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.

Prognostic value of single-subject grey matter networks in early multiple sclerosis.

The identification of prognostic markers in early multiple sclerosis (MS) is challenging and requires reliable measures that robustly predict future disease trajectories. Ideally, such measures should make inferences at the individual level to inform clinical decisions. This study investigated the prognostic value of longitudinal structural networks to predict 5-year Expanded Disability Status Scale (EDSS) progression in patients with relapsing-remitting MS (RRMS). We hypothesized that network measures, derived from MRI, outperform conventional MRI measurements at identifying patients at risk of developing disability progression. This longitudinal, multicentre study within the Magnetic Resonance Imaging in MS (MAGNIMS) network included 406 patients with RRMS (mean age = 35.7 ± 9.1 years) followed up for 5 years (mean follow-up = 5.0 ± 0.6 years). EDSS was determined to track disability accumulation. A group of 153 healthy subjects (mean age = 35.0 ± 10.1 years) with longitudinal MRI served as controls. All subjects underwent MRI at baseline and again 1 year after baseline. Grey matter atrophy over 1 year and white matter lesion load were determined. A single-subject brain network was reconstructed from T1-weighted scans based on grey matter atrophy measures derived from a statistical parameter mapping-based segmentation pipeline. Key topological measures, including network degree, global efficiency and transitivity, were calculated at single-subject level to quantify network properties related to EDSS progression. Areas under receiver operator characteristic (ROC) curves were constructed for grey matter atrophy and white matter lesion load, and the network measures and comparisons between ROC curves were conducted. The applied network analyses differentiated patients with RRMS who experience EDSS progression over 5 years through lower values for network degree [H(2) = 30.0, P < 0.001] and global efficiency [H(2) = 31.3, P < 0.001] from healthy controls but also from patients without progression. For transitivity, the comparisons showed no difference between the groups [H(2) = 1.5, P = 0.474]. Most notably, changes in network degree and global efficiency were detected independent of disease activity in the first year. The described network reorganization in patients experiencing EDSS progression was evident in the absence of grey matter atrophy. Network degree and global efficiency measurements demonstrated superiority of network measures in the ROC analyses over grey matter atrophy and white matter lesion load in predicting EDSS worsening (all P-values < 0.05). Our findings provide evidence that grey matter network reorganization over 1 year discloses relevant information about subsequent clinical worsening in RRMS. Early grey matter restructuring towards lower network efficiency predicts disability accumulation and outperforms conventional MRI predictors.

Predicting disease severity in multiple sclerosis using multimodal data and machine learning.

BACKGROUND: Multiple sclerosis patients would benefit from machine learning algorithms that integrates clinical, imaging and multimodal biomarkers to define the risk of disease activity. METHODS: We have analysed a prospective multi-centric cohort of 322 MS patients and 98 healthy controls from four MS centres, collecting disability scales at baseline and 2 years later. Imaging data included brain MRI and optical coherence tomography, and omics included genotyping, cytomics and phosphoproteomic data from peripheral blood mononuclear cells. Predictors of clinical outcomes were searched using Random Forest algorithms. Assessment of the algorithm performance was conducted in an independent prospective cohort of 271 MS patients from a single centre. RESULTS: We found algorithms for predicting confirmed disability accumulation for the different scales, no evidence of disease activity (NEDA), onset of immunotherapy and the escalation from low- to high-efficacy therapy with intermediate to high-accuracy. This accuracy was achieved for most of the predictors using clinical data alone or in combination with imaging data. Still, in some cases, the addition of omics data slightly increased algorithm performance. Accuracies were comparable in both cohorts. CONCLUSION: Combining clinical, imaging and omics data with machine learning helps identify MS patients at risk of disability worsening.

Immune cell subpopulations and serum neurofilament light chain are associated with increased risk of disease worsening in multiple sclerosis.

Changes is lymphocyte subpopulations in peripheral blood have been proposed as biomarkers for evaluation of disease activity in multiple sclerosis (MS). Serum neurofilament light chain (sNfL) is a biomarker reflecting neuro-axonal injury in MS that could be used to monitor disease activity, response to drugs and to prognosticate disease course. Here we show a moderate correlation between sNfL and lymphocyte cell subpopulations, and our data furthermore suggest that sNfL and specific immune cell subpopulations together could predict future disease worsening in MS.

T cell responses to SARS-CoV-2 vaccination differ by disease-modifying therapy for multiple sclerosis.

Immune responses in people with multiple sclerosis (pwMS) receiving disease-modifying therapies (DMTs) have been of significant interest throughout the COVID-19 pandemic. Lymphocyte-targeting immunotherapies, including anti-CD20 treatments and sphingosine-1-phosphate receptor (S1PR) modulators, attenuate Ab responses after vaccination. Evaluation of cellular responses after vaccination, therefore, is of particular importance in these populations. In this study, we used flow cytometry to analyze CD4 and CD8 T cell functional responses to SARS-CoV-2 spike peptides in healthy control study participants and pwMS receiving 5 different DMTs. Although pwMS receiving rituximab and fingolimod therapies had low Ab responses after both 2 and 3 vaccine doses, T cell responses in pwMS taking rituximab were preserved after a third vaccination, even when an additional dose of rituximab was administered between vaccine doses 2 and 3. PwMS taking fingolimod had low detectable T cell responses in peripheral blood. CD4 and CD8 T cell responses to SARS-CoV-2 variants of concern Delta and Omicron were lower than to the ancestral Wuhan-Hu-1 variant. Our results indicate the importance of assessing both cellular and humoral responses after vaccination and suggest that, even in the absence of robust Ab responses, vaccination can generate immune responses in pwMS.

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.

Quantitative proteomics reveals protein dysregulation during T cell activation in multiple sclerosis patients compared to healthy controls.

BACKGROUND: Multiple sclerosis (MS) is an autoimmune, neurodegenerative disorder with a strong genetic component that acts in a complex interaction with environmental factors for disease development. CD4+ T cells are pivotal players in MS pathogenesis, where peripherally activated T cells migrate to the central nervous system leading to demyelination and axonal degeneration. Through a proteomic approach, we aim at identifying dysregulated pathways in activated T cells from MS patients as compared to healthy controls. METHODS: CD4+ T cells were purified from peripheral blood from MS patients and healthy controls by magnetic separation. Cells were left unstimulated or stimulated in vitro through the TCR and costimulatory CD28 receptor for 24 h prior to sampling. Electrospray liquid chromatography-tandem mass spectrometry was used to measure protein abundances. RESULTS: Upon T cell activation the abundance of 1801 proteins was changed. Among these proteins, we observed an enrichment of proteins expressed by MS-susceptibility genes. When comparing protein abundances in T cell samples from healthy controls and MS patients, 18 and 33 proteins were differentially expressed in unstimulated and stimulated CD4+ T cells, respectively. Moreover, 353 and 304 proteins were identified as proteins exclusively induced upon T cell activation in healthy controls and MS patients, respectively and dysregulation of the Nur77 pathway was observed only in samples from MS patients. CONCLUSIONS: Our study highlights the importance of CD4+ T cell activation for MS, as proteins that change in abundance upon T cell activation are enriched for proteins encoded by MS susceptibility genes. The results provide evidence for proteomic disturbances in T cell activation in MS, and pinpoint to dysregulation of the Nur77 pathway, a biological pathway known to limit aberrant effector T cell responses.

Exploring Retinal Blood Vessel Diameters as Biomarkers in Multiple Sclerosis.

We aimed to determine whether retinal vessel diameters and retinal oxygen saturation in newly diagnosed patients with multiple sclerosis (pwMS) are different from those of a healthy population. Retinal blood vessel diameters were measured using imaging with a spectrophotometric non-invasive retinal oximeter. Twenty-three newly diagnosed untreated relapsing-remitting MS (RRMS) patients (mean age: 32.2 ± 7.5 years, age range = 18-50 years, 56.5% female) were measured and compared to 23 age- and sex-matched healthy controls (HCs) (mean age: 34.8 ± 8.1 years). Patients with Optic Neuritis were excluded. Retinal venular diameter (143.8 µm versus 157.8 µm: mean; p = 0.0013) and retinal arteriolar diameter (112.6 µm versus 120.6 µm: mean; p = 0.0089) were smaller in pwMS when compared with HCs, respectively. There was no significant difference in the oxygen saturation in retinal venules and arterioles in pwMS (mean: 60.0% and 93.7%; p = 0.5980) compared to HCs (mean: 59.3% and 91.5%; p = 0.8934), respectively. There was a significant difference in the median low contrast visual acuity (2.5% contrast) between the pwMS and the HC groups (p = 0.0143) Retinal arteriolar and venular diameter may have potential as objective biomarkers for MS.

Serum neurofilament light chain concentration predicts disease worsening in multiple sclerosis.

BACKGROUND: Serum neurofilament light (sNfL) chain is a promising biomarker reflecting neuro-axonal injury in multiple sclerosis (MS). However, the ability of sNfL to predict outcomes in real-world MS cohorts requires further validation. OBJECTIVE: The aim of the study is to investigate the associations of sNfL concentration, magnetic resonance imaging (MRI) and retinal optical coherence tomography (OCT) markers with disease worsening in a longitudinal European multicentre MS cohort. METHODS: MS patients (n = 309) were prospectively enrolled at four centres and re-examined after 2 years (n = 226). NfL concentration was measured by single molecule array assay in serum. The patients' phenotypes were thoroughly characterized with clinical examination, retinal OCT and MRI brain scans. The primary outcome was disease worsening at median 2-year follow-up. RESULTS: Patients with high sNfL concentrations (⩾8 pg/mL) at baseline had increased risk of disease worsening at median 2-year follow-up (odds ratio (95% confidence interval) = 2.8 (1.5-5.3), p = 0.001). We found no significant associations of MRI or OCT measures at baseline with risk of disease worsening. CONCLUSION: Serum NfL concentration was the only factor associated with disease worsening, indicating that sNfL is a useful biomarker in MS that might be relevant in a clinical setting.

Brain disconnectome mapping derived from white matter lesions and serum neurofilament light levels in multiple sclerosis: A longitudinal multicenter study.

BACKGROUND AND OBJECTIVES: Connectivity-based approaches incorporating the distribution and magnitude of the extended brain network aberrations caused by lesions may offer higher sensitivity for axonal damage in patients with multiple sclerosis (MS) than conventional lesion characteristics. Using individual brain disconnectome mapping, we tested the longitudinal associations between putative imaging-based brain network aberrations and levels of serum neurofilament light chain (NfL) as a neuroaxonal injury biomarker. METHODS: MS patients (n = 312, mean age 42.9 years, 71 % female) and healthy controls (HC) (n = 59, mean age 39.9 years, 78 % female) were prospectively enrolled at four European MS centres, and reassessed after two years (MS, n = 242; HC, n = 30). Post-processing of 3 Tesla (3 T) MRI data was performed at one centre using a harmonized pipeline, and disconnectome maps were calculated using BCBtoolkit based on individual lesion maps. Global disconnectivity (GD) was defined as the average disconnectome probability in each patient's white matter. Serum NfL concentrations were measured by single molecule array (Simoa). Robust linear mixed models (rLMM) with GD or T2-lesion volume (T2LV) as dependent variables, patient as a random factor, serum NfL, age, sex, timepoint for visit, diagnosis, treatment, and center as fixed factors were run. RESULTS: rLMM revealed significant associations between GD and serum NfL (t = 2.94, p = 0.003), age (t = 4.21, p = 2.5 × 10-5), and longitudinal changes in NfL (t = -2.29, p = 0.02), but not for sex (t = 0.63, p = 0.53) or treatments (t = 0.80-0.83, p = 0.41-0.42). Voxel-wise analyses revealed significant associations between dysconnectivity in cerebellar and brainstem regions and serum NfL (t = 7.03, p < 0.001). DISCUSSION: In our prospective multi-site MS cohort, rLMMs demonstrated that the extent of global and regional brain disconnectivity is sensitive to a systemic biomarker of axonal damage, serum NfL, in patients with MS. These findings provide a neuroaxonal correlate of advanced disconnectome mapping and provide a platform for further investigations of the functional and potential clinical relevance of brain disconnectome mapping in patients with brain disorders.

Deep neural networks learn general and clinically relevant representations of the ageing brain.

The discrepancy between chronological age and the apparent age of the brain based on neuroimaging data - the brain age delta - has emerged as a reliable marker of brain health. With an increasing wealth of data, approaches to tackle heterogeneity in data acquisition are vital. To this end, we compiled raw structural magnetic resonance images into one of the largest and most diverse datasets assembled (n=53542), and trained convolutional neural networks (CNNs) to predict age. We achieved state-of-the-art performance on unseen data from unknown scanners (n=2553), and showed that higher brain age delta is associated with diabetes, alcohol intake and smoking. Using transfer learning, the intermediate representations learned by our model complemented and partly outperformed brain age delta in predicting common brain disorders. Our work shows we can achieve generalizable and biologically plausible brain age predictions using CNNs trained on heterogeneous datasets, and transfer them to clinical use cases.

Risk of fingolimod rebound after switching to cladribine or rituximab in multiple sclerosis.

BACKGROUND: A sudden onset of extensive disease activity, including severe clinical relapse and extensive brain or spinal magnetic resonance imaging (MRI) lesions, termed "rebound" disease activity has been reported after withdrawal of fingolimod in patients with multiple sclerosis (MS). OBJECTIVE: To compare the risk of rebound after switching from fingolimod to cladribine or rituximab in MS. METHODS: All patients switching from fingolimod to cladribine or rituximab were included in a retrospective cohort study utilizing prospectively collected data from two university hospitals with different treatment strategies. RESULTS: A total of 73 patients with at least 6 months follow-up after switching were identified, 33 patients had switched from fingolimod to cladribine and 40 patients to rituximab. No patients in the rituximab group and seven (21.1%) in the cladribine group qualified for rebound disease activity. Ten (30.3%) of the patients using cladribine and five (12.5%) of the patients using rituximab experienced a relapse. MRI disease activity was seen in 18 (54.5%) and eight (20.0%) of the patients using cladribine and rituximab, respectively. Younger age and previous high relapse rate were associated with increased risk of rebound in the cladribine group. CONCLUSIONS: We identify a lower risk of rebound during the first year after switching from fingolimod to rituximab compared to cladribine, indicating a better initial clinical outcome with the former treatment strategy.

Cardiometabolic risk factors associated with brain age and accelerate brain ageing.

The structure and integrity of the ageing brain is interchangeably linked to physical health, and cardiometabolic risk factors (CMRs) are associated with dementia and other brain disorders. In this mixed cross-sectional and longitudinal study (interval mean = 19.7 months), including 790 healthy individuals (mean age = 46.7 years, 53% women), we investigated CMRs and health indicators including anthropometric measures, lifestyle factors, and blood biomarkers in relation to brain structure using MRI-based morphometry and diffusion tensor imaging (DTI). We performed tissue specific brain age prediction using machine learning and performed Bayesian multilevel modeling to assess changes in each CMR over time, their respective association with brain age gap (BAG), and their interaction effects with time and age on the tissue-specific BAGs. The results showed credible associations between DTI-based BAG and blood levels of phosphate and mean cell volume (MCV), and between T1-based BAG and systolic blood pressure, smoking, pulse, and C-reactive protein (CRP), indicating older-appearing brains in people with higher cardiometabolic risk (smoking, higher blood pressure and pulse, low-grade inflammation). Longitudinal evidence supported interactions between both BAGs and waist-to-hip ratio (WHR), and between DTI-based BAG and systolic blood pressure and smoking, indicating accelerated ageing in people with higher cardiometabolic risk (smoking, higher blood pressure, and WHR). The results demonstrate that cardiometabolic risk factors are associated with brain ageing. While randomized controlled trials are needed to establish causality, our results indicate that public health initiatives and treatment strategies targeting modifiable cardiometabolic risk factors may also improve risk trajectories and delay brain ageing.

Functional connectivity in multiple sclerosis modelled as connectome stability: A 5-year follow-up study.

BACKGROUND: Brain functional connectivity (FC) in multiple sclerosis (MS) is abnormal compared to healthy controls (HCs). More longitudinal studies in MS are needed to evaluate whether FC stability is clinically relevant. OBJECTIVE: To compare functional magnetic resonance imaging (fMRI)-based FC between MS and HC, and to determine the relationship between longitudinal FC changes and structural brain damage, cognitive performance and physical disability. METHODS: T1-weighted MPRAGE and resting-state fMRI (1.5T) were acquired from 70 relapsing-remitting MS patients and 94 matched HC at baseline (mean months since diagnosis 14.0 ± 11) and from 60 MS patients after 5 years. Independent component analysis and network modelling were used to measure longitudinal FC stability and cross-sectional comparisons with HC. Linear mixed models, adjusted for age and sex, were used to calculate correlations. RESULTS: At baseline, patients with MS showed FC abnormalities both within networks and in single connections compared to HC. Longitudinal analyses revealed functional stability and no significant relationships with clinical disability, cognitive performance, lesion or brain volume. CONCLUSION: FC abnormalities occur already at the first decade of MS, yet we found no relevant clinical correlations for these network deviations. Future large-scale longitudinal fMRI studies across a range of MS subtypes and outcomes are required.

Blood neurofilament light concentration at admittance: a potential prognostic marker in COVID-19.

OBJECTIVE: To test the hypotheses that blood biomarkers for nervous system injury, serum concentrations of neurofilament light chain protein (NfL) and glial fibrillary acidic protein (GFAp) can serve as biomarkers for disease severity in COVID-19 patients. METHODS: Forty-seven inpatients with confirmed COVID-19 had blood samples drawn on admission for assessing serum biomarkers of CNS injury by Single molecule array (Simoa), NfL and GFAp. Concentrations of NfL and GFAp were analyzed in relation to symptoms, clinical signs, inflammatory biomarkers and clinical outcomes. We used multivariate linear models to test for differences in biomarker concentrations in the subgroups, accounting for confounding effects. RESULTS: In total, 21% (n = 10) of the patients were admitted to an intensive care unit, and the overall mortality rate was 13% (n = 6). Non-survivors had higher serum concentrations of NfL (p < 0.001) upon admission than patients who were discharged alive both in adjusted analyses (p = 2.6 × 10-7) and unadjusted analyses (p = 0.001). The concentrations of NfL in non-survivors increased over repeated measurements; whereas, the concentrations in survivors were stable. The GFAp concentration was also significantly higher in non-survivors than survivors (p = 0.02). CONCLUSION: Increased concentrations of NfL and GFAp in COVID-19 patients on admission may indicate increased mortality risk. Measurement of blood biomarkers for nervous system injury can be useful to detect and monitor CNS injury in COVID-19.

LesionQuant for Assessment of MRI in Multiple Sclerosis-A Promising Supplement to the Visual Scan Inspection.

Background and Goals: Multiple sclerosis (MS) is a central nervous system inflammatory disease where magnetic resonance imaging (MRI) is an important tool for diagnosis and disease monitoring. Quantitative measurements of lesion volume, lesion count, distribution of lesions, and brain atrophy have a potentially significant value for evaluating disease progression. We hypothesize that utilizing software designed for evaluating MRI data in MS will provide more accurate and detailed analyses compared to the visual neuro-radiological evaluation. Methods: A group of 56 MS patients (mean age 35 years, 70% females and 96% relapsing-remitting MS) was examined with brain MRI one and 5 years after diagnosis. The T1 and FLAIR brain MRI sequences for all patients were analyzed using the LesionQuant (LQ) software. These data were compared with data from structured visual evaluations of the MRI scans performed by neuro-radiologists, including assessments of atrophy, and lesion count. The data from LQ were also compared with data from other validated research methods for brain segmentation, including assessments of whole brain volume and lesion volume. Correlations with clinical tests like the timed 25-foot walk test (T25FT) were performed to explore additional value of LQ analyses. Results: Lesion count assessments by LQ and by the neuro-radiologist were significantly correlated one year (cor = 0.92, p = 2.2 × 10-16) and 5 years (cor = 0.84, p = 2.7 × 10-16) after diagnosis. Analyzes of the intra- and interrater variability also correlated significantly (cor = 0.96, p < 0.001, cor = 0.97, p < 0.001). Significant positive correlation was found between lesion volume measured by LQ and by the software Cascade (cor = 0.7, p < 0.001. LQ detected a reduction in whole brain percentile >10 in 10 patients across the time-points, whereas the neuro-radiologist assessment identified six of these. The neuro-radiologist additionally identified five patients with increased atrophy in the follow-up period, all of them displayed decreasing low whole brain percentiles (median 11, range 8-28) in the LQ analysis. Significant positive correlation was identified between lesion volume measured by LQ and test performance on the T25FT both at 1 and 5 years after diagnosis. Conclusion: For the number of MS lesions at both time-points, we demonstrated strong correlations between the assessments done by LQ and the neuro-radiologist. Lesion volume evaluated with LQ correlated with T25FT performance. LQ-analyses classified more patients to have brain atrophy than the visual neuro-radiological evaluation. In conclusion, LQ seems like a promising supplement to the evaluation performed by neuro-radiologists, providing an automated tool for evaluating lesions in MS patients and also detecting early signs of atrophy in both a longitudinal and cross-sectional setting.

The genetic architecture of human brainstem structures and their involvement in common brain disorders.

Brainstem regions support vital bodily functions, yet their genetic architectures and involvement in common brain disorders remain understudied. Here, using imaging-genetics data from a discovery sample of 27,034 individuals, we identify 45 brainstem-associated genetic loci, including the first linked to midbrain, pons, and medulla oblongata volumes, and map them to 305 genes. In a replication sample of 7432 participants most of the loci show the same effect direction and are significant at a nominal threshold. We detect genetic overlap between brainstem volumes and eight psychiatric and neurological disorders. In additional clinical data from 5062 individuals with common brain disorders and 11,257 healthy controls, we observe differential volume alterations in schizophrenia, bipolar disorder, multiple sclerosis, mild cognitive impairment, dementia, and Parkinson's disease, supporting the relevance of brainstem regions and their genetic architectures in common brain disorders.

Impact of treatment on cellular immunophenotype in MS: A cross-sectional study.

OBJECTIVE: To establish cytometry profiles associated with disease stages and immunotherapy in MS. METHODS: Demographic/clinical data and peripheral blood samples were collected from 227 patients with MS and 82 sex- and age-matched healthy controls (HCs) enrolled in a cross-sectional study at 4 European MS centers (Spain, Italy, Germany, and Norway). Flow cytometry of isolated peripheral blood mononuclear cells was performed in each center using specifically prepared antibody-cocktail Lyotubes; data analysis was centralized at the Genoa center. Differences in immune cell subsets were assessed between groups of untreated patients with relapsing-remitting or progressive MS (RRMS or PMS) and HCs and between groups of patients with RRMS taking 6 commonly used disease-modifying drugs. RESULTS: In untreated patients with MS, significantly higher frequencies of Th17 cells in the RRMS population compared with HC and lower frequencies of B-memory/B-regulatory cells as well as higher percentages of B-mature cells in patients with PMS compared with HCs emerged. Overall, the greatest deviation in immunophenotype in MS was observed by treatment rather than disease course, with the strongest impact found in fingolimod-treated patients. Fingolimod induced a decrease in total CD4+ T cells and in B-mature and B-memory cells and increases in CD4+ and CD8+ T-regulatory and B-regulatory cells. CONCLUSIONS: Our highly standardized, multisite cytomics data provide further understanding of treatment impact on MS immunophenotype and could pave the way toward monitoring immune cells to help clinical management of MS individuals.

Publisher Correction: Common brain disorders are associated with heritable patterns of apparent aging of the brain.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.