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.

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.

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.

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.

White matter changes measured by multi-component MR Fingerprinting in multiple sclerosis.

T2-hyperintense lesions are the key imaging marker of multiple sclerosis (MS). Previous studies have shown that the white matter surrounding such lesions is often also affected by MS. Our aim was to develop a new method to visualize and quantify the extent of white matter tissue changes in MS based on relaxometry properties. We applied a fast, multi-parametric quantitative MRI approach and used a multi-component MR Fingerprinting (MC-MRF) analysis. We assessed the differences in the MRF component representing prolongedrelaxation time between patients with MS and controls and studied the relation between this component's volume and structural white matter damage identified on FLAIR MRI scans in patients with MS. A total of 48 MS patients at two different sites and 12 healthy controls were scanned with FLAIR and MRF-EPI MRI scans. MRF scans were analyzed with a joint-sparsity multi-component analysis to obtain magnetization fraction maps of different components, representing tissues such as myelin water, white matter, gray matter and cerebrospinal fluid. In the MS patients, an additional component was identified with increased transverse relaxation times compared to the white matter, likely representing changes in free water content. Patients with MS had a higher volume of the long- component in the white matter of the brain compared to healthy controls (B (95%-CI) = 0.004 (0.0006-0.008), p = 0.02). Furthermore, this MRF component had a moderate correlation (correlation coefficient R 0.47) with visible structural white matter changes on the FLAIR scans. Also, the component was found to be more extensive compared to structural white matter changes in 73% of MS patients. In conclusion, our MRF acquisition and analysis captured white matter tissue changes in MS patients compared to controls. In patients these tissue changes were more extensive compared to visually detectable white matter changes on FLAIR scans. Our method provides a novel way to quantify the extent of white matter changes in MS patients, which is underestimated using only conventional clinical MRI scans.

mRNA COVID-19 Vaccination Does Not Exacerbate Symptoms or Trigger Neural Antibody Responses in Multiple Sclerosis.

BACKGROUND AND OBJECTIVE: In people with multiple sclerosis (pwMS), concern for potential disease exacerbation or triggering of other autoimmune disorders contributes to vaccine hesitancy. We assessed the humoral and T-cell responses to SARS-CoV-2 after mRNA vaccination, changes in disease activity, and development of antibodies against central or peripheral nervous system antigens. METHODS: This was a prospective 1-year longitudinal observational study of pwMS and a control group of patients with other inflammatory neurologic disorders (OIND) who received an mRNA vaccine. Blood samples were obtained before the first dose (T1), 1 month after the first dose (T2), 1 month after the second dose (T3), and 6 (T4), 9 (T5), and 12 (T6) months after the first dose. Patients were assessed for the immune-specific response, annualized relapse rate (ARR), and antibodies to onconeuronal, neural surface, glial, ganglioside, and nodo-paranodal antigens. RESULTS: Among 454 patients studied, 390 had MS (22 adolescents) and 64 OIND; the mean (SD) age was 44 (14) years; 315 (69%) were female; and 392 (87%) were on disease-modifying therapies. Antibodies to the receptor-binding domain were detected in 367 (86%) patients at T3 and 276 (83%) at T4. After a third dose, only 13 (22%) of 60 seronegative patients seroconverted, and 255 (92%) remained seropositive at T6. Cellular responses were present in 381 (93%) patients at T3 and in 235 (91%) patients at T6 including all those receiving anti-CD20 therapies and in 79% of patients receiving fingolimod. At T3 (429 patients) or T6 (395 patients), none of the patients had developed CNS autoantibodies. Seven patients had neural antibodies that were already present before immunization (3 adult patients with MS had MOG-IgG, 2 with MG and 1 with MS had neuronal cell surface antibodies [unknown antigen], and 1 with MS had myelin antibody reactivity [unknown antigen]. Similarly, no antibodies against PNS antigens were identified at T3 (427 patients). ARR was lower in MS and not significantly different in patients with OIND. Although 182 (40%) patients developed SARS-CoV-2 infection, no cases of severe COVID-19 or serious adverse events occurred. DISCUSSION: In this study, mRNA COVID-19 vaccination was safe and did not exacerbate the autoimmune disease nor triggered neural autoantibodies or immune-mediated neurologic disorders. The outcome of patients who developed SARS-CoV-2 infection was favorable.

Language impairments in people with autoimmune neurological diseases: A scoping review.

INTRODUCTION: Autoimmune neurological diseases (ANDs) are a specific type of autoimmune disease that affect cells within the central and peripheral nervous system. ANDs trigger various physical/neuropsychiatric symptoms. However, language impairments in people with ANDs are not well characterized. Here we aimed to determine the kinds of language impairment that most commonly emerge in 10 ANDs, the characteristics of the patients (demographic, neurological damage), and the assessment methods used. METHODS: We followed the PRISMA Extension for Scoping Reviews (PRISMA-ScR). PubMed and Google Scholar were searched. We used a list of search terms containing 10 types of ANDs (e.g., multiple sclerosis, acute disseminated encephalomyelitis) in combination with the terms aphasia, dysphasia, fluency, language, listening, morphology, phonology, pragmatics, reading, semantics, speaking, syntax, writing. The reference lists and citations of the relevant papers were also investigated. The type of AND, patient characteristics, neurological damage and examination technique, language tests administered, and main findings were noted for each study meeting the inclusion criteria. RESULTS: We found 171 studies meeting our inclusion criteria. These comprised group studies and case studies. Language impairments differed largely among types of ANDs. Neurological findings were mentioned in most of the papers, but specific language tests were rarely used. CONCLUSIONS: Language symptoms in people with ANDs are commonly reported. These are often not full descriptions or only focus on specific time points in the course of the disease. Future research needs to assess specific language functions in people with ANDs and relate their language impairments to brain damage at different stages of disease evolution.

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.

The Acute Optic Neuritis Network (ACON): Study protocol of a non-interventional prospective multicenter study on diagnosis and treatment of acute optic neuritis.

Optic neuritis (ON) often occurs at the presentation of multiple sclerosis (MS), neuromyelitis optica spectrum disorders (NMOSD), and myelin oligodendrocyte glycoprotein (MOG) antibody-associated disease (MOGAD). The recommended treatment of high-dose corticosteroids for ON is based on a North American study population, which did not address treatment timing or antibody serostatus. The Acute Optic Neuritis Network (ACON) presents a global, prospective, observational study protocol primarily designed to investigate the effect of time to high-dose corticosteroid treatment on 6-month visual outcomes in ON. Patients presenting within 30 days of the inaugural ON will be enrolled. For the primary analysis, patients will subsequently be assigned into the MS-ON group, the aquapotin-4-IgG positive ON (AQP4-IgG+ON) group or the MOG-IgG positive ON (MOG-IgG+ON) group and then further sub-stratified according to the number of days from the onset of visual loss to high-dose corticosteroids (days-to-Rx). The primary outcome measure will be high-contrast best-corrected visual acuity (HC-BCVA) at 6 months. In addition, multimodal data will be collected in subjects with any ON (CIS-ON, MS-ON, AQP4-IgG+ON or MOG-IgG+ON, and seronegative non-MS-ON), excluding infectious and granulomatous ON. Secondary outcomes include low-contrast best-corrected visual acuity (LC-BCVA), optical coherence tomography (OCT), magnetic resonance imaging (MRI) measurements, serum and cerebrospinal fluid (CSF) biomarkers (AQP4-IgG and MOG-IgG levels, neurofilament, and glial fibrillary protein), and patient reported outcome measures (headache, visual function in daily routine, depression, and quality of life questionnaires) at presentation at 6-month and 12-month follow-up visits. Data will be collected from 28 academic hospitals from Africa, Asia, the Middle East, Europe, North America, South America, and Australia. Planned recruitment consists of 100 MS-ON, 50 AQP4-IgG+ON, and 50 MOG-IgG+ON. This prospective, multimodal data collection will assess the potential value of early high-dose corticosteroid treatment, investigate the interrelations between functional impairments and structural changes, and evaluate the diagnostic yield of laboratory biomarkers. This analysis has the ability to substantially improve treatment strategies and the accuracy of diagnostic stratification in acute demyelinating ON. Trial registration: ClinicalTrials.gov, identifier: NCT05605951.

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.

Association of Serum Neurofilament Light Chain Levels at Disease Onset With Disability Worsening in Patients With a First Demyelinating Multiple Sclerosis Event Not Treated With High-Efficacy Drugs.

Importance: The value of serum neurofilament light chain (sNfL) levels for predicting long-term disability in patients with multiple sclerosis (MS) remains controversial. Objective: To assess whether high sNfL values are associated with disability worsening in patients who underwent their first demyelinating MS event. Design, Setting, and Participants: This multicenter cohort study included patients who underwent their first demyelinating event suggestive of MS at Hospital Universitario Ramón y Cajal (development cohort; June 1, 1994, to September 31, 2021, with follow-up until August 31, 2022) and 8 Spanish hospitals (validation cohort; October 1, 1995, to August 4, 2020, with follow-up until August 16, 2022). Exposures: Clinical evaluations at least every 6 months. Main Outcomes and Measures: The main outcomes were 6-month confirmed disability worsening (CDW) and an Expanded Disability Status Scale (EDSS) score of 3. Levels of sNfL were measured in blood samples obtained within 12 months after disease onset using a single molecule array kit. The cutoffs used were sNfL level of 10 pg/mL and a standardized score (z score) of 1.5. Multivariable Cox proportional hazards regression models were used to evaluate outcomes. Results: Of the 578 patients included in the study, 327 were in the development cohort (median age at sNfL analysis, 34.1 years [IQR, 27.2-42.7 years]; 226 female [69.1%]) and 251 patients were in the validation cohort (median age at sNfL analysis, 33.3 years [IQR, 27.4-41.5 years]; 184 female [73.3%]). The median follow-up was 7.10 years (IQR, 4.18-10.0 years). Levels of sNfL greater than 10 pg/mL were independently associated with higher risk of 6-month CDW and an EDSS of 3 in the development cohort (6-month CDW: hazard ratio [HR], 2.39; 95% CI, 1.39-4.12; P = .002; EDSS of 3: HR, 4.12; 95% CI, 2.18-7.77; P < .001) and the validation cohort (6-month CDW: HR, 1.61; 95% CI, 1.07-2.42; P = .02; EDSS of 3: HR, 2.03; 95% CI, 1.23-3.33; P = .005). Highly effective disease-modifying treatments were associated with lower risks of 6-month CDW and an EDSS of 3 in patients with high baseline sNfL values. Conclusions and Relevance: This cohort study found that high sNfL values obtained within the first year of disease were associated with long-term disability worsening in MS, suggesting that sNfL level measurement may help identify optimal candidates for highly effective disease-modifying treatments.

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.

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.

Susac Syndrome: Description of a Single-Centre Case Series.

This study describes the clinical characteristics, diagnostic results, treatment regimens, and clinical course of a cohort of patients with Susac syndrome (SS). It is a retrospective observational study of all patients with the diagnosis of SS evaluated at the Hospital Clinic (Barcelona, Spain) between March 2006 and November 2020. Nine patients were diagnosed with SS. The median time from the onset of the symptoms to diagnosis was five months (IQR 9.0), and the median follow-up time was 44 months (IQR 63.5). There was no clear predominance of sex, and mean age of symptoms onset was 36 years (range 19-59). Six patients (67%) presented with incomplete classical clinical triad, but this eventually developed in six patients during the disease course. Encephalopathy, focal neurological signs, visual disturbances, and hearing loss were the most frequent manifestations. Brain magnetic resonance imaging showed callosal lesions in all patients. Most were in remission within two years. Only four patients met the proposed criteria for definite SS. When SS is suspected, a detailed diagnostic workup should be performed and repeated over time to identify the clinical manifestations that will lead to a definite diagnosis.

Intense long-term training impairs brain health compared with moderate exercise: Experimental evidence and mechanisms.

The consequences of extremely intense long-term exercise for brain health remain unknown. We studied the effects of strenuous exercise on brain structure and function, its dose-response relationship, and mechanisms in a rat model of endurance training. Five-week-old male Wistar rats were assigned to moderate (MOD) or intense (INT) exercise or a sedentary (SED) group for 16 weeks. MOD rats showed the highest motivation and learning capacity in operant conditioning experiments; SED and INT presented similar results. In vivo MRI demonstrated enhanced global and regional connectivity efficiency and clustering as well as a higher cerebral blood flow (CBF) in MOD but not INT rats compared with SED. In the cortex, downregulation of oxidative phosphorylation complex IV and AMPK activation denoted mitochondrial dysfunction in INT rats. An imbalance in cortical antioxidant capacity was found between MOD and INT rats. The MOD group showed the lowest hippocampal brain-derived neurotrophic factor levels. The mRNA and protein levels of inflammatory markers were similar in all groups. In conclusion, strenuous long-term exercise yields a lesser improvement in learning ability than moderate exercise. Blunting of MOD-induced improvements in CBF and connectivity efficiency, accompanied by impaired mitochondrial energetics and, possibly, transient local oxidative stress, may underlie the findings in intensively trained rats.

Clinical characterisation of patients in the post-acute stage of anti-NMDA receptor encephalitis: a prospective cohort study and comparison with patients with schizophrenia spectrum disorders.

BACKGROUND: Anti-NMDA receptor (NMDAR) encephalitis is associated with a post-acute stage that is not well known. We aimed to describe the clinical features of this stage, similarities with schizophrenia spectrum disorders, and the factors that predict cognitive-psychiatric outcomes and could serve as prognostic biomarkers. METHODS: In this prospective cohort study, participants (aged 12-60 years) with anti-NMDAR encephalitis during the post-acute stage visited Hospital Clínic de Barcelona (Barcelona, Spain) on three occasions (at study entry [V1], at 6 months [V2], and at 12 months [V3]) and underwent comprehensive neuropsychiatric evaluations. Similar evaluations were done in a group of age-matched participants with schizophrenia spectrum disorders and a group of age-matched and sex-matched healthy participants also recruited from Hospital Clínic de Barcelona. We analysed differences between and within groups in the longitudinal follow-up using multilevel linear mixed-effect models, adjusting for group, age, sex, and socioeconomic status to control for possible confounding. FINDINGS: Between Jan 1, 2017, and Sept 30, 2020, 82 participants were recruited, 28 (34%) with anti-NMDAR encephalitis, 27 (33%) with schizophrenia spectrum disorders, and 27 (33%) healthy participants. Although, by V1 (median 4 months [IQR 3-7] from disease onset), many acute-stage symptoms in participants with anti-NMDAR encephalitis had resolved (acute stage median modified Rankin Scale [mRS] score 5 [IQR 4-5] vs V1 mRS score 2 [1-2]; p<0·0001), 25 (89%) participants showed deficits in at least one cognitive domain. In this group, 15 (68%) of 22 cognitive domain variables were impaired at V1, whereas only eight (36%) were altered at V3 (p=0·016). In participants with schizophrenia spectrum disorders, 11 (50%) of 22 variables (all shared with participants with anti-NMDAR encephalitis) were impaired at V1, without changes at V3. Two acute-stage features of anti-NMDAR encephalitis (ie, decreased consciousness and no improvement within the first 4 weeks of treatment) predicted cognitive domain outcomes, and a visuospatial task (ie, serial biases) at V1 showed potential in predicting learning and memory outcomes. At V1, all psychiatric symptom clusters were similarly altered in participants with anti-NMDAR encephalitis and in those with schizophrenia spectrum disorders, but only those in individuals with anti-NMDAR encephalitis subsequently improved (p=0·031). The greatest cognitive-psychiatric improvement in participants with anti-NMDAR encephalitis occurred between V1 and V2. During this interval, four (14%) participants with anti-NMDAR encephalitis would have met the diagnostic criteria of schizophrenia if CSF antibody findings had not been investigated. INTERPRETATION: The cognitive-psychiatric symptoms of anti-NMDAR encephalitis in the post-acute stage resembled those of stabilised schizophrenia, but only those in participants with anti-NMDAR encephalitis progressively improved, predominantly during V1-V2. These findings are important for clinical trials on anti-NMDAR encephalitis and suggest that prompt cognitive-psychosocial rehabilitation might be a valuable intervention. FUNDING: Instituto Salud Carlos III, NEURON Network of European Funding for Neuroscience Research, National Alliance for Research in Schizophrenia and Affective Disorders, and la Caixa Health-Research Foundation.

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.

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.

Cognitive Performance and Health-Related Quality of Life in Patients with Neuromyelitis Optica Spectrum Disorder.

Background: The frequency of cognitive impairment (CI) reported in neuromyelitis optica spectrum disorder (NMOSD) is highly variable, and its relationship with demographic and clinical characteristics is poorly understood. We aimed to describe the cognitive profile of NMOSD patients, and to analyse the cognitive differences according to their serostatus; furthermore, we aimed to assess the relationship between cognition, demographic and clinical characteristics, and other aspects linked to health-related quality of life (HRQoL). Methods: This cross-sectional study included 41 patients (median age, 44 years; 85% women) from 13 Spanish centres. Demographic and clinical characteristics were collected along with a cognitive z-score (Rao's Battery) and HRQoL patient-centred measures, and their relationship was explored using linear regression. We used the Akaike information criterion to model which characteristics were associated with cognition. Results: Fourteen patients (34%) had CI, and the most affected cognitive domain was visual memory. Cognition was similar in AQP4-IgG-positive and -negative patients. Gender, mood, fatigue, satisfaction with life, and perception of stigma were associated with cognitive performance (adjusted R2 = 0.396, p < 0.001). Conclusions: The results highlight the presence of CI and its impact on HRQoL in NMOSD patients. Cognitive and psychological assessments may be crucial to achieve a holistic approach in patient care.