) BPF. The relative importance of the different qMRI thalamic measures—together with T2LV and BPF—in (1) explaining neurologic disability (as measured with the EDSS), (2) explaining multidomain cognitive performance (as measured with the MuSIC score), and (3) distinguishing between progressive and relapsing-remitting phenotypes was ranked using multivariable least absolute shrinkage and selection operator (LASSO) regression models. We performed 10-fold cross-validation to select the optimal regularization parameter (lambda). For each patient, we calculated the deviation of the thalamic qMRI metrics (and the BPF) from the HC population, accounting for age and sex. Specifically, we fitted a linear regression in HCs with the MRI measure as the dependent variable and age and sex as independent variables. The deviation from the regression line (residual of the fit) of each MRI measure in PwMS was the variable entered in the LASSO models.42

Longitudinal changes in thalamic qMRI metrics were quantified with mixed-effect models, using the qMRI measurements at each given time point as dependent variables. Models included time (to estimate the rate of change), age at baseline, and sex as covariates and a random intercept for participants. To estimate annual percentage change from the slope over time, the qMRI metrics were log-transformed. Comparisons in the rates of change between (1) PwMS and HCs and (2) patients with inactive PMS and patients with active RRMS were performed by introducing in the mixed-effect models the interaction term between the group and time. Similarly, the association between the rates of qMRI change and (1) baseline EDSS, (2) baseline T2LV, (3) baseline normalized thalamic volume, (4) disease activity over follow-up, (5) EDSS progression over follow-up, (6) change in T2LV over follow-up, and (7) change in BPF over follow-up was explored by introducing in the mixed-effect models the interaction term between the variables of interest and time. EDSS progression was defined as an increase in the EDSS score of ≥1.5 points if the baseline EDSS score was 0, ≥1.0 points if the baseline EDSS score was 1.0–5.5, or ≥0.5 points if the baseline EDSS score was greater than 5.5.43 Mixed-effect models were also used to investigate baseline demographic, clinical, and MRI predictors of subsequent thalamic atrophy.

Sensitivity analyses were conducted to (1) exclude a significant impact of partial volume effect on the estimations of thalamic qMRI measures (by comparing the qMRI values derived with the original thalamic masks with those obtained with thalamic masks that underwent a 1-voxel erosion process); (2) compare the estimations of NDI obtained with SMT with those derived from NODDI; (3) explore between-group differences in thalamic microstructure accounting for treatment effect and presence of disease activity over the follow-up; (4) explore between-group differences in subgroups matched for age and sex; and (5) investigate regional microstructural changes in thalamic nuclei defined with a different probabilistic atlas (as implemented in FreeSurfer; eFigures 1–5).

Additional analyses were conducted to explore (1) between-group differences in thalamic measures of orientation dispersion index (ODI) and isotropic volume fraction (ISOVF) derived with NODDI (eTables 1–4); (2) the extent of microstructural changes as a function of the distance from the CSF (eFigure 6); (3) the correlation between different thalamic qMRI metrics (eFigure 7); (4) the impact of T2-hyperintense lesion burden specifically within thalamocortical bundles as a proxy for thalamic disconnectivity (eTables 5–9); and (5) the relative importance of demographic, clinical, and conventional MRI measures and the global and regional thalamic qMRI metrics explored in the study, in explaining clinical outcomes using random forests (eFigures 8–10).

Further methodological details are available in eMethods.

Standard Protocol Approvals, Registrations, and Patient Consents

Study approval was obtained from the local ethics committee (IRM of Northwest Switzerland); informed consent was obtained from all participants before study entry.

Data Availability

The data that support the findings of this study are available on reasonable request.

Results

The main cohort's demographic, clinical, and MRI characteristics are summarized in Table 1. Baseline characteristics of patients undergoing clinical and MRI follow-up are reported in eTable 10. The qMRI contrasts included in the study are illustrated in Figure 2.

Table 1 Baseline Demographic, Clinical, and Conventional MRI Characteristics of the Cohort

	Healthy controls	Patients with active RRMS	Patients with inactive PMS	p Values	
n	105	101	82	—	
Female/male	58/47	66/35	44/38	RRMS vs HCs: 0.18a	
				PMS vs HCs: 0.95a	
				PMS vs RRMS: 0.15a	
Mean (SD) [range] age, y	37.8 (13.0) [18.1–69.0]	37.6 (10.9) [18.3–62.8]	58.1 (9.5) [36.9–77.2]	RRMS vs HCs: 0.92b	
				PMS vs HCs: <0.001b	
				PMS vs RRMS: <0.001b	
Median [IQR] disease duration, y	—	2.5 [0.6–9.3]	15.4 [8.0–25.0]	RRMS vs HCs:/	
				PMS vs HCs:/	
				PMS vs RRMS: <0.001c	
Disease-modifying therapy	—				
 Platform, n (%)		3 (3)	4 (5)	RRMS vs HCs:/	
 Oral, n (%)		36 (36)	15 (18)	PMS vs HCs:/	
 Monoclonal antibodies, n (%)		50 (50)	48 (59)	PMS vs RRMS: <0.001a	
 Untreated, n (%)		12 (12)	15 (18)		
Median [IQR] EDSS score	—	2.0 [1.5–2.5]	5.0 [4.0–6.0]	RRMS vs HCs:/	
				PMS vs HCs:/	
				PMS vs RRMS: < 0.001c	
Mean (SD) SDMT z-score*	0.55 (1.16)	0.29 (1.15)	−0.49 (1.50)	RRMS vs HCs: 0.19b	
				PMS vs HCs: <0.001b	
				PMS vs RRMS: 0.004b	
Mean (SD) VLMT z-score*	1.00 (1.17)	0.89 (1.06)	0.08 (1.08)	RRMS vs HCs: 0.57b	
				PMS vs HCs: <0.001b	
				PMS vs RRMS: <0.001b	
Mean (SD) MuSIC score**	27.0 (3.5)	25.9 (4.6)	19.5 (6.4)	RRMS vs HCs: 0.10b	
				PMS vs HCs: <0.001b	
				PMS vs RRMS: <0.001b	
Mean (SD) BPF	0.74 (0.02)	0.73 (0.02)	0.69 (0.03)	RRMS vs HCs: 0.005d	
				PMS vs HCs: <0.001d	
				PMS vs RRMS: <0.001d	
Median [IQR] T2LV, ml	0.0 [0.0–0.2]	4.0 [1.2–10.3]	12.6 [5.8–26.8]	RRMS vs HCs: <0.001c	
				PMS vs HCs: <0.001c	
				PMS vs RRMS: <0.001c	
Abbreviations: BPF = brain parenchymal fraction; EDSS = Expanded Disability Status Scale; IQR = interquartile range; MuSIC = Multiple Sclerosis Inventory and Cognition; PMS = progressive multiple sclerosis; PPMS = primary progressive multiple sclerosis; RRMS = relapsing-remitting multiple sclerosis; SDMT = Symbol Digit Modalities Test; SPMS = secondary progressive multiple sclerosis; VLMT = Verbal Learning and Memory Test.

a Chi-square test.

b Welch t-test.

c Mann-Whitney U test.

d General linear model, age and sex-adjusted. *Available in 100 healthy controls and 99 patients with multiple sclerosis. **Available in 100 healthy controls and 98 patients with multiple sclerosis. Platform disease-modifying therapies included glatiramer acetate and interferon-beta preparations; oral disease-modifying therapies included dimethyl fumarate, fingolimod, siponimod, and teriflunomide; monoclonal antibody disease-modifying therapies included natalizumab, ocrelizumab, and rituximab.

Figure 2 qMRI Contrasts Included in the Study

The images displayed were obtained by averaging data from all healthy controls included in the study, after nonlinear registration to the MNI152 template. MP2RAGE = magnetization-prepared 2 rapid gradient-echo; MVF = myelin volume fraction; NDI = neurite density index; QSM = quantitative susceptibility mapping; qT1 = quantitative T1-relaxometry.

Owing to insufficient image quality, data from 5 PwMS for MVF analysis and 3 PwMS for NDI analysis were excluded.

Thalamic qMRI Metrics at Baseline

PwMS vs HCs

At baseline, PwMS had reduced thalamic volume (β = −0.232; p < 0.0001) compared with HCs. In addition, in the normal-appearing thalamic tissue, PwMS displayed decreased MVF (β = −0.120; p = 0.032) and QSM (β = −0.138; p = 0.013) values, along with increased qT1 values (β = 0.145; p = 0.016); no difference in NDI values was observed between groups (Figure 3).

Figure 3 Comparisons in Thalamic qMRI Metrics Between Patients With Multiple Sclerosis and Healthy Controls at Baseline (in the Entire Thalamus and Thalamic Nuclei)

The difference between groups is graphically displayed on a segmentation of the thalamus, with colors reflecting the effect size (expressed in terms of standardized regression coefficient, β). Only differences reaching statistical significance are displayed. MVF = myelin volume fraction; NDI = neurite density index; QSM = quantitative susceptibility mapping; qT1 = quantitative T1-relaxometry.

The greatest between-group difference in MVF, QSM, and qT1 values was evident in the thalamic area closest to the CSF (Figure 4). Graphical representations of between-group comparisons at the thalamic nuclei level are displayed in Figure 3 and eFigure 11 while a comprehensive description of the results is reported in eTables 11–12.

Figure 4 Between-Group Comparisons in Thalamic qMRI Metrics in the 3 Bands Presenting Increasing Distance From the CSF

The 3 bands are associated with different colors, graphically displayed in the panel on top. The height of the bars reflects the magnitude of the effect size (β) of the between-group comparisons. *p < 0.05; **p < 0.01; ***p < 0.001. HCs = healthy controls; MS = multiple sclerosis; MVF = myelin volume fraction; NDI = neurite density index; QSM = quantitative susceptibility mapping; qT1 = quantitative T1-relaxometry.

The differences in thalamic qMRI measures between PwMS and HCs were mainly driven by the subset of patients with inactive PMS (Figure 5). While patients with active RRMS differed from HCs only in thalamic volume (β = −0.145; p = 0.015), patients with inactive PMS differed from HCs in thalamic volume (β = −0.490; p < 0.0001), MVF (β = −0.328; p < 0.0001), qT1 (β = 0.324; p < 0.0001), and QSM (β = −0.258; p = 0.0005) values. Patients with inactive PMS also showed significant differences in thalamic volume (β = −0.356; p < 0.0001), MVF (β = −0.310; p < 0.0001), qT1 (β = 0.270; p = 0.0007), and QSM (β = −0.183; p = 0.013) values compared with patients with active RRMS. The most substantial differences between patients with inactive PMS and patients with active RRMS were observed in the thalamic region close to the CSF for MVF and qT1 and in the central thalamic area for QSM (eFigure 12).

Figure 5 Between-Group Comparisons of Baseline Thalamic qMRI Metrics

*p < 0.05; **p < 0.01; ***p < 0.001. HCs = healthy controls; MVF = myelin volume fraction; NDI = neurite density index; PMS = progressive multiple sclerosis; QSM = quantitative susceptibility mapping; qT1 = quantitative T1-relaxometry; RRMS = relapsing-remitting multiple sclerosis.

Thalamic Lesions vs Normal-Appearing Thalamic Tissue

T2-hyperintense thalamic lesions were identified in 66 PwMS. The proportion of patients presenting with thalamic lesions was not statistically different between PMS and RRMS groups (43% vs 31%; p = 0.09). Of 112 observed thalamic lesions, 92 were discrete ovoid lesions and 20 were more diffuse periventricular lesional areas. 51.1% of thalamic lesions exhibited the central vein sign, with a higher prevalence in ovoid lesions compared with periventricular lesions (57.4% vs 30.0%; p = 0.031). No lesions displayed a paramagnetic rim (eTable 13). The distribution of lesions across different thalamic nuclei is detailed in eTable 14. Compared with the normal-appearing perilesional tissue, thalamic lesions exhibited reduced MVF and NDI and increased qT1 values (all p < 0.0001). No differences were measured in QSM values between thalamic lesions and the perilesional normal-appearing tissue (eTable 15).

Association Between Thalamic qMRI Measures and Clinical/MRI Features in PwMS

Thalamic volume was associated with the EDSS score (β = −0.471; p < 0.0001), cognitive performance in all tests considered in the study (SDMT: β = 0.345; p = 0.0002; VLMT: β = 0.300; p = 0.0009; MuSIC: β = 0.461; p < 0.0001), T2LV (β = −0.650; p < 0.0001), and BPF (β = 0.824; p < 0.0001).

Various associations with clinical and conventional MRI measures were evident also for qMRI measures of microstructural integrity within the normal-appearing thalamic tissue. Specifically, MVF was associated with the EDSS score (β = −0.246; p = 0.0009), MuSIC score (β = 0.307; p = 0.004), T2LV (β = −0.249; p = 0.0007), and BPF (β = 0.361; p < 0.0001); qT1 was associated with the EDSS score (β = 0.198; p = 0.007), SDMT (β = −0.193; p = 0.036), MuSIC score (β = −0.197; p = 0.034), T2LV (β = 0.229; p = 0.002), and BPF (β = −0.317; p < 0.0001); NDI was associated with the EDSS score (β = −0.198; p = 0.008) and BPF (β = 0.188; p = 0.012); QSM was associated with the EDSS score (β = −0.224; p = 0.002), T2LV (β = −0.248; p = 0.0007), and BPF (β = 0.312; p < 0.0001) (eTable 16).

In the LASSO regression models, the variables selected as predictors of the EDSS score, in order of importance, were BPF (β = −0.382), T2LV (β = 0.155), thalamic MVF (β = −0.051), thalamic QSM (β = −0.022), and thalamic NDI (β = −0.012) [R2 of the model: 0.296]; the variables selected as predictors of the MuSIC score, in order of importance, were BPF (β = 0.262), normalized thalamic volume (β = 0.100), and thalamic MVF (β = 0.031) [R2 of the model: 0.248]; in the model for discrimination between PMS and RRMS, the selected features, in order of importance, were BPF (odds ratio [OR] = 0.356), thalamic MVF (OR = 0.712), T2LV (OR = 1.231), normalized thalamic volume (OR = 0.783), and thalamic NDI (OR = 0.876) [area under the curve of the model: 0.836].

Longitudinal Thalamic qMRI Changes

PwMS vs HCs

During the 2-year follow-up, PwMS had accelerated thalamic atrophy compared with HCs [mean difference in annualized percentage change (MD-ApC): −0.88 (95% CI −1.56 to −0.20); p = 0.013]. PwMS also exhibited an accelerated rate of i) decrease in thalamic MVF values [MD-ApC: −1.50 (95% CI −2.91 to −0.07); p = 0.041] and ii) increase in qT1 values [MD-ApC: 0.92 (95% CI 0.54–1.30); p < 0.0001].

Compared with HCs, patients with active RRMS had an accelerated rate of increase in qT1 values [MD-ApC: 0.74 (95% CI 0.33–1.15); p = 0.0005]; the difference in thalamic atrophy rates approached but did not reach statistical significance [MD-ApC: −0.71 (95% CI −1.46 to 0.04); p = 0.066]. Compared with HCs, patients with inactive PMS had accelerated thalamic atrophy [MD-ApC: −1.20 (95% CI −2.07 to −0.32); p = 0.008], as well as accelerated rates of decrease in thalamic MVF values [MD-ApC: −2.52 (95% CI −4.28 to −0.72); p = 0.007] and increase in thalamic qT1 values [MD-ApC: 1.24 (95% CI 0.76–1.73); p < 0.0001]. Patients with inactive PMS had accelerated rates of qT1 increase also when compared with patients with active RRMS [MD-ApC: 0.50 (95% CI 0.03–0.97); p = 0.039]. No differences in the rates of change between groups were measured in the other thalamic qMRI metrics (Figure 6).

Figure 6 Between-Group Comparisons of Longitudinal Thalamic qMRI Metric Changes

*p < 0.05; **p < 0.01; ***p < 0.001. HCs = healthy controls; MD-APC: mean difference in annual percentage change; MS = multiple sclerosis; MVF = myelin volume fraction; NDI = neurite density index; PMS = progressive multiple sclerosis; RRMS = relapsing-remitting multiple sclerosis; QSM = quantitative susceptibility mapping; qT1 = quantitative T1-relaxometry.

Association Between Quantitative Thalamic Changes and Clinical/MRI Features in PwMS

Higher baseline T2LV was associated with accelerated thalamic MVF reduction (β = −0.118; p = 0.039) and qT1 increase (β = 0.088; p = 0.007). Similarly, the increase in T2LV over follow-up was associated with an acceleration in thalamic MVF reduction (β = −0.100; p = 0.038) and qT1 increase (β = 0.075; p = 0.008). Patients with disease activity over follow-up had accelerated qT1 increase compared with those without (β = 0.106; p = 0.034). While the baseline EDSS score was not associated with the rates of thalamic qMRI changes, the EDSS progression over follow-up correlated with accelerated rates of decrease in NDI (β = −0.067; p = 0.039) and QSM (β = −0.077; p = 0.027) values. Baseline normalized thalamic volume was linked to the rates of change in MVF (β = 0.462; p = 0.035), qT1 (β = −0.577; p < 0.0001), and NDI (β = 0.343; p = 0.036) values. The rate of brain volume loss over follow-up was associated with the rate of qT1 change (β = −0.059; p = 0.018) (eTable 17).

Baseline variables associated with the subsequent rate of thalamic volume loss in PwMS included age (β = −0.045; p = 0.034), T2LV (β = −0.084; p = 0.004), and MVF and qT1 in the thalamic region close to the CSF (β = 0.111; p = 0.028 and β = −0.425; p = 0.0001, respectively). In HCs, baseline age was the only significant predictor of the rate of thalamic volume loss (β = −0.103; p = 0.0002) (eTable 18).

Sensitivity Analyses

Excellent agreement was observed between qMRI values obtained with the original thalamic masks and with 1-voxel eroded thalamic masks [qT1: ICC = 0.91 (95% CI 0.90–0.93); MVF: ICC = 0.93 (95% CI 0.91–0.94); NDI: ICC = 0.95 (95% CI 0.94–0.96); QSM: ICC = 0.98 (95% CI 0.98–0.98)].

Good agreement was also measured between the estimations of NDI obtained with SMT and NODDI [ICC = 0.85 (95% CI 0.83–0.88)].

Significant between-group differences in thalamic microstructural changes were confirmed also when accounting for treatment effect (eTables 19–22) and disease activity over the follow-up (eTable 23) and in subgroups matched for age, sex, and disease duration (eTables 24–35).

Discussion

We conducted an in vivo multiparametric qMRI study to investigate the pathophysiologic processes occurring in the thalamus of PwMS. Our selection of qMRI contrasts, which leverage different biophysical tissue properties,18 aimed to provide a comprehensive characterization of microstructural changes linked to demyelination, neuroaxonal loss, and disturbances in iron homeostasis.

The study revealed substantial thalamic alterations in PwMS compared with HCs at baseline and accelerated rates of thalamic degeneration over a 2-year follow-up. Remarkably, the observed thalamic qMRI changes were more severe in patients with PMS and exhibited extensive correlations with both MRI-derived measures of disease burden and clinical measures of neurologic disability and cognitive impairment.

Thalamic pathology in PwMS manifested at a macroscopic level by significant thalamic atrophy. At a microstructural level, thalamic damage was demonstrated by extensive qMRI alterations, not limited to focal lesions but diffusely extending to the tissue without visible changes on conventional FLAIR images. The reduction in thalamic volume that we observed in PwMS, especially in those with PMS, is a well-recognized hallmark of the disease, reflecting heterogeneous neurodegenerative processes.3,12 Owing to its dependence on widespread pathophysiologic mechanisms involving the entire CNS, thalamic atrophy has been proposed as a marker of the overall extent of MS-related damage12 and used as an end point in clinical trials.3 Nevertheless, thalamic volume loss is a relatively unspecific marker, representing the final result of a complex interplay between various pathologic mechanisms occurring at the microstructural level. These microstructural changes in our study were demonstrated by alterations in qT1, MVF, and NDI values within thalamic lesions and qT1, MVF, and QSM values within the normal-appearing thalamic tissue.

qT1, which measures the time of recovery of longitudinal magnetization, was prolonged in both thalamic lesions and normal-appearing tissue, indicating microstructural and macrostructural tissue damage.18,19 Alterations in qT1 have been previously reported in the thalamus of PwMS.44,45 Notably, while qT1 exhibits overall low specificity to MS pathology, it is highly sensitive to a large spectrum of pathologic changes, encompassing demyelination, axonal loss, iron loss, and accumulation of free water.18

As a more specific indicator of macromolecular damage (including demyelination and cellular loss), we observed a diffuse reduction in thalamic MVF values. MVF maps were derived from MTsat images, which quantify the exchange of magnetization between free protons and macromolecular protons.46 Previous studies have investigated magnetization transfer changes in the thalamus of PwMS, providing mixed results; in a recent meta-analysis, no difference in thalamic magnetization transfer values was measured between patients with RRMS and controls.47 In line with these findings, in our cohort, patients with active RRMS did not differ from HCs in MVF levels within the normal-appearing tissue; conversely, a substantial reduction in MVF values was observed in patients with inactive PMS, reflecting a remarkable degree of demyelination and macromolecular damage in the thalamus of this subgroup of patients compared with both HCs and patients with active RRMS. In addition, MVF also showed high sensitivity to the focal damage in thalamic lesions.

We also observed significant alterations in NDI values in thalamic lesions compared with the surrounding tissue, suggesting increased neuroaxonal loss. By contrast, no differences were found in the normal-appearing thalamic tissue between PwMS and HCs. It is noteworthy that in our study, we chose to model microstructural diffusion processes using SMT because of its effectiveness in addressing challenges posed by fiber crossing populations and orientation dispersion, particularly prominent within gray matter regions.21 Nonetheless, it is important to note that, like NODDI, SMT is primarily designed for assessing white matter and limitations may arise when exploring structures such as the thalamus.

Previous research on magnetic susceptibility in the thalamus of PwMS has yielded conflicting results.48,49 In our cohort, we observed a significant decrease in thalamic QSM values among PwMS, consistent with findings in other QSM studies.45,49,50 An intriguing interpretation involves the depletion of iron from oligodendrocytes, potentially triggered by chronic microglia activation.49 On the contrary, focal thalamic lesions within our cohort exhibited no discernible changes in QSM values when compared with perilesional tissue. Notably, QSM is influenced by not only paramagnetic iron concentration but also diamagnetic myelin content.18 Therefore, we can speculate that a substantial reduction in myelin content within focal lesions might have mitigated the effect of a potential concomitant decline in iron levels, given the opposing impacts of iron and myelin on the QSM signal.18

Regionally, the observed qMRI changes in PwMS were predominant in the tissue in proximity to the CSF. Accordingly, a distinctive “ependymal-in” gradient of thalamic damage, characterized by neuroaxonal loss and microglia activation, has been described in MS.7 This phenomenon is consistently observed across diverse stages of the disease, being evident from the earliest phases of pediatric MS to the progressive stages in patients with advanced disease.6-8 In our cohort, qMRI changes were also evident regionally in multiple distinct thalamic nuclei. Of interest, a prevalent involvement of the posterior and medial thalamic compartments was evident. Notably, these compartments, which are closest to the CSF, have previously been shown to be more frequently atrophic in MS.51 Recent research has also indicated that these compartments are more affected by the disconnection of thalamocortical projections due to MS lesions.52

The clinical relevance of the observed qMRI thalamic changes was demonstrated by their extensive correlations with both conventional MRI and clinical measures of disease burden. All qMRI metrics showed robust associations with both the EDSS score and BPF—a proxy measure of cumulative neurodegenerative processes within the CNS. Furthermore, select qMRI measures exhibited correlations with the T2-hyperintense lesion load and clinical measures of cognitive impairment, including SDMT and MuSIC scores. Notably, qMRI metrics of microstructural integrity also proved to substantially contribute to explaining neurologic and cognitive disability and supporting the differentiation between active RRMS and inactive PMS phenotypes, in multivariable LASSO regression models including normalized thalamic volume, BPF, and T2LV as additional predictors.

In our study cohort, significant differences between PwMS and HCs emerged also in the longitudinal rates of thalamic qMRI changes over a 2-year follow-up period. In comparison with HCs, PwMS exhibited accelerated thalamic atrophy and enhanced rates of MVF decrease and qT1 increase, reflecting accentuated neurodegenerative and demyelinating processes. As for cross-sectional findings, between-group differences in longitudinal rates of change were substantially driven by the subgroup of patients with an inactive PMS phenotype, in whom the neurodegenerative processes were notably accentuated. Nevertheless, a significant difference was measurable between the subgroup of patients with active RRMS and HCs in the pace of thalamic qT1 increase. This observation indicates the accumulation of subtle microstructural changes within the normal-appearing thalamic tissue even in patients with a typical RRMS phenotype. In longitudinal studies, thalamic volume has been shown to decline faster in PwMS compared with HCs, but not differently across disease phenotypes12,14; such a pattern is in line with our results. In our study, this lack of difference in thalamic atrophy rates between disease phases was confirmed even between phenotypes specifically selected to represent the extremes of the clinical spectrum: active RRMS, dominated by neuroinflammatory processes, and inactive PMS, governed by neurodegenerative mechanisms. Conversely, qT1 emerged as a valuable metric for differentiating between patients with active RRMS and inactive PMS. Specifically, patients with inactive PMS exhibited a notable acceleration in qT1 prolongation, suggesting enhanced accumulation of microstructural alterations.

Similar to the cross-sectional analyses, the longitudinal changes of thalamic qMRI metrics showed substantial clinical relevance. Specifically, longitudinal qMRI changes proved to be associated with MRI markers of disease burden, including the accumulation of T2LV and the rate of brain volume loss over time. Moreover, significant differences in longitudinal qMRI changes were observed between patients with clinical stability and those exhibiting disability progression over the clinical follow-up period. Specifically, patients manifesting disease progression over the 2-year interval exhibited an accelerated reduction in thalamic NDI and QSM values, suggestive of hastened neuroaxonal and iron loss.

Overall, our findings suggest 2 concurrent mechanisms as crucial drivers of thalamic degeneration in PwMS: an indirect damage due to the transection of thalamic projections by white matter lesions and a direct surface-in gradient of damage possibly resulting from toxic factors in the CSF. The impact of focal white matter lesions was evidenced by the association between T2LV and more severe microstructural alterations, as well as accelerated pathologic degeneration over time. Furthermore, the burden of T2LV within thalamocortical projections was significantly more impactful in explaining thalamic microstructural and macrostructural degeneration compared with the general T2LV. The role of surface-in degeneration was suggested by the pronounced pathologic changes in the thalamic regions near the CSF. Notably, the extent of microstructural degeneration in these thalamic areas, but not in other regions, was a significant predictor of the subsequent rate of thalamic volume loss.

Strengths of our study include the large sample size and the inclusion of multiple advanced qMRI contrasts obtained with a standardized acquisition protocol allowing for an extensive concomitant investigation of different pathophysiologic processes. In addition, the availability of clinical and advanced MRI follow-up enabled the investigation of the trajectories of thalamic damage over time, along with their predictors and clinical implications. Furthermore, the study design allowed us to reliably demonstrate the added value of microstructural thalamic changes in explaining disease severity beyond other well-established MRI markers. This study also has some limitations. First, follow-up data were not available for the entire cohort; although the loss to follow-up did not seem to disproportionately affect any specific subgroup of patients, the potential for biases cannot be entirely excluded. Second, given the exploratory nature of this study, we limited the application of multiple comparisons correction to instances where multiple regions of interest were simultaneously investigated. Considering the distinct primary focus of the various qMRI contrasts, each probing different pathophysiologic processes with minimal overlap, we chose not to apply multiple comparisons correction for the remaining hypotheses. This decision was intended to mitigate the risk of type II errors while aligning with the overarching objectives of the investigation. Third, disease progression over the follow-up period was determined solely based on 2 EDSS assessments, conducted at baseline and after 2 years. The absence of an additional follow-up assessment to establish a “confirmed disability progression” criterion potentially limits the accuracy in estimating the actual incidence of disease progression. Fourth, although we ensured the absence of neurologic or psychiatric comorbidities in all participants, we did not systematically collect information on cardiovascular risk factors, which might potentially have had an influence on the extent of the observed neurodegenerative changes.

In conclusion, our extensive qMRI protocol revealed substantial pathologic alterations in the thalamus of PwMS. These changes, extending beyond focal lesions to involve normal-appearing tissue, indicated significant micro/macrostructural alterations, including demyelination and perturbations in iron homeostasis. Thalamic damage accumulated faster in PwMS, particularly in those with inactive PMS, and exhibited extensive clinical correlations. Collectively, these findings contribute to a deeper understanding of the pathologic changes in the thalamus of PwMS, underscoring the clinical importance of thalamic damage and its link to disease progression.

Acknowledgment

The authors thank all the participants for taking part in this study and Marguerite Limberg for her role in enrolling participants into the study.

Study Funding

The authors report no targeted funding.

Disclosure

A. Cagol is supported by EUROSTAR E!113682 HORIZON2020, and received speaker honoraria from Novartis; M. Weigel has received research funding by Biogen for developing spinal cord MRI; M. Barakovic is an employee of Hays plc and a consultant for F. Hoffmann-La Roche Ltd; A. Lutti was supported by the Swiss National Science Foundation (Grant Number: 320030_184784) and the ROGER DE SPOELBERCH foundation; P. Calabrese has received honoraria for speaking at scientific meetings, serving at scientific advisory boards, steering committees and consulting activities from Abbvie, Actelion, Almirall, Bayer-Schering, Biogen, BMS, EISAI, Genzyme, Lundbeck, Merck Serono, Novartis, Sanofi-Aventis, Schwabe and Teva, he also receives research Grants from the Swiss Insurance Medicine (SIM) and the Swiss National Research Foundation; J. Kuhle received speaker fees, research support, travel support, and/or served on advisory boards by Swiss MS Society, Swiss National Research Foundation (320030_189140/1), University of Basel, Progressive MS Alliance, Bayer, Biogen, Celgene, Merck, Novartis, Octave Bioscience, Roche, Sanofi; L. Kappos has received no personal compensation, his institutions (University Hospital Basel/Foundation Clinical Neuroimmunology and Neuroscience Basel) have received and used exclusively for research support: payments for steering committee and advisory board participation, consultancy services, and participation in educational activities from: Actelion, Bayer, BMS, df-mp Molnia & Pohlmann, Celgene, Eli Lilly, EMD Serono, Genentech, Glaxo Smith Kline, Janssen, Japan Tobacco, Merck, MH Consulting, Minoryx, Novartis, F. Hoffmann-La Roche Ltd, Senda Biosciences Inc., Sanofi, Santhera, Shionogi BV, TG Therapeutics, and Wellmera, and license fees for Neurostatus-UHB products; grants from Novartis, Innosuisse, and Roche; M.P. Sormani received consulting fees from Biogen, Merck, Novartis, Roche, Sanofi, Immunic, Alexion; C. Granziera: The University Hospital Basel (USB), as the employer of C.G., has received the following fees which were used exclusively for research support: (1) advisory board and consultancy fees from Actelion, Genzyme-Sanofi, Novartis, GeNeuro and Roche; (2) speaker fees from Genzyme-Sanofi, Novartis, GeNeuro and Roche; (3) research support from Siemens, GeNeuro, Roche, Cristina Granziera is supported by the Swiss National Science Foundation (SNSF) grant PP00P3_176984, the Stiftung zur Förderung der gastroenterologischen und allgemeinen klinischen Forschung and the EUROSTAR E!113682 HORIZON2020; all other authors report no competing interests. Go to Neurology.org/NN for full disclosures.

Appendix Authors

Name	Location	Contribution	
Alessandro Cagol, MD	Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel; Department of Neurology, University Hospital Basel; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Switzerland; Dipartimento di Scienze della Salute, Università degli Studi di Genova, Italy	Drafting/revision of the manuscript for content, including medical writing for content; study concept or design; analysis or interpretation of data	
Mario Ocampo-Pineda, PhD	Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel; Department of Neurology, University Hospital Basel; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Switzerland	Drafting/revision of the manuscript for content, including medical writing for content; analysis or interpretation of data	
Po-Jui Lu, PhD	Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel; Department of Neurology, University Hospital Basel; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Switzerland	Drafting/revision of the manuscript for content, including medical writing for content; analysis or interpretation of data	
Matthias Weigel, PhD	Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel; Department of Neurology, University Hospital Basel; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel; Division of Radiological Physics, Department of Radiology, University Hospital Basel, Switzerland	Drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data	
Muhamed Barakovic, PhD	Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel; Department of Neurology, University Hospital Basel; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Switzerland	Drafting/revision of the manuscript for content, including medical writing for content; analysis or interpretation of data	
Lester Melie-Garcia, PhD	Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel; Department of Neurology, University Hospital Basel; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Switzerland	Drafting/revision of the manuscript for content, including medical writing for content; analysis or interpretation of data	
Xinjie Chen, MD	Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel; Department of Neurology, University Hospital Basel; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Switzerland	Drafting/revision of the manuscript for content, including medical writing for content; analysis or interpretation of data	
Antoine Lutti, PhD	Laboratory for Research in Neuroimaging, Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne, Switzerland	Drafting/revision of the manuscript for content, including medical writing for content	
Pasquale Calabrese, PhD	Neuropsychology and Behavioral Neurology Unit, Division of Cognitive and Molecular Neuroscience, University of Basel, Switzerland	Drafting/revision of the manuscript for content, including medical writing for content	
Jens Kuhle, MD, PhD	Department of Neurology, University Hospital Basel; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland	Drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data	
Ludwig Kappos, MD	Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel; Department of Neurology, University Hospital Basel; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Switzerland	Drafting/revision of the manuscript for content, including medical writing for content	
Maria Pia Sormani, PhD	Dipartimento di Scienze della Salute, Università degli Studi di Genova; IRCCS Ospedale Policlinico San Martino, Genova, Italy	Drafting/revision of the manuscript for content, including medical writing for content; study concept or design; analysis or interpretation of data	
Cristina Granziera, MD, PhD	Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Switzerland	Drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data; study concept or design; analysis or interpretation of data	

Glossary

BPF brain parenchymal fraction

EDSS Expanded Disability Status Scale

EPI echo planar imaging

FLAIR fluid-attenuated inversion recovery

HC healthy control

MTsat magnetization transfer saturation

MuSIC Multiple Sclerosis Inventory Cognition

MVF myelin volume fraction

MS multiple sclerosis

NDI neurite density index

NODDI Neurite Orientation Dispersion and Density Imaging

OR odds ratio

PMS progressive MS

PwMS patients with MS

qMRI quantitative MRI

QSM quantitative susceptibility mapping

RRMS relapsing-remitting MS

SDMT Symbol Digit Modalities Test

SMT spherical mean technique

T2LV T2-hyperintense lesion volume

TIV total intracranial volume

VLMT Verbal Learning and Memory Test
==== Refs
References

1. Geurts JJ, Barkhof F. Grey matter pathology in multiple sclerosis. Lancet Neurol. 2008;7 (9 ):841-851. doi:10.1016/S1474-4422(08)70191-1 18703006
2. Sastre-Garriga J, Pareto D, Battaglini M, et al. MAGNIMS consensus recommendations on the use of brain and spinal cord atrophy measures in clinical practice. Nat Rev Neurol. 2020;16 (3 ):171-182. doi:10.1038/s41582-020-0314-x 32094485
3. Ontaneda D, Raza PC, Mahajan KR, et al. Deep grey matter injury in multiple sclerosis: a NAIMS consensus statement. Brain. 2021;144 (7 ):1974-1984. doi:10.1093/brain/awab132 33757115
4. Mehndiratta A, Treaba CA, Barletta V, et al. Characterization of thalamic lesions and their correlates in multiple sclerosis by ultra-high-field MRI. Mult Scler. 2021;27 (5 ):674-683. doi:10.1177/1352458520932804 32584159
5. Trapp BD, Peterson J, Ransohoff RM, Rudick R, Mörk S, Bö L. Axonal transection in the lesions of multiple sclerosis. N Engl J Med. 1998;338 (5 ):278-285. doi:10.1056/nejm199801293380502 9445407
6. Fadda G, Brown RA, Magliozzi R, et al. A surface-in gradient of thalamic damage evolves in pediatric multiple sclerosis. Ann Neurol. 2019;85 (3 ):340-351. doi:10.1002/ana.25429 30719730
7. Magliozzi R, Fadda G, Brown RA, et al. “Ependymal-in” gradient of thalamic damage in progressive multiple sclerosis. Ann Neurol. 2022;92 (4 ):670-685. doi:10.1002/ana.26448 35748636
8. De Meo E, Storelli L, Moiola L, et al. In vivo gradients of thalamic damage in paediatric multiple sclerosis: a window into pathology. Brain. 2021;144 (1 ):186-197. doi:10.1093/brain/awaa379 33221873
9. Liu Z, Pardini M, Yaldizli Ö, et al. Magnetization transfer ratio measures in normal-appearing white matter show periventricular gradient abnormalities in multiple sclerosis. Brain. 2015;138 (Pt 5 ):1239-1246. doi:10.1093/brain/awv065 25823475
10. Witte ME, Mahad DJ, Lassmann H, van Horssen J. Mitochondrial dysfunction contributes to neurodegeneration in multiple sclerosis. Trends Mol Med. 2014;20 (3 ):179-187. doi:10.1016/j.molmed.2013.11.007 24369898
11. Haider L, Simeonidou C, Steinberger G, et al. Multiple sclerosis deep grey matter: the relation between demyelination, neurodegeneration, inflammation and iron. J Neurol Neurosurg Psychiatry. 2014;85 (12 ):1386-1395. doi:10.1136/jnnp-2014-307712 24899728
12. Azevedo CJ, Cen SY, Khadka S, et al. Thalamic atrophy in multiple sclerosis: a magnetic resonance imaging marker of neurodegeneration throughout disease. Ann Neurol. 2018;83 (2 ):223-234. doi:10.1002/ana.25150 29328531
13. Houtchens MK, Benedict RHB, Killiany R, et al. Thalamic atrophy and cognition in multiple sclerosis. Neurology. 2007;69 (12 ):1213-1223. doi:10.1212/01.wnl.0000276992.17011.b5 17875909
14. Eshaghi A, Prados F, Brownlee WJ, et al. Deep gray matter volume loss drives disability worsening in multiple sclerosis. Ann Neurol. 2018;83 (2 ):210-222. doi:10.1002/ana.25145 29331092
15. Geurts JJG, Reuling IEW, Vrenken H, et al. MR spectroscopic evidence for thalamic and hippocampal, but not cortical, damage in multiple sclerosis. Magn Reson Med. 2006;55 (3 ):478-483. doi:10.1002/mrm.20792 16463353
16. Cifelli A, Arridge M, Jezzard P, Esiri MM, Palace J, Matthews PM. Thalamic neurodegeneration in multiple sclerosis. Ann Neurol. 2002;52 (5 ):650-653. doi:10.1002/ana.10326 12402265
17. Louapre C, Govindarajan ST, Giannì C, et al. Heterogeneous pathological processes account for thalamic degeneration in multiple sclerosis: insights from 7 T imaging. Mult Scler. 2018;24 (11 ):1433-1444. doi:10.1177/1352458517726382 28803512
18. Granziera C, Wuerfel J, Barkhof F, et al. Quantitative magnetic resonance imaging towards clinical application in multiple sclerosis. Brain. 2021;144 (5 ):1296-1311. doi:10.1093/brain/awab029 33970206
19. Helms G. Tissue properties from quantitative MRI. In: Brain Mapping: An Encyclopedic Reference, Vol 1 . Academic Press; 2015:287-294. doi:10.1016/B978-0-12-397025-1.00297-9
20. Henkelman RM, Stanisz GJ, Graham SJ. Magnetization transfer in MRI: a review. NMR Biomed. 2001;14 (2 ):57-64. doi:10.1002/nbm.683 11320533
21. Kaden E, Kelm ND, Carson RP, Does MD, Alexander DC. Multi-compartment microscopic diffusion imaging. Neuroimage. 2016;139 :346-359. doi:10.1016/j.neuroimage.2016.06.002 27282476
22. Thompson AJ, Banwell BL, Barkhof F, et al. Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol. 2018;17 (2 ):162-173. doi:10.1016/S1474-4422(17)30470-2 29275977
23. Von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP. STROBE Initiative. The strengthening the reporting of observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies. Bull World Health Organ. 2007;85 (11 ):867-872. doi:10.2471/BLT.07.045120 18038077
24. Kurtzke JF. Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS). Neurology. 1983;33 (11 ):1444-1452. doi:10.1212/wnl.33.11.1444 6685237
25. Neurostatus. Accessed January 30, 2024. neurostatus.net/
26. Smith A. Symbol digit modalities test; 1973. Accessed November 24, 2021. communicate-ed.org.uk/assets/downloads/SDMT_Formula_Chart_Communicate-ed_2.pdf
27. Verbaler Lern-und Merkfähigkeitstest (VLMT) 2001 C. Helmstaedter, M. Lendt & S. Lux, [BIO, DIA, KLI], neurops. Verfahren, 2001.
28. Calabrese P, Kalbe E, Kessler J. Ein neuropsychologisches screening zur erfassung kognitiver störungen bei MS-patienten: Das Multiple Sklerose Inventarium Cognition (MUSIC). psychoNeuro. 2004;30 (7 ):384-388. doi:10.1055/s-2004-831083
29. Rahmanzadeh R, Weigel M, Lu PJ, et al. A comparative assessment of myelin-sensitive measures in multiple sclerosis patients and healthy subjects. NeuroImage Clin. 2022;36 :103177. doi:10.1016/j.nicl.2022.103177 36067611
30. Marques JP, Kober T, Krueger G, van der Zwaag W, Van de Moortele PF, Gruetter R. MP2RAGE, a self bias-field corrected sequence for improved segmentation and T1-mapping at high field. Neuroimage. 2010;49 (2 ):1271-1281. doi:10.1016/j.neuroimage.2009.10.002 19819338
31. Helms G, Dathe H, Dechent P. Quantitative FLASH MRI at 3T using a rational approximation of the Ernst equation. Magn Reson Med. 2008;59 (3 ):667-672. doi:10.1002/mrm.21542 18306368
32. Helms G, Dechent P. Increased SNR and reduced distortions by averaging multiple gradient echo signals in 3D FLASH imaging of the human brain at 3T. J Magn Reson Imaging. 2009;29 (1 ):198-204. doi:10.1002/jmri.21629 19097114
33. Cercignani M, Giulietti G, Dowell NG, et al. Characterizing axonal myelination within the healthy population: a tract-by-tract mapping of effects of age and gender on the fiber g-ratio. Neurobiol Aging. 2017;49 :109-118. doi:10.1016/j.neurobiolaging.2016.09.016 27792897
34. Mancini M, Giulietti G, Dowell N, et al. Introducing axonal myelination in connectomics: a preliminary analysis of g-ratio distribution in healthy subjects. Neuroimage. 2018;182 :351-359. doi:10.1016/j.neuroimage.2017.09.018 28917698
35. Liu T, Xu W, Spincemaille P, Avestimehr AS, Wang Y. Accuracy of the morphology enabled dipole inversion (MEDI) algorithm for quantitative susceptibility mapping in MRI. IEEE Trans Med Imaging. 2012;31 (3 ):816-824. doi:10.1109/TMI.2011.2182523 22231170
36. La Rosa F, Abdulkadir A, Fartaria MJ, et al. Multiple sclerosis cortical and WM lesion segmentation at 3T MRI: a deep learning method based on FLAIR and MP2RAGE. NeuroImage Clin. 2020;27 :102335. doi:10.1016/j.nicl.2020.102335 32663798
37. Patenaude B, Smith SM, Kennedy DN, Jenkinson M. A Bayesian model of shape and appearance for subcortical brain segmentation. Neuroimage. 2011;56 (3 ):907-922. doi:10.1016/j.neuroimage.2011.02.046 21352927
38. de Sitter A, Burggraaff J, Bartel F, et al . Development and evaluation of a manual segmentation protocol for deep grey matter in multiple sclerosis: Towards accelerated semi-automated references. Neuroimage Clin. 2021;30 :102659. doi:10.1016/j.nicl.2021 33882422
39. Su JH, Thomas FT, Kasoff WS, et al. Thalamus Optimized Multi Atlas Segmentation (THOMAS): fast, fully automated segmentation of thalamic nuclei from structural MRI. Neuroimage. 2019;194 :272-282. doi:10.1016/j.neuroimage.2019.03.021 30894331
40. Cerri S, Puonti O, Meier DS, et al. A contrast-adaptive method for simultaneous whole-brain and lesion segmentation in multiple sclerosis. Neuroimage. 2021;225 :117471. doi:10.1016/j.neuroimage.2020.117471 33099007
41. The R Project for Statistical Reporting. Accessed January 30, 2024. R-project.org
42. Eshaghi A, Wottschel V, Cortese R, et al. Gray matter MRI differentiates neuromyelitis optica from multiple sclerosis using random forest. Neurology. 2016;87 (23 ):2463-2470. doi:10.1212/WNL.0000000000003395 27807185
43. Lorscheider J, Buzzard K, Jokubaitis V, et al. Defining secondary progressive multiple sclerosis. Brain. 2016;139 (Pt 9 ):2395-2405. doi:10.1093/brain/aww173 27401521
44. Vrenken H, Geurts JJG, Knol DL, et al. Whole-brain T1 mapping in multiple sclerosis: global changes of normal-appearing gray and white matter. Radiology. 2006;240 (3 ):811-820. doi:10.1148/radiol.2403050569 16868279
45. Pontillo G, Petracca M, Monti S, et al. Unraveling deep gray matter atrophy and iron and myelin changes in multiple sclerosis. AJNR Am J Neuroradiol. 2021;42 (7 ):1223-1230. doi:10.3174/ajnr.A7093 33888456
46. Moccia M, Van De Pavert S, Eshaghi A, et al. Pathologic correlates of the magnetization transfer ratio in multiple sclerosis. Neurology. 2020;95 (22 ):E2965–E2976. doi:10.1212/WNL.0000000000010909 32938787
47. York EN, Thrippleton MJ, Meijboom R, Hunt DPJ, Waldman AD. Quantitative magnetization transfer imaging in relapsing-remitting multiple sclerosis: a systematic review and meta-analysis. Brain Commun. 2022;4 (2 ):fcac088. doi:10.1093/braincomms/fcac088 35652121
48. Tranfa M, Pontillo G, Petracca M, et al. Quantitative MRI in multiple sclerosis: from theory to application. AJNR Am J Neuroradiol. 2022;43 (12 ):1688-1695. doi:10.3174/ajnr.A7536 35680161
49. Schweser F, Raffaini Duarte Martins AL, Hagemeier J, et al. Mapping of thalamic magnetic susceptibility in multiple sclerosis indicates decreasing iron with disease duration: a proposed mechanistic relationship between inflammation and oligodendrocyte vitality. Neuroimage. 2018;167 :438-452. doi:10.1016/J.NEUROIMAGE.2017.10.063 29097315
50. Zivadinov R, Tavazzi E, Bergsland N, et al. Brain iron at quantitative MRI is associated with disability in multiple sclerosis. Radiology. 2018;289 (2 ):487-496. doi:10.1148/radiol.2018180136 30015589
51. Blyau S, Koubiyr I, Saranathan M, et al. Differential vulnerability of thalamic nuclei in multiple sclerosis. Mult Scler. 2023;29 (2 ):295-300. doi:10.1177/13524585221114247 35959722
52. Koubiyr I, Yamamoto T, Blyau S, et al. Vulnerability of thalamic nuclei at CSF Interface during the entire course of multiple sclerosis. Neurol Neuroimmunol Neuroinflamm. 2024;11 (3 ):e200222. doi:10.1212/NXI.0000000000200222 38635941
