Predicting disability progression and cognitive worsening in multiple sclerosis using patterns of grey matter volumes

Cognitive impairment in multiple sclerosis: from phenomenology to neurobiological mechanisms

Multiple sclerosis (MS) is an autoimmune-mediated disease of the central nervous system characterized by inflammation, demyelination and chronic progressive neurodegeneration. Among its broad and unpredictable range of clinical symptoms, cognitive impairment (CI) is a common and disabling feature greatly affecting the patients' quality of life. Its prevalence is 20% up to 88% with a wide variety depending on the phenotype of MS, with highest frequency and severity in primary progressive MS. Involving different cognitive domains, CI is often associated with depression and other neuropsychiatric symptoms, but usually not correlated with motor and other deficits, suggesting different pathophysiological mechanisms. While no specific neuropathological data for CI in MS are available, modern research has provided evidence that it arises from the disease-specific brain alterations. Multimodal neuroimaging, besides structural changes of cortical and deep subcortical gray and white matter, exhibited dysfunction of fronto-parietal, thalamo-hippocampal, default mode and cognition-related networks, disruption of inter-network connections and involvement of the γ-aminobutyric acid (GABA) system. This provided a conceptual framework to explain how aberrant pathophysiological processes, including oxidative stress, mitochondrial dysfunction, autoimmune reactions and disruption of essential signaling pathways predict/cause specific disorders of cognition. CI in MS is related to multi-regional patterns of cerebral disturbances, although its complex pathogenic mechanisms await further elucidation. This article, based on systematic analysis of PubMed, Google Scholar and Cochrane Library, reviews current epidemiological, clinical, neuroimaging and pathogenetic evidence that could aid early identification of CI in MS and inform about new therapeutic targets and strategies.

Isolated cognitive impairment in people with multiple sclerosis: frequency, MRI patterns and its development over time

OBJECTIVES

To study the frequency of isolated (i.e., single-domain) cognitive impairments, domain specific MRI correlates, and its longitudinal development in people with multiple sclerosis (PwMS).

METHODS

348 PwMS (mean age 48 ± 11 years, 67% female, 244RR/52SP/38PP) underwent neuropsychological testing (extended BRB-N) at baseline and at five-year follow-up. At baseline, structural MRI was acquired. Isolated cognitive impairment was defined as a Z-score of at least 1.5 SD below normative data in one domain only (processing speed, memory, executive functioning/working memory, and attention). Multi-domain cognitive impairment was defined as being affected in ≥ 2 domains, and cognitively preserved otherwise. For PwMS with isolated cognitive impairment, MRI correlates were explored using linear regression. Development of isolated cognitive impairment over time was evaluated based on reliable change index.

RESULTS

At baseline, 108 (31%) PwMS displayed isolated cognitive impairment, 148 (43%) PwMS displayed multi-domain cognitive impairment. Most PwMS with isolated cognitive impairment were impaired on executive functioning/working memory (EF/WM; N = 37), followed by processing speed (IPS; N = 25), memory (N = 23), and attention (N = 23). Isolated IPS impairment was explained by a model of cortical volume and fractional anisotropy (adj. R2 = 0.539, p < 0.001); memory by a model with cortical volume and hippocampal volume (adj. R2 = 0.493, p = 0.002); EF/WM and attention were not associated with any MRI measure. At follow-up, cognitive decline was present in 11/16 (69%) of PwMS with isolated IPS impairment at baseline. This percentage varied between 18 and 31% of PwMS with isolated cognitive impairment in domains other than IPS at baseline.

CONCLUSION

Isolated cognitive impairment is frequently present in PwMS and can serve as a proxy for further decline, particularly when it concerns processing speed. Cortical and deep grey matter atrophy seem to play a pivotal role in isolated cognitive impairment. Timely detection and patient-tailored intervention, predominantly for IPS, may help to postpone further cognitive decline.

Spinal cord and brain atrophy patterns in neuromyelitis optica spectrum disorder and multiple sclerosis

BACKGROUND

Spinal cord and brain atrophy are common in neuromyelitis optica spectrum disorder (NMOSD) and relapsing-remitting multiple sclerosis (RRMS) but harbor distinct patterns accounting for disability and cognitive impairment.

METHODS

This study included 209 NMOSD and 304 RRMS patients and 436 healthy controls. Non-negative matrix factorization was used to parse differences in spinal cord and brain atrophy at subject level into distinct patterns based on structural MRI. The weights of patterns were obtained using a linear regression model and associated with Expanded Disability Status Scale (EDSS) and cognitive scores. Additionally, patients were divided into cognitive impairment (CI) and cognitive preservation (CP) groups.

RESULTS

Three patterns were observed in NMOSD: (1) Spinal Cord-Deep Grey Matter (SC-DGM) pattern was associated with high EDSS scores and decline of visuospatial memory function; (2) Frontal-Temporal pattern was associated with decline of language learning function; and (3) Cerebellum-Brainstem pattern had no observed association. Patients with CI had higher weights of SC-DGM pattern than CP group. Three patterns were observed in RRMS: (1) DGM pattern was associated with high EDSS scores, decreased information processing speed, and decreased language learning and visuospatial memory functions; (2) Frontal-Temporal pattern was associated with overall cognitive decline; and (3) Occipital pattern had no observed association. Patients with CI trended to have higher weights of DGM and Frontal-Temporal patterns than CP group.

CONCLUSION

This study estimated the heterogeneity of spinal cord and brain atrophy patterns in NMOSD and RRMS patients at individual level, and evaluated the clinical relevance of these patterns, which may contribute to stratifying participants for targeted therapy.

Interpretable and Intuitive Machine Learning Approaches for Predicting Disability Progression in Relapsing-Remitting Multiple Sclerosis Based on Clinical and Gray Matter Atrophy Indicators

RATIONALE AND OBJECTIVES

To investigate whether clinical and gray matter (GM) atrophy indicators can predict disability in relapsing-remitting multiple sclerosis (RRMS) and to enhance the interpretability and intuitiveness of a predictive machine learning model.

MATERIALS AND METHODS

145 and 50 RRMS patients with structural MRI and at least 1-year follow-up Expanded Disability Status Scale (EDSS) results were retrospectively enrolled and placed in the discovery and external test cohorts, respectively. Six clinical and radiomics feature-based machine learning classifiers were trained and tested to predict disability progression in the discovery cohort and validated in the external test set. Partial dependence plot (PDP) analysis and a Shiny web application were conducted to enhance the interpretability and intuitiveness.

RESULTS

In the discovery cohort, 98 patients had disability stability, and 47 patients were classified as having disability progression. In the external test set, 35 patients were disability stable, and 15 patients had disability progression. Models trained with both clinical and radiomics features (area under the curve (AUC), 0.725-0.950) outperformed those trained with clinical (AUC, 0.600-0.740) or radiomics features only (AUC, 0.615-0.945). Among clinical+ radiomics feature models, the logistic regression (LR) classifier-based model performed best, with an AUC of 0.950. Only the radiomics feature-only models were applied in the external test set due to the data collection problem and showed fair performance, with AUCs ranging from 0.617 to 0.753. PDP analysis showed that female patients and those with lower volume, surface area, and symbol digit modalities test (SDMT) scores; greater mean curvature and age; and no disease modifying therapy (DMT) had increased probabilities of disease progression. Finally, a Shiny web application (https://lauralin1104.shinyapps.io/LRshiny/) was developed to calculate the risk of disability progression.

CONCLUSION

Interpretable and intuitive machine learning approaches based on clinical and GM atrophy indicators can help physicians predict disability progression in RRMS patients for clinical decision-making and patient management.

Associations Between Cognitive Impairment and Neuroimaging in Patients with Multiple Sclerosis

OBJECTIVE

Multiple sclerosis (MS) is a debilitating inflammatory and neurodegenerative disease which commonly involves cognitive dysfunction. Magnetic resonance imaging (MRI) studies have shown that patients with MS (pwMS) have diffuse patterns of brain atrophy, however, the relationship between the presentation of cognitive dysfunction and brain tissue loss remains understudied. Given the integral function of thalamus as a central nervous system relay center and its involvement in various brain circuits, thalamic atrophy may play a key role in the development and progression of cognitive dysfunction. The purpose of this study is to examine the relationship between cognitive impairment in pwMS and thalamic atrophy.

METHODS

A total of 121 pwMS who had neuropsychological testing and quantitative MRI within 1 year of each were retrospectively identified. Grouped LASSO linear regression with 10-fold cross validation was used to estimate each neuropsychological test score with thalamic volume as the focal predictor and all other demographic and MRI metrics as covariates.

RESULTS

Rates of impairment ranged from 19% to 44%. Results showed notable associations between thalamic volume and Symbol Digit Modalities Test (β = 0.11), Brief Visuospatial Memory Test, delayed (β = 0.12), California Verbal Learning Test, delayed and total (β = 0.24 and β = 0.15 respectively), and Trail Making Test Part A (β = -0.01), after adjusting for covariates.

CONCLUSIONS

These findings demonstrate an independent association between thalamic volumes and processing speed and memory performance, after accounting for demographic, clinical, and other MRI variables, among pwMS.

Group independent components underpin responses to items from a depression scale

OBJECTIVE

The aim of the present study is to investigate the brain circuits or networks that underpin diagnostically specific tasks by means of group independent component analysis for FMRI toolbox (GIFT). We hypothesised that there will be neural network patterns of activation and deactivation, which correspond to real-time performance on clinical self-evaluation scales.

METHODS

In total, 20 healthy controls (HC) and 22 patients with major depressive episode have been included. All subjects were scanned with functional magnetic resonance imaging (fMRI) with paradigm composed of diagnostic clinical self-assessment depression scale contrasted to neutral scale. The data were processed with group independent component analysis for functional MRI toolbox and statistical parametric mapping.

RESULTS

The results have demonstrated that there exist positively or negatively modulated brain networks during processing of diagnostic specific task questions for depressive disorder. There have also been confirmed differences in the networks processing diagnostic versus off blocks between patients and controls in anterior cingulate cortex and middle frontal gyrus. Diagnostic conditions (depression scale) when contrasted to neutral conditions demonstrate differential activity of right superior frontal gyrus and right middle cingulate cortex in the comparison of patients with HC.

CONCLUSION

Potential neuroimaging of state-dependent biomarkers has been directly linked with clinical assessment self-evaluation scale, administered as stimuli simultaneously with the fMRI acquisition. It may be regarded as further evidence in support of the convergent capacity of both methods to distinguish groups by means of incremental translational cross-validation.

An update on the role of magnetic resonance imaging in predicting and monitoring multiple sclerosis progression

INTRODUCTION

While magnetic resonance imaging (MRI) is established in diagnosing and monitoring disease activity in multiple sclerosis (MS), its utility in predicting and monitoring disease progression is less clear.

AREAS COVERED

The authors consider changing concepts in the phenotypic classification of MS, including progression independent of relapses; pathological processes underpinning progression; advances in MRI measures to assess them; how well MRI features explain and predict clinical outcomes, including models that assess disease effects on neural networks, and the potential role for machine learning.

EXPERT OPINION

Relapsing-remitting and progressive MS have evolved from being viewed as mutually exclusive to having considerable overlap. Progression is likely the consequence of several pathological elements, each important in building more holistic prognostic models beyond conventional phenotypes. MRI is well placed to assess pathogenic processes underpinning progression, but we need to bridge the gap between MRI measures and clinical outcomes. Mapping pathological effects on specific neural networks may help and machine learning methods may be able to optimize predictive markers while identifying new, or previously overlooked, clinically relevant features. The ever-increasing ability to measure features on MRI raises the dilemma of what to measure and when, and the challenge of translating research methods into clinically useable tools.

Current knowledge on multiple sclerosis pathophysiology, disability progression assessment and treatment options, and the role of autologous hematopoietic stem cell transplantation

Multiple sclerosis (MS) is an autoimmune disease of the central nervous system (CNS) that affects nearly 2.8 million people each year. MS distinguishes three main types: relapsing-remitting MS (RRMS), secondary progressive MS (SPMS) and primary progressive MS (PPMS). RRMS is the most common type, with the majority of patients eventually progressing to SPMS, in which neurological development is constant, whereas PPMS is characterized by a progressive course from disease onset. New or additional insights into the role of effector and regulatory cells of the immune and CNS systems, Epstein-Barr virus (EBV) infection, and the microbiome in the pathophysiology of MS have emerged, which may lead to the development of more targeted therapies that can halt or reverse neurodegeneration. Depending on the type and severity of the disease, various disease-modifying therapies (DMTs) are currently used for RRMS/SPMS and PPMS. As a last resort, and especially in highly active RRMS that does not respond to DMTs, autologous hematopoietic stem cell transplantation (AHSCT) is performed and has shown good results in reducing neuroinflammation. Nevertheless, the question of its potential role in preventing disability progression remains open. The aim of this review is to provide a comprehensive update on MS pathophysiology, assessment of MS disability progression and current treatments, and to examine the potential role of AHSCT in preventing disability progression.

Causal relationship between multiple sclerosis and cortical structure: a Mendelian randomization study

BACKGROUND

Observational studies have suggested an association between multiple sclerosis (MS) and cortical structure, but the results have been inconsistent.

OBJECTIVE

We used two-sample Mendelian randomization (MR) to assess the causal relationship between MS and cortical structure.

METHODS

MS data as the exposure trait, including 14,498 cases and 24,091 controls, were obtained from the International Multiple Sclerosis Genetics Consortium. Genome-wide association study (GWAS) data for cortical surface area (SAw/nw) and thickness (THw/nw) in 51,665 individuals of European ancestry were obtained from the ENIGMA Consortium. The inverse-variance weighted (IVW) method was used as the primary analysis for MR. Sensitivity analyses were conducted to evaluate heterogeneity and pleiotropy. Enrichment analysis was performed on MR analyses filtered by sensitivity analysis.

RESULTS

After IVW and sensitivity analysis filtering, only six surviving MR results provided suggestive evidence supporting a causal relationship between MS and cortical structure, including lingual SAw (p = .0342, beta (se) = 5.7127 (2.6969)), parahippocampal SAw (p = .0224, beta (se) = 1.5577 (0.6822)), rostral middle frontal SAw (p = .0154, beta (se) = - 9.0301 (3.7281)), cuneus THw (p = .0418, beta (se) =  - 0.0020 (0.0010)), lateral orbitofrontal THw (p = .0281, beta (se) = 0.0025 (0.0010)), and lateral orbitofrontal THnw (p = .0417, beta (se) = 0.0029 (0.0014)). Enrichment analysis suggested that leukocyte cell-related pathways, JAK-STAT signaling pathway, NF-kappa B signaling pathway, cytokine-cytokine receptor interaction, and prolactin signaling pathway may be involved in the effect of MS on cortical morphology.

CONCLUSION

Our results provide evidence supporting a causal relationship between MS and cortical structure. Enrichment analysis suggests that the pathways mediating brain morphology abnormalities in MS patients are mainly related to immune and inflammation-driven pathways.

An investigation of the association between focal damage and global network properties in cognitively impaired and cognitively preserved patients with multiple sclerosis

Introduction

The presence of focal cortical and white matter damage in patients with multiple sclerosis (pwMS) might lead to specific alterations in brain networks that are associated with cognitive impairment. We applied microstructure-weighted connectomes to investigate (i) the relationship between global network metrics and information processing speed in pwMS, and (ii) whether the disruption provoked by focal lesions on global network metrics is associated to patients' information processing speed.

Materials and methods

Sixty-eight pwMS and 92 healthy controls (HC) underwent neuropsychological examination and 3T brain MRI including multishell diffusion (dMRI), 3D FLAIR, and MP2RAGE. Whole-brain deterministic tractography and connectometry were performed on dMRI. Connectomes were obtained using the Spherical Mean Technique and were weighted for the intracellular fraction. We identified white matter lesions and cortical lesions on 3D FLAIR and MP2RAGE images, respectively. PwMS were subdivided into cognitively preserved (CPMS) and cognitively impaired (CIMS) using the Symbol Digit Modalities Test (SDMT) z-score at cut-off value of -1.5 standard deviations. Statistical analyses were performed using robust linear models with age, gender, and years of education as covariates, followed by correction for multiple testing.

Results

Out of 68 pwMS, 18 were CIMS and 50 were CPMS. We found significant changes in all global network metrics in pwMS vs HC (p < 0.05), except for modularity. All global network metrics were positively correlated with SDMT, except for modularity which showed an inverse correlation. Cortical, leukocortical, and periventricular lesion volumes significantly influenced the relationship between (i) network density and information processing speed and (ii) modularity and information processing speed in pwMS. Interestingly, this was not the case, when an exploratory analysis was performed in the subgroup of CIMS patients.

Discussion

Our study showed that cortical (especially leukocortical) and periventricular lesions affect the relationship between global network metrics and information processing speed in pwMS. Our data also suggest that in CIMS patients increased focal cortical and periventricular damage does not linearly affect the relationship between network properties and SDMT, suggesting that other mechanisms (e.g. disruption of local networks, loss of compensatory processes) might be responsible for the development of processing speed deficits.

Rapid, non-contact multifocal visual assessment in multiple sclerosis

OBJECTIVE

Previous work on temporally sparse multifocal methods suggests that the results are correlated with disability and progression in people with multiple sclerosis (PwMS). Here, we assess the diagnostic power of three cortically mediated sparse multifocal pupillographic objective perimetry (mfPOP) methods that quantified response-delay and light-sensitivity at up to 44 regions of both visual fields concurrently.

METHODS

One high-spatial-resolution mfPOP method, P129, and two rapid medium-resolution methods, W12 and W20, were tested on 44 PwMS and controls. W12 and W20 took 82 s to test both visual fields concurrently, providing response delay and sensitivity at each field location, while P129 took 7 min. Diagnostic power was assessed using areas under the receiver operating characteristic (AUROC) curves and effect-size (Hedges' g). Linear models examined significance. Concurrent testing of both eyes permitted assessment of between-eye asymmetries.

RESULTS

Per-region response delays and asymmetries achieved AUROCs of 86.6% ± 4.72% (mean ± SE) in relapsing-remitting MS, and 96.5% ± 2.30% in progressive MS. Performance increased with increasing disability scores, with even moderate EDSS 2 to 4.5 PwMS producing AUROCs of 82.1 to 89.8%, Hedge's g values up to 2.06, and p = 4.0e - 13. All tests performed well regardless of any history of optic neuritis. W12 and W20 performed as well or better than P129.

CONCLUSION

Overall, the 82-s tests (W12 and W20) performed better than P129. The results suggest that mfPOP assesses a correlate of disease severity rather than a history of inflammation, and that it may be useful in the clinical management of PwMS.

Neural correlates of digital measures shown by structural MRI: a post-hoc analysis of a smartphone-based remote assessment feasibility study in multiple sclerosis

BACKGROUND

A study was undertaken to evaluate remote monitoring via smartphone sensor-based tests in people with multiple sclerosis (PwMS). This analysis aimed to explore regional neural correlates of digital measures derived from these tests.

METHODS

In a 24-week, non-randomized, interventional, feasibility study (NCT02952911), sensor-based tests on the Floodlight Proof-of-Concept app were used to assess cognition (smartphone-based electronic Symbol Digit Modalities Test), upper extremity function (Draw a Shape Test, Pinching Test), and gait and balance (Static Balance Test, Two-Minute Walk Test, U-Turn Test). In this post-hoc analysis, digital measures and standard clinical measures (e.g., Nine-Hole Peg Test [9HPT]) were correlated against regional structural magnetic resonance imaging outcomes. Seventy-six PwMS aged 18-55 years with an Expanded Disability Status Scale score of 0.0-5.5 were enrolled from two different sites (USA and Spain). Sixty-two PwMS were included in this analysis.

RESULTS

Worse performance on digital and clinical measures was associated with smaller regional brain volumes and larger ventricular volumes. Whereas digital and clinical measures had many neural correlates in common (e.g., putamen, globus pallidus, caudate nucleus, lateral occipital cortex), some were observed only for digital measures. For example, Draw a Shape Test and Pinching Test measures, but not 9HPT score, correlated with volume of the hippocampus (r = 0.37 [drawing accuracy over time on the Draw a Shape Test]/ - 0.45 [touching asynchrony on the Pinching Test]), thalamus (r = 0.38/ - 0.41), and pons (r = 0.35/ - 0.35).

CONCLUSIONS

Multiple neural correlates were identified for the digital measures in a cohort of people with early MS. Digital measures showed associations with brain regions that clinical measures were unable to demonstrate, thus providing potential novel information on functional ability compared with standard clinical assessments.

Changes in diffusion tensor imaging indices in basal ganglia and thalamus of patients with Relapsing-Remitting Multiple Sclerosis and relation with clinical conditions: A case-control study

Background

Multiple sclerosis (MS) is recognized as the most prevalent autoimmune abnormality of the CNS. T1WI, T2WI, and FLAIR are limited in the quantification of tissue damage and detection of tissue alterations in white and grey matter in MS. This study aimed to the evaluation of changes in DTI indices in these patients at the thalamus and basal ganglia.

Methods

30 relapsing-remitting MS (RRMS) cases and 30 normal individuals were included. Conventional MRI (T2, FLAIR) was acquired to confirm NAGM in MS patients. A T1 MPRAGE protocol was used to normalize DTI images. FSL, SPM, and Explore DTI software were employed to reach Mean Diffusivities (MD), Axial Diffusivities (AD), Fractional anisotropy (FA), and Radial Diffusivity (RD) at the thalamus and the basal ganglia.

Results

The FA and RD of the thalamus were decreased in healthy controls compared to MS cases (0.319 vs. 0.296 and 0.0009 vs. 0.0006, respectively) (P < 0.05). The AD value in the thalamus and the FA value in the caudate nucleus were significantly lower in MS cases than in controls (0.0009 vs. 0.0011 and 0.16 vs. 0.18, respectively) (P < 0.05). MD values in the thalamus or basal ganglia were not significantly different between groups.

Conclusions

DTI measures including FA, RD, and AD have a good diagnostic performance in detecting microstructural changes in the normal-appearing thalamus in cases with RRMS while they had no significant relationship with clinical signs in terms of EDSS.

Availability of data and material

Not applicable.

Brain volumetric correlates of remotely versus in-person administered symbol digit modalities test in multiple sclerosis

BACKGROUND

Prior studies in multiple sclerosis (MS) support reliability of telehealth-delivered cognitive batteries, although, to date, none have reported relationships of cognitive test performance to neural correlates across administration modalities. In this study we aimed to compare brain-behavior relationships, using the Symbol Digit Modalities Test (SDMT), the most reliable and sensitive cognitive measure in MS, measured from patients seen via telehealth versus in-person.

METHODS

SDMT was administered to individuals with MS either in-person (N=60, mean age=39.7) or remotely via video conference (N=51, mean age=47.4). Magnetic resonance imaging (MRI) data was collected in 3-Tesla scanners. Using 3-dimensional T1 images cerebral, cortical, deep gray, cerebral white matter and thalamic nuclei volumes were calculated. Using a meta-analysis approach with an interaction term for participant group, individual regression models were run for each MRI measure having SDMT scores as the outcome variable in each model. In addition, the correlation and average difference between In-person and Remote group associations across the MRI measures were calculated. Finally, for each MRI variable I² score was quantified to test the heterogeneity between the groups.

RESULTS

Administration modality did not affect the association of SDMT performance with MRI measures. Brain tissue volumes showing high associations with the SDMT scores in one group also showed high associations in the other (r = 0.83; 95% CI = [0.07, 0.86]). The average difference between the In-person and the Remote group associations was not significant (β_Remote - β_In-person = 0.14, 95% CI = [-0.04, 0.34]). Across MRI measures, the average I² value was 14%, reflecting very little heterogeneity in the relationship of SDMT performance to brain volume.

CONCLUSION

We found consistent relationships to neural correlates across in-person and remote SDMT administration modalities. Hence, our study extended the findings of the previous studies demonstrating the feasibility of remote administration of the SDMT.

Objective perimetry and progression of multiple sclerosis

Introduction

We re-examined the per-region response amplitudes and delays obtained from multifocal pupillographic objective perimetry (mfPOP) after 10 years in 44 persons living with multiple sclerosis (PwMS), both to examine which parts of the visual field had progressed in terms of response properties and to examine if the baseline data could predict the overall progression of disease.

Methods

Expanded Disability Status Scale (EDSS) scores were assessed in 2009 and 2019. Both eyes of each participant were concurrently tested at 44 locations/eye on both occasions. Several measures of clinical progression were examined, using logistic regression to determine the odds of progression.

Results

At the second examination the 44 PwMS (31 females) were aged 61.0 ± 12.2 y. Mean EDSS had not changed significantly (3.69 ± 1.23 in 2009, 3.81 ± 2.00 in 2019). mfPOP delay increased progressively from inferior to superior regions of the visual fields while amplitudes demonstrated a temporal to nasal gradient. The mean of the 3 most delayed visual field regions was correlated with progression of MS by 2019 (p = 0.023). Logistic regression indicated a significant association between delay and odds of progression (p = 0.045): an individual with 3 regions at least 1 SD (40 ms) slower than the mean in 2009 had 2.05× (±SE: 1.43× to 2.95×) the odds of progression by 2019. A 1 SD shorter delay was associated with 2.05× lower odds of progression. Amplitude changes were not predictive of progression.

Significance

mfPOP may provide a rapid, convenient method of monitoring and predicting MS progression.

The network collapse in multiple sclerosis: An overview of novel concepts to address disease dynamics

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Multiple sclerosis (MS) can be considered as a network disorder.

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This review discusses network concepts in order to understand progression in MS.

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Damage is hypothesized to lead to a “network collapse” and clinical progression.

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New concepts are discussed that will likely influence the field in the near future.

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These include brain wiring, how regions communicate and robustness to damage.

Multiple sclerosis is a neuroinflammatory and neurodegenerative disorder of the central nervous system that can be considered a network disorder. In MS, lesional pathology continuously disconnects structural pathways in the brain, forming a disconnection syndrome. Complex functional network changes then occur that are poorly understood but closely follow clinical status. Studying these structural and functional network changes has been and remains crucial to further decipher complex symptoms like cognitive impairment and physical disability. Recent insights especially implicate the importance of monitoring network hubs in MS, like the thalamus and default-mode network which seem especially hit hard. Such network insights in MS have led to the hypothesis that as the network continues to become disconnected and dysfunctional, exceeding a certain threshold of network efficiency loss leads to a “network collapse”. After this collapse, crucial network hubs become rigid and overloaded, and at the same time a faster neurodegeneration and accelerated clinical (and cognitive) progression can be seen. As network neuroscience has evolved, the MS field can now move towards a clearer classification of the network collapse itself and specific milestone events leading up to it. Such an updated network-focused conceptual framework of MS could directly impact clinical decision making as well as the design of network-tailored rehabilitation strategies. This review therefore provides an overview of recent network concepts that have enhanced our understanding of clinical progression in MS, especially focusing on cognition, as well as new concepts that will likely move the field forward in the near future.

Neurophysiological and behavioural correlates of ocrelizumab therapy on manual dexterity in patients with primary progressive multiple sclerosis

Background

Hand dexterity impairment is a key feature of disability in people with primary progressive multiple sclerosis (PPMS). So far, ocrelizumab, a recombinant humanized monoclonal antibody that selectively depletes CD20-expressing B cells, is the only therapy approved for PPMS and recent analysis reported its ability to reduce the risk of upper limb disability progression. However, the neural mechanisms underlying hand impairment in PPMS and the brain networks behind the effect of ocrelizumab on manual dexterity are not fully understood.

Objective

Main aims of our study were: (i) to investigate neurophysiological and behavioural correlates of hand function impairment in subjects with PPMS, and (ii) to use neurophysiologic and behavioural measures to track the effects of ocrelizumab therapy on manual dexterity.

Methods

Seventeen PPMS patients and 17 healthy-controls underwent routine neurophysiological protocols assessing the integrity of cortico-spinal and somatosensory pathways and advanced transcranial magnetic stimulation (TMS) protocols evaluating inhibitory (short and long interval intracortical inhibition, short-latency afferent inhibition) and facilitatory (motor thresholds, intracortical facilitation, short-interval intracortical facilitation) circuits in the primary motor cortex. All subjects also underwent behavioural analysis of hand dexterity by means of nine-hole peg test and finger movement analysis, and hand strength with handgrip and three-point pinch test. Neurophysiological and clinical assessments of hand functionality were also performed after 1 year of ocrelizumab therapy.

Results

At baseline PPMS patients displayed a significant impairment of hand dexterity and strength compared to healthy controls (all p < 0.03). Neurophysiological study disclosed prolonged latencies of standard somatosensory and motor evoked potentials (all p < 0.025) and an overall reduction of intracortical excitability at TMS protocols, involving both excitatory and inhibitory circuits. Importantly, hand dexterity impairment, indexed by delayed 9HPT, correlated with TMS protocols investigating cortical sensorimotor integration (short-latency afferent inhibition, SAI), p = 0.009. Both parameters, 9HPT (p = 0.01) and SAI (p = 0.01), displayed a significant improvement after 1 year of therapy with ocrelizumab.

Conclusion

Intracortical sensorimotor networks are involved in hand dexterity dysfunction of PPMS. Ocrelizumab therapy displays a beneficial effect on hand dexterity impairment most likely through intracortical networks implicated in fast sensorimotor integration.

What Have Failed, Interrupted, and Withdrawn Antibody Therapies in Multiple Sclerosis Taught Us?

In the past two decades, monoclonal antibodies (mAbs) have revolutionized the treatment of multiple sclerosis (MS). However, a remarkable number of mAbs failed due to negative study results were withdrawn because of unexpected serious adverse events (SAEs) or due to studies being halted for other reasons. While trials with positive outcomes are usually published in prestigious journals, negative trials are merely published as abstracts or not at all. This review summarizes MS mAbs that have either failed in phase II-III trials, have been interrupted for various reasons, or withdrawn from the market since 2015. The main conclusions that can be drawn from these 'negative' experiences are as follows. mAbs that have been proven to be safe in other autoimmune conditions, will not have the same safety profile in MS due to immunopathogenetic differences in these diseases (e.g., daclizumab). Identification of SAEs in clinical trials is difficult highlighting the importance of phase IV studies. Memory B cells are central players in MS immunopathogenesis (e.g., tabalumab). The pathophysiological mechanisms of disease progression are independent of leukocyte 'outside-in' traffic which drives relapses in MS. Therefore, therapies for progressive MS must be able to sufficiently cross the blood-brain barrier. Sufficiently long trial duration and multicomponent outcome measures are important for clinical studies in progressive MS. The success of trials on remyelination-promoting therapies mainly depends on the sufficient high dose of mAb, the optimal readout for 'proof of concept', time of treatment initiation, and appropriate selection of patients. Failed strategies are highly important to better understand assumed immunopathophysiological mechanisms and optimizing future trial designs.

Towards Multimodal Machine Learning Prediction of Individual Cognitive Evolution in Multiple Sclerosis

Multiple sclerosis (MS) manifests heterogeneously among persons suffering from it, making its disease course highly challenging to predict. At present, prognosis mostly relies on biomarkers that are unable to predict disease course on an individual level. Machine learning is a promising technique, both in terms of its ability to combine multimodal data and through the capability of making personalized predictions. However, most investigations on machine learning for prognosis in MS were geared towards predicting physical deterioration, while cognitive deterioration, although prevalent and burdensome, remained largely overlooked. This review aims to boost the field of machine learning for cognitive prognosis in MS by means of an introduction to machine learning and its pitfalls, an overview of important elements for study design, and an overview of the current literature on cognitive prognosis in MS using machine learning. Furthermore, the review discusses new trends in the field of machine learning that might be adopted for future studies in the field.