Individual Prognostication of Disease Activity and Disability Worsening in Multiple Sclerosis With Retinal Layer Thickness z Scores.

BACKGROUND AND OBJECTIVES: Retinal optical coherence tomography (OCT) provides promising prognostic imaging biomarkers for future disease activity in multiple sclerosis (MS). However, raw OCT-derived measures have multiple dependencies, supporting the need for establishing reference values adjusted for possible confounders. The purpose of this study was to investigate the capacity for age-adjusted z scores of OCT-derived measures to prognosticate future disease activity and disability worsening in people with MS (PwMS). METHODS: We established age-adjusted OCT reference data using generalized additive models for location, scale, and shape for peripapillary retinal nerve fiber layer (pRNFL) and ganglion cell-inner plexiform layer (GCIP) thicknesses, involving 910 and 423 healthy eyes, respectively. Next, we transformed the retinal layer thickness of PwMS from 3 published studies into age-adjusted z scores (pRNFL-z and GCIP-z) based on the reference data. Finally, we investigated the association of pRNFL-z or GCIP-z as predictors with future confirmed disability worsening (Expanded Disability Status Scale score increase) or disease activity (failing of the no evidence of disease activity [NEDA-3] criteria) as outcomes. Cox proportional hazards models or logistic regression analyses were applied according to the original studies. Optimal cutoffs were identified using the Akaike information criterion as well as location with the log-rank and likelihood-ratio tests. RESULTS: In the first cohort (n = 863), 172 PwMS (24%) had disability worsening over a median observational period of 2.0 (interquartile range [IQR]:1.0-3.0) years. Low pRNFL-z (≤-2.04) were associated with an increased risk of disability worsening (adjusted hazard ratio (aHR) [95% CI] = 2.08 [1.47-2.95], p = 3.82e-5). In the second cohort (n = 170), logistic regression analyses revealed that lower pRNFL-z showed a higher likelihood for disability accumulation at the two-year follow-up (reciprocal odds ratio [95% CI] = 1.51[1.06-2.15], p = 0.03). In the third cohort (n = 78), 46 PwMS (59%) did not maintain the NEDA-3 status over a median follow-up of 2.0 (IQR: 1.9-2.1) years. PwMS with low GCIP-z (≤-1.03) had a higher risk of showing disease activity (aHR [95% CI] = 2.14 [1.03-4.43], p = 0.04). Compared with raw values with arbitrary cutoffs, applying the z score approach with optimal cutoffs showed better performance in discrimination and calibration (higher Harrell's concordance index and lower integrated Brier score). DISCUSSION: In conclusion, our work demonstrated reference cohort-based z scores that account for age, a major driver for disease progression in MS, to be a promising approach for creating OCT-derived measures useable across devices and toward individualized prognostication.

Altered brain perfusion and oxygen levels relate to sleepiness and attention in post-COVID syndrome.

OBJECTIVE: Persisting neurological symptoms after COVID-19 affect up to 10% of patients and can manifest in fatigue and cognitive complaints. Based on recent evidence, we evaluated whether cerebral hemodynamic changes contribute to post-COVID syndrome (PCS). METHODS: Using resting-state functional magnetic resonance imaging, we investigated brain perfusion and oxygen level estimates in 47 patients (44.4 ± 11.6 years; F:M = 38:9) and 47 individually matched healthy control participants. Group differences were calculated using two-sample t-tests. Multivariable linear regression was used for associations of each regional perfusion and oxygen level measure with cognition and sleepiness measures. Exploratory hazard ratios were calculated for each brain metric with clinical measures. RESULTS: Patients presented with high levels of fatigue (79%) and daytime sleepiness (45%). We found widespread decreased brain oxygen levels, most evident in the white matter (false discovery rate adjusted-p-value (p-FDR) = 0.038) and cortical grey matter (p-FDR = 0.015). Brain perfusion did not differ between patients and healthy participants. However, delayed patient caudate nucleus perfusion was associated with better executive function (p-FDR = 0.008). Delayed perfusion in the cortical grey matter and hippocampus were associated with a reduced risk of daytime sleepiness (hazard ratio (HR) = 0.07, p = 0.037 and HR = 0.06, p = 0.034). Decreased putamen oxygen levels were associated with a reduced risk of poor cognitive outcome (HR = 0.22, p = 0.019). Meanwhile, lower thalamic oxygen levels were associated with a higher risk of cognitive fatigue (HR = 6.29, p = 0.017). INTERPRETATION: Our findings of lower regional brain blood oxygen levels suggest increased cerebral metabolism in PCS, which potentially holds a compensatory function. These hemodynamic changes were related to symptom severity, possibly representing metabolic adaptations.

Evaluation of machine learning-based classification of clinical impairment and prediction of clinical worsening in multiple sclerosis.

BACKGROUND: Robust predictive models of clinical impairment and worsening in multiple sclerosis (MS) are needed to identify patients at risk and optimize treatment strategies. OBJECTIVE: To evaluate whether machine learning (ML) methods can classify clinical impairment and predict worsening in people with MS (pwMS) and, if so, which combination of clinical and magnetic resonance imaging (MRI) features and ML algorithm is optimal. METHODS: We used baseline clinical and structural MRI data from two MS cohorts (Berlin: n = 125, Amsterdam: n = 330) to evaluate the capability of five ML models in classifying clinical impairment at baseline and predicting future clinical worsening over a follow-up of 2 and 5 years. Clinical worsening was defined by increases in the Expanded Disability Status Scale (EDSS), Timed 25-Foot Walk Test (T25FW), 9-Hole Peg Test (9HPT), or Symbol Digit Modalities Test (SDMT). Different combinations of clinical and volumetric MRI measures were systematically assessed in predicting clinical outcomes. ML models were evaluated using Monte Carlo cross-validation, area under the curve (AUC), and permutation testing to assess significance. RESULTS: The ML models significantly determined clinical impairment at baseline for the Amsterdam cohort, but did not reach significance for predicting clinical worsening over a follow-up of 2 and 5 years. High disability (EDSS ≥ 4) was best determined by a support vector machine (SVM) classifier using clinical and global MRI volumes (AUC = 0.83 ± 0.07, p = 0.015). Impaired cognition (SDMT Z-score ≤ -1.5) was best determined by a SVM using regional MRI volumes (thalamus, ventricles, lesions, and hippocampus), reaching an AUC of 0.73 ± 0.04 (p = 0.008). CONCLUSION: ML models could aid in classifying pwMS with clinical impairment and identify relevant biomarkers, but prediction of clinical worsening is an unmet need.

Multiple sclerosis endophenotypes identified by high-dimensional blood signatures are associated with distinct disease trajectories.

One of the biggest challenges in managing multiple sclerosis is the heterogeneity of clinical manifestations and progression trajectories. It still remains to be elucidated whether this heterogeneity is reflected by discrete immune signatures in the blood as a surrogate of disease pathophysiology. Accordingly, individualized treatment selection based on immunobiological principles is still not feasible. Using two independent multicentric longitudinal cohorts of patients with early multiple sclerosis (n = 309 discovery and n = 232 validation), we were able to identify three distinct peripheral blood immunological endophenotypes by a combination of high-dimensional flow cytometry and serum proteomics, followed by unsupervised clustering. Longitudinal clinical and paraclinical follow-up data collected for the cohorts revealed that these endophenotypes were associated with disease trajectories of inflammation versus early structural damage. Investigating the capacity of immunotherapies to normalize endophenotype-specific immune signatures revealed discrete effect sizes as illustrated by the limited effect of interferon-ß on endophenotype 3-related immune signatures. Accordingly, patients who fell into endophenotype 3 subsequently treated with interferon-ß exhibited higher disease progression and MRI activity over a 4-year follow-up compared with treatment with other therapies. We therefore propose that ascertaining a patient's blood immune signature before immunomodulatory treatment initiation may facilitate prediction of clinical disease trajectories and enable personalized treatment decisions based on pathobiological principles.

CD4+ T cell mitochondrial genotype in Multiple Sclerosis: a cross-sectional and longitudinal analysis.

Multiple Sclerosis (MS) is a chronic autoimmune demyelinating disease of the central nervous system (CNS), with a largely unknown etiology, where mitochondrial dysfunction likely contributes to neuroaxonal loss and brain atrophy. Mirroring the CNS, peripheral immune cells from patients with MS, particularly CD4+ T cells, show inappropriate mitochondrial phenotypes and/or oxidative phosphorylation (OxPhos) insufficiency, with a still unknown contribution of mitochondrial DNA (mtDNA). We hypothesized that mitochondrial genotype in CD4+ T cells might influence MS disease activity and progression. Thus, we performed a retrospective cross-sectional and longitudinal study on patients with a recent diagnosis of either Clinically Isolated Syndrome (CIS) or Relapsing-Remitting MS (RRMS) at two timepoints: 6 months (VIS1) and 36 months (VIS2) after disease onset. Our primary outcomes were the differences in mtDNA extracted from CD4+ T cells between: (I) patients with CIS/RRMS (PwMS) at VIS1 and age- and sex-matched healthy controls (HC), in the cross-sectional analysis, and (II) different diagnostic evolutions in PwMS from VIS1 to VIS2, in the longitudinal analysis. We successfully performed mtDNA whole genome sequencing (mean coverage: 2055.77 reads/base pair) in 183 samples (61 triplets). Nonetheless, mitochondrial genotype was not associated with a diagnosis of CIS/RRMS, nor with longitudinal diagnostic evolution.

Regional spinal cord volumes and pain profiles in AQP4-IgG + NMOSD and MOGAD.

Objective: Aquaporin-4-antibody-seropositive (AQP4-IgG+) Neuromyelitis Optica Spectrum Disorder (NMOSD) and Myelin Oligodendrocyte Glycoprotein Antibody-Associated Disorder (MOGAD) are relapsing neuroinflammatory diseases, frequently leading to chronic pain. In both diseases, the spinal cord (SC) is often affected by myelitis attacks. We hypothesized that regional SC volumes differ between AQP4-IgG + NMOSD and MOGAD and that pain intensity is associated with lower SC volumes. To evaluate changes in the SC white matter (WM), gray matter (GM), and pain intensity in patients with recent relapses (myelitis or optic neuritis), we further profiled phenotypes in a case series with longitudinal imaging and clinical data. Methods: Cross-sectional data from 36 participants were analyzed in this retrospective study, including 20 AQP4-IgG + NMOSD and 16 MOGAD patients. Pain assessment was performed in all patients by the Brief Pain Inventory and painDETECT questionnaires. Segmentation of SC WM, GM, cervical cord volumes (combined volume of WM + GM) was performed at the C2/C3 cervical level. WM% and GM% were calculated using the cervical cord volume as a whole per patient. The presence of pain, pain severity, and clinical disability was evaluated and tested for associations with SC segmentations. Additionally, longitudinal data were deeply profiled in a case series of four patients with attacks between two MRI visits within one year. Results: In AQP4-IgG + NMOSD, cervical cord volume was associated with mean pain severity within 24 h (ß = -0.62, p = 0.009) and with daily life pain interference (ß = -0.56, p = 0.010). Cross-sectional analysis showed no statistically significant SC volume differences between AQP4-IgG + NMOSD and MOGAD. However, in AQP4-IgG + NMOSD, SC WM% tended to be lower with increasing time from the last attack (ß = -0.41, p = 0.096). This tendency was not observed in MOGAD. Our case series including two AQP4-IgG + NMOSD patients revealed SC GM% increased by roughly 2% with either a myelitis or optic neuritis attack between visits. Meanwhile, GM% decreased by 1-2% in two MOGAD patients with a myelitis attack between MRI visits. Conclusion: In AQP4-IgG + NMOSD, lower cervical cord volume was associated with increased pain. Furthermore, cord GM changes were detected between MRI visits in patients with disease-related attacks in both groups. Regional SC MRI measures are pertinent for monitoring disease-related cord pathology in AQP4-IgG + NMOSD and MOGAD.

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.

Cortical Thickness Patterns of Cognitive Impairment Phenotypes in Drug-Resistant Temporal Lobe Epilepsy.

OBJECTIVE: In temporal lobe epilepsy (TLE), a taxonomy classifying patients into 3 cognitive phenotypes has been adopted: minimally, focally, or multidomain cognitively impaired (CI). We examined gray matter (GM) thickness patterns of cognitive phenotypes in drug-resistant TLE and assessed potential use for predicting postsurgical cognitive outcomes. METHODS: TLE patients undergoing presurgical evaluation were categorized into cognitive phenotypes. Network edge weights and distances were calculated using type III analysis of variance F-statistics from comparisons of GM regions within each TLE cognitive phenotype and age- and sex-matched healthy participants. In resected patients, logistic regression models (LRMs) based on network analysis results were used for prediction of postsurgical cognitive outcome. RESULTS: A total of 124 patients (63 females, mean age ± standard deviation [SD] = 36.0 ± 12.0 years) and 117 healthy controls (63 females, mean age ± SD = 36.1 ± 12.0 years) were analyzed. In the multidomain CI group (n = 66, 53.2%), 28 GM regions were significantly thinner compared to healthy controls. Focally impaired patients (n = 37, 29.8%) showed 13 regions, whereas minimally impaired patients (n = 21, 16.9%) had 2 significantly thinner GM regions. Regions affected in both multidomain and focally impaired patients included the anterior cingulate cortex, medial prefrontal cortex, medial temporal, and lateral temporal regions. In 69 (35 females, mean age ± SD = 33.6 ± 18.0 years) patients who underwent surgery, LRMs based on network-identified GM regions predicted postsurgical verbal memory worsening with a receiver operating curve area under the curve of 0.70 ± 0.15. INTERPRETATION: A differential pattern of GM thickness can be found across different cognitive phenotypes in TLE. Including magnetic resonance imaging with clinical measures associated with cognitive profiles has potential in predicting postsurgical cognitive outcomes in drug-resistant TLE. ANN NEUROL 2024;95:984-997.

An exploratory research report on brain mineralization in postoperative delirium and cognitive decline.

Delirium is a severe postoperative complication associated with poor overall and especially neurocognitive prognosis. Altered brain mineralization is found in neurodegenerative disorders but has not been studied in postoperative delirium and postoperative cognitive decline. We hypothesized that mineralization-related hypointensity in susceptibility-weighted magnetic resonance imaging (SWI) is associated with postoperative delirium and cognitive decline. In an exploratory, hypothesis-generating study, we analysed a subsample of cognitively healthy patients ≥65 years who underwent SWI before (N = 65) and 3 months after surgery (N = 33). We measured relative SWI intensities in the basal ganglia, hippocampus and posterior basal forebrain cholinergic system (pBFCS). A post hoc analysis of two pBFCS subregions (Ch4, Ch4p) was conducted. Patients were screened for delirium until the seventh postoperative day. Cognitive testing was performed before and 3 months after surgery. Fourteen patients developed delirium. After adjustment for age, sex, preoperative cognition and region volume, only pBFCS hypointensity was associated with delirium (regression coefficient [90% CI]: B = -15.3 [-31.6; -0.8]). After adjustments for surgery duration, age, sex and region volume, perioperative change in relative SWI intensities of the pBFCS was associated with cognitive decline 3 months after surgery at a trend level (B = 6.8 [-0.9; 14.1]), which was probably driven by a stronger association in subregion Ch4p (B = 9.3 [2.3; 16.2]). Brain mineralization, particularly in the cerebral cholinergic system, could be a pathomechanism in postoperative delirium and cognitive decline. Evidence from our studies is limited because of the small sample and a SWI dataset unfit for iron quantification, and the analyses presented here should be considered exploratory.

Superficial white matter integrity in neuromyelitis optica spectrum disorder and multiple sclerosis.

Background: Superficial white matter (SWM) is a particularly vulnerable area of white matter adjacent to cerebral cortex that was shown to be a sensitive marker of disease severity in several neurological and psychiatric disorders, including multiple sclerosis (MS), but has not been studied in neuromyelitis optica spectrum disorder (NMOSD). Objective: To compare the integrity of SWM between MS patients, NMOSD patients and healthy controls, and explore the correlation of SWM integrity with cognitive performance and overall disability. Methods: Forty NMOSD patients, 48 MS patients and 52 healthy controls were included in the study. Mean diffusivity (MD) values obtained by diffusion tensor imaging were used as a measure of SWM integrity. Cognitive performance and overall disability were assessed with standardized tests. Results: Superficial white matter MD was increased in MS patients compared to healthy controls. Higher MD was associated with poorer spatial memory (most prominently in right temporal and right limbic lobe) and poorer information processing speed in MS patients. After adjusting for age, no significant differences of SWM MD were observed between NMOSD patients and healthy controls. Conclusion: Integrity of SWM is compromised in MS, but not in NMOSD, and can serve as a sensitive marker of disease severity.

The T1-weighted/T2-weighted ratio as a biomarker of anti-NMDA receptor encephalitis.

BACKGROUND: Anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis rarely causes visible lesions in conventional MRI, yet advanced imaging detects extensive white matter damage. To improve prognostic capabilities, we evaluate the T1-weighted/T2-weighted (T1w/T2w) ratio, a measure of white matter integrity computable from clinical MRI sequences, in NMDAR encephalitis and examine its associations with cognitive impairment. METHODS: T1-weighted and T2-weighted MRI were acquired cross-sectionally at 3 Tesla in 53 patients with NMDAR encephalitis (81% women, mean age 29 years) and 53 matched healthy controls. Quantitative and voxel-wise group differences in T1w/T2w ratios and associations with clinical and neuropsychological outcomes were assessed. P-values were false discovery rate (FDR) adjusted where multiple tests were conducted. RESULTS: Patients with NMDAR encephalitis had significantly lower T1w/T2w ratios across normal appearing white matter (p=0.009, Hedges' g=-0.51), which was associated with worse verbal episodic memory performance (r=0.39, p=0.005, p(FDR)=0.026). White matter integrity loss was observed in the corticospinal tract, superior longitudinal fascicle, optic radiation and callosal body with medium to large effects (Cohen's d=[0.42-1.17]). In addition, patients showed decreased T1w/T2w ratios in the hippocampus (p=0.002, p(FDR)=0.005, Hedges' g=-0.62), amygdala (p=0.002, p(FDR)=0.005, Hedges' g=-0.63) and thalamus (p=0.010, p(FDR)=0.019, Hedges' g=-0.51). CONCLUSIONS: The T1w/T2w ratio detects microstructural changes in grey and white matter of patients with NMDAR encephalitis that correlate with cognitive performance. Computable from conventional clinical MRI sequences, this measure shows promise in bridging the clinico-radiological dissociation in NMDAR encephalitis and could serve as an imaging outcome measure in clinical trials.

Interactions of optic radiation lesions with retinal and brain atrophy in early multiple sclerosis.

OBJECTIVE: Retrograde trans-synaptic neuroaxonal degeneration is considered a key pathological factor of subclinical retinal neuroaxonal damage in multiple sclerosis (MS). We aim to evaluate the longitudinal association of optic radiation (OR) lesion activity with retinal neuroaxonal damage and its role in correlations between retinal and brain atrophy in people with clinically isolated syndrome and early MS (pweMS). METHODS: Eighty-five pweMS were retrospectively screened from a prospective cohort (Berlin CIS cohort). Participants underwent 3T magnetic resonance imaging (MRI) for OR lesion volume and brain atrophy measurements and optical coherence tomography (OCT) for retinal layer thickness measurements. All pweMS were followed with serial OCT and MRI over a median follow-up of 2.9 (interquartile range: 2.6-3.4) years. Eyes with a history of optic neuritis prior to study enrollment were excluded. Linear mixed models were used to analyze the association of retinal layer thinning with changes in OR lesion volume and brain atrophy. RESULTS: Macular ganglion cell-inner plexiform layer (GCIPL) thinning was more pronounced in pweMS with OR lesion volume increase during follow-up compared to those without (Difference: -0.82 µm [95% CI:-1.49 to -0.15], p = 0.018). Furthermore, GCIPL thinning correlated with both OR lesion volume increase (ß [95% CI] = -0.27 [-0.50 to -0.03], p = 0.028) and brain atrophy (ß [95% CI] = 0.47 [0.25 to 0.70], p < 0.001). Correlations of GCIPL changes with brain atrophy did not differ between pweMS with or without OR lesion increase ( η p 2 = 5.92e-7 , p = 0.762). INTERPRETATION: Faster GCIPL thinning rate is associated with increased OR lesion load. Our results support the value of GCIPL as a sensitive biomarker reflecting both posterior visual pathway pathology and global brain neurodegeneration.

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.

Immunoglobulin A Antibodies Against Myelin Oligodendrocyte Glycoprotein in a Subgroup of Patients With Central Nervous System Demyelination.

Importance: Differential diagnosis of patients with seronegative demyelinating central nervous system (CNS) disease is challenging. In this regard, evidence suggests that immunoglobulin (Ig) A plays a role in the pathogenesis of different autoimmune diseases. Yet little is known about the presence and clinical relevance of IgA antibodies against myelin oligodendrocyte glycoprotein (MOG) in CNS demyelination. Objective: To investigate the frequency of MOG-IgA and associated clinical features in patients with demyelinating CNS disease and healthy controls. Design, Setting, and Participants: This longitudinal study comprised 1 discovery and 1 confirmation cohort derived from 5 centers. Participants included patients with suspected or confirmed demyelinating diseases and healthy controls. MOG-IgA, MOG-IgG, and MOG-IgM were measured in serum samples and cerebrospinal fluid (CSF) of patients, who were assessed from September 2012 to April 2022. Main Outcomes and Measures: Frequency and clinical features of patients who were seropositive for MOG-IgA and double-seronegative for aquaporin 4 (AQP4) IgG and MOG-IgG. Results: After the exclusion of 5 participants with coexisting AQP4-IgG and MOG-IgA, MOG-IgG, and/or MOG-IgM, 1339 patients and 110 healthy controls were included; the median follow-up time was 39 months (range, 0-227 months). Of included patients with isolated MOG-IgA, 11 of 18 were female (61%), and the median age was 31.5 years (range, 3-76 years). Among patients double-seronegative for AQP4-IgG and MOG-IgG (1126/1339; 84%), isolated MOG-IgA was identified in 3 of 50 patients (6%) with neuromyelitis optica spectrum disorder, 5 of 228 patients (2%) with other CNS demyelinating diseases, and 10 of 848 patients (1%) with multiple sclerosis but in none of the healthy controls (0/110). The most common disease manifestation in patients seropositive for isolated MOG-IgA was myelitis (11/17 [65%]), followed by more frequent brainstem syndrome (7/16 [44%] vs 14/75 [19%], respectively; P = .048), and infrequent manifestation of optic neuritis (4/15 [27%] vs 46/73 [63%], respectively; P = .02) vs patients with MOG-IgG. Among patients fulfilling 2017 McDonald criteria for multiple sclerosis, MOG-IgA was associated with less frequent CSF-specific oligoclonal bands (4/9 [44%] vs 325/351 [93%], respectively; P < .001) vs patients with multiple sclerosis who were MOG-IgG/IgA seronegative. Further, most patients with isolated MOG-IgA presented clinical attacks after recent infection or vaccination (7/11 [64%]). Conclusion and Relevance: In this study, MOG-specific IgA was identified in a subgroup of patients who were double-seronegative for AQP4-/MOG-IgG, suggesting that MOG-IgA may be a novel diagnostic biomarker for patients with CNS demyelination.

Functional connectome fingerprinting and stability in multiple sclerosis.

Background: Functional connectome fingerprinting can identify individuals based on their functional connectome. Previous studies relied mostly on short intervals between fMRI acquisitions. Objective: This cohort study aimed to determine the stability of connectome-based identification and their underlying signatures in patients with multiple sclerosis and healthy individuals with long follow-up intervals. Methods: We acquired resting-state fMRI in 70 patients with multiple sclerosis and 273 healthy individuals with long follow-up times (up to 4 and 9 years, respectively). Using functional connectome fingerprinting, we examined the stability of the connectome and additionally investigated which regions, connections and networks supported individual identification. Finally, we predicted cognitive and behavioural outcome based on functional connectivity. Results: Multiple sclerosis patients showed connectome stability and identification accuracies similar to healthy individuals, with longer time delays between imaging sessions being associated with accuracies dropping from 89% to 76%. Lesion load, brain atrophy or cognitive impairment did not affect identification accuracies within the range of disease severity studied. Connections from the fronto-parietal and default mode network were consistently most distinctive, i.e., informative of identity. The functional connectivity also allowed the prediction of individual cognitive performances. Conclusion: Our results demonstrate that discriminatory signatures in the functional connectome are stable over extended periods of time in multiple sclerosis, resulting in similar identification accuracies and distinctive long-lasting functional connectome fingerprinting signatures in patients and healthy individuals.

MRI to differentiate multiple sclerosis, neuromyelitis optica, and myelin oligodendrocyte glycoprotein antibody disease.

Differentiating multiple sclerosis (MS) from other relapsing inflammatory autoimmune diseases of the central nervous system such as neuromyelitis optica spectrum disorder (NMOSD) and myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) is crucial in clinical practice. The differential diagnosis may be challenging but making the correct ultimate diagnosis is critical, since prognosis and treatments differ, and inappropriate therapy may promote disability. In the last two decades, significant advances have been made in MS, NMOSD, and MOGAD including new diagnostic criteria with better characterization of typical clinical symptoms and suggestive imaging (magnetic resonance imaging [MRI]) lesions. MRI is invaluable in making the ultimate diagnosis. An increasing amount of new evidence with respect to the specificity of observed lesions as well as the associated dynamic changes in the acute and follow-up phase in each condition has been reported in distinct studies recently published. Additionally, differences in brain (including the optic nerve) and spinal cord lesion patterns between MS, aquaporin4-antibody-positive NMOSD, and MOGAD have been described. We therefore present a narrative review on the most relevant findings in brain, spinal cord, and optic nerve lesions on conventional MRI for distinguishing adult patients with MS from NMOSD and MOGAD in clinical practice. In this context, cortical and central vein sign lesions, brain and spinal cord lesions characteristic of MS, NMOSD, and MOGAD, optic nerve involvement, role of MRI at follow-up, and new proposed diagnostic criteria to differentiate MS from NMOSD and MOGAD were discussed.

iPSC-derived reactive astrocytes from patients with multiple sclerosis protect cocultured neurons in inflammatory conditions.

Multiple sclerosis (MS) is the most common chronic central nervous system inflammatory disease. Individual courses are highly variable, with complete remission in some patients and relentless progression in others. We generated induced pluripotent stem cells (iPSCs) to investigate possible mechanisms in benign MS (BMS), compared with progressive MS (PMS). We differentiated neurons and astrocytes that were then stressed with inflammatory cytokines typically associated with MS phenotypes. TNF-α/IL-17A treatment increased neurite damage in MS neurons from both clinical phenotypes. In contrast, TNF-α/IL-17A-reactive BMS astrocytes cultured with healthy control neurons exhibited less axonal damage compared with PMS astrocytes. Accordingly, single-cell transcriptomic BMS astrocyte analysis of cocultured neurons revealed upregulated neuronal resilience pathways; these astrocytes showed differential growth factor expression. Furthermore, supernatants from BMS astrocyte/neuronal cocultures rescued TNF-α/IL-17-induced neurite damage. This process was associated with a unique LIF and TGF-ß1 growth factor expression, as induced by TNF-α/IL-17 and JAK-STAT activation. Our findings highlight a potential therapeutic role of modulation of astrocyte phenotypes, generating a neuroprotective milieu. Such effects could prevent permanent neuronal damage.

A review on multiple sclerosis prognostic findings from imaging, inflammation, and mental health studies.

Magnetic resonance imaging (MRI) of the brain is commonly used to detect where chronic and active lesions are in multiple sclerosis (MS). MRI is also extensively used as a tool to calculate and extrapolate brain health by way of volumetric analysis or advanced imaging techniques. In MS patients, psychiatric symptoms are common comorbidities, with depression being the main one. Even though these symptoms are a major determinant of quality of life in MS, they are often overlooked and undertreated. There has been evidence of bidirectional interactions between the course of MS and comorbid psychiatric symptoms. In order to mitigate disability progression in MS, treating psychiatric comorbidities should be investigated and optimized. New research for the prediction of disease states or phenotypes of disability have advanced, primarily due to new technologies and a better understanding of the aging brain.

Structural brain changes in patients with post-COVID fatigue: a prospective observational study.

Background: Post-COVID syndrome is a severe long-term complication of COVID-19. Although fatigue and cognitive complaints are the most prominent symptoms, it is unclear whether they have structural correlates in the brain. We therefore explored the clinical characteristics of post-COVID fatigue, describe associated structural imaging changes, and determine what influences fatigue severity. Methods: We prospectively recruited 50 patients from neurological post-COVID outpatient clinics (age 18-69 years, 39f/8m) and matched non-COVID healthy controls between April 15 and December 31, 2021. Assessments included diffusion and volumetric MR imaging, neuropsychiatric, and cognitive testing. At 7.5 months (median, IQR 6.5-9.2) after the acute SARS-CoV-2 infection, moderate or severe fatigue was identified in 47/50 patients with post-COVID syndrome who were included in the analyses. As a clinical control group, we included 47 matched multiple sclerosis patients with fatigue. Findings: Our diffusion imaging analyses revealed aberrant fractional anisotropy of the thalamus. Diffusion markers correlated with fatigue severity, such as physical fatigue, fatigue-related impairment in everyday life (Bell score) and daytime sleepiness. Moreover, we observed shape deformations and decreased volumes of the left thalamus, putamen, and pallidum. These overlapped with the more extensive subcortical changes in MS and were associated with impaired short-term memory. While fatigue severity was not related to COVID-19 disease courses (6/47 hospitalised, 2/47 with ICU treatment), post-acute sleep quality and depressiveness emerged as associated factors and were accompanied by increased levels of anxiety and daytime sleepiness. Interpretation: Characteristic structural imaging changes of the thalamus and basal ganglia underlie the persistent fatigue experienced by patients with post-COVID syndrome. Evidence for pathological changes to these subcortical motor and cognitive hubs provides a key to the understanding of post-COVID fatigue and related neuropsychiatric complications. Funding: Deutsche Forschungsgemeinschaft (DFG) and German Ministry of Education and Research (BMBF).