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Artificial intelligence (AI) has meant a turning point in data analysis, allowing predictions of unseen outcomes with precedented levels of accuracy. In multiple sclerosis (MS), a chronic inflammatory-demyelinating condition of the central nervous system with a complex pathogenesis and potentially devastating consequences, AI-based models have shown promising preliminary results, especially when using neuroimaging data as model input or predictor variables. The application of AI-based methodologies to serum/blood and CSF biomarkers has been less explored, according to the literature, despite its great potential. In this review, we aimed to investigate and summarise the recent advances in AI methods applied to body fluid biomarkers in MS, highlighting the key features of the most representative studies, while illustrating their limitations and future directions.

Objective Conventional magnetic resonance imaging (MRI) of the multiple sclerosis spinal cord is limited by low specificity regarding the underlying pathological processes, and new MRI metrics assessing microscopic damage are required. We aim to show for the first time that neurite orientation dispersion (i.e., variability in axon/dendrite orientations) is a new biomarker that uncovers previously undetected layers of complexity of multiple sclerosis spinal cord pathology. Also, we validate against histology a clinically viable MRI technique for dispersion measurement (neurite orientation dispersion and density imaging,NODDI), to demonstrate the strong potential of the new marker. Methods We related quantitative metrics from histology and MRI in four post mortem spinal cord specimens (two controls; two progressive multiple sclerosis cases). The samples were scanned at high field, obtaining maps of neurite density and orientation dispersion from NODDI and routine diffusion tensor imaging (DTI) indices. Histological procedures provided markers of astrocyte, microglia, myelin and neurofilament density, as well as neurite dispersion. Results We report from both NODDI and histology a trend toward lower neurite dispersion in demyelinated lesions, indicative of reduced neurite architecture complexity. Also, we provide unequivocal evidence that NODDI‐derived dispersion matches its histological counterpart (P < 0.001), while DTI metrics are less specific and influenced by several biophysical substrates. Interpretation Neurite orientation dispersion detects a previously undescribed and potentially relevant layer of microstructural complexity of multiple sclerosis spinal cord pathology. Clinically feasible techniques such as NODDI may play a key role in clinical trial and practice settings, as they provide histologically meaningful dispersion indices.

Spinal cord injury causes a cascade of physiological responses, which may trigger a subsequent neurotoxic increase in intracellular sodium. This can lead to neurodegeneration, both at and beyond the site of injury, causing clinical symptoms and loss of function. However, in vivo measurements of tissue sodium remain challenging. Here we utilise sodium magnetic resonance spectroscopy ( 23 Na-MRS) at 3T to measure tissue sodium concentration (TSC) and its association with microstructural measures and macromolecular MRI metrics in the cervical spinal cord, distal to the site of injury. Twenty people with cervical myelopathy and twenty healthy controls, were studied. Associations with motor and sensory impairments were explored using ASIA and jOAMEQ scores. No significant difference in TSC in the cervical myelopathy group (39 ± 10 mM) relative to healthy controls (35 ± 13 mM) was found. However, patients had a significantly lower cord-cross-sectional area than controls (70 ± 9 mm 2 vs. 82 ± 9 mm 2 , p  < 0.001). Lower-extremity function positively correlated with intracellular volume fraction ( p  = 0.031). In conclusion, using 23 Na-MRS, TSC in cervical myelopathy patients was successfully measured. Differences in TSC relative to healthy controls did not reach significance, despite a significant reduction in cord-cross-sectional area. However, lower intracellular volume fraction, indicating reduced neurite density distal to the site of injury, was associated with physical impairment.

Background: Investigation of atrophy data from a pivotal natalizumab trial has demonstrated an increased rate of volume loss, compared to placebo, after the first year of therapy. It was considered to be probably due to a pseudoatrophy effect. Objective: To assess grey and white matter volume changes and their relation to global brain volume changes and to baseline inflammation, for patients under natalizumab therapy. Methods: We selected 45 patients on natalizumab therapy for at least 24 months, with magnetic resonance imaging (MRI) scans at baseline, 12 and 24 months. We calculated the percentage brain volume change (PBVC) for the first and second year, using SIENA software. Grey and white matter fractions (GMF and WMF, respectively) for the first year were calculated with SPM5, using lesion masks. After quality checks, six patients were excluded. We studied the predictive variables of change in brain volumes. Results: The PBVC decrease was faster during the first year (−1.10% ± 1.43%), as compared to the second (−0.51% ± 0.96%) (p = 0.037). These differences were more marked in patients with baseline gadolinium-enhancing lesions (p = 0.005). Mean GMF and WMF changes during the first year of treatment were +1.15% (n.s.) and −1.72% (p = 0.017), respectively. The presence of active lesions at baseline MRI predicted PBVC (p = 0.022) and WMF change (p = 0.026) during the first year of treatment, after adjusting for age and corticosteroid treatment. No predictors were found for GMF volume changes. Conclusion: Early brain volume loss during natalizumab therapy is mainly due to WMF volume loss and it is related to the inflammatory activity present at the onset of therapy. We found that the pseudoatrophy effect is mostly due to white matter volume changes.

Epidemiological studies identified insufficient and poor-quality sleep as independent risk factors for multiple sclerosis (MS). The glymphatic system, active during slow-wave sleep, clears brain waste through perivascular astrocytic aquaporin-4 (AQP4) channels. The presence of antigens induces a transient, physiological lowering of glymphatic flux as a first step of an inflammatory response. A possible hypothesis linking infection with the Epstein–Barr virus, a well identified causal step in MS, and the development of the disease is that mechanisms such as poor sleep or less functional AQP4 polymorphisms may sustain glymphatic flow reduction. Such chronic glymphatic reduction would trigger a vicious circle in which the persistence of antigens and an inflammatory response maintains glymphatic dysfunction. In addition, viral proteins that persist in demyelinated plaques can depolarize AQP4, further restricting waste elimination and sustaining local inflammation. This review examines the epidemiological evidence connecting sleep and MS risk, and the mechanistic findings showing how poor sleep and other glymphatic modulators heighten inflammatory signaling implicated in MS pathogenesis. Deepening knowledge of glymphatic functioning in MS could open new avenues for personalized prevention and therapy.

One significant characteristic of Multiple Sclerosis (MS), a chronic inflammatory demyelinating disease of the central nervous system, is the evolution of highly variable patterns of white matter lesions. Based on geostatistical metrics, the MS-Lesion Pattern Discrimination Plot reduces complex three- and four-dimensional configurations of MS-White Matter Lesions to a well-arranged and standardized two-dimensional plot that facilitates follow-up, cross-sectional and medication impact analysis. Here, we present a script that generates the MS-Lesion Pattern Discrimination Plot, using the widespread statistical computing environment R. Input data to the script are Nifti-1 or Analyze-7.5 files with individual MS-White Matter Lesion masks in Montreal Normal Brain geometry. The MS-Lesion Pattern Discrimination Plot, variogram plots and associated fitting statistics are output to the R console and exported to standard graphics and text files. Besides reviewing relevant geostatistical basics and commenting on implementation details for smooth customization and extension, the paper guides through generating MS-Lesion Pattern Discrimination Plots using publicly available synthetic MS-Lesion patterns. The paper is accompanied by the R script LDPgenerator.r, a small sample data set and associated graphics for comparison.

Background: In patients with relapsing–remitting multiple sclerosis (RRMS), a scoring system based on new magnetic resonance imaging (MRI) active lesions, relapses and sustained disability progression after a 1-year treatment with IFNβ predicted patient disability progression over time; however, this score had not been tested in patients receiving glatiramer acetate (GA). Objective: The objective of this study was to evaluate whether this previous scoring system can also be applied to patients treated with GA. Methods: This was a prospective, longitudinal study of 151 RRMS patients treated with GA. Their scores were constructed, based on the clinical and MRI activity after 1 year of therapy. Regression analysis was performed, in order to identify the response variables. Results: The total possible score range was 0–3. Patients with a score of ≥ 2 and those with clinical activity (with or without MRI activity) during their first year of treatment were at increased risk of continuing with relapses and/or sustained disability in the next 2 years (odds ratio (OR): 38.8; p < 0.0001 and OR: 7.8; p < 0.009, respectively). Conclusions: In RRMS patients treated with GA, a combination of clinical activity measures may have prognostic value for identifying patients with disease activity in the next 2 years of therapy.

We aimed to investigate the ability of natalizumab (NTZ)-treated patients to assume treatment-associated risks and the factors involved in such risk acceptance. From a total of 185 patients, 114 patients on NTZ as of July 2011 carried out a comprehensive survey. We obtained disease severity perception scores, personality traits' scores, and risk-acceptance scores (RAS) so that higher RAS indicated higher risk acceptance. We recorded JC virus status (JCV+/-), prior immunosuppression, NTZ treatment duration, and clinical characteristics. NTZ patients were split into subgroups (A-E), depending on their individual PML risk. Some 22 MS patients on first-line drugs (DMD) acted as controls. No differences between treatment groups were observed in disease severity perception and personality traits. RAS were higher in NTZ than in DMD patients (p<0.01). Perception of the own disease as a more severe condition tended to predict higher RAS (p=0.07). Higher neuroticism scores predicted higher RAS in the NTZ group as a whole (p=0.04), and in high PML-risk subgroups (A-B) (p=0.02). In low PML-risk subgroups (C-E), higher RAS were associated with a JCV+ status (p=0.01). Neither disability scores nor pre-treatment relapse rate predicted RAS in either group. Risk acceptance is a multifactorial phenomenon, which might be partly explained by an adaptive process, in light of the higher risk acceptance amongst NTZ-treated patients and, especially, amongst those who are JCV seropositive but still have low PML risk, but which seems also intimately related to personality traits.

Increasing numbers of drugs are being developed for the treatment of multiple sclerosis (MS). Measurement of relevant outcomes is key for assessing the efficacy of new drugs in clinical trials and for monitoring responses to disease-modifying drugs in individual patients. Most outcomes used in trial and clinical settings reflect either clinical or neuroimaging aspects of MS (such as relapse and accrual of disability or the presence of visible inflammation and brain tissue loss, respectively). However, most measures employed in clinical trials to assess treatment effects are not used in routine practice. In clinical trials, the appropriate choice of outcome measures is crucial because the results determine whether a drug is considered effective and therefore worthy of further development; in the clinic, outcome measures can guide treatment decisions, such as choosing a first-line disease-modifying drug or escalating to second-line treatment. This Review discusses clinical, neuroimaging and composite outcome measures for MS, including patient-reported outcome measures, used in both trials and the clinical setting. Its aim is to help clinicians and researchers navigate through the multiple options encountered when choosing an outcome measure. Barriers and limitations that need to be overcome to translate trial outcome measures into the clinical setting are also discussed.