Background

A deep-learning prototype method, called CVSNet, was recently introduced for the automated detection of the central vein sign (CVS) in brain lesions and demonstrated effective and accurate discrimination of multiple sclerosis (MS) from its mimics. However, this method solely considered focal lesions displaying the central vein sign (CVS+) or not (CVS−), therefore requiring a manual pre-selection of the lesions to be evaluated by eliminating the so-called excluded lesions (CVSe) as defined by the NAIMS criteria. CVSe lesions may however play an important role in differential diagnosis. Moreover, extending the automated CVS classification to these lesions would facilitate the integration of CVSNet with existing MS lesion segmentation algorithms in a fully automated pipeline.

Objectives

To develop an improved version of the CVSNet prototype method able to classify all types of lesions (CVS+, CVS− and CVSe).

Methods

Patients with an established MS or CIS diagnosis (RRMS 29; SPMS 10; PPMS 10; CIS 1; mean ± SD age: 50 ± 11 years; male/female: 23/27), and healthy controls (n=8; mean ± SD age: 41 ± 9 years; male/female: 5/3), underwent 3T brain MRI (MAGNETOM Skyra and MAGNETOM Prisma, Siemens Healthcare, Erlangen, Germany, or Achieva, Philips Healthcare, Best, Netherlands). Brain lesions were automatically segmented and manually corrected by a single rater. CVS assessment was conducted on FLAIR* images by two raters, according to the NAIMS guidelines, yielding 1542 CVS+, 1004 CVS−, and 1131 CVSe lesions. A convolutional neural network (CNN) based on the CVSnet architecture was trained with different configurations using 3021 samples (1261 CVS+, 847 CVS−, and 913 CVSe) and evaluated in 656 unseen samples (281 CVS+, 157 CVS−, and 218 CVSe, from 13 patients) for final testing. The configurations relied on different combinations of the following channels as input: (i) FLAIR*, (ii) T2*, (iii) lesion mask, and (iv) CSF and brain tissue concentration maps obtained from a partial-volume estimation algorithm. Lesion-wise classification performance was evaluated for the different configurations by estimating the sensitivity, specificity, and accuracy for each lesion class.

Results

The results were similar across the different configurations. The best performance in the unseen testing set was obtained when all channels were used as input (sensitivity: 0.71, 0.73; specificity: 0.71, 0.81; and accuracy: 0.71, 0.79 for CVS+, CVS−, respectively). For CVSe, this approach achieved 0.52 sensitivity, 0.94 specificity, and 0.80 accuracy.

Conclusions

We introduced a modified CVSNet prototype method that can analyze the presence of the central vein for all types of brain lesions, enabling its integration with current MS lesion segmentation algorithms. This new feature will allow a fully automated assessment of the CVS in patients’ brains, speeding up the evaluation of CVS as a diagnostic biomarker for differentiating MS from mimicking diseases.

Background

Postmortem magnetic resonance imaging (MRI) of formalin-fixed healthy and diseased human brains with ultra-high spatial resolution has the great potential to depict tissue architecture in fine detail, allowing a deeper understanding of pathological processes. Whole-brain imaging is important since it provides neuroanatomic relationships, reference points across distant brain regions, and a comprehensive view of pathologies affecting the brain. However, ultra-high-resolution whole-brain postmortem MRI is challenging and has been so far almost exclusively performed at 7T with specialized hardware.

Objectives

To develop a 3D isotropic 160µm ultra-high-resolution imaging (URI) approach for human whole-brain ex vivo acquisitions on a standard clinical 3T MRI system. To explore the sensitivity and specificity of the approach to specific pathological features of multiple sclerosis (MS).

Methods

A fixed whole human brain from a patient with secondary progressive MS was investigated. Acquisitions were performed on a clinical 3T Siemens Prisma^fit MRI system with standard hardware components. URI is based on a gradient echo sequence similar to the 7T approach by Edlow et al. 2019. However, it allows to acquire an isotropic 160µm resolution with low hardware demands and to directly reconstruct the image data on the standard 3T MRI system. URI images display a strong, susceptibility-enhanced tissue contrast.

Results

The reconstructed URI images depicted with remarkable quality the diseased human MS brain at 3T field strength. URI allowed to distinguish fine anatomical details such as the subpial molecular layer, the stria of Gennari as well as some intrathalamic nuclei. Additionally, because of the unprecedented spatial resolution and contrast at 3T, URI permitted to easily identify the presence of subpial lesions, detailed features of intracortical lesions such the presence of incomplete/complete iron rims or patterns of iron accumulation in the entire lesion core in both cortical and white matter lesions (CLs/WMLs), lesions affecting the convoluted layers of the cerebellar cortex and nascent submillimetric CLs/WMLs.

Conclusions

URI provides a comprehensive microscopic insight into the whole-human brain at 3T through the micrometric resolution and a tissue-specific, susceptibility-enhanced contrast. We propose URI as an excellent approach to investigate microscopic brain changes of complex pathologies like MS.

Background

Pregnancy in MS typically goes along with reduced disease activity in the third trimester, followed by an increase in relapse frequency postpartum. Neurofilament light chain levels in serum (NfL) is a specific biomarker of neuroaxonal injury. Increased NfL levels are associated with relapses and MRI activity, while disease modifying treatment (DMT) response is reflected by a decrease of NfL.

Objectives

The objective of this study was to evaluate whether interrupting DMT due to pregnancy leads to increased NfL levels in MS.

Methods

We investigated prospectively documented pregnancies in the Swiss MS Cohort Study. Serum samples were collected 6- or 12-monthly and were analyzed by Simoa NF-light® assay. Uni- and multivariable mixed effect models were used to investigate associations between clinical characteristics and longitudinal NfL levels.

Results

We investigated 72 pregnancies in 63 relapsing MS patients (median age 31.4; disease duration 7.1 years; EDSS 1.5 at last visit before birth). In total, 433 samples were included: 92 during pregnancy or up to initiation of DMT but max. 9 months postpartum (pregnancy/post-partum period, pp), 167 prior to pp and 174 after the pp. Four patients had no DMT before, during and after pregnancy. DMT was continued in 13/72 pregnancies (>6 months during pregnancy: 6 rituximab/ocrelizumab, 4 natalizumab, 1 interferon-beta 1a i.m., 1 fingolimod and 1 glatiramer acetate). In univariable analysis, NfL levels were on average 22% higher during vs. outside the pp (β: 1.22, 95%CI: 1.10-1.35; p<0.001). We observed 29 relapses during the pp. In a multivariable analysis, relapses (within 120 days before serum sampling) were associated with 98% higher NfL (β: 1.98, 95%CI: 1.75-2.25; p<0.001); NfL was 7% higher per EDSS step increase (β: 1.07, 95%CI: 1.01-1.12; p=0.013) and on average 13% higher during vs. outside the pp (β: 1.13, 95%CI: 1.03-1.24; p=0.009). The effect of the pp on NfL disappeared after including DMT exposure (yes/no) at the sampling timepoint to the model (β:1.07, 95%CI: 0.97-1.18; p=0.178). Patients sampled during DMT had on average 12% lower NfL levels compared to patients without (β:0.88, 95%CI: 0.79-0.98; p=0.019).

Conclusions

Higher NfL levels were found during pp. This increase was independent of relapses suggesting increased subclinical disease activity during this time span. After including DMT into the model the effect of pregnancy on NfL disappeared: strategies allowing to continue DMT during pregnancy may be warranted.

Background

Serum neurofilament light chain (sNfL) reflects neuro-axonal damage and may qualify as a biomarker of suboptimal response to disease modifying therapy (DMT).

Objectives

To investigate the predictive value of sNfL in clinically isolated syndrome (CIS) and relapsing-remitting (RR) MS patients with established DMT for future MS disease activity in the Swiss MS Cohort Study.

Methods

All patients were on DMT for at least 3 months. sNfL was measured 6 or 12-monthly with the NF-light^®assay. The association between sNfL and age was modeled using a generalized additive model for location scale and shape. Z-scores (sNfLz) were derived thereof, reflecting the deviation of a patient sNfL value from the mean value of same age healthy controls (n=8865 samples). We used univariable mixed logistic regression models to investigate the association between sNfLz and the occurrence of clinical events (relapses, EDSS worsening [≥1.5 steps if EDSS 0; ≥1.0 if 1.0-5.5 or ≥0.5 if >5.5] in the following year in all patients, and in those fulfilling NEDA-3 criteria (no relapses, EDSS worsening, contrast enhancing or new/enlarging T2 lesions in brain MRI, based on previous year). We combined sNfLz with clinical and MRI measures of MS disease activity in the previous year (EDA-3) in a multivariable mixed logistic regression model for predicting clinical events in the following year.

Results

sNfL was measured in 1062 patients with 5192 longitudinal samples (median age 39.7 yrs; EDSS 2.0; 4.1% CIS, 95.9% RRMS; median follow-up 5 yrs). sNfLz predicted clinical events in the following year (OR 1.21 [95%CI 1.11-1.36], p<0.001, n=4624). This effect increased in magnitude with increasing sNfLz (sNfLz >1: OR 1.41 [95%CI 1.15-1.73], p=0.001; >1.5: OR 1.80 [95%CI 1.43-2.28], p<0.001; >2: OR 2.33 [95%CI 1.74-3.14], p<0.001). Similar results were found for the prediction of future new/enlarging T2 lesions and brain volume loss. In the multivariable model, new/enlarging T2 lesions (OR 1.88 [95%CI 1.13-3.12], p=0.016) and sNfLz>1.5 (OR 2.18 [95%CI 1.21-3.90], p=0.009) predicted future clinical events (n=853), while previous EDSS worsening, previous relapses and current contrast enhancement did not. In NEDA-3 patients, change of sNfLz (per standard deviation) was associated with a 37% increased risk of clinical events in the subsequent year (OR 1.37 [95%CI 1.04-1.78], p=0.025, n=587).

Conclusions

Our data support the value of sNfL levels, beyond the NEDA3 concept, for treatment monitoring in MS clinical practice.

Background

Quantitative susceptibility mapping (QSM) identifies iron accumulation and myelin loss in smoldering white matter lesions (WMLs). Yet, QSM may be also used to provide a broader understanding of focal and diffuse MS pathology.

Objectives

To study QSM features across WMLs, to assess myelin and axonal loss in WMLs with different QSM features and to quantify QSM pathology in normal-appearing white and cortical grey matter (NAWM, NAGM).

Methods

Ninety-one MS patients (62 RRMS, 29 PMS) and 72 healthy controls (HC) underwent QSM, myelin water imaging (MWI) and multishell diffusion at 3T MRI. In WMLs, cortical lesions (CLs), NAWM and NAGM, we extracted mean QSM, myelin water fraction (MWF) and neurite density index (NDI). WMLs were classified into 5 groups according to their appearance on 3D-EPI QSM: (i) isointense; (ii) with hyperintense rim, Rim+ (iii); with hypointense rim relative to the lesion core, hypo Rim; (iv) hyperintense; (v) hypointense. Mann-Whitney and Kruskal-Wallis test with Dunn’s correction for multiple comparison were used to compare (a) lesion types and (b) specific lesions vs all other WMLs. Voxel-wise comparisons of NAWM QSM were performed using Threshold-Free Cluster Enhancement (TFCE) clustering. Cortical analysis of QSM NAGM and GM-HC was performed using FreeSurfer and compared using a General Linear model (GLM).

Results

Of 1136 WMLs in QSM maps, we detected: (i) 314 (27.6%), (ii) 183 (16.1%), (iii) 16 (1.41%), (iv) 577 (50.8%) and (v) 46 (4.05%) WML. All WML exhibited lower NDI than NAWM and WM-HC (P<0.0001). Isointense lesions exhibited higher NDI (P=0.0115) and MWF (P<0.0001) than other WMLs. Rim + and hyperintense lesions exhibited lower MWF than NAWM and WM-HC (P<0.0001). Rim + lesions showed lower MWF and NDI than other WML types (P<0.001). Hypo Rim+ lesions and hypointense lesions exhibited higher MWF than other WMLs (P=0.0006, P<0.05). Hyperintense lesions exhibited lower MWF than other WMLs types (P<0.01) except Rim+ lesions. TFCE and vertex-wise cortical surface analysis showed areas throughout the NA tissue, where QSM is either lower or higher compared to healthy tissue in HC and in PMS compared to RMS (P<0.01).

Conclusions

QSM is sensitive to diffuse and focal pathology with various myelin and axonal characteristics. We hypothesize that isointense WMLs show high repair activity, hypointense WMLs are remyelinated lesions and hyperintense WMLs are chronic inactive lesions. MRI-histopathology work is ongoing to confirm these findings.

Background

In multiple sclerosis (MS), perilesional chronic inflammation appears on in vivo 3T susceptibility-based magnetic resonance imaging (MRI) as non-gadolinium-enhancing paramagnetic rim lesions (PRL). A higher PRL burden has been recently associated with a more aggressive disease course. The visual detection of PRL by experts is time-consuming and can be subjective.

Objectives

To develop a multimodal convolutional neural network (CNN) capable of automatically detecting PRL on 3D-T2*w-EPI unwrapped phase and 3D-T2w-FLAIR images.

Methods

124 MS cases (87 relapsing remitting MS, 16 primary progressive MS and 21 secondary progressive MS) underwent 3T MRI (MAGNETOM Prisma and MAGNETOM Skyra, Siemens Healthcare, Erlangen, Germany). Two neurologists visually inspected FLAIR magnitude and EPI phase images and annotated 462 PRL. 4857 lesions detected by an automatic segmentation (La Rosa et al. 2019) without overlap with PRL were considered non-PRL. The prototype RimNet was built upon two single CNNs, each fed with 3D patches centered on candidate lesions in phase and FLAIR images, respectively. A two-step feature-map fusion, initially after the first convolutional block and then before the fully connected layers, enhances the extraction of low and high-level multimodal features. For comparison, two unimodal CNNs were trained with phase and FLAIR images. The areas under the ROC curve (AUC) were used for evaluation (DeLong et al. 1988). The operating point was set at a lesion-wise specificity of 0.95. The patient-wise assessment was conducted by using a clinically relevant threshold of four rim+ lesions per patient (Absinta et al. 2019).

Results

RimNet (AUC=0.943) outperformed the phase and FLAIR image unimodal networks (AUC=0.913 and 0.855, respectively, P’s <0.0001). At the operating point, RimNet showed higher lesion-wise sensitivity (70.6%) than the unimodal phase network (62.1%), but lower than the experts (77.7%). At the patient level, RimNet performed with sensitivity of 86.8% and specificity of 90.7%. Individual expert ratings yielded averaged sensitivity and specificity values of 76.3% and 99.4%, respectively.

Conclusions

The excellent performance of RimNet supports its further development as an assessment tool to automatically detect PRL in MS. Interestingly, the unimodal FLAIR network performed reasonably well despite the absence of a paramagnetic rim, suggesting that morphometric features such as volume or shape might be a distinguishable feature of PRL.

Background

Microstructural biophysical models reconstructed from advanced diffusion MRI (dMRI) data provide quantitative measures (qMs), which inform about the brain tissue microenvironment, based on different assumptions.

Objectives

To compare the sensitivity of available qMs to focal pathology in multiple sclerosis (MS), and to explore which qMs– or combinations of qMs – are best correlated with patients’ disability.

Methods

dMRI (1.8 mm isotropic resolution, 149 directions, b-values were 0, 700, 1000, 2000, 3000 s/mm²) was acquired from 67 relapsing-remitting and 33 progressive MS patients (median EDSS: 2.5). The qMs for the isotropic and intra-axonal compartments were derived from the following available models: Ball and Stick, NODDI, SMT-NODDI, MCMDI, NODDIDA, DIAMOND, Microstructure Bayesian approach (MB) and microstructure fingerprinting. In total, 13 qMs were included and subject-wise normalized within brain tissue (nqMs).

To identify the nqMs sensitive to focal pathology, an attention-based convolutional neural network (aCNN) was built to (a) classify randomly sampled WM lesion and perilesional WM patches and (b) generate attention weights (AWs) representing the relative importance of the qMs in the classification. Twenty patients were randomly selected in the test dataset (709 lesion patches and 746 perilesional WM patches), and the rest were in the cross-validation (CV) dataset (2925 lesion patches and 3176 perilesional WM patches). The performance metric was the area under the receiver operating characteristic curve (AUC). Because of the correlation between the nqMs, which may influence the relative AWs, we performed 10-fold CV and selected the nqMS that most contributed to the classification.

To assess which nqMS – or combination of nqMS was best correlated with EDSS, we used Spearman’s correlation coefficient (ρ) with two-sided 20000 permutation tests and followed by Bonferroni correction.

Results

The test AUC was 0.911 indicating the aCNN learned the right AWs to differentiate lesions and perilesional WM. The most discriminating nqMs included isotropic and intra-axonal compartments from MB, the neural density index (NDI) from the NODDI and the intra-axonal compartment from MCMDI.

The sum of isotropic and intra-axonal compartments of the MB (sMB) showed the strongest correlation with EDSS (ρ=-0.40,corr. p<0.0001) followed by the sum of sMB and NDI (ρ=-0.30,corr. p<0.05), and the sum of sMB and intra-axonal compartment from MCMDI (ρ=-0.32,corr. p<0.05). None of the selected nqMs as a single measure and their other combinations correlated with EDSS.

Conclusions

By performing aCNN-aided selection of the openly available WM quantitative measures, we have identified the measures most sensitive to MS focal pathology; furthermore, we have derived a new contrast that – by combining the measures of isotropic and intracellular diffusion – strongly correlated with patients’ disability.

Background

Compared to the conventional magnetization-prepared rapid gradient-echo imaging (MPRAGE) MRI, magnetization prepared 2 rapid acquisition gradient echoes (MP2RAGE) (Marques, J. P., 2010) shows a higher brain tissue and lesion contrast in multiple sclerosis (MS) patients. This specialized sequence is, however, mainly limited to research settings and not widely acquired in clinical routine.

Objectives

To synthesize realistic-looking MP2RAGE images from MPRAGE acquisitions via generative adversarial network (GAN) and verify if these improve the performance of automatic MS lesion and brain tissue segmentation tools.

Methods

We propose a GAN inspired by the pix2pix framework (Isola, P., 2018) which takes as input 2D slices of 3D MPRAGE images and generates a synthetic MP2RAGE (synMP2RAGE). Differently from pix2pix, the generator of the GAN combines three loss functions: a pixel-wise L1 loss, an adversarial loss, and a perceptual loss. Our framework is trained on 12 healthy controls and 8 MS patients and tested on 36 MS patients, for which an expert manually delineated cortical and white matter lesions. Imaging was performed with a 3T MRI scanner (Siemens Healthcare, Erlangen, Germany) with a 1x1x1.2 mm resolution. Evaluation is performed with reference-based metrics and through automatic segmentation of MS lesions (La Rosa, F., 2020) and brain tissue (Avants, B.B., 2011).

Results

Considering as reference the acquired MP2RAGE, synMP2RAGE achieves a peak signal-to-noise ratio of 31.39, normalized root mean square error of 0.13, and structural similarity index of 0.98, overperforming the MPRAGE (29.49, 0.17, 0.97, respectively) for all metrics. Compared to the initial MPRAGE it also significantly improves the lesion and tissue segmentation masks in terms of the Dice coefficient and volume difference (p-values < 0.001). On the contrary, no significant differences between the real and synMP2RAGE are found in the patient-wise comparison of the lesions’ segmentation (p-values > 0.05), whereas they are significant between MPRAGE and MP2RAGE (p-value < 0.001).

Conclusions

Our proposed framework successfully translates MPRAGE to MP2RAGE, synthesizing realistic-looking images which improve the performance of automatic segmentation tools tested on MS patients. In accordance with previous claims (Finck, T., 2020), these results confirm that GANs can be helpful in the automatic analysis of MRI images.

Background

Quantification of the activity and progression of multiple sclerosis (MS) is an important tool for research on MS as well as its clinical treatment. Currently, disease activity and progression assessments rely on qualitative clinical evaluations or the acquisition of radiographic data such as magnetic resonance imaging (MRI).

Objectives

Quantifying MS disease activity (DA) and progression (DP) instead through the use of blood biomarkers would provide a significant reduction in barriers to such testing (e.g. monetary cost, time and specialized personnel requirements, invasiveness, operational difficulty, etc.). The use of an ensemble of proteins representing various biological pathways involved in MS pathophysiology would also provide useful insights into this complex and heterogeneous disease.

Methods

We investigated proteomic biomarkers associated with different levels of MS DA and DP using 205 blood serum samples from 88 patients (University Hospital Basel), extracting protein levels using Proximity Extension Assays (PEA) from Olink^TM. We then conducted a focused statistical analysis on 21 proteins that were selected for a custom MS assay panel development project based on their association with endpoints in previous studies. We corrected these protein levels using clinical data, including: age, sex, disease duration, age of the bio-banked sample, and medication status. We then compared protein levels to five different radiographic and clinical endpoints.

– Primary Endpoint: Gadolinium (Gd) enhanced lesion count

– Secondary Endpoints: T2 lesion volume, Expanded Disability Status Scale (EDSS) score, Clinically Defined Relapse Status, and Annualized Relapse Rate (ARR)

Results

In this report, we examine the univariate performance of selected proteins on the prediction of all five endpoints, comparing it to that of several multivariate machine learning techniques. We draw distinctions between the highest performing models for each endpoint and draw connections to the underlying biology governing MS activity and progression.

Conclusions

We found significant improvements in predictive power from the use of multivariate models in comparison to even the highest performing univariate techniques. The samples analyzed in this study will be re-assayed for validation purposes alongside additional cohorts in the forthcoming 21-plex custom MS proteomic assay panel.

Background

Additional biomarkers reflecting disease activity and predicting severity of multiple sclerosis (MS) are urgently needed.

Objectives

To explore whether intrathecal immunoglobulin (Ig) M synthesis is associated with time from disease onset to first relapse, MS Severity Score (MSSS) and time to first initiation of high efficacy disease modifying treatments (DMT) in patients with relapsing MS in the Swiss Multiple Sclerosis Cohort study.

Methods

487patients were categorized by presence of CSF oligoclonal IgG bands (OCGB) and quantitative intrathecal IgG and IgM production (Intrathecal Fraction, IF). Treatments were classified according to "no therapy", "platform", "oral" and "high efficacy". Multivariable Cox proportional hazard models or a multivariable linear model, adjusted for relevant covariables, were used to assess time from disease onset to described endpoints and associations with the MSSS.

Results

OCGB were present in 89.3%, IgGIF in 66.3%, IgMIF in 26.9% and IgAIF in 11.9% of patients. Patients with IgMIF had a shorter interval from disease onset to first relapse (HR 1.887 [CI 1.181, 3.014], p<0.01) compared to those without OCGB and IgGIF and IgMIF. Quantitatively, patients with IgMIF above versus below the median had a 1.75- fold increased hazard of occurrence of a first relapse (HR 1.746 [CI 1.097, 2.781]; p=0.019). IgMIF positive patients had on average a 1.24 steps higher MSSS compared with those without any intrathecal Ig synthesis (estimate: 1.243 [CI 0.501,1.986], p<0.01), followed by patients with OCGB and quantitative production of IgGIF (estimate: 0.966 [CI 0.283, 1.650], p<0.01) and patients with only OCGB (estimate: 0.716 [CI -0.030, 1.461], p=0.060). Accordingly, patients with IgMIF production had a shorter interval to initiation of high efficacy DMT (HR 2.788 [CI 1.306, 5.951], p<0.01). Quantitatively, above versus below median IgMIF was associated with a 2.36-fold risk of escalation to a high efficacy DMT (HR 2.361 [CI 1.304, 4.277]; p<0.01).

Conclusions

In relapsing MS, presence of intrathecally produced IgM is associated with higher disease activity, more severe disease course and earlier use of high efficacy treatments. Intrathecally produced IgM may qualify as useful prognostic biomarker for therapeutic decision making in early stage of disease.

Background

Intrathecal IgM synthesis was reported to be associated with higher clinical disease activity and severity. We found an association also with earlier use of high efficacy treatments in relapsing MS (RMS).

Objectives

To explore whether patients with intrathecal IgM synthesis show a) higher serum neurofilament light chain levels (sNfL) as a reflection of neuronal damage, or b) signs of increased disease severity in cerebral MRI, in patients with RMS followed in the Swiss MS Cohort Study.

Methods

487 patients were categorized by presence of oligoclonal IgG bands (OCGB) and intrathecally produced IgG/M:

1) OCGB-/IgG-/IgM- (reference [ref]);

2) OCGB+/IgG-/IgM-;

3) OCGB+/IgG+/IgM- and

4) OCGB+/IgG+/IgM+.

sNfL was measured (at baseline and every 6- or 12 months) with the NF-light^® assay. Age-dependent sNfL z-scores (sNfLz) were modelled in 8865 healthy control samples to reflect the deviation of a patient sNfL value compared to mean values observed in same age healthy controls. Yearly T2 lesion number and occurrence of new/enlarging T2 lesions were automatically assessed in cerebral MRIs and checked manually. Contrast enhancing lesions (CEL) were manually quantified. Linear or negative binomial mixed models were used to investigate the associations between the four CSF Ig patterns and longitudinal sNfLz and MRI measures, adjusted for DMT and other covariates.

Results

IgM+ patients had higher sNfLz vs reference (estimate 0.50 [CI 0.12, 0.89], p=0.011), whereas those with only OCGB+ (0.11 [-0.28, 0.50], p=0.582) or with OCGB+/IgG+ (0.20 [-0.16, 0.56], p=0.270) did not (n=2970 observations). This was confirmed when analyzing only untreated patients adjusting for T2 and CEL numbers (1.16 [0.47, 1.86], p<0.01 vs 0.58 [-0.11, 1.27], p=0.1022 vs 0.51 [-0.11, 1.13], p=0.108 vs ref, respectively) (n=234).

IgM+ patients had 2.28-fold more T2 lesions ([1.51, 3.44], p<0.01) vs ref; for patients with only OCGB+ (1.61 [1.07, 2.43], p=0.0237) or OCGB+/IgG+ (1.58 [CI 1.08, 2.32], p=0.0179) (n=1580) this association was weaker.

IgM+ was associated with a 2.47-fold risk for new/enlarging T2 lesions on yearly follow-up MRIs vs ref (2.47 [1.28, 4.78], p<0.01) but not the two other patient groups (1.84 [CI 0.93; 3.65], p=0.0799 and 1.61 [CI 0.87; 2.95], p=0.1280) (n=861).

Conclusions

Intrathecal IgM synthesis was consistently associated with quantitative measures of neuro-axonal injury and disease severity in RMS. Our findings strongly support the clinical utiliy of this biomarker.

Background

In relapsing MS, blood NfL has emerged as a promising biomarker of disease activity and worsening. The ability of serum NfL (sNfL) to detect relapse-independent disability progression is less well established.

Objectives

We investigated whether patients followed in the Swiss Multiple Sclerosis Cohort (SMSC) without any relapses during follow-up, had higher sNfL levels when experiencing confirmed disability progression independent of relapses (PIRA) as compared to stable patients. Secondly, we explored whether baseline (BL) sNfL could predict PIRA.

Methods

BL and 6- or 12-monthly follow-up sNfL were measured by Simoa NF-light^® assay in 4608 samples from 806 relapse-free MS patients and 8865 serum samples from 4133 healthy controls (median age 45 yrs). Age-dependent sNfL z-scores (sNfLz) were modeled in healthy controls using a generalised additive model for location scale and shape to reflect the deviation of a patient sNfL value from the mean value of same age healthy controls. PIRA was defined as an EDSS increase of ≥1.5 steps if baseline EDSS 0, ≥1.0 if 1.0-5.5, or ≥0.5 if >5.5, confirmed after ≥6 months. We used mixed effects models to investigate the association between PIRA, clinical parameters, disease modifying treatment, and log(sNfL) as dependent variable at each sampling. The predictive value of BL sNfLz was investigated by uni- and multivariable Cox proportional hazards models.

Results

806 (4608 samples) of 1399 patients in the SMSC did not experience relapses during a median follow-up of 4.7 years (57.6%; BL: 715 RRMS, 43 SPMS, 48 PPMS; median age 42 yrs; samples/patient: 5; EDSS 2.0). PIRA occurred in 153/806 (19.0%). In a multivariable model, sNfL was positively associated with age (1.7%/year [95%CI 1.5;2.0], p<0.001) and EDSS at BL (7.6%/step, [5.8;9.6], p<0.001), whereas it was decreased when sampled during monoclonal antibody therapy (-10.8%, [-14.7;-6.6], p<0.001) or oral MS treatments (-10.4%, [-14.1;-6.5%], p<0.001) as compared to untreated timepoints. Importantly, patients experiencing PIRA had 11.6% higher sNfL levels, compared with stable patients (4.5;19.2, p=0.001). The hazard of future PIRA increased by 23.5% (8.3;40.8, p=0.002) per 1 standard deviation higher BL sNfLz. This finding was confirmed after adjusting for age, EDSS score and treatment at BL (27.8%, [11.5;46.5], p<0.001; sNfLz > 2: 2.5-fold risk [95%CI 1.7-3.9], p<0.001 for PIRA event vs. sNfLz < 2).

Conclusions

Our data support the value of sNfL to capture and predict neuro-axonal injury leading to disability progression independent from relapses.

Background

Serum neurofilament light chain (sNfL) levels reflect neuroaxonal damage and relate to disease activity in MS. sNfL may qualify as well as a biomarker of suboptimal treatment response to disease modifying therapies (DMT). Establishment of age-dependent reference ranges in healthy controls is a prerequisite for developing this biomarker for clinical use.

Objectives

To compare on-treatment sNfL levels with values from a healthy control cohort and to investigate the effect of DMTs on sNfL levels in patients from the Swiss MS Cohort Study.

Methods

sNfL was measured (at baseline and every 6- or 12 months) with the NF-light® assay. Age-dependent sNfL z-scores (sNfLz) were modeled in healthy controls using a generalized additive model for location scale and shape to reflect the deviation of a patient sNfL value from the mean value of same age healthy controls. Linear mixed models were used to investigate the associations between clinical characteristics, DMT and longitudinal sNfLz. Interaction terms and splines were used to model sNfLz and for comparison log(NfL), and their dynamics under treatment.

Results

sNfL was measured in 1368 patients with 7550 longitudinal samples (baseline: median age: 41.9 yrs; 5.4% CIS, 83.2% RRMS, 5.6% SPMS, 5.8% PPMS; median EDSS: 2.0; median follow-up: 4.6 yrs) and 4133 healthy controls with 8865 samples (median age: 44.8 yrs). In the multivariable model, sNfLz increased with EDSS (0.131/step, [95% CI 0.101;0.161]), recent (<120 days) relapse (0.739 [0.643;0.835]) decreased with age (-0.014/year [-0.02;-0.009]), and time on DMT (-0.040/year [-0.054;-0.027]); sNfLz were lower when sampled while on more effective DMT (oral versus platform injectables: -0.229 [-0.344;-0.144]; monoclonal antibodies (mAB) versus platform injectables: -0.349 [-0.475;-0.224]), (p<0.001 for all associations). sNfLz were inversely associated with the hierarchy in efficacy of mAB over orals and orals over platform therapies with regard to slope and extent of decrease (interaction between time under DMT and DMT class: p<0.001). sNfLz, but not log(NfL) showed normalization of sNfL levels by mAB to healthy control levels.

Conclusions

The dynamic change of sNfLz on DMT reflects closely their relative clinical efficacy and is more meaningful than log(sNfL) by excluding age as a confounding factor. Use of sNfLz based on a large normative database as an age-independent sNfL measure improves the accuracy of the sNfL signal and hence their clinical utility.

Background

Background: The envelope protein of the human endogenous retrovirus type W (HERV-W ENV) is expressed in chronic active MS lesions. Preclinical models have shown that HERV-W ENV activates microglia, prevents maturation of oligodendrocyte precursor cells, and leads to neuronal death. Following the effects of a B-cell depleting, anti-inflammatory therapy, rituximab (RTX), with temelimab (TML), a humanized, IgG4-κ monoclonal antibody against HERV-W ENV represents a novel therapeutic approach against neurodegenerative features of MS.

Objectives

Objective: To present safety preclinical results on the interaction of RTX and TML, and the trial design of the ProTEct-MS study.

Methods

Design/Methods: Interactions between RTX and TML were studied in vitro in high density-peripheral blood mononuclear cells (PBMCs) and ex-vivo in a whole blood loop system from fresh human blood.

ProTEct-MS is a randomized, double-blind, placebo controlled, parallel group study. Enrolment commenced in 2020/6 and will be completed in 2020/12. Patients with RMS (2017 McDonald criteria) (N=40) being previously treated for ³12 months with RTX are randomized (1:1:1:1) to monthly iv TML (18, 36 or 54mg/kg) and placebo for 48 weeks.

Eligibility criteria: age 18-55 yrs, Expanded Disability Status Scale (EDSS) of 2.5-5.5 at screening; clinical worsening in ³1 neurological domain as assessed by EDSS, 6MWT or T25FW, or cognitive functioning as assessed by SDMT over the last year.

Primary objective: assessment of safety and tolerability of TML

Secondary outcome measures: MRI: change of brain atrophy, lesion volume and magnetization transfer ratio

Results

Results: Co-administration of TML with RTX was overall comparable to vehicle for all blood parameters assessed including cytokine levels of all five donors tested in both in vitro and ex-vivo assays. Co-administration of TML with RTX did not affect the functionality profile of either compound. By September 2020, 25% of patients are expected to be randomized, providing baseline clinical and MRI characteristics.

Conclusions

Conclusions: Preclinical safety experiments of the drug combination showed no evidence against the use of TML following RTX in humans. ProTEct-MS study patients represent a RMS cohort with progression in absence of relapse activity (PIRA,) i.e. whose present clinical condition is stable under RTX therapy, enabling TML's effects on attenuating mechanisms of progression to be measured without interference by acute inflammatory activity.

Background

Myelin water imaging (MWI) and neurite orientation dispersion and density imaging (NODDI) provide sensitive surrogate markers of myelin and axonal content in lesions and normal-appearing tissue. However, to date, there is scarce information about the relationship of these measures with (i) disability; and (ii) the axonal damage specific biomarker serum neurofilament light chain (sNfL).

Objectives

To explore the correlation of MWI and NODDI measures in MS lesions and in normal-appearing (NA) brain tissue with disability and sNfL.

Methods

Ninety-one MS patients (62 relapsing-remitting MS-RRMS and 29 progressive MS-PMS) underwent MWI and NODDI. Mean myelin water fraction (MWF) and neurite density index (NDI) were extracted in white matter lesions (WMLs), cortical lesions (CLs), NA white matter (NAWM) and cortical NA gray matter (CNAGM). For sNfL, a logarithmic transformation was applied to comply with normality assumption. Correlation studies between MRI measures, sNfL and EDSS were performed using linear models, with age and gender as covariates. The models were performed for the whole sample and for patients with clinical deficits only (EDSS >1).

Results

MWF and NDI did not correlate with EDSS when the entire cohort was considered (P>0.05). However, for those patients with clinical deficits (EDSS> 1), NDI in WMLs was associated with EDSS (NDI: P<0.01, beta=-10.00; N=74). We also found that MWF and NDI in WMLs were related to sNfL (MWF: P<0.01, beta=0.13; NDI: P<0.01, beta=-3.60). Again, this correlation was stronger in patients with EDSS>1 (MWF: P<0.01, beta=0.13; NDI: P <0.01, beta=-3.60).

Conclusions

Imaging surrogate markers of myelin and axon pathology in WML – and not in CLs and NA tissues - are correlated with disability and sNfL. Interestingly, associations between those imaging markers and disability/sNFL were more evident in patients with clinical deficits as compared to those without neurological deficits.

Background

Advanced diffusion-weighted MRI (DW-MRI) sequences, in combination with biophysical models, provide unprecedented information on the microstructural properties of both healthy and pathological brain tissue.

Nevertheless, it is nowadays challenging to identify the most accurate biophysical model to describe focal microstructural pathology in multiple sclerosis (MS) patients, due to the lack of appropriate comparative studies.

Objectives

To investigate the specificity and sensitivity of 124 independent features derived from 20 diffusion microstructural models to differentiate specific features of tissue alterations in white matter (WM) lesions compared to the surrounding normal-appearing WM (NAWM).

Methods

The study included 102 MS patients: RRMS: 66%, SPMS: 18%, PPMS: 16%, mean age 46±14; female 64%, disease duration 12.16±18.18 years, median Expanded Disability Status Scale (EDSS): 2.5.

DW-MRI data were acquired with 1.8mm isotropic resolution isotropic and with the b-values [0, 700, 1000, 2000, 3000] s/mm².

Lesion masks were generated with a deep learning network algorithm and manually corrected if required. Voxels of NAWM tissue were randomly chosen outside the lesion masks.

The following microstructural models were applied: DTI, Non-parametric DTI, DKI, Ball and Stick, Ball and Sticks, Ball and Rockets, NODDI-Watson, AMICO-NODDI, NODDI-Bingham, SMT-NODDI, NODDIDA, SMT, MCMDI, CHARMED, IVIM, sIVIM, Microstructure Fingerprinting, Microstructure Bayesian, DIAMOND, and DIAMOND isotropic-restricted.

The classification was performed using logistic regression on 300’000 voxels, equally divided in lesion and NAWM voxels. Features were scored according to the Area Under the Curve (AUC), sensitivity, and specificity.

Results

The intra-axonal signal fraction of the Microstructure Bayesian approach scored maximum with AUC=0.87, for threshold=0.5 sensitivity=0.79, sensitivity=0.83. AUC = 0.86 were attributed to the intra-axonal signal fraction of Ball and rockets, NODDI-Watson, AMICO-NODDI, NODDI-Bingham, SMT-NODDI and the extra-axonal perpendicular signal fraction of the Microstructure Bayesian approach. Low AUC scores (<0.75) were achieved by DTI and parameters not related to signal fractions, e.g. orientation dispersion.

Conclusions

Among available microstructural models, the Microstructure Bayesian appeared to best differentiate voxels with microstructural damage in WM lesions compared to NAWM. Very similar, albeit slightly lower accuracy, was achieved by NODDI-based models. In general, models with estimates intra-axonal signal fraction tend to perform better in this type of classification, showing that intra-axonal component may be the dominant factor in distinguishing the two types of tissue. Further analysis will explore the advantage of including combinations of independent features.

Background

LeMan-PV is a prototype that performs cross-sectional and longitudinal detection of Multiple Sclerosis (MS) lesions, which has been validated on conventional (cMRI) and advanced magnetic resonance imaging at 3T (Fartaria et al. 2019). Since this software provides a report that is available shortly after image acquisition, it may be ideal as clinical decision-support tool.

Objectives

To assess LeMan-PV as clinical decision-support tool in a monocentric real-world cMRI dataset from the Swiss Multiple Sclerosis Cohort.

Methods

262 MS patients underwent cMRI at Basel University Hospital in a mean of 3.5 follow-up sessions, with an average of 399 days between two consecutive sessions. cMRI sequences were acquired at 1.5T and 3T in 725 and 195 sessions, respectively. Cross-sectional and longitudinal MS lesions segmentation (i.e. identification of new and enlarging lesions - NLs, ELs) was performed using the LeMAN-PV prototype software. An expert neuroradiologist performed a radiological reading of the number of NLs and ELs in the most recent acquisition by comparing it to the previous one (ground truth, GT), considering only lesions with a diameter larger than 3 mm. The minimum volume thresholds to identify an NL and an EL were chosen by minimizing the patient-wise error between the automated count and the expert ground truth. Two scenarios were evaluated by first assuming disease activity if one or more EL were present, and second by considering activity if NL were present in the new acquisition.

Results

The volume thresholds chosen were 11 and 12 mm³ for ELs and NLs, respectively. For those, LeMan-PV detected 11% more of both ELs and NLs than the neuroradiologist. In the patient-wise evaluation of cases with both sessions acquired at 1.5T (70%), LeMan-PV showed sensitivities of 93% and 78% and specificities of 62% and 43% when evaluating ELs and NLs. For the 3T pairs of sessions (8%), values were 68% and 72% for ELs and 73% and 68% for NLs. Finally, for cases with a first acquisition at 1.5T and a second at 3T (22%), values were 76% and 73% for ELs and 71% and 65% for NLs.

Conclusions

The count of new and enlarging MS lesions using LeMan-PV were close to the one performed by an expert neuroradiologist; the software performed better when assessing disease activity via detection of enlarging lesions rather than by identifying new lesions. More 3T data is being currently collected at 3T to provide a size-matched inter-scanner comparison.

Background

Graph theory is used to study brain connectivity, i.e. connectomes, estimated with diffusion magnetic resonance imaging (dMRI). Previous studies have already investigated the correlation between some network measures and the Expansion Disability Status Scale (EDSS), which assesses the clinical worsening of multiple sclerosis (MS) patients.

Objectives

We investigated connectivity changes between healthy controls (HC) and relapsing remitting (RR) patients and tested whether such differences correlate with EDSS, comparing the effectiveness of various definitions of “connection strength” using different microstructural models.

Methods

dMRI was acquired for 67 HC (39F, 37±7yrs) and 49 RR (33F, 37±4yrs). Connectomes were created with deterministic tractography and weighting the connections by 1) number of streamlines (NOS) between grey-matter regions and, 2) mean value of quantitative scalar maps, estimated using state-of-the-art microstructural models, along the streamlines, notably: fractional anisotropy, FA; axial AD, radial RD and mean diffusivity MD; Intra Neurite and Isotropic Volume Fractions, ICVF and ISOVF; orientation dispersion, OD; Neurite volume fraction, INTRA; Extra-neurite transverse and mean diffusivity EXTRATRANS and EXTRAMD. We computed 5 network measures from each connectome: Density (ratio between actual and possible connections); Efficiency (capability of transferring and processing information); Modularity (network segregation); Clustering Coefficient (degree to which nodes tend to cluster together); Mean Strength (average of the sum of the edge weights connected to a node).

Results

The network measures that significantly differ between the 2 groups were: Efficiency for ICVF p=0.031, AD p<0.01, RD p<0.01, EXTRATRANS p=0.019 and MD p<0.01 connectomes; Clustering Coefficient for AD p=0.015, RD p=0.013, EXTRATRANS p=0.021 and MD p<0.01 connectomes; Mean Strength for ICVF p=0.019, INTRA p=0.037, AD p=0.011, RD p<0.01, EXTRATRANS p=0.014 and MD p<0.01 connectomes. Only Modularity significantly correlate with EDSS for NOS p=0.047, FA p=0.049, ICVF p=0.041 and INTRA p=0.030 connectomes. All tests accounted for age, sex and density as confounding factor.

Conclusions

The maps discriminating more HC from MS patients were AD, RD, MD and EXTRATRANS. The microstructure features along the tracts with the highest correlation to EDSS were those investigating axonal integrity (FA, ICVF and INTRA). Modularity was the metric most correlated with EDSS.

Background

The pathogenesis of neurodegeneration in multiple sclerosis (MS) is multifactorial and the determinants of brain atrophy rates are not completely understood.

Objectives

To investigate the association between annualized atrophy rate (AAR) of multiple brain measures (regional cortical thickness (CTh), volumes of basal ganglia, thalamus, white matter, gray matter, brain and brain parenchymal fraction (BPF)) and: (1) annualized rate of new and enlarging white matter lesions (WMLs); (2) annualized rate of resolved WMLs; (3) occurrence of progression independent of relapse activity (PIRA) during follow-up.

Methods

We included 1573 1.5T or 3T brain MRI scans from 378 patients of the Swiss MS Cohort Study (331 relapsing-remitting MS (RRMS), 27 clinically isolated syndrome (CIS), 11 secondary-progressive MS (SPMS), 9 primary-progressive MS (PPMS); 70% female; median age: 41.9 yrs; disease duration: 8.3 yrs; EDSS: 2.0; follow-up time: 4.0 yrs). Longitudinal changes in WMLs were obtained using an automated prototype (LeMan-PV). Brain volumes and CTh AARs were obtained using FreeSurfer longitudinal pipeline (v6.0) after WMLs filling. In patients fulfilling PIRA an EDSS progression had to be confirmed ≥6 months after the index event. Multivariable generalized linear models were used to model the association between AAR (dependent variable) and independent variables (1-3), correcting for age, sex, disease duration and baseline EDSS. p-values were adjusted for Bonferroni multiple comparison correction; for vertex-wise CTh analysis, Monte Carlo Z simulation was performed (cluster threshold p<0.05).

Results

We found positive associations between annualized rate of new and enlarging WMLs and (i) CTh AAR of 8 extensive clusters (bilateral frontal, temporal and occipital regions and right insula, all p<0.01) and (ii) AAR of: caudate bilaterally (p=0.02), white matter volume, brain volume and BPF (p<0.001 for all).

We also found a negative association between annualized rate of resolved WMLs and CTh AAR in 3 cortical clusters (right insula, precentral area and anterior cingulate region, all p<0.05); no associations with AAR of volumes emerged.

57 patients fulfilled PIRA whereas 295 experienced no EDSS progression events: no significant differences in AAR measures were found between these two groups.

Conclusions

In a large cohort of MS patients, with a median follow-up of 4 years, local radiological inflammatory and reparative activity were associated with AAR in multiple brain regions. PIRA did not seem to be related to increased AAR in any of the regions studied.

Background

Neurofilament light polypeptide (NfL) is a neurofilament protein highly expressed in myelinated axons. Increased serum NfL (sNfL) concentration indicates the presence of axonal damage in patients with multiple sclerosis (MS). Until now, the potential effects of this axonal damage on brain connectivity have never been investigated.

Objectives

We studied the relationship between active inflammation measured by sNFL and structural connectivity alterations detectable by global network metrics estimated with diffusion MRI.

Methods

Diffusion MRI, T1-weighted and FLAIR sequences were acquired on 74 patients (44F, 44.9±14.6yrs, 50 relapsing-remitting and 24 progressive) and sNfL levels were measured from blood samples in the same session. Volume of white-matter lesions was computed on FLAIR with an automatic in-house tool. To build the connectomes we 1) performed deterministic tractography on diffusion MRI, 2) segmented the grey matter in 85 regions using T1 images, and 3) quantified the connection strength of each pair of regions by counting the streamlines between them. From each connectome we extracted 5 global metrics: Density (ratio between actual and possible connections), Efficiency (capability of transferring and processing information); Modularity (network segregation); Clustering Coefficient (degree to which nodes tend to cluster together); Mean Strength (average of the sum of the edge weights connected to a node). Since discrepancies in density may affect other metrics, we first tested its correlation with sNFL, then we performed partial correlations of the last 4 metrics with sNFL using age, sex and density as covariates.

Results

We found negative correlation between density and sNfL (R=-0.252 p=0.05) indicating that high axonal damage is associated with reduced number of connections. Efficiency and mean strength showed a strong anti-correlation with sNfL (R=-0.325 p=0.011 and R=-0.475 p<0.001), while modularity and clustering coefficient seemed not related to axonal damage (R=0.183 p=0.162 and R=-0.215 p=0.099). Finally, a positive association with sNfL was found for both the lesions volume and the Expansion Disability Status Scale (p=0.011 R=0.323 and p=0.038 R=0.267), confirming previous results.

Conclusions

We showed that high values of sNfL are associated with global connectivity damage (reduced number of connections, efficiency and mean strength) confirming the utility of network-based connectivity metrics to assess MS disease impact.

Background

Postmortem MRI provides precious insights into the relation of MRI metrics to pathoanatomical features of multiple sclerosis (MS) and can help to understand the basis of damage and repair.

Objectives

To investigate the respective features of MS lesions in the cortex and in the white matter using multiparametric postmortem MR imaging at 3T and identify discriminant characteristics of white matter lesion subgroups.

Methods

We scanned three fixed brains of secondary-progressive MS patients (mean disease duration 15.3 years) on a standard clinical 3T-MRI scanner with following sequences: Magnetization Transfer Saturation (MTsat), T1-relaxometry (T1-rt), Myelin Water Fraction (MWF) and Diffusion Tensor - Fractional Anisotropy (DTI-FA). We compared these metrics between (i) cortical lesions (CL, n=118) and normal-appearing grey matter (NAGM, n=186) and (ii) white matter lesions (WML, n=140) and normal-appearing white matter (NAWM, n=53) using a Mann-Whitney U test. Then, we analyzed the differences between different subgroups of WML (periventricular lesions -PVL-, n=38, WM part of leukocortical lesions -WMLCL-, n=36, subcortical lesions -SCL-, n=66, and areas of “dirty white matter” -DWM-, n=15) by performing a Kruskal-Wallis test and a Mann-Whitney U tests for direct comparison. Bonferroni correction for multiple-testing was applied.

Results

CL exhibited lower MTsat (p<0.001), higher T1-rt (p<0.001) and MWF (p<0.01) than normal appearing cortical tissue. WML showed lower MTsat (p<0.001), higher T1-rt (p<0.001), and lower MWF (p<0.001) than normal appearing white matter. DTI-FA did not differ between CL/WML and NAWM/NAGM. MTsat values were lower in the PVL (p<0.001) and higher in the DWM (p<0.001) in comparison to all other lesion subgroups. T1-rt were higher in PVL (p<0.001) compared to the other lesion subgroups. MWF values were higher in DWM and SCL (p<0.01), not statistically different between PVL and WMLCL. DTI-FA values were lower in WMLCL in comparison to all other subgroups (p<0.01) and did not differ between the other categories.

Conclusions

Postmortem MRI metrics in WML/CL as well as in different subgroups of WML, are compatible with myelin damage and tissue destruction. Interestingly, MWF was higher in CL than in NAGM, which might correspond to a predominance of “myelin blistering” pathology in the cortex. Ongoing work aims to directly correlate our findings with detailed histopathological characterization including electron microscopy of myelin damage.

Background

Gadolinium (Gd)-based contrast agents are widely used to assess disease activity and treatment response by MRI in multiple sclerosis (MS). There is, however, increasing concern about their safety as their repeated administration may lead to brain parenchymal accumulation, while preclinical models suggest that they induce mitochondrial toxicity and neuronal cell death. Moreover, recent reports have demonstrated that three-dimensional (3D) T2-weighted Fluid-Attenuated-Inversion-Recovery (FLAIR) is highly sensitive in detecting new or enlarging MS lesions.

Objectives

To explore whether the presence of contrast enhancing lesions (CEL) based on Gd injection is more sensitive in detecting lesional activity in clinically stable MS patients in comparison to the analysis of new or enlarging MS lesions by 3D FLAIR.

Methods

MS patients being part of the observational, multicenter Swiss Multiple Sclerosis Cohort Study (SMSC) with contrast enhanced T1-weighted (T1w) images were included. Clinical stability was defined as no relapse and no Expanded Disability Status Scale (EDSS) increase during at least twelve months prior to MRI. Presence of CEL was assessed on contrast enhanced T1w images. Presence of new or enlarging T2w lesions was assessed manually on 3D FLAIR in an independent analysis by a different investigator in clinically stable MS patients presenting with CEL.

Results

3930 MRI scans (3.0 Tesla n=1497 (38%)) in 1057 participants (685 women, median age 42.0 years, 941 with relapsing MS, 116 with progressive MS, median EDSS 2.0 (range 1.5-3.5), median disease duration 7.4 years) were included.

Of 2620 MRI scans (66.7%) acquired in clinically stable conditions 46 (1.8%) demonstrated CEL. In all of these, new or enlarging T2w lesions were detectable by 3D FLAIR when a previous MRI was available for comparison (previous MRI available in 29/46; median number of new or enlarging T2w lesions: 3 (range 1-41, total number 176); median number of CEL: 1 (range 1-4, total number 47)).

Conclusions

In our large cohort from clinical practice, the assessment of new or enlarging lesions by 3D FLAIR was equally sensitive as the quantification of CEL to detect disease activity in clinically stable MS patients, challenging current practice of the use of Gd-enhanced MRI for monitoring of MS in clinical routine.

Background

Advanced diffusion-weighted MRI (DW-MRI) sequences, in combination with biophysical models, provide new information on the microstructural properties of the tissue.

Objectives

To investigate the differences in intra-axonal signal fraction (IASF) between perilesional normal-appearing white matter (pl-NAWM), white matter lesions (WML) without (rim-) and with paramagnetic rim (rim+) comparing eight biophysical diffusion models.

Methods

The study included 102 MS patients: RRMS: 66%, SPMS: 18%, PPMS: 16%, mean age 46±14; female 64%, disease duration 12.16±18.18 yrs, median EDSS: 2.5.

DW-MRI data were acquired with 1.8mm isotropic resolution and b-values [0, 700, 1000, 2000, 3000] s/mm².

Lesion masks were generated with a deep-learning-based method and manually corrected if required; pl-NAWM was defined as a region of 3-voxels around each WML; 225 paramagnetic rim lesions were manually identified based on 3D EPI and 2330 were labelled as rim-.

The following microstructural models were applied: Ball and Stick, Ball and Rockets, AMICO-NODDI, SMT-NODDI, MCMDI, NODDIDA, CHARMED, Microstructure Bayesian approach.

Delta (WML - pl-NAWM) was calculated for each WML, and one-side Mann Whitney U was used to compare the delta between models, followed by Bonferroni to correct for multiple testing.

Mean difference and Cohen's d was used to assess differences between lesions with extensive axonal damage (rim+) and other WML (rim-).

Results

All models applied in this study reported low IASF in rim+ WML, medium IASF in rim- WML and relatively high IASF in pl-NAWM. However, a broad spectrum of IASF values was identified from the different models: relatively simple models such as Ball and Stick and CHARMED, showed low delta IASF within lesions, while MCMDI models reported the highest significant difference compared to other models (p<0.0001). The comparison between WML and pl-NAWM mean IASF across models showed that MCDMI exhibited the highest difference (mean 0.13, Cohen’s d 1.34). AMICO-NODDI and SMT-NODDI showed close results (mean difference 0.12/0.12 and Cohen’s d 1.46/1.51).

The models best discriminating IASF between rim+ and rim- lesions were MCMDI and NODIDDA (mean 0.08/0.07, Cohen’s d -0.69/-0.70).

Conclusions

We compared eight WM diffusion models for assessment of intralesional axonal damage in MS patients. The comparison between WML and pl-NAWM showed that robustness of the method, identified with SMT-based and NODDI-based models, it is crucial. For the comparison between lesions with a high level of damage (rim +) and other WML, the diffusivity estimation appeared to play an important role. The method which appeared both robust and able to estimate the diffusivity of the tissue was MCMDI, which performed best in both cases.

Background

Neuromyelitis optica spectrum disease (NMOSD) and multiple sclerosis (MS) have a different pathophysiology. Accumulating evidence suggests that the choroid plexus plays a pivotal role in the pathogenesis of MS. However, MRI data comparing the choroid plexus volume between MS and NMOSD are scarce.

Objectives

To compare the choroid plexus volume in MS vs. NMOSD in vivo using high-resolution 3D MRI data. Migraine patients and healthy individuals served as control groups.

Methods

We included 95 MS patients [45% secondary progressive (SP); mean age 51.0±11.5 years; disease duration 20.8±10.4 years, 62% female; median Expanded Disability Status Scale (EDSS) 4.0], 43 NMOSD patients [28/43 anti-aquaporin 4 antibody positive; 11/43 anti-myelin oligodendrocyte glycoprotein antibody positive; 87% female; mean age 50.0±13.8 years; disease duration 6.8±7.3 years, median EDSS 3.0], 38 migraine patients [mean age 39±13 years, 79% female; 15/38 migraine with aura] and 65 healthy individuals [HCs, mean age 41±17 years, 48% female]. The choroid plexus of the lateral ventricles and T2-weighted (T2w) white matter lesions (WMLs) were segmented fully automated on T1-weighted (T1w) magnetization-prepared rapid gradient echo (MPRAGE) images and fluid attenuated inversion recovery sequences (FLAIR, voxel size of both sequences 1x1x1 mm³), respectively, using a supervised deep learning algorithm (multi-dimensional gated recurrent units). Total intracranial volume (TIV) and lateral ventricle volumes were assessed fully automated using Freesurfer. All outputs were reviewed and manually corrected (if necessary) using 3D-Slicer by trained raters who were blinded to the clinical information. Group differences were analyzed using multivariable generalized linear models (GLMs) adjusted for age, gender, TIV and lateral ventricle volume. Cohens’ d was used to calculate the standardized difference between the respective groups. Given p-values are adjusted for multiple comparisons (Bonferroni).

Results

Mean choroid plexus was larger in MS compared to NMOSD (1907±455 vs. 1467±408 µl; p<0.001, d=0.86), HCs (1663±424 µl; p=0.007, d=1.17) and migraine (1527±366 µl; p=0.02, d=0.72). There was no statistical difference in the choroid plexus volume between NMOSD, migraine and HCs. The choroid plexus was marginally larger in RRMS than SPMS (1959±482 vs. 1875±476 µl; p=0.28; d=0.17) and in untreated MS patients compared to MS patients on disease modifying therapy (2111±382 vs. 1876±459 µl; p=0.36). However, these differences did not reach statistical significance after correction for multiple comparisons. There was no association between the choroid plexus volume and total T2w WML volume in MS.

Conclusions

Patients with MS have larger choroid plexus than HCs, migraine and NMOSD patients. Further studies are warranted to investigate the respective roles of the choroid plexus in the pathogenesis of MS and NMOSD.