Grey Matter Atrophy and its Relationship with White Matter Lesions in Patients with Myelin Oligodendrocyte Glycoprotein Antibody-associated Disease, Aquaporin-4 Antibody-Positive Neuromyelitis Optica Spectrum Disorder, and Multiple Sclerosis.

OBJECTIVE: To evaluate: (1) the distribution of gray matter (GM) atrophy in myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD), aquaporin-4 antibody-positive neuromyelitis optica spectrum disorder (AQP4+NMOSD), and relapsing-remitting multiple sclerosis (RRMS); and (2) the relationship between GM volumes and white matter lesions in various brain regions within each disease. METHODS: A retrospective, multicenter analysis of magnetic resonance imaging data included patients with MOGAD/AQP4+NMOSD/RRMS in non-acute disease stage. Voxel-wise analyses and general linear models were used to evaluate the relevance of regional GM atrophy. For significant results (p < 0.05), volumes of atrophic areas are reported. RESULTS: We studied 135 MOGAD patients, 135 AQP4+NMOSD, 175 RRMS, and 144 healthy controls (HC). Compared with HC, MOGAD showed lower GM volumes in the temporal lobes, deep GM, insula, and cingulate cortex (75.79 cm3); AQP4+NMOSD in the occipital cortex (32.83 cm3); and RRMS diffusely in the GM (260.61 cm3). MOGAD showed more pronounced temporal cortex atrophy than RRMS (6.71 cm3), whereas AQP4+NMOSD displayed greater occipital cortex atrophy than RRMS (19.82 cm3). RRMS demonstrated more pronounced deep GM atrophy in comparison with MOGAD (27.90 cm3) and AQP4+NMOSD (47.04 cm3). In MOGAD, higher periventricular and cortical/juxtacortical lesions were linked to reduced temporal cortex, deep GM, and insula volumes. In RRMS, the diffuse GM atrophy was associated with lesions in all locations. AQP4+NMOSD showed no lesion/GM volume correlation. INTERPRETATION: GM atrophy is more widespread in RRMS compared with the other two conditions. MOGAD primarily affects the temporal cortex, whereas AQP4+NMOSD mainly involves the occipital cortex. In MOGAD and RRMS, lesion-related tract degeneration is associated with atrophy, but this link is absent in AQP4+NMOSD. ANN NEUROL 2024;96:276-288.

Multiscale networks in multiple sclerosis.

Complex diseases such as Multiple Sclerosis (MS) cover a wide range of biological scales, from genes and proteins to cells and tissues, up to the full organism. In fact, any phenotype for an organism is dictated by the interplay among these scales. We conducted a multilayer network analysis and deep phenotyping with multi-omics data (genomics, phosphoproteomics and cytomics), brain and retinal imaging, and clinical data, obtained from a multicenter prospective cohort of 328 patients and 90 healthy controls. Multilayer networks were constructed using mutual information for topological analysis, and Boolean simulations were constructed using Pearson correlation to identified paths within and among all layers. The path more commonly found from the Boolean simulations connects protein MK03, with total T cells, the thickness of the retinal nerve fiber layer (RNFL), and the walking speed. This path contains nodes involved in protein phosphorylation, glial cell differentiation, and regulation of stress-activated MAPK cascade, among others. Specific paths identified were subsequently analyzed by flow cytometry at the single-cell level. Combinations of several proteins (GSK3AB, HSBP1 or RS6) and immune cells (Th17, Th1 non-classic, CD8, CD8 Treg, CD56 neg, and B memory) were part of the paths explaining the clinical phenotype. The advantage of the path identified from the Boolean simulations is that it connects information about these known biological pathways with the layers at higher scales (retina damage and disability). Overall, the identified paths provide a means to connect the molecular aspects of MS with the overall phenotype.

Predictive value of retinal atrophy for cognitive decline across disease duration in multiple sclerosis.

BACKGROUND: We investigated the association between changes in retinal thickness and cognition in people with MS (PwMS), exploring the predictive value of optical coherence tomography (OCT) markers of neuroaxonal damage for global cognitive decline at different periods of disease. METHOD: We quantified the peripapillary retinal nerve fibre (pRFNL) and ganglion cell-inner plexiform (GCIPL) layers thicknesses of 207 PwMS and performed neuropsychological evaluations. The cohort was divided based on disease duration (≤5 years or >5 years). We studied associations between changes in OCT and cognition over time, and assessed the risk of cognitive decline of a pRFNL≤88 µm or GCIPL≤77 µm and its predictive value. RESULTS: Changes in pRFNL and GCIPL thickness over 3.2 years were associated with evolution of cognitive scores, in the entire cohort and in patients with more than 5 years of disease (p<0.01). Changes in cognition were related to less use of disease-modifying drugs, but not OCT metrics in PwMS within 5 years of onset. A pRFNL≤88 µm was associated with earlier cognitive disability (3.7 vs 9.9 years) and higher risk of cognitive deterioration (HR=1.64, p=0.022). A GCIPL≤77 µm was not associated with a higher risk of cognitive decline, but a trend was observed at ≤91.5 µm in PwMS with longer disease (HR=1.81, p=0.061). CONCLUSIONS: The progressive retinal thinning is related to cognitive decline, indicating that cognitive dysfunction is a late manifestation of accumulated neuroaxonal damage. Quantifying the pRFNL aids in identifying individuals at risk of cognitive dysfunction.

Serum biomarker levels predict disability progression in patients with primary progressive multiple sclerosis.

BACKGROUND: We aimed to investigate the potential of serum biomarker levels to predict disability progression in a multicentric real-world cohort of patients with primary progressive multiple sclerosis (PPMS). METHODS: A total of 141 patients with PPMS from 18 European MS centres were included. Disability progression was investigated using change in Expanded Disability Status Scale (EDSS) score over three time intervals: baseline to 2 years, 6 years and to the last follow-up. Serum levels of neurofilament light chain (sNfL), glial fibrillar acidic protein (sGFAP) and chitinase 3-like 1 (sCHI3L1) were measured using single-molecule array assays at baseline. Correlations between biomarker levels, and between biomarkers and age were quantified using Spearman's r. Univariable and multivariable linear models were performed to assess associations between biomarker levels and EDSS change over the different time periods. RESULTS: Median (IQR) age of patients was 52.9 (46.4-58.5) years, and 58 (41.1%) were men. Median follow-up time was 9.1 (7.0-12.6) years. Only 8 (5.7%) patients received treatment during follow-up. sNfL and sGFAP levels were moderately correlated (r=0.43) and both weakly correlated with sCHI3L1 levels (r=0.19 and r=0.17, respectively). In multivariable analyses, levels of the three biomarkers were associated with EDSS changes across all time periods. However, when analysis was restricted to non-inflammatory patients according to clinical and radiological parameters (n=64), only sCHI3L1 levels remained associated with future EDSS change. CONCLUSIONS: Levels of sNfL, sGFAP and sCHI3L1 are prognostic biomarkers associated with disability progression in patients with PPMS, being CHI3L1 findings less dependent on the inflammatory component associated with disease progression.

Clinical and MRI measures to identify non-acute MOG-antibody disease in adults.

MRI and clinical features of myelin oligodendrocyte glycoprotein (MOG)-antibody disease may overlap with those of other inflammatory demyelinating conditions posing diagnostic challenges, especially in non-acute phases and when serologic testing for MOG antibodies is unavailable or shows uncertain results. We aimed to identify MRI and clinical markers that differentiate non-acute MOG-antibody disease from aquaporin 4 (AQP4)-antibody neuromyelitis optica spectrum disorder and relapsing remitting multiple sclerosis, guiding in the identification of patients with MOG-antibody disease in clinical practice. In this cross-sectional retrospective study, data from 16 MAGNIMS centres were included. Data collection and analyses were conducted from 2019 to 2021. Inclusion criteria were: diagnosis of MOG-antibody disease; AQP4-neuromyelitis optica spectrum disorder and multiple sclerosis; brain and cord MRI at least 6 months from relapse; and Expanded Disability Status Scale (EDSS) score on the day of MRI. Brain white matter T2 lesions, T1-hypointense lesions, cortical and cord lesions were identified. Random forest models were constructed to classify patients as MOG-antibody disease/AQP4-neuromyelitis optica spectrum disorder/multiple sclerosis; a leave one out cross-validation procedure assessed the performance of the models. Based on the best discriminators between diseases, we proposed a guide to target investigations for MOG-antibody disease. One hundred and sixty-two patients with MOG-antibody disease [99 females, mean age: 41 (±14) years, median EDSS: 2 (0-7.5)], 162 with AQP4-neuromyelitis optica spectrum disorder [132 females, mean age: 51 (±14) years, median EDSS: 3.5 (0-8)], 189 with multiple sclerosis (132 females, mean age: 40 (±10) years, median EDSS: 2 (0-8)] and 152 healthy controls (91 females) were studied. In young patients (<34 years), with low disability (EDSS < 3), the absence of Dawson's fingers, temporal lobe lesions and longitudinally extensive lesions in the cervical cord pointed towards a diagnosis of MOG-antibody disease instead of the other two diseases (accuracy: 76%, sensitivity: 81%, specificity: 84%, P < 0.001). In these non-acute patients, the number of brain lesions < 6 predicted MOG-antibody disease versus multiple sclerosis (accuracy: 83%, sensitivity: 82%, specificity: 83%, P < 0.001). An EDSS < 3 and the absence of longitudinally extensive lesions in the cervical cord predicted MOG-antibody disease versus AQP4-neuromyelitis optica spectrum disorder (accuracy: 76%, sensitivity: 89%, specificity: 62%, P < 0.001). A workflow with sequential tests and supporting features is proposed to guide better identification of patients with MOG-antibody disease. Adult patients with non-acute MOG-antibody disease showed distinctive clinical and MRI features when compared to AQP4-neuromyelitis optica spectrum disorder and multiple sclerosis. A careful inspection of the morphology of brain and cord lesions together with clinical information can guide further analyses towards the diagnosis of MOG-antibody disease in clinical practice.

Using The Virtual Brain to study the relationship between structural and functional connectivity in patients with multiple sclerosis: a multicenter study.

The relationship between structural connectivity (SC) and functional connectivity (FC) captured from magnetic resonance imaging, as well as its interaction with disability and cognitive impairment, is not well understood in people with multiple sclerosis (pwMS). The Virtual Brain (TVB) is an open-source brain simulator for creating personalized brain models using SC and FC. The aim of this study was to explore SC-FC relationship in MS using TVB. Two different model regimes have been studied: stable and oscillatory, with the latter including conduction delays in the brain. The models were applied to 513 pwMS and 208 healthy controls (HC) from 7 different centers. Models were analyzed using structural damage, global diffusion properties, clinical disability, cognitive scores, and graph-derived metrics from both simulated and empirical FC. For the stable model, higher SC-FC coupling was associated with pwMS with low Single Digit Modalities Test (SDMT) score (F=3.48, P$\lt$0.05), suggesting that cognitive impairment in pwMS is associated with a higher SC-FC coupling. Differences in entropy of the simulated FC between HC, high and low SDMT groups (F=31.57, P$\lt$1e-5), show that the model captures subtle differences not detected in the empirical FC, suggesting the existence of compensatory and maladaptive mechanisms between SC and FC in MS.

Removing the effects of the site in brain imaging machine-learning - Measurement and extendable benchmark.

Multisite machine-learning neuroimaging studies, such as those conducted by the ENIGMA Consortium, need to remove the differences between sites to avoid effects of the site (EoS) that may prevent or fraudulently help the creation of prediction models, leading to impoverished or inflated prediction accuracy. Unfortunately, we have shown earlier that current Methods Aiming to Remove the EoS (MAREoS, e.g., ComBat) cannot remove complex EoS (e.g., including interactions between regions). And complex EoS may bias the accuracy. To overcome this hurdle, groups worldwide are developing novel MAREoS. However, we cannot assess their effectiveness because EoS may either inflate or shrink the accuracy, and MAREoS may both remove the EoS and degrade the data. In this work, we propose a strategy to measure the effectiveness of a MAREoS in removing different types of EoS. FOR MAREOS DEVELOPERS, we provide two multisite MRI datasets with only simple true effects (i.e., detectable by most machine-learning algorithms) and two with only simple EoS (i.e., removable by most MAREoS). First, they should use these datasets to fit machine-learning algorithms after applying the MAREoS. Second, they should use the formulas we provide to calculate the relative accuracy change associated with the MAREoS in each dataset and derive an EoS-removal effectiveness statistic. We also offer similar datasets and formulas for complex true effects and EoS that include first-order interactions. FOR MACHINE-LEARNING RESEARCHERS, we provide an extendable benchmark website to show: a) the types of EoS they should remove for each given machine-learning algorithm and b) the effectiveness of each MAREoS for removing each type of EoS. Relevantly, a MAREoS only able to remove the simple EoS may suffice for simple machine-learning algorithms, whereas more complex algorithms need a MAREoS that can remove more complex EoS. For instance, ComBat removes all simple EoS as needed for predictions based on simple lasso algorithms, but it leaves residual complex EoS that may bias the predictions based on standard support vector machine algorithms.

Longitudinal Retinal Changes in MOGAD.

OBJECTIVE: Patients with myelin oligodendrocyte glycoprotein antibody (MOG-IgG)-associated disease (MOGAD) suffer from severe optic neuritis (ON) leading to retinal neuro-axonal loss, which can be quantified by optical coherence tomography (OCT). We assessed whether ON-independent retinal atrophy can be detected in MOGAD. METHODS: Eighty patients with MOGAD and 139 healthy controls (HCs) were included. OCT data was acquired with (1) Spectralis spectral domain OCT (MOGAD: N = 66 and HCs: N = 103) and (2) Cirrus high-definition OCT (MOGAD: N = 14 and HCs: N = 36). Macular combined ganglion cell and inner plexiform layer (GCIPL) and peripapillary retinal nerve fiber layer (pRNFL) were quantified. RESULTS: At baseline, GCIPL and pRNFL were lower in MOGAD eyes with a history of ON (MOGAD-ON) compared with MOGAD eyes without a history of ON (MOGAD-NON) and HCs (p < 0.001). MOGAD-NON eyes had lower GCIPL volume compared to HCs (p < 0.001) in the Spectralis, but not in the Cirrus cohort. Longitudinally (follow-up up to 3 years), MOGAD-ON with ON within the last 6-12 months before baseline exhibited greater pRNFL thinning than MOGAD-ON with an ON greater than 12 months ago (p < 0.001). The overall MOGAD cohort did not exhibit faster GCIPL thinning compared with the HC cohort. INTERPRETATION: Our study suggests the absence of attack-independent retinal damage in patients with MOGAD. Yet, ongoing neuroaxonal damage or edema resolution seems to occur for up to 12 months after ON, which is longer than what has been reported with other ON forms. These findings support that the pathomechanisms underlying optic nerve involvement and the evolution of OCT retinal changes after ON is distinct in patients with MOGAD. ANN NEUROL 2022;92:476-485.

Diffusion-based structural connectivity patterns of multiple sclerosis phenotypes.

BACKGROUND: We aimed to describe the severity of the changes in brain diffusion-based connectivity as multiple sclerosis (MS) progresses and the microstructural characteristics of these networks that are associated with distinct MS phenotypes. METHODS: Clinical information and brain MRIs were collected from 221 healthy individuals and 823 people with MS at 8 MAGNIMS centres. The patients were divided into four clinical phenotypes: clinically isolated syndrome, relapsing-remitting, secondary progressive and primary progressive. Advanced tractography methods were used to obtain connectivity matrices. Then, differences in whole-brain and nodal graph-derived measures, and in the fractional anisotropy of connections between groups were analysed. Support vector machine algorithms were used to classify groups. RESULTS: Clinically isolated syndrome and relapsing-remitting patients shared similar network changes relative to controls. However, most global and local network properties differed in secondary progressive patients compared with the other groups, with lower fractional anisotropy in most connections. Primary progressive participants had fewer differences in global and local graph measures compared with clinically isolated syndrome and relapsing-remitting patients, and reductions in fractional anisotropy were only evident for a few connections. The accuracy of support vector machine to discriminate patients from healthy controls based on connection was 81%, and ranged between 64% and 74% in distinguishing among the clinical phenotypes. CONCLUSIONS: In conclusion, brain connectivity is disrupted in MS and has differential patterns according to the phenotype. Secondary progressive is associated with more widespread changes in connectivity. Additionally, classification tasks can distinguish between MS types, with subcortical connections being the most important factor.

Recent advances in the longitudinal segmentation of multiple sclerosis lesions on magnetic resonance imaging: a review.

Multiple sclerosis (MS) is a chronic autoimmune disease characterized by demyelinating lesions that are often visible on magnetic resonance imaging (MRI). Segmentation of these lesions can provide imaging biomarkers of disease burden that can help monitor disease progression and the imaging response to treatment. Manual delineation of MRI lesions is tedious and prone to subjective bias, while automated lesion segmentation methods offer objectivity and speed, the latter being particularly important when analysing large datasets. Lesion segmentation can be broadly categorised into two groups: cross-sectional methods, which use imaging data acquired at a single time-point to characterise MRI lesions; and longitudinal methods, which use imaging data from the same subject acquired at two or more different time-points to characterise lesions over time. The main objective of longitudinal segmentation approaches is to more accurately detect the presence of new MS lesions and the growth or remission of existing lesions, which may be effective biomarkers of disease progression and treatment response. This paper reviews articles on longitudinal MS lesion segmentation methods published over the past 10 years. These are divided into traditional machine learning methods and deep learning techniques. PubMed articles using longitudinal information and comparing fully automatic two time point segmentations in any step of the process were selected. Nineteen articles were reviewed. There is an increasing number of deep learning techniques for longitudinal MS lesion segmentation that are promising to help better understand disease progression.

Prognosis of a second clinical event from baseline MRI in patients with a CIS: a multicenter study using a machine learning approach.

PURPOSE: To predict the occurrence of a second clinical event in patients with a CIS suggestive of MS, from baseline magnetic resonance imaging (MRI), by means of a pattern recognition approach. METHODS: Two hundred sixty-six patients with a CIS were recruited from four participating centers. Over a follow-up of 3 years, 130 patients had a second clinical episode and 136 did not. Grey matter and white matter T1-hypointensities masks segmented from 3D T1-weighted images acquired on 3 T scanners were used as features for the classification approach. Differences between CIS that remained CIS and those that developed a second event were assessed at a global level and at a regional level, arranging the regions according to their contribution to the classification model. RESULTS: All classification metrics were around or even below 50% for both global and regional approaches. Accuracies did not change when T1-hypointensity maps were added to the model; just the specificity was increased up to 80%. Among the 30 regions with the largest contribution, 26 were grey matter and 4 were white matter regions. For grey matter, regions contributing showed either a larger or a smaller volume in the group of patients that remained CIS, compared to those with a second event. The volume of T1-hypointensities was always larger for the group that presented a second event. CONCLUSIONS: Prediction of a second clinical event in CIS patients from baseline MRI seems to present a highly heterogeneous pattern, leading to very low classification accuracies. Adding the T1-hypointensity maps does not seem to improve the accuracy of the classification model.

Immune tolerance in multiple sclerosis and neuromyelitis optica with peptide-loaded tolerogenic dendritic cells in a phase 1b trial.

There are adaptive T-cell and antibody autoimmune responses to myelin-derived peptides in multiple sclerosis (MS) and to aquaporin-4 (AQP4) in neuromyelitis optica spectrum disorders (NMOSDs). Strategies aimed at antigen-specific tolerance to these autoantigens are thus indicated for these diseases. One approach involves induction of tolerance with engineered dendritic cells (tolDCs) loaded with specific antigens. We conducted an in-human phase 1b clinical trial testing increasing concentrations of autologous tolDCs loaded with peptides from various myelin proteins and from AQP4. We tested this approach in 12 patients, 8 with MS and 4 with NMOSD. The primary end point was the safety and tolerability, while secondary end points were clinical outcomes (relapses and disability), imaging (MRI and optical coherence tomography), and immunological responses. Therapy with tolDCs was well tolerated, without serious adverse events and with no therapy-related reactions. Patients remained stable clinically in terms of relapse, disability, and in various measurements using imaging. We observed a significant increase in the production of IL-10 levels in PBMCs stimulated with the peptides as well as an increase in the frequency of a regulatory T cell, known as Tr1, by week 12 of follow-up. In this phase 1b trial, we concluded that the i.v. administration of peptide-loaded dendritic cells is safe and feasible. Elicitation of specific IL-10 production by peptide-specific T cells in MS and NMOSD patients indicates that a key element in antigen specific tolerance is activated with this approach. The results warrant further clinical testing in larger trials.

Fully automated delineation of the optic radiation for surgical planning using clinically feasible sequences.

Quadrantanopia caused by inadvertent severing of Meyer's Loop of the optic radiation is a well-recognised complication of temporal lobectomy for conditions such as epilepsy. Dissection studies indicate that the anterior extent of Meyer's Loop varies considerably between individuals. Quantifying this for individual patients is thus an important step to improve the safety profile of temporal lobectomies. Previous attempts to delineate Meyer's Loop using diffusion MRI tractography have had difficulty estimating its full anterior extent, required manual ROI placement, and/or relied on advanced diffusion sequences that cannot be acquired routinely in most clinics. Here we present CONSULT: a pipeline that can delineate the optic radiation from raw DICOM data in a completely automated way via a combination of robust pre-processing, segmentation, and alignment stages, plus simple improvements that bolster the efficiency and reliability of standard tractography. We tested CONSULT on 696 scans of predominantly healthy participants (539 unique brains), including both advanced acquisitions and simpler acquisitions that could be acquired in clinically acceptable timeframes. Delineations completed without error in 99.4% of the scans. The distance between Meyer's Loop and the temporal pole closely matched both averages and ranges reported in dissection studies for all tested sequences. Median scan-rescan error of this distance was 1 mm. When tested on two participants with considerable pathology, delineations were successful and realistic. Through this, we demonstrate not only how to identify Meyer's Loop with clinically feasible sequences, but also that this can be achieved without fundamental changes to tractography algorithms or complex post-processing methods.

Accelerated white matter lesion analysis based on simultaneous T1 and T2∗ quantification using magnetic resonance fingerprinting and deep learning.

PURPOSE: To develop an accelerated postprocessing pipeline for reproducible and efficient assessment of white matter lesions using quantitative magnetic resonance fingerprinting (MRF) and deep learning. METHODS: MRF using echo-planar imaging (EPI) scans with varying repetition and echo times were acquired for whole brain quantification of T1 and T2∗ in 50 subjects with multiple sclerosis (MS) and 10 healthy volunteers along 2 centers. MRF T1 and T2∗ parametric maps were distortion corrected and denoised. A CNN was trained to reconstruct the T1 and T2∗ parametric maps, and the WM and GM probability maps. RESULTS: Deep learning-based postprocessing reduced reconstruction and image processing times from hours to a few seconds while maintaining high accuracy, reliability, and precision. Mean absolute error performed the best for T1 (deviations 5.6%) and the logarithmic hyperbolic cosinus loss the best for T2∗ (deviations 6.0%). CONCLUSIONS: MRF is a fast and robust tool for quantitative T1 and T2∗ mapping. Its long reconstruction and several postprocessing steps can be facilitated and accelerated using deep learning.

A multidisciplinary registry of patients with autoimmune and immune-mediated diseases with symptomatic COVID-19 from a single center.

BACKGROUND AND AIM: There is increasing interest regarding SARS-CoV-2 infection in patients with autoimmune and immune-mediated inflammatory diseases (AI/IMID) with some discrepancies in different cohorts about their risk and outcomes. The aim was to describe a multidisciplinary cohort of patients with AI/IMID and symptomatic SARS-CoV-2 infection in a single tertiary center and analyze sociodemographic, clinical, and therapeutic factors associated with poor outcomes. METHODS: A retrospective observational study was conducted from the 1st of March until May 29th, 2020 in a University tertiary hospital in Barcelona, Spain. Patients with an underlying AI/IMID and symptomatic SARS-CoV-2 infection were identified in our local SARS-CoV-2 infection database. Controls (2:1) were selected from the same database and matched by age and gender. The primary outcome was severe SARS-CoV-2 infection, which was a composite endpoint including admission to the intensive care unit (ICU), need for mechanical ventilation (MV), and/or death. Several covariates including age, sex, and comorbidities among others were combined into a multivariate model having severe SARS-CoV-2 as the dependent variable. Also, a sensitivity analysis was performed evaluating AID and IMID separately. RESULTS: The prevalence of symptomatic SARS-CoV-2 infection in a cohort of AI/IMID patients was 1.3%. Eighty-five patients with AI/IMID and symptomatic SARS-CoV-2 were identified, requiring hospitalization in 58 (68%) cases. A total of 175 patients admitted for SARS-CoV-2 (58 with AI/IMID and 117 matched-controls) were analyzed. In logistic regression analysis, a significant inverse association between AI/IMID group and severe SARS-CoV-2 (OR 0.28; 95% CI 0.12-0.61; p = 0.001), need of MV (OR 0.20; IC 95% 0.05-0.71; p = 0.014), and ICU admission (OR 0.25; IC 95% 0.10-0.62; p = 0.003) was found. CONCLUSIONS: Patients with AI/IMID who require admission for SARS-CoV-2 infection have a lower risk of developing severe disease, including the need to stay in the ICU and MV.

Oligoclonal IgM bands in the cerebrospinal fluid of patients with relapsing MS to inform long-term MS disability.

BACKGROUND: Prognostic markers are needed to guide multiple sclerosis (MS) management in the context of large availability of disease-modifying drugs (DMDs). OBJECTIVE: To investigate the role of cerebrospinal fluid (CSF) markers to inform long-term MS outcomes. METHODS: Demographic features, IgM index, oligoclonal IgM bands (OCMB), lipid-specific OCMB, CSF neurofilament light chain protein levels, expanded disability status scale (EDSS), relapses and DMD use over the study period and peripapillary retinal nerve fiber layer (pRNFL) and ganglion cell plus inner plexiform layer (GCIPL) thicknesses in non-optic neuritis eyes (end of follow-up) were collected from relapsing MS (RMS) patients with CSF obtained ⩽2 years after MS onset prospectively followed at the Hospital Clinic of Barcelona. We assessed associations between CSF markers and MS outcomes using multivariable models. RESULTS: A total of 89 patients (71 females; median 32.9 years of age) followed over a median of 9.6 years were included. OCMB were associated with a 33% increase in the annualized relapse rate (ARR; p = 0.06), higher odds for high-efficacy DMDs use (OR = 4.8; 95% CI = (1.5, 16.1)), thinner pRNFL (ß = -4.4; 95% CI = (-8.6, -0.2)) and GCIPL (ß = -2.9; 95% CI = (-5.9, +0.05)), and higher rates to EDSS ⩾ 3.0 (HR = 4.4; 95% CI = (1.6, 11.8)) and EDSS ⩾ 4.0 (HR = 5.4; 95% CI = (1.1, 27.1)). No overall associations were found for other CSF markers. CONCLUSION: The presence of OCMB was associated with unfavorable long-term outcomes. OCMB should be determined in RMS to inform long-term prognosis.

Lesion probability mapping in MS patients using a regression network on MR fingerprinting.

BACKGROUND: To develop a regression neural network for the reconstruction of lesion probability maps on Magnetic Resonance Fingerprinting using echo-planar imaging (MRF-EPI) in addition to [Formula: see text], [Formula: see text], NAWM, and GM- probability maps. METHODS: We performed MRF-EPI measurements in 42 patients with multiple sclerosis and 6 healthy volunteers along two sites. A U-net was trained to reconstruct the denoised and distortion corrected [Formula: see text] and [Formula: see text] maps, and to additionally generate NAWM-, GM-, and WM lesion probability maps. RESULTS: WM lesions were predicted with a dice coefficient of [Formula: see text] and a lesion detection rate of [Formula: see text] for a threshold of 33%. The network jointly enabled accurate [Formula: see text] and [Formula: see text] times with relative deviations of 5.2% and 5.1% and average dice coefficients of [Formula: see text] and [Formula: see text] for NAWM and GM after binarizing with a threshold of 80%. CONCLUSION: DL is a promising tool for the prediction of lesion probability maps in a fraction of time. These might be of clinical interest for the WM lesion analysis in MS patients.

Targeted resequencing reveals rare variants enrichment in multiple sclerosis susceptibility genes.

Although genome-wide association studies have identified a number of common variants associated with multiple sclerosis (MS) susceptibility, little is known about the relevance of rare variants. Here, we aimed to explore the role of rare variants in 14 MS risk genes (FCRL1, RGS1, TIMMDC1, HHEX, CXCR5, LTBR, TSFM, GALC, TRAF3, STAT3, TNFSF14, IFI30, CD40, and CYP24A1) by targeted resequencing in an Iberian population of 524 MS cases and 546 healthy controls. Four rare variants-enriched regions within CYP24A1, FCRL1, RGS1, and TRAF3 were identified as significantly associated with MS. Functional studies revealed significantly decreased regulator of G protein signaling 1 (RGS1) gene expression levels in peripheral blood mononuclear cells from MS patients with RGS1 rare variants compared to noncarriers, whereas no significant differences in gene expression were observed for CYP24A1, FCRL1, and TRAF3 between rare variants carriers and noncarriers. Immunophenotyping showed significant decrease in RGS1 expression in peripheral blood B lymphocytes from MS patients with RGS1 rare variants relative to noncarriers. Lastly, peripheral blood mononuclear cell from MS patients carrying RGS1 rare variants showed significantly lower induction of RGS1 gene expression by interferon-ß compared to MS patients lacking RGS1 variants. The presence of rare variants in RGS1 reinforce the ideas of high genetic heterogeneity and a role of rare variants in MS pathogenesis.

Evaluation of treatment response in adults with relapsing MOG-Ab-associated disease.

BACKGROUND: Myelin oligodendrocyte glycoprotein antibodies (MOG-Ab) are related to several acquired demyelinating syndromes in adults, but the therapeutic approach is currently unclear. We aimed to describe the response to different therapeutic strategies in adult patients with relapsing MOG-Ab-associated disease. METHODS: This is a retrospective study conducted in France and Spain including 125 relapsing MOG-Ab patients aged ≥ 18 years. First, we performed a survival analysis to investigate the relapse risk between treated and non-treated patients, performing a propensity score method based on the inverse probability of treatment weighting. Second, we assessed the annualised relapse rates (ARR), Expanded Disability Status Scale (EDSS) and visual acuity pre-treatment and on/end-treatment. RESULTS: Median age at onset was 34.1 years (range 18.0-67.1), the female to male ratio was 1.2:1, and 96% were Caucasian. At 5 years, 84% (95% confidence interval [CI], 77.1-89.8) patients relapsed. At the last follow-up, 66 (52.8%) received maintenance therapy. Patients initiating immunosuppressants (azathioprine, mycophenolate mophetil [MMF], rituximab) were at lower risk of new relapse in comparison to non-treated patients (HR, 0.41; 95CI%, 0.20-0.82; p = 0.011). Mean ARR (standard deviation) was reduced from 1.05(1.20) to 0.43(0.79) with azathioprine (n = 11; p = 0.041), from 1.20(1.11) to 0.23(0.60) with MMF (n = 11; p = 0.033), and from 1.08(0.98) to 0.43(0.89) with rituximab (n = 26; p = 0.012). Other immunosuppressants (methotrexate/mitoxantrone/cyclophosphamide; n = 5), or multiple sclerosis disease-modifying drugs (MS-DMD; n = 9), were not associated with significantly reduced ARR. Higher rates of freedom of EDSS progression were observed with azathioprine, MMF or rituximab. CONCLUSION: In adults with relapsing MOG-Ab-associated disease, immunosuppressant therapy (azathioprine, MMF and rituximab) is associated with reduced risk of relapse and better disability outcomes. Such an effect was not found in the few patients treated with MS-DMD.

Hippocampal-related memory network in multiple sclerosis: A structural connectivity analysis.

BACKGROUND: We used graph theoretical analysis to quantify structural connectivity of the hippocampal-related episodic memory network and its association with memory performance in multiple sclerosis (MS) patients. METHODS: Brain diffusion and T1-weighted sequences were obtained from 71 MS patients and 50 healthy controls (HCs). A total of 30 gray matter regions (selected a priori) were used as seeds to perform probabilistic tractography and create connectivity matrices. Global, nodal, and edge graph theoretical properties were calculated. In patients, verbal and visuospatial memory was assessed. RESULTS: MS patients showed decreased network strength, assortativity, transitivity, global efficiency, and increased average path length. Several nodes had decreased strength and communicability in patients, whereas insula and left temporo-occipital cortex increased communicability. Patients had widespread decreased streamline count (SC) and communicability of edges, although a few ones increased their connectivity. Worse memory performance was associated with reduced network efficiency, decreased right hippocampus strength, and reduced SC and communicability of edges related to medial temporal lobe, thalamus, insula, and occipital cortex. CONCLUSION: Impaired structural connectivity occurs in the hippocampal-related memory network, decreasing the efficiency of information transmission. Network connectivity measures correlate with episodic memory, supporting the relevance of structural integrity in preserving memory processes in MS.