Dynamics and heterogeneity of brain damage in multiple sclerosis

Modeling and simulation for prediction of multiple sclerosis progression

In light of extensive work that has created a wide range of techniques for predicting the course of multiple sclerosis (MS) disease, this paper attempts to provide an overview of these approaches and put forth an alternative way to predict the disease progression. For this purpose, the existing methods for estimating and predicting the course of the disease have been categorized into clinical, radiological, biological, and computational or artificial intelligence-based markers. Weighing the weaknesses and strengths of these prognostic groups is a profound method that is yet in need and works directly at the level of diseased connectivity. Therefore, we propose using the computational models in combination with established connectomes as a predictive tool for MS disease trajectories. The fundamental conduction-based Hodgkin-Huxley model emerged as promising from examining these studies. The advantage of the Hodgkin-Huxley model is that certain properties of connectomes, such as neuronal connection weights, spatial distances, and adjustments of signal transmission rates, can be taken into account. It is precisely these properties that are particularly altered in MS and that have strong implications for processing, transmission, and interactions of neuronal signaling patterns. The Hodgkin-Huxley (HH) equations as a point-neuron model are used for signal propagation inside a small network. The objective is to change the conduction parameter of the neuron model, replicate the changes in myelin properties in MS and observe the dynamics of the signal propagation across the network. The model is initially validated for different lengths, conduction values, and connection weights through three nodal connections. Later, these individual factors are incorporated into a small network and simulated to mimic the condition of MS. The signal propagation pattern is observed after inducing changes in conduction parameters at certain nodes in the network and compared against a control model pattern obtained before the changes are applied to the network. The signal propagation pattern varies as expected by adapting to the input conditions. Similarly, when the model is applied to a connectome, the pattern changes could give an insight into disease progression. This approach has opened up a new path to explore the progression of the disease in MS. The work is in its preliminary state, but with a future vision to apply this method in a connectome, providing a better clinical tool.

Breaking boundaries: the transformative role of exercise in managing multiple sclerosis

Multiple sclerosis (MS) is a prevalent cause of physical disability in adults, with inflammation-induced demyelination and neurodegeneration contributing to its etiology. This comprehensive review explores the multifaceted benefits of exercise in managing MS, including improvements in aerobic capacity, balance, muscle strength, immune and hormonal functions and mood. Various exercise modalities, such as aerobic, resistance, flexibility, and balance training, are discussed, along with tailored protocols for MS patients. Recommended exercise strategies are: aerobic exercise: 2-3x/week; 10-30 minutes (40 %-60 % of maximum heart rate (HRmax), HIIT: 1x/week, five 30-90-second intervals at 90 %-100 % HRmax, Resistance training: 2-3x/week, 5-10 exercises; 1-3 sets for each exercise, 8-15 repetitions/set. The review also examines the impact of exercise on neuroplasticity, cardiovascular responses, cytokine modulation, stress hormone regulation, brain structure, and function and fatigue perception. Emphasizing the importance of exercise in enhancing the quality of life for individuals with MS, the review proposes exercise prescriptions and highlights the promising link between physical activity, brain health, and improved hormonal and immune status in MS patients. This review aims to inform future research and guide clinical practices for effective MS management.

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.

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.

Could Mathematics be the Key to Unlocking the Mysteries of Multiple Sclerosis?

Multiple sclerosis (MS) is an autoimmune, neurodegenerative disease that is driven by immune system-mediated demyelination of nerve axons. While diseases such as cancer, HIV, malaria and even COVID have realised notable benefits from the attention of the mathematical community, MS has received significantly less attention despite the increasing disease incidence rates, lack of curative treatment, and long-term impact on patient well-being. In this review, we highlight existing, MS-specific mathematical research and discuss the outstanding challenges and open problems that remain for mathematicians. We focus on how both non-spatial and spatial deterministic models have been used to successfully further our understanding of T cell responses and treatment in MS. We also review how agent-based models and other stochastic modelling techniques have begun to shed light on the highly stochastic and oscillatory nature of this disease. Reviewing the current mathematical work in MS, alongside the biology specific to MS immunology, it is clear that mathematical research dedicated to understanding immunotherapies in cancer or the immune responses to viral infections could be readily translatable to MS and might hold the key to unlocking some of its mysteries.

Combining in vivo proton exchange rate (k ex) MRI with quantitative susceptibility mapping to further stratify the gadolinium-negative multiple sclerosis lesions

Background

Conventional gadolinium (Gd)-enhanced MRI is currently used for stratifying the lesion activity of multiple sclerosis (MS) despite limited correlation with disability and disease activity. The stratification of MS lesion activity needs further improvement to better support clinics.

Purpose

To investigate if the novel proton exchange rate (k _ex ) MRI combined with quantitative susceptibility mapping (QSM) may help to further stratify non-enhanced (Gd-negative) MS lesions.

Materials and methods

From December 2017 to December 2020, clinically diagnosed relapsing-remitting MS patients who underwent MRI were consecutively enrolled in this IRB-approved retrospective study. The customized MRI protocol covered conventional T₂-weighted, T₂-fluid-attenuated-inversion-recovery, pre- and post-contrast T₁-weighted imaging, and quantitative sequences, including k _ex MRI based on direct-saturation removed omega plots and QSM. Each MS lesion was evaluated based on its Gd-enhancement as well as its susceptibility and k _ex elevation compared to the normal appearing white matter. The difference and correlation concerning lesion characteristics and imaging contrasts were analyzed using the Mann-Whitney U test or Kruskal-Wallis test, and Spearman rank analysis with p < 0.05 considered significant.

Results

A total of 322 MS lesions from 30 patients were identified with 153 Gd-enhanced and 169 non-enhanced lesions. We found that the k _ex elevation of all lesions significantly correlated with their susceptibility elevation (r = 0.30, p < 0.001). Within the 153 MS lesions with Gd-enhancement, ring-enhanced lesions showed higher k _ex elevation than the nodular-enhanced ones' (p < 0.001). Similarly, lesions with ring-hyperintensity in QSM also had higher k _ex elevation than the lesions with nodular-QSM-hyperintensity (p < 0.001). Of the 169 Gd-negative lesions, three radiological patterns were recognized according to lesion manifestations on the k _ex map and QSM images: Pattern I (k _ex ⁺ and QSM⁺, n = 114, 67.5%), Pattern II (only k _ex ⁺ or QSM⁺, n = 47, 27.8%) and Pattern III (k _ex ^- and QSM^-, n = 8, 4.7%). Compared to Pattern II and III, Pattern I had higher k _ex (p < 0.001) and susceptibility (p < 0.05) elevation. The percentage of Pattern I of each subject was negatively correlated with the disease duration (r = -0.45, p = 0.015).

Conclusion

As a potential imaging biomarker for inflammation due to oxidative stress, in vivo k _ex MRI combined with QSM is promising in extending the clinical classification of MS lesions beyond conventional Gd-enhanced MRI.

Multiple sclerosis clinical decision support system based on projection to reference datasets

OBJECTIVE

Multiple sclerosis (MS) is a multifactorial disease with increasingly complicated management. Our objective is to use on-demand computational power to address the challenges of dynamically managing MS.

METHODS

A phase 3 clinical trial data (NCT00906399) were used to contextualize the medication efficacy of peg-interferon beta-1a vs placebo on patients with relapsing-remitting MS (RRMS). Using a set of reference patients (PORs), selected based on adequate features similar to those of an individual patient, we visualize disease activity by measuring the percentage of relapses, accumulation of new T2 lesions on MRI, and worsening EDSS during the clinical trial.

RESULTS

We developed MS Vista, a functional prototype of clinical decision support system (CDSS), with a user-centered design and distributed infrastructure. MS Vista shows the medication efficacy of peginterferon beta-1a versus placebo for each individual patient with RRMS. In addition, MS Vista initiated the integration of a longitudinal magnetic resonance imaging (MRI) viewer and interactive dual physician-patient data display to facilitate communication.

INTERPRETATION

The pioneer use of PORs for each individual patient enables personalized analytics sustaining the dialog between neurologists, patients and caregivers with quantified evidence.

Reaction-diffusion models in weighted and directed connectomes

Connectomes represent comprehensive descriptions of neural connections in a nervous system to better understand and model central brain function and peripheral processing of afferent and efferent neural signals. Connectomes can be considered as a distinctive and necessary structural component alongside glial, vascular, neurochemical, and metabolic networks of the nervous systems of higher organisms that are required for the control of body functions and interaction with the environment. They are carriers of functional epiphenomena such as planning behavior and cognition, which are based on the processing of highly dynamic neural signaling patterns. In this study, we examine more detailed connectomes with edge weighting and orientation properties, in which reciprocal neuronal connections are also considered. Diffusion processes are a further necessary condition for generating dynamic bioelectric patterns in connectomes. Based on our high-precision connectome data, we investigate different diffusion-reaction models to study the propagation of dynamic concentration patterns in control and lesioned connectomes. Therefore, differential equations for modeling diffusion were combined with well-known reaction terms to allow the use of connection weights, connectivity orientation and spatial distances.

Three reaction-diffusion systems Gray-Scott, Gierer-Meinhardt and Mimura-Murray were investigated. For this purpose, implicit solvers were implemented in a numerically stable reaction-diffusion system within the framework of neuroVIISAS. The implemented reaction-diffusion systems were applied to a subconnectome which shapes the mechanosensitive pathway that is strongly affected in the multiple sclerosis demyelination disease. It was found that demyelination modeling by connectivity weight modulation changes the oscillations of the target region, i.e. the primary somatosensory cortex, of the mechanosensitive pathway.

In conclusion, a new application of reaction-diffusion systems to weighted and directed connectomes has been realized. Because the implementation were performed in the neuroVIISAS framework many possibilities for the study of dynamic reaction-diffusion processes in empirical connectomes as well as specific randomized network models are available now.

Brain atrophy patterns in multiple sclerosis patients treated with natalizumab and its clinical correlates

BACKGROUND

Multiple sclerosis (MS) is defined as a demyelinating disorder of the central nervous system, witnessing over the past years a remarkable progress in the therapeutic approaches of the inflammatory process. Yet, the ongoing neurodegenerative process is still ambiguous, under-assessed, and probably under-treated. Atrophy and cognitive dysfunction represent the radiological and clinical correlates of such process. In this study, we evaluated the effect of one specific MS treatment, which is natalizumab (NTZ), on brain atrophy evolution in different anatomical regions and its correlation with the cognitive profile and the physical disability.

METHODS

We recruited 20 patients diagnosed with relapsing-remitting MS (RR-MS) and treated with NTZ. We tracked brain atrophy in different anatomical structures using MRI scans processed with an automated image segmentation technique. We also assessed the progression of physical disability and the cognitive function and its link with the progression of atrophy.

RESULTS

During the first 2 years of treatment, a significant volume loss was noted within the corpus callosum and the cerebellum gray matter (GM). The annual atrophy rate of the cortical GM, the cerebellum GM, the thalamus, the amygdala, the globus pallidus, and the hippocampus correlated with greater memory impairment. As for the third and fourth years of treatment, a significant atrophy revolved around the gray matter, mainly the cortical one. We also noted an increase of the thalamus volume.

CONCLUSION

Atrophy in RR-MS patients treated with NTZ is regional and targeting highly cognitive regions mainly of the subcortical gray matter and the cerebellum. The cerebellum atrophy was a marker of physical disability progression. NTZ did not accelerate the atrophy process in MS and may play a neuroprotective role by increasing the thalamus volume.

Visual Evoked Potentials to Monitor Myelin Cuprizone-Induced Functional Changes

The visual system is one of the most accessible routes to study the central nervous system under pathological conditions, such as in multiple sclerosis (MS). Non-invasive visual evoked potential (VEP) and optical coherence tomography (OCT) were used to assess visual function and neuroretinal thickness in C57BL/6 taking 0.2% cuprizone for 7 weeks and at 5, 8, 12, and 15 days after returning to a normal diet. VEPs were significantly delayed starting from 4 weeks on cuprizone, with progressive recovery off cuprizone, becoming significant at day 8, complete at day 15. In contrast, OCT and neurofilament staining showed no significant axonal thinning. Optic nerve histology indicated that whilst there was significant myelin loss at 7 weeks on the cuprizone diet compared with healthy mice, at 15 days off cuprizone diet demyelination was significantly less severe. The number of Iba 1⁺ cells was found increased in cuprizone mice at 7 weeks on and 15 days off cuprizone. The combined use of VEPs and OCT allowed us to characterize non-invasively, in vivo, the functional and structural changes associated with demyelination and remyelination in a preclinical model of MS. This approach contributes to the non-invasive study of possible effective treatments to promote remyelination in demyelinating pathologies.

Dyschromatopsia in multiple sclerosis reflects diffuse chronic neurodegeneration beyond anatomical landmarks

To formulate and validate a dyschromatopsia linear regression model in patients with multiple sclerosis (MS). 64 MS patients (50 to formulate the model and 14 for its validation) underwent neurological (Expanded Disability Status Scale, EDSS), color vision (Farnsworth D15 test), and peripapillary retinal nerve fiber layer (pRNFL) and retinal evaluation with spectral-domain optical coherence tomography (SD-OCT). Neuroradiological examination permitted to obtain brain parenchymal fraction (BPF) and cervical spinal cord volume (SC). Ophthalmic parameters were calculated as the average of both non-optic neuritis (ON) eyes, and in case the patient had previous ON, the value of the fellow non-ON eye was taken. The influence of sex, age, disease duration, and history of disease-modifying treatment (first- or second-line DMT) was tested as covariables that could influence color perception. Color confusion index (log CCI) correlated with pRNFL (r =  - 0.322, p = 0.009), ganglion cell layer (GCL, r =  - 0.321, p = 0.01), BPF (r =  - 0.287, p = 0.021), SC volume (r =  - 0.33, p = 0.008), patients' age (r = 0.417, p = 0.001), disease duration (r = 0.371, p = 0.003), and EDSS (r = 0.44, p = 0.001). The following CCI equation was obtained: log (CCI) = 0.316-0.224 BPF - 0.187 SC volume (mm3) + 0.226 age (years) + 0.012 disease duration (years) - 0.372 GCL (µm). CCI correlates with MS clinical and paraclinical established biomarkers suggesting chronic diffuse neurodegeneration in MS operates at brain, SC, and retina linking all three compartments. Color vision outcome can be calculated through the aforementioned variables for clinical and research purposes.

Toxic heavy metal concentrations in multiple sclerosis patients: A systematic review and meta-analysis

The present meta-analysis was performed to assess the association between MS patients and control subjects in terms of their circulating levels of arsenic (As), lead (Pb), mercury (Hg), and cadmium (Cd). We searched Medline/PubMed, Scopus, Web of Science, and Embase up until June 2020 to identify all studies that examined the concentrations of heavy metals in MS patients. Statistical tests used to assess inter-study heterogeneity were Cochrane's Q test and the I² statistic. Given the observed significant heterogeneity, the random-effects model was employed to pool the weighted mean differences (WMDs) and the corresponding 95 % confidence intervals (CIs). Out of a total of 1181 articles, 16 studies with 1650 participants (772 patients with MS and 878 controls) were included in this review meta-analysis. Pooled results using random-effects model showed that the levels of Pb (WMD= 0.73 µg/L, 95 % CI= 0.33 to 1.12, P< 0.001), As (WMD= 2.48 μg/L, 95 % CI= 1.44 to 3.53, P <0.001; I²= 98.9 %, P <0.001), and Cd (WMD= 0.17 μg/L, 95 % CI= 0.09 to 0.26, P <0.001) were significantly higher in MS patients than those of the controls. However, there were no significant differences in the levels of Hg (WMD= -0.14 µg/L, 95 % CI= -0.77 to 0.49, P= 0.658) among both groups. Sensitivity analysis indicated that after excluding one-by-one study, the overall pooled WMD of Pb was changed. This meta-analysis showed that patients with MS had significantly higher levels of circulatory As and Cd compared to the controls. Yet, there was no statistically significant difference between circulating levels of Hg and Pb among MS patients and controls. See also Figure 1(Fig. 1).

Design and Validation of an Expanded Disability Status Scale Model in Multiple Sclerosis

INTRODUCTION

We aimed to develop and validate an Expanded Disability Status Scale (EDSS) model through clinical, optical coherence tomography (OCT), and magnetic resonance imaging (MRI) measures.

METHODS

Sixty-four multiple sclerosis (MS) patients underwent peripapillary retinal nerve fiber layer and segmented macular layers evaluation through OCT (Spectralis, Heidelberg Engineering). Brain parenchymal fraction was quantified through Freesurfer, while cervical spinal cord (SC) volume was assessed manually guided by Spinal Cord Toolbox software analysis. EDSS, neuroradiological, and OCT assessment were carried out within 3 months. OCT parameters were calculated as the average of both nonoptic neuritis (ON) eyes, and in case the patient had previous ON, the value of the fellow non-ON eye was taken. Brain lesion volume, sex, age, disease duration, and history of disease-modifying treatment (1st or 2nd line disease-modifying treatments) were tested as covariables of the EDSS score.

RESULTS

EDSS values correlated with patient's age (r = 0.543, p = 0.001), SC volume (r = -0.301, p = 0.034), and ganglion cell layer (GCL, r = -0.354, p = 0.012). Using these correlations, an ordinal regression model to express probability of diverse EDSS scores were designed, the highest of which was the most probable (Nagelkerke R2 = 43.3%). Using EDSS cutoff point of 4.0 in a dichotomous model, compared to a cutoff of 2.0, permits the inclusion of GCL as a disability predictor, in addition to age and SC.

CONCLUSIONS

MS disability measured through EDSS is an age-dependent magnitude that is partly conditioned by SC and GCL. Further studies assessing paraclinical disability predictors are needed.

Involvement of various chemokine/chemokine receptor axes in trafficking and oriented locomotion of mesenchymal stem cells in multiple sclerosis patients

Multiple sclerosis (MS) is a specific type of chronic immune-mediated disease in which the immune responses are almost run against the central nervous system (CNS). Despite intensive research, a known treatment for MS disease yet to be introduced. Thus, the development of novel and safe medications needs to be considered for the disease management. Application of mesenchymal stem cells (MSCs) as an emerging approach was recruited forthe treatment of MS. MSCs have several sources and they can be derived from the umbilical cord, adipose tissue, and bone marrow. Chemokines are low molecular weight proteins that their functional activities are achieved by binding to the cell surface G protein-coupled receptors (GPCRs). Chemokine and chemokine receptors are of the most important and effective molecules in MSC trafficking within the different tissues in hemostatic and non-hemostatic circumstances. Chemokine/chemokine receptor axes play a pivotal role in the recruitment and oriented trafficking of immune cells both towards and within the CNS and it appears that chemokine/chemokine receptor signaling may be the most important leading mechanisms in the pathogenesis of MS. In this article, we hypothesized that the chemokine/chemokine receptor axes network have crucial and efficacious impacts on behavior of the MSCs, nonetheless, the exact responsibility of these axes on the targeted tropism of MSCs to the CNS of MS patients yet remained to be fully elucidated. Therefore, we reviewed the ability of MSCs to migrate and home into the CNS of MS patients via expression of various chemokine receptors in response to chemokines expressed by cells of CNS tissue, to provide a great source of knowledge.

Predicting Neuropsychological Impairment in Relapsing Remitting Multiple Sclerosis: The Role of Clinical Measures, Treatment, and Neuropsychiatry Symptoms

OBJECTIVE

This retrospective observational study aimed to define neuropsychological impairment (NI) profiles and determine the influence of clinical, demographic, and neuropsychiatric measures in specific cognitive domains in a cohort of relapsing-remitting multiple sclerosis (RRMS) patients.

METHODS

Ninety-one RRMS patients underwent a neurological examination and a brief neuropsychological assessment. Patients were classified according to the disease-modifying therapies (DMTs) received (platform or high-efficacy). Differences between groups and multiple regression analyses were performed to determine the predictive value of the assessed measures in cognitive performance.

RESULTS

More than two-thirds of the patients showed NI. Specifically, mild to moderate NI was presented in approximately half of the participants. Paced Auditory Serial Addition Test (PASAT-3) and Symbol Digit Modalities Test (SDMT) were the most frequently impaired cognitive tests (45.3% and 41.3%, respectively) followed by phonemic verbal fluency (PVF) (27.8%). Expanded Disability Status Scale (EDSS), age, depressive symptoms, and disease duration were the best predictors of SDMT (R2 = .34; p < .01), whereas disease duration, EDSS, and anxiety-state levels predicted PASAT-3 (R2 = .33, p < .01). Educational level, age, EDSS, and depressive symptoms demonstrated the strongest association with PVF (R2 = .31, p < .01).

CONCLUSIONS

Our results indicated a significant prevalence of NI in RRMS patients that was not dependent on the DMT type. In addition to the meaningful working memory (PASAT-3) and information processing speed (SDMT) impairments found, PVF deficits may also be an important marker of cognitive impairment in RRMS patients. This study supports the relevance of standard clinical measures and reinforces the importance of quantifying clinical and neuropsychiatric symptoms to predict subsequent cognitive performance on a similar multiple sclerosis phenotype and disease stage.

Cortical fractal dimension predicts disability worsening in Multiple Sclerosis patients

BACKGROUND

Fractal geometry measures the morphology of the brain and detects CNS damage. We aimed to assess the longitudinal changes on brain's fractal geometry and its predictive value for disease worsening in patients with Multiple Sclerosis (MS).

METHODS

We prospectively analyzed 146 consecutive patients with relapsing-remitting MS with up to 5 years of clinical and brain MRI (3 T) assessments. The fractal dimension and lacunarity were calculated for brain regions using box-counting methods. Longitudinal changes were analyzed in mixed-effect models and the risk of disability accumulation were assessed using Cox Proportional Hazard regression analysis.

RESULTS

There was a significant decrease in the fractal dimension and increases of lacunarity in different brain regions over the 5-year follow-up. Lower cortical fractal dimension increased the risk of disability accumulation for the Expanded Disability Status Scale [HR 0.9734, CI 0.8420-0.9125; Harrell C 0.59; Wald p 0.038], 9-hole peg test [HR 0.9734, CI 0.8420-0.9125; Harrell C 0.59; Wald p 0.0083], 2.5% low contrast vision [HR 0.4311, CI 0.2035-0.9133; Harrell C 0.58; Wald p 0.0403], symbol digit modality test [HR 2.215, CI 1.043-4.705; Harrell C 0.65; Wald p 0.0384] and MS Functional Composite-4 [HR 0.55, CI 0.317-0.955; Harrell C 0.59; Wald p 0.0029].

CONCLUSIONS

Fractal geometry analysis of brain MRI identified patients at risk of increasing their disability in the next five years.

A mathematical model of the multiple sclerosis plaque

Multiple sclerosis is an autoimmune disease that affects white matter in the central nervous system. It is one of the primary causes of neurological disability among young people. Its characteristic pathological lesion is called a plaque, a zone of inflammatory activity and tissue destruction that expands radially outward by destroying the myelin and oligodendrocytes of white matter. The present paper develops a mathematical model of the multiple sclerosis plaques. Although these plaques do not provide reliable information of the clinical disability in MS, they are nevertheless useful as a primary outcome measure of Phase II trials. The model consists of a system of partial differential equations in a simplified geometry of the lesion, consisting of three domains: perivascular space, demyelinated plaque, and white matter. The model describes the activity of various pro- and anti-inflammatory cells and cytokines in the plaque, and quantifies their effect on plaque growth. We show that volume growth of plaques are in qualitative agreement with reported clinical studies of several currently used drugs. We then use the model to explore treatments with combinations of such drugs, and with experimental drugs. We finally consider the benefits of early vs. delayed treatment.

Androgen Therapy in Neurodegenerative Diseases

Neurodegenerative diseases, including Alzheimer disease (AD), Parkinson disease (PD), multiple sclerosis (MS), amyotrophic lateral sclerosis (ALS), and Huntington disease, are characterized by the loss of neurons as well as neuronal function in multiple regions of the central and peripheral nervous systems. Several studies in animal models have shown that androgens have neuroprotective effects in the brain and stimulate axonal regeneration. The presence of neuronal androgen receptors in the peripheral and central nervous system suggests that androgen therapy might be useful in the treatment of neurodegenerative diseases. To illustrate, androgen therapy reduced inflammation, amyloid-β deposition, and cognitive impairment in patients with AD. As well, improvements in remyelination in MS have been reported; by comparison, only variable results are observed in androgen treatment of PD. In ALS, androgen administration stimulated motoneuron recovery from progressive damage and regenerated both axons and dendrites. Only a few clinical studies are available in human individuals despite the safety and low cost of androgen therapy. Clinical evaluations of the effects of androgen therapy on these devastating diseases using large populations of patients are strongly needed.

Retinal and brain damage during multiple sclerosis course: inflammatory activity is a key factor in the first 5 years

Understanding of the role of focal inflammation, a treatable feature, on neuro-axonal injury, is paramount to optimize neuroprotective strategy in MS. To quantify the impact of focal inflammatory activity on the rate of neuro-axonal injury over the MS course. We quantified the annualized rates of change in peripapillary retinal nerve fiber layer, ganglion cell plus inner plexiform layer (GCIPL), whole-brain, gray matter and thalamic volumes in patients with and without focal inflammatory activity in 161 patients followed over 5 years. We used mixed models including focal inflammatory activity (the presence of at least one relapse or a new/enlarging T2-FLAIR or gadolinium- enhancing lesion), and its interaction with time adjusted by age, sex, use of disease-modifying therapies and steroids, and prior optic neuritis. The increased rate of neuro-axonal injury during the first five years after onset was more prominent among active patients, as reflected by the changes in GCIPL thickness (p = 0.02), whole brain (p = 0.002) and thalamic volumes (p < 0.001). Thereafter, rates of retinal and brain changes stabilized and were similar in active and stable patients. Focal inflammatory activity is associated with neurodegeneration early in MS which reinforces the use of an early intensive anti-inflammatory therapy to prevent neurodegeneration in MS.

The Potential of Computational Modeling to Predict Disease Course and Treatment Response in Patients with Relapsing Multiple Sclerosis

As of today, 20 disease-modifying drugs (DMDs) have been approved for the treatment of relapsing multiple sclerosis (MS) and, based on their efficacy, they can be grouped into moderate-efficacy DMDs and high-efficacy DMDs. The choice of the drug mostly relies on the judgment and experience of neurologists and the evaluation of the therapeutic response can only be obtained by monitoring the clinical and magnetic resonance imaging (MRI) status during follow up. In an era where therapies are focused on personalization, this study aims to develop a modeling infrastructure to predict the evolution of relapsing MS and the response to treatments. We built a computational modeling infrastructure named Universal Immune System Simulator (UISS), which can simulate the main features and dynamics of the immune system activities. We extended UISS to simulate all the underlying MS pathogenesis and its interaction with the host immune system. This simulator is a multi-scale, multi-organ, agent-based simulator with an attached module capable of simulating the dynamics of specific biological pathways at the molecular level. We simulated six MS patients with different relapsing-remitting courses. These patients were characterized based on their age, sex, presence of oligoclonal bands, therapy, and MRI lesion load at the onset. The simulator framework is made freely available and can be used following the links provided in the availability section. Even though the model can be further personalized employing immunological parameters and genetic information, we generated a few simulation scenarios for each patient based on the available data. Among these simulations, it was possible to find the scenarios that realistically matched the real clinical and MRI history. Moreover, for two patients, the simulator anticipated the timing of subsequent relapses, which occurred, suggesting that UISS may have the potential to assist MS specialists in predicting the course of the disease and the response to treatment.

MAPK pathway and B cells overactivation in multiple sclerosis revealed by phosphoproteomics and genomic analysis

Dysregulation of signaling pathways in multiple sclerosis (MS) can be analyzed by phosphoproteomics in peripheral blood mononuclear cells (PBMCs). We performed in vitro kinetic assays on PBMCs in 195 MS patients and 60 matched controls and quantified the phosphorylation of 17 kinases using xMAP assays. Phosphoprotein levels were tested for association with genetic susceptibility by typing 112 single-nucleotide polymorphisms (SNPs) associated with MS susceptibility. We found increased phosphorylation of MP2K1 in MS patients relative to the controls. Moreover, we identified one SNP located in the PHDGH gene and another on IRF8 gene that were associated with MP2K1 phosphorylation levels, providing a first clue on how this MS risk gene may act. The analyses in patients treated with disease-modifying drugs identified the phosphorylation of each receptor's downstream kinases. Finally, using flow cytometry, we detected in MS patients increased STAT1, STAT3, TF65, and HSPB1 phosphorylation in CD19⁺ cells. These findings indicate the activation of cell survival and proliferation (MAPK), and proinflammatory (STAT) pathways in the immune cells of MS patients, primarily in B cells. The changes in the activation of these kinases suggest that these pathways may represent therapeutic targets for modulation by kinase inhibitors.

Mercury Involvement in Neuronal Damage and in Neurodegenerative Diseases

Neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, and multiple sclerosis are characterized by a chronic and selective process of neuronal cell death. Although the causes of neurodegenerative diseases remain still unknown, it is now a well-established idea that more factors, such as genetic, endogenous, and environmental, are involved. Among environmental causes, the accumulation of mercury, a heavy metal considered a toxic agent, was largely studied as a probable factor involved in neurodegenerative disease course. Mercury exists in three main forms: elemental mercury, inorganic mercury, and organic mercury (methylmercury and ethylmercury). Sources of elemental mercury can be natural (volcanic emission) or anthropogenic (coal-fired electric utilities, waste combustion, hazardous-waste incinerators, and gold extraction). Moreover, mercury is still used as an antiseptic, as a medical preservative, and as a fungicide. Dental amalgam can emit mercury vapor. Mercury vapor, being highly volatile and lipid soluble, can cross the blood-brain barrier and the lipid cell membranes and can be accumulated into the cells in its inorganic forms. Also, methylmercury can pass through blood-brain and placental barriers, causing serious damage in the central nervous system. This review describes the toxic effects of mercury in cell cultures, in animal models, and in patients with neurodegenerative diseases. In vitro experiments showed that mercury exposure was principally involved in oxidative stress and apoptotic processes. Moreover, motor and cognitive impairment and neural loss have been confirmed in various studies performed in animal models. Finally, observational studies on patients with neurodegenerative diseases showed discordant data about a possible mercury involvement.

Tolerogenic Dendritic Cells as a Promising Antigen-Specific Therapy in the Treatment of Multiple Sclerosis and Neuromyelitis Optica From Preclinical to Clinical Trials

The identification of activated T-lymphocytes restricted to myelin-derived immunogenic peptides in multiple sclerosis (MS) and aquaporin-4 water channel in neuromyelitis optica (NMO) in the blood of patients opened the possibility for developing highly selective and disease-specific therapeutic approaches. Antigen presenting cells and in particular dendritic cells (DCs) represent a strategy to inhibit pro-inflammatory T helper cells. DCs are located in peripheral and lymphoid tissues and are essential for homeostasis of T cell-dependent immune responses. The expression of a particular set of receptors involved in pathogen recognition confers to DCs the property to initiate immune responses. However, in the absence of danger signals different DC subsets have been revealed to induce active tolerance by inducing regulatory T cells, inhibiting pro-inflammatory T helper cells responses or both. Interestingly, several protocols to generate clinical-grade tolerogenic DC (Tol-DC) in vitro have been described, offering the possibility to restore the homeostasis to central nervous system-related antigens. In this review, we discuss about different DC subsets and their role in tolerance induction, the different protocols to generate Tol-DCs and preclinical studies in animal models as well as describe recent characterization of Tol-DCs for clinical application in autoimmune diseases and in particular in MS and NMO patients. In addition, we discuss the clinical trials ongoing based on Tol-DCs to treat different autoimmune diseases.

Autoimmune comorbidities in multiple sclerosis: what is the influence on brain volumes? A case-control MRI study

BACKGROUND

Several studies indicated that multiple sclerosis (MS) is frequently associated with other autoimmune diseases. However, it is little known if the coexistence of these conditions may influence the radiologic features of MS, and in particular the brain volumes.

OBJECTIVES

To evaluate the effect of autoimmune comorbidities on brain atrophy in a large case-control MS population.

METHODS

A group of MS patients affected by a second autoimmune disorder, and a control MS group without any comorbidity, were recruited. Patients underwent a brain MRI and volumes of whole brain (WB), white matter (WM), and gray matter (GM) with cortical GM were estimated by SIENAX.

RESULTS

The sample included 286 MS patients, of which 30 (10.5%) subjects with type 1 diabetes (T1D), 53 (18.5%) with autoimmune thyroiditis (AT) and 4 (0.1%) with celiac disease. Multiple regression analysis found an association between T1D and lower GM (p = 0.038) and cortical GM (p = 0.036) volumes, independent from MS clinical features and related to T1D duration (p < 0.01), while no association was observed with AT and celiac disease.

CONCLUSIONS

Our data support the importance of considering T1D as possible factors influencing the brain atrophy in MS. Further studies are needed to confirm our data and to clarify the underlying mechanisms.