Brain atrophy and lesion load predict long term disability in multiple sclerosis

White matter tract density index is associated with disability in multiple sclerosis

BACKGROUND

The association between common neuroradiological markers of multiple sclerosis (MS) and clinical disability is weak. Given that the disability in patients with MS may depend on the underlying structural connectivity of the brain, our study aimed to examine the association between white matter tracts affected by MS and the patients' disability using a new tract density index (TDI).

METHOD

This study included 53 patients diagnosed with MS, examined between 2019 and 2020. Manual lesion segmentation was performed on fluid-attenuated inversion recovery (FLAIR) images, and the density of white matter tracts encompassing the lesion (i.e., TDI) was calculated. Correlation analysis was employed to assess the association between TDI and disability. Additionally, the relationship between disability, TDI, and lesion-derived network metrics was examined by computing a partial correlation network.

RESULTS

The TDI significantly correlated with the expanded disability status scale (EDSS) (r = 0.30, p = 0.03). Furthermore, the patient's disability is linked solely through TDI to lesion-derived network metrics -a key metric that 'bridges' the gap between the brain lesion and disability.

CONCLUSIONS

In this study, MS lesions encompassing regions with high white matter tract density were associated and linked with severe physical disability. These findings indicate that TDI may be an outcome predictor that may connect radiologic findings to clinical practice.

Advanced Brain Imaging in Central Nervous System Demyelinating Diseases

In recent decades, advances in neuroimaging have profoundly transformed our comprehension of central nervous system demyelinating diseases. Remarkable technological progress has enabled the integration of cutting-edge acquisition and postprocessing techniques, proving instrumental in characterizing subtle focal changes, diffuse microstructural alterations, and macroscopic pathologic processes. This review delves into state-of-the-art modalities applied to multiple sclerosis, neuromyelitis optica spectrum disorders, and myelin oligodendrocyte glycoprotein antibody-associated disease. Furthermore, it explores how this dynamic landscape holds significant promise for the development of effective and personalized clinical management strategies, encompassing support for differential diagnosis, prognosis, monitoring treatment response, and patient stratification.

Explainable machine learning on baseline MRI predicts multiple sclerosis trajectory descriptors

Multiple sclerosis (MS) is a multifaceted neurological condition characterized by challenges in timely diagnosis and personalized patient management. The application of Artificial Intelligence (AI) to MS holds promises for early detection, accurate diagnosis, and predictive modeling. The objectives of this study are: 1) to propose new MS trajectory descriptors that could be employed in Machine Learning (ML) regressors and classifiers to predict patient evolution; 2) to explore the contribution of ML models in discerning MS trajectory descriptors using only baseline Magnetic Resonance Imaging (MRI) studies. This study involved 446 MS patients who had a baseline MRI, at least two measurements of Expanded Disability Status Scale (EDSS), and a 1-year follow-up. Patients were divided into two groups: 1) for model development and 2) for evaluation. Three descriptors: β1, β2, and EDSS(t), were related to baseline MRI parameters using regression and classification XGBoost models. Shapley Additive Explanations (SHAP) analysis enhanced model transparency by identifying influential features. The results of this study demonstrate the potential of AI in predicting MS progression using the proposed patient trajectories and baseline MRI scans, outperforming classic Multiple Linear Regression (MLR) methods. In conclusion, MS trajectory descriptors are crucial; incorporating AI analysis into MRI assessments presents promising opportunities to advance predictive capabilities. SHAP analysis enhances model interpretation, revealing feature importance for clinical decisions.

Global and Regional Deep Learning Models for Multiple Sclerosis Stratification From MRI

BACKGROUND

The combination of anatomical MRI and deep learning-based methods such as convolutional neural networks (CNNs) is a promising strategy to build predictive models of multiple sclerosis (MS) prognosis. However, studies assessing the effect of different input strategies on model's performance are lacking.

PURPOSE

To compare whole-brain input sampling strategies and regional/specific-tissue strategies, which focus on a priori known relevant areas for disability accrual, to stratify MS patients based on their disability level.

STUDY TYPE

Retrospective.

SUBJECTS

Three hundred nineteen MS patients (382 brain MRI scans) with clinical assessment of disability level performed within the following 6 months (~70% training/~15% validation/~15% inference in-house dataset) and 440 MS patients from multiple centers (independent external validation cohort).

FIELD STRENGTH/SEQUENCE

Single vendor 1.5 T or 3.0 T. Magnetization-Prepared Rapid Gradient-Echo and Fluid-Attenuated Inversion Recovery sequences.

ASSESSMENT

A 7-fold patient cross validation strategy was used to train a 3D-CNN to classify patients into two groups, Expanded Disability Status Scale score (EDSS) ≥ 3.0 or EDSS < 3.0. Two strategies were investigated: 1) a global approach, taking the whole brain volume as input and 2) regional approaches using five different regions-of-interest: white matter, gray matter, subcortical gray matter, ventricles, and brainstem structures. The performance of the models was assessed in the in-house and the independent external cohorts.

STATISTICAL TESTS

Balanced accuracy, sensitivity, specificity, area under receiver operating characteristic (ROC) curve (AUC).

RESULTS

With the in-house dataset, the gray matter regional model showed the highest stratification accuracy (81%), followed by the global approach (79%). In the external dataset, without any further retraining, an accuracy of 72% was achieved for the white matter model and 71% for the global approach.

DATA CONCLUSION

The global approach offered the best trade-off between internal performance and external validation to stratify MS patients based on accumulated disability.

EVIDENCE LEVEL

4 TECHNICAL EFFICACY: Stage 2.

Addition of quantitative MRI to the routine clinical care of patients with multiple sclerosis-Results from the MAGNON project

BACKGROUND

The revised Lublin classification offers a framework for categorizing multiple sclerosis (MS) according to the clinical course and imaging results. Diagnosis of secondary progressive MS (SPMS) is often delayed by a period of uncertainty. Several quantitative magnetic resonance imaging (qMRI) markers are associated with progressive disease states, but they are not usually available in clinical practice.

METHODS

The MAGNON project enrolled 629 patients (early relapsing-remitting MS (RRMS), n = 51; RRMS with suspected SPMS, n = 386; SPMS, n = 192) at 55 centers in Germany. Routine magnetic resonance imaging (MRI) scans at baseline and after 12 months were analyzed using a centralized automatic processing pipeline to quantify lesions and normalized brain and thalamic volume. Clinical measures included relapse activity, disability, and MS phenotyping. Neurologists completed questionnaires before and after receiving the qMRI reports.

RESULTS

According to the physicians' reports, qMRI results changed their assessment of the patient in 31.8% (baseline scan) and 27.6% (follow-up scan). For ∼50% of patients with RRMS with suspected SPMS, reports provided additional information that the patient was transitioning to SPMS. In >25% of all patients, this information influenced the physicians' assessment of the patient's current phenotype. However, actual changes of treatment were reported only in a minority of these patients.

CONCLUSIONS

The MAGNON results suggest that standardized qMRI reports may be integrated into the routine clinical care of MS patients and support the application of the Lublin classification as well as treatment decisions. The highest impact was reported in patients with suspected SPMS, indicating a potential to reduce diagnostic uncertainty.

The role of ethnicity and native-country income in multiple sclerosis: the Italian multicentre study (MS-MigIT)

OBJECTIVE

Multiple sclerosis (MS) is a complex disorder in which environmental and genetic factors interact modifying disease risk and course. This multicentre, case-control study involving 18 Italian MS Centres investigated MS course by ethnicity and native-country economic status in foreign-born patients living in Italy.

METHODS

We identified 457 MS patients who migrated to Italy and 893 age- and sex-matched native-born Italian patients. In our population, 1225 (93.2%) subjects were White Europeans and White Northern Americans (WENA) and 89 (6.8%) patients were from other ethnical groups (OEG); 1109 (82.1%) patients were born in a high-income (HI) Country and 241 (17.9%) in a low-middle-income (LMI) Country. Medical records and patients interviews were used to collect demographic and disease data.

RESULTS

We included 1350 individuals (973 women and 377 men); mean (SD) age was 45.0 (11.7) years. At onset, 25.45% OEG patients vs 12.47% WENA (p = 0.039) had > 3 STIR spine lesions. At recruitment, the same group featured mean (SD) EDSS score of 2.85 (2.23) vs 2.64 (2.28) (p = 0.044) reached in 8.9 (9.0) vs 12.0 (9.0) years (p = 0.018) and underwent 1.10 (4.44) vs. 0.99 (0.40) annual MRI examinations (p = 0.035). At disease onset, patients from LMI countries had higher EDSS score than HI patients (2.40 (1.43) vs 1.99 (1.17); p = 0.032).

DISCUSSION

Our results suggested that both ethnicity and socio-economic status of native country shape MS presentation and course and should be considered for an appropriate management of patients. To the best of our knowledge, this is the first study reporting on the impact of ethnicity in MS at an individual level and beyond an ecological population-perspective.

Predictors of multiple sclerosis progression: A systematic review of conventional magnetic resonance imaging studies

INTRODUCTION

Multiple Sclerosis (MS) is a chronic neurodegenerative disorder that affects the central nervous system (CNS) and results in progressive clinical disability and cognitive decline. Currently, there are no specific imaging parameters available for the prediction of longitudinal disability in MS patients. Magnetic resonance imaging (MRI) has linked imaging anomalies to clinical and cognitive deficits in MS. In this study, we aimed to evaluate the effectiveness of MRI in predicting disability, clinical progression, and cognitive decline in MS.

METHODS

In this study, according to PRISMA guidelines, we comprehensively searched the Web of Science, PubMed, and Embase databases to identify pertinent articles that employed conventional MRI in the context of Relapsing-Remitting and progressive forms of MS. Following a rigorous screening process, studies that met the predefined inclusion criteria were selected for data extraction and evaluated for potential sources of bias.

RESULTS

A total of 3028 records were retrieved from database searching. After a rigorous screening, 53 records met the criteria and were included in this study. Lesions and alterations in CNS structures like white matter, gray matter, corpus callosum, thalamus, and spinal cord, may be used to anticipate disability progression. Several prognostic factors associated with the progression of MS, including presence of cortical lesions, changes in gray matter volume, whole brain atrophy, the corpus callosum index, alterations in thalamic volume, and lesions or alterations in cross-sectional area of the spinal cord. For cognitive impairment in MS patients, reliable predictors include cortical gray matter volume, brain atrophy, lesion characteristics (T2-lesion load, temporal, frontal, and cerebellar lesions), white matter lesion volume, thalamic volume, and corpus callosum density.

CONCLUSION

This study indicates that MRI can be used to predict the cognitive decline, disability progression, and disease progression in MS patients over time.

Magnetic resonance imaging prognostic factors for survival and relapse in dogs with meningoencephalitis of unknown origin

Introduction

Canine meningoencephalitis of unknown origin (MUO) is a debilitating disease associated with high mortality. The prognostic value of magnetic resonance imaging (MRI) findings for predicting survival at 12 months and long-term relapse remains uncertain.

Methods

This was a retrospective cohort study evaluating the prognostic value of different MRI variables using multivariable logistic regression and Cox proportional hazards analysis.

Results

In total, 138 dogs were presumptively diagnosed with MUO. The most common location for lesions identified on MRI were the white matter tracts of the corona radiata and corpus callosum, followed by the frontal, sensorimotor and temporal cortices. Lower T2 lesion load (p = 0.006, OR = 0.942, CI = 0.902-0.983) was associated with longer survival and higher T1 post-contrast lesion load (p = 0.023, OR = 1.162, CI = 1.021-1.322) was associated with relapse.

Discussion

This study has identified prognostic factors that may help identify dogs at higher risk of death and relapse and therefore guide treatment recommendations.

Prognostic relevance of MRI in early relapsing multiple sclerosis: ready to guide treatment decision making?

Magnetic resonance imaging (MRI) of the brain and spinal cord plays a crucial role in the diagnosis and monitoring of multiple sclerosis (MS). There is conclusive evidence that brain and spinal cord MRI findings in early disease stages also provide relevant insight into individual prognosis. This includes prediction of disease activity and disease progression, the accumulation of long-term disability and the conversion to secondary progressive MS. The extent to which these MRI findings should influence treatment decisions remains a subject of ongoing discussion. The aim of this review is to present and discuss the current knowledge and scientific evidence regarding the utility of MRI at early MS disease stages for prognostic classification of individual patients. In addition, we discuss the current evidence regarding the use of MRI in order to predict treatment response. Finally, we propose a potential approach as to how MRI data may be categorized and integrated into early clinical decision making.

Recent Advances in Diagnostic, Prognostic, and Disease-Monitoring Biomarkers in Multiple Sclerosis

Multiple sclerosis (MS) is a highly heterogeneous disease. Currently, a combination of clinical features, MRI, and cerebrospinal fluid markers are used in clinical practice for diagnosis and treatment decisions. In recent years, there has been considerable effort to develop novel biomarkers that better reflect the pathologic substrates of the disease to aid in diagnosis and early prognosis, evaluation of ongoing inflammatory activity, detection and monitoring of disease progression, prediction of treatment response, and monitoring of disease-modifying treatment safety. In this review, the authors provide an overview of promising recent developments in diagnostic, prognostic, and disease-monitoring/treatment-response biomarkers in MS.

Disease progression in the first 5 years of treatment in multiple sclerosis: Predictive value of early brain and lesion volume changes

BACKGROUND

Whether the degree of inflammation (and its resolution) and neurodegeneration after treatment initiation predicts disease progression in multiple sclerosis (MS) remains unclear.

OBJECTIVES

To assess the predictive value of magnetic resonance imaging (MRI)-derived brain and lesion volume (LV) changes in years 1 and 2 of treatment for disease progression.

METHODS

Patients receiving early interferon beta-1a treatment in REFLEX/REFLEXION (N = 262) were included. Predictive regression models included new/enlarging LV (positive activity), disappearing/shrinking LV (negative activity), and global/central atrophy during years 1 and 2.

RESULTS

Faster global atrophy and/or pseudo-atrophy and positive lesion activity in years 1 and 2 related to an increased probability and faster conversion to clinically definite multiple sclerosis (CDMS). Negative lesion activity in year 1 and slower central atrophy in year 2 were predictive of confirmed disability progression (9-Hole Peg Test). Positive lesion activity in year 2 was predictive of faster global atrophy, while positive lesion activity in years 1 and 2 was predictive of faster central atrophy.

CONCLUSIONS

A higher degree of global atrophy and/or pseudo-atrophy in year 1 was predictive of CDMS. Positive lesion activity in any year was related to CDMS and neurodegeneration. Disability was related to negative lesion activity in year 1 and slower central atrophy in year 2.

A longitudinal analysis of brain volume changes in myelin oligodendrocyte glycoprotein antibody-associated disease

BACKGROUND AND PURPOSE

Myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) is a relapsing demyelinating condition. There are several cross-sectional studies showing evidence of brain atrophy in people with MOGAD (pwMOGAD), but longitudinal brain volumetric assessment is still an unmet need. Current recommendations do not include monitoring with MRI and assume distinct attacks. Evidence of ongoing axon loss will have diagnostic and therapeutic implications. In this study, we assessed brain volume changes in pwMOGAD over a mean follow-up period of 2 years and compared this to changes in people with multiple sclerosis (pwMS).

METHODS

This is a retrospective single-center study over a 7-year period from 2014 to 2021. MRI brain scans at the time of diagnosis and follow-up in remission were collected from 14 Caucasian pwMOGAD, confirmed by serum myelin oligodendrocyte glycoprotein immunoglobulin G antibody presence, detected by live cell-based assays. Total brain volume (TBV), white matter (WM), gray matter (GM), and demyelinating lesion volumes were assessed automatically using the Statistical Parametric Mapping and FMRIB automated segmentation tools. MRI brain scans at diagnosis and follow-up on remission were collected from 32-matched pwMS for comparison. Statistical analysis was done using analysis of variance.

RESULTS

There is evidence of TBV loss, affecting particularly GM, over an approximately 2-year follow-up period in pwMOGAD (p < .05), comparable to pwMS. WM and lesion volume change over the same period were not statistically significant (p > .1).

CONCLUSION

We found evidence of loss of GM and TBV over time  in pwMOGAD, similar to pwMS, although the WM and lesion volumes were unchanged.

Short-term brain atrophy evolution after initiation of immunotherapy in a real-world multiple sclerosis cohort

BACKGROUND AND PURPOSE

In multiple sclerosis (MS), brain atrophy measurements have emerged as an important biomarker reflecting neurodegeneration and disability progression. However, due to several potential confounders, investigation of brain atrophy in clinical routine and even in controlled clinical studies can be challenging. The aim of this study was to investigate the short-term dynamics of brain atrophy development after initiation of disease-modifying therapy (DMT) in a "real-world setting."

METHODS

In this retrospective study, we included MS patients starting DMT (natalizumab, fingolimod, dimethyl fumarate, or interferon-ß1a) or without DMT, availability of a baseline MRI, and two annual follow-up scans on the same MRI system. Two-timepoint percentage brain volume changes (PBVCs) were calculated.

RESULTS

Fifty-five MS patients (12 patients starting DMT with natalizumab, 7 fingolimod, 14 dimethyl fumarate, 11 interferon-ß1a, and 11 patients without DMT) were included. We found the highest PBVCs in the first 12 months after initiation of natalizumab treatment. Furthermore, the PBVCs in our study were very much comparable to the results observed by other groups, as well as for fingolimod, dimethyl fumarate, and interferon-ß1a.

CONCLUSION

We found PBVCs that are comparable to the results of previous studies, suggesting that brain atrophy, assessed on 3D MRI data sets acquired on the same 3T MRI, provides a robust MS biomarker.

Prevalence of 2-year "No evidence of disease activity" (NEDA-3 and NEDA-4) in relapsing-remitting multiple sclerosis. A real-world study

BACKGROUND

No evidence of disease activity (NEDA) is becoming a gold standard in the evaluation of disease modifying therapies (DMT) in relapsing-remitting multiple sclerosis (RRMS). NEDA-3 status is the absence of relapses, new activity on brain MRI, and disability progression. NEDA-4 meets all NEDA-3 criteria plus lack of brain atrophy.

OBJECTIVE

Aim of this study was to investigate the prevalence of two-year NEDA-3, NEDA-4, six-month delayed NEDA-3 (6mdNEDA-3), and six-month delayed NEDA-4 (6mdNEDA-4) in a cohort of patients with RRMS. Six-month delayed measures were introduced to consider latency of action of drugs.

METHODS

Observational retrospective monocentric study. All the patients with RRMS starting DMT between 2015 and 2018, and with 2-year of follow-up, were included. Annualized brain volume loss (a-BVL) was calculated by SIENA software.

RESULTS

We included 108 patients, the majority treated with first line DMT. At 2-year follow-up, 35 % of patients were NEDA-3 (50 % 6mdNEDA-3), and 17 % NEDA-4 (28 % 6mdNEDA-4). Loss of NEDA-3 status was mainly driven by MRI activity (70 %), followed by relapses (56 %), and only minimally by disability progression (7 %).

CONCLUSION

In our cohort 2-year NEDA status, especially including lack of brain atrophy, was hard to achieve. Further studies are needed to establish the prognostic value of NEDA-3 and NEDA4 in the long-term follow-up.

A real-world clinical validation for AI-based MRI monitoring in multiple sclerosis

Modern management of MS targets No Evidence of Disease Activity (NEDA): no clinical relapses, no magnetic resonance imaging (MRI) disease activity and no disability worsening. While MRI is the principal tool available to neurologists for monitoring clinically silent MS disease activity and, where appropriate, escalating treatment, standard radiology reports are qualitative and may be insensitive to the development of new or enlarging lesions. Existing quantitative neuroimaging tools lack adequate clinical validation. In 397 multi-center MRI scan pairs acquired in routine practice, we demonstrate superior case-level sensitivity of a clinically integrated AI-based tool over standard radiology reports (93.3% vs 58.3%), relative to a consensus ground truth, with minimal loss of specificity. We also demonstrate equivalence of the AI-tool with a core clinical trial imaging lab for lesion activity and quantitative brain volumetric measures, including percentage brain volume loss (PBVC), an accepted biomarker of neurodegeneration in MS (mean PBVC -0.32% vs -0.36%, respectively), whereas even severe atrophy (>0.8% loss) was not appreciated in radiology reports. Finally, the AI-tool additionally embeds a clinically meaningful, experiential comparator that returns a relevant MS patient centile for lesion burden, revealing, in our cohort, inconsistencies in qualitative descriptors used in radiology reports. AI-based image quantitation enhances the accuracy of, and value-adds to, qualitative radiology reporting. Scaled deployment of these tools will open a path to precision management for patients with MS.

Brain Atrophy as an Outcome of Disease-Modifying Therapy for Remitting-Relapsing Multiple Sclerosis

Introduction

Currently, clinical trials of DMTs strive to determine their effect on neuroinflammation and neurodegeneration. We aimed to determine the impact of currently used DMTs on brain atrophy and disability in RRMS. The main goal of this review is to evaluate the neuroprotective potential of MS therapy and assess its impact on disability.

Methods

We performed a systematic analysis of clinical trials that used brain atrophy as an outcome or performed post hoc analysis of volumetric MRI parameters to assess the neuroprotective potential of applied therapies. Trials between 2008 and 2019 that included published results of brain parenchymal fraction (BPF) change and brain volume loss (BVL) in the period from baseline to week 96 or longer were considered.

Results

Twelve from 146 clinical trials met the inclusion criteria and were incorporated into the analysis. DMTs that presented a large reduction in BVL also exhibited robust effects on clinical disability worsening, e.g., alemtuzumab with a 42% risk reduction in 6-month confirmed disability accumulation (p = 0.0084), ocrelizumab with a 40% risk reduction in 6-month confirmed disability progression (p = 0.003), and other DMTs (cladribine and teriflunomide) with moderate influence on brain atrophy were also associated with a marked impact on disability worsening. Dimethyl fumarate (DEFINE) and fingolimod (FREEDOMS I) initially exhibited significant effect on BVL; however, this effect was not confirmed in further clinical trials: CONFIRM and FREEDOMS II, respectively. Peg-IFN-β1a shows a modest effect on BVL and disability worsening.

Conclusion

Our results show that BVL in one of the components of clinical disability worsening, together with other variables (lesion volume and annualized relapse rate). Standardization of atrophy measurement technique as well as harmonization of disability worsening and progression criteria in further clinical trials are of utmost importance as they enable a reliable comparison of neuroprotective potential of DMTs.

Learning from pseudo-labels: deep networks improve consistency in longitudinal brain volume estimation

Introduction

Brain atrophy is a critical biomarker of disease progression and treatment response in neurodegenerative diseases such as multiple sclerosis (MS). Confounding factors such as inconsistent imaging acquisitions hamper the accurate measurement of brain atrophy in the clinic. This study aims to develop and validate a robust deep learning model to overcome these challenges; and to evaluate its impact on the measurement of disease progression.

Methods

Voxel-wise pseudo-atrophy labels were generated using SIENA, a widely adopted tool for the measurement of brain atrophy in MS. Deformation maps were produced for 195 pairs of longitudinal 3D T1 scans from patients with MS. A 3D U-Net, namely DeepBVC, was specifically developed overcome common variances in resolution, signal-to-noise ratio and contrast ratio between baseline and follow up scans. The performance of DeepBVC was compared against SIENA using McLaren test-retest dataset and 233 in-house MS subjects with MRI from multiple time points. Clinical evaluation included disability assessment with the Expanded Disability Status Scale (EDSS) and traditional imaging metrics such as lesion burden.

Results

For 3 subjects in test-retest experiments, the median percent brain volume change (PBVC) for DeepBVC and SIENA was 0.105 vs. 0.198% (subject 1), 0.061 vs. 0.084% (subject 2), 0.104 vs. 0.408% (subject 3). For testing consistency across multiple time points in individual MS subjects, the mean (± standard deviation) PBVC difference of DeepBVC and SIENA were 0.028% (± 0.145%) and 0.031% (±0.154%), respectively. The linear correlation with baseline T2 lesion volume were r = -0.288 (p < 0.05) and r = -0.249 (p < 0.05) for DeepBVC and SIENA, respectively. There was no significant correlation of disability progression with PBVC as estimated by either method (p = 0.86, p = 0.84).

Discussion

DeepBVC is a deep learning powered brain volume change estimation method for assessing brain atrophy used T1-weighted images. Compared to SIENA, DeepBVC demonstrates superior performance in reproducibility and in the context of common clinical scan variances such as imaging contrast, voxel resolution, random bias field, and signal-to-noise ratio. Enhanced measurement robustness, automation, and processing speed of DeepBVC indicate its potential for utilisation in both research and clinical environments for monitoring disease progression and, potentially, evaluating treatment effectiveness.

CSF Markers of Oxidative Stress Are Associated with Brain Atrophy and Iron Accumulation in a 2-Year Longitudinal Cohort of Early MS

In this prospective longitudinal study, we quantified regional brain volume and susceptibility changes during the first two years after the diagnosis of multiple sclerosis (MS) and identified their association with cerebrospinal fluid (CSF) markers at baseline. Seventy patients underwent MRI (T1 and susceptibility weighted images processed to quantitative susceptibility maps, QSM) with neurological examination at the diagnosis and after two years. In CSF obtained at baseline, the levels of oxidative stress, products of lipid peroxidation, and neurofilaments light chain (NfL) were determined. Brain volumetry and QSM were compared with a group of 58 healthy controls. In MS patients, regional atrophy was identified in the striatum, thalamus, and substantia nigra. Magnetic susceptibility increased in the striatum, globus pallidus, and dentate and decreased in the thalamus. Compared to controls, MS patients developed greater atrophy of the thalamus, and a greater increase in susceptibility in the caudate, putamen, globus pallidus and a decrease in the thalamus. Of the multiple calculated correlations, only the decrease in brain parenchymal fraction, total white matter, and thalamic volume in MS patients negatively correlated with increased NfL in CSF. Additionally, negative correlation was found between QSM value in the substantia nigra and peroxiredoxin-2, and QSM value in the dentate and lipid peroxidation levels.

Detection of grey matter microstructural substrates of neurodegeneration in multiple sclerosis

Multiple sclerosis features complex pathological changes in grey matter that begin early and eventually lead to diffuse atrophy. Novel approaches to image grey-matter microstructural alterations in vivo are highly sought after and would enable more sensitive monitoring of disease activity and progression. This cross-sectional study aimed to assess the sensitivity of high-gradient diffusion MRI for microstructural tissue damage in cortical and deep grey matter in people with multiple sclerosis and test the hypothesis that reduced cortical cell body density is associated with cortical and deep grey-matter volume loss. Forty-one people with multiple sclerosis (age 24-72, 14 females) and 37 age- and sex-matched healthy controls were scanned on a 3 T Connectom MRI scanner equipped with 300 mT/m gradients using a multi-shell diffusion MRI protocol. The soma and neurite density imaging model was fitted to high-gradient diffusion MRI data to obtain estimates of intra-neurite, intra-cellular and extra-cellular signal fractions and apparent soma radius. Cortical and deep grey-matter microstructural imaging metrics were compared between multiple sclerosis and healthy controls and correlated with grey-matter volume, clinical disability and cognitive outcomes. People with multiple sclerosis showed significant cortical and deep grey-matter volume loss compared with healthy controls. People with multiple sclerosis showed trends towards lower cortical intra-cellular signal fraction and significantly lower intra-cellular and higher extra-cellular signal fractions in deep grey matter, especially the thalamus and caudate, compared with healthy controls. Changes were most pronounced in progressive disease and correlated with the Expanded Disability Status Scale, but not the Symbol Digit Modalities Test. In multiple sclerosis, normalized thalamic volume was associated with thalamic microstructural imaging metrics. Whereas thalamic volume loss did not correlate with cortical volume loss, cortical microstructural imaging metrics were significantly associated with thalamic volume, and not with cortical volume. Compared with the short diffusion time (Δ = 19 ms) achievable on the Connectom scanner, at the longer diffusion time of Δ = 49 ms attainable on clinical scanners, multiple sclerosis-related changes in imaging metrics were generally less apparent with lower effect sizes in cortical and deep grey matter. Soma and neurite density imaging metrics obtained from high-gradient diffusion MRI data provide detailed grey-matter characterization beyond cortical and thalamic volumes and distinguish multiple sclerosis-related microstructural pathology from healthy controls. Cortical cell body density correlates with thalamic volume, appears sensitive to the microstructural substrate of neurodegeneration and reflects disability status in people with multiple sclerosis, becoming more pronounced as disability worsens.

Scanner agnostic large-scale evaluation of MS lesion delineation tool for clinical MRI

Introduction

Patients with MS are MRI scanned continuously throughout their disease course resulting in a large manual workload for radiologists which includes lesion detection and size estimation. Though many models for automatic lesion segmentation have been published, few are used broadly in clinic today, as there is a lack of testing on clinical datasets. By collecting a large, heterogeneous training dataset directly from our MS clinic we aim to present a model which is robust to different scanner protocols and artefacts and which only uses MRI modalities present in routine clinical examinations.

Methods

We retrospectively included 746 patients from routine examinations at our MS clinic. The inclusion criteria included acquisition at one of seven different scanners and an MRI protocol including 2D or 3D T2-w FLAIR, T2-w and T1-w images. Reference lesion masks on the training (n = 571) and validation (n = 70) datasets were generated using a preliminary segmentation model and subsequent manual correction. The test dataset (n = 100) was manually delineated. Our segmentation model https://github.com/CAAI/AIMS/ was based on the popular nnU-Net, which has won several biomedical segmentation challenges. We tested our model against the published segmentation models HD-MS-Lesions, which is also based on nnU-Net, trained with a more homogenous patient cohort. We furthermore tested model robustness to data from unseen scanners by performing a leave-one-scanner-out experiment.

Results

We found that our model was able to segment MS white matter lesions with a performance comparable to literature: DSC = 0.68, precision = 0.90, recall = 0.70, f1 = 0.78. Furthermore, the model outperformed HD-MS-Lesions in all metrics except precision = 0.96. In the leave-one-scanner-out experiment there was no significant change in performance (p < 0.05) between any of the models which were only trained on part of the dataset and the full segmentation model.

Conclusion

In conclusion we have seen, that by including a large, heterogeneous dataset emulating clinical reality, we have trained a segmentation model which maintains a high segmentation performance while being robust to data from unseen scanners. This broadens the applicability of the model in clinic and paves the way for clinical implementation.

The association between brain volume loss and disability in multiple sclerosis: A systematic review

BACKGROUND

Multiple sclerosis (MS) is a chronic, inflammatory, demyelinating, degenerative disease of the central nervous system that affects approximately 2.8 million people worldwide. Compelling evidence from observational studies and clinical trials indicates a strong association between brain volume loss (BVL) and the accumulation of disability in MS. However, the considerable heterogeneity in study designs and methods of assessment of BVL invites questions concerning the generalizability of the reported findings. Therefore, we conducted this systematic review to characterize the relationship between BVL and physical disability in patients with MS.

METHODS

A systematic literature search of MEDLINE and EMBASE databases was performed supplemented by gray literature searches. The following study designs were included: prospective/retrospective cohort, cross-sectional and case-control. Only English language articles published from 2010 onwards were eligible for final inclusion. There were no restrictions on MS subtype, age, or ethnicity. Of the 1620 citations retrieved by the structured searches, 50 publications met our screening criteria and were included in the final data set.

RESULTS

Across all BVL measures, there was considerable heterogeneity in studies regarding the underlying study population, the definitions of BVL and image analysis methodologies, the physical disability measure used, the measures of association reported and whether the analysis conducted was univariable or multivariable. A total of 36 primary studies providing data on the association between whole BVL and physical disability in MS collectively suggest that whole brain atrophy is associated with greater physical disability progression in MS patients. Similarly, a total of 15 primary studies providing data on the association between ventricular atrophy and physical disability in MS suggest that ventricular atrophy is associated with greater physical disability progression in MS patients. Along similar lines, the existing evidence based on a total of 13 primary studies suggests that gray matter atrophy is associated with greater physical disability progression in MS patients. Four primary studies suggest that corpus callosum atrophy is associated with greater physical disability progression in MS patients. The majority of the existing evidence (6 primary studies) suggests no association between white matter atrophy and physical disability in MS. It is difficult to assign a relationship between basal ganglia volume loss and physical disability as well as medulla oblongata width and physical disability in MS due to very limited data.

CONCLUSION

The evidence gathered from this systematic review, although very heterogeneous, suggests that whole brain atrophy is associated with greater physical disability progression in MS patients. Our review can help define future imaging biomarkers for physical disability progression and treatment monitoring in MS.

Temporal evolution of new T1-weighted hypo-intense lesions and central brain atrophy in patients with a first clinical demyelinating event treated with subcutaneous interferon β-1a

OBJECTIVE

Evaluate the effect of subcutaneous interferon β-1a (sc IFN β-1a) versus placebo on the evolution of T1-weighted MRI lesions and central brain atrophy in in patients with a first clinical demyelinating event (FCDE).

METHODS

Post hoc analysis of baseline-to-24 month MRI data from patients with an FCDE who received sc IFN β-1a 44 μg once- (qw) or three-times-weekly (tiw), or placebo, in REFLEX. Patients were grouped according to treatment regimen or conversion to clinically definite MS (CDMS) status. The intensity of new lesions on unenhanced T1-weighted images was classified as T1 iso- or hypo-intense (black holes) and percentage ventricular volume change (PVVC) was assessed throughout the study.

RESULTS

In patients not converting to CDMS, sc IFN β-1a tiw or qw, versus placebo, reduced the overall number of new lesions (P < 0.001 and P = 0.005) and new T1 iso-intense lesions (P < 0.001 and P = 0.002) after 24 months; only sc IFN β-1a tiw was associated with fewer T1 hypo-intense lesions versus placebo (P < 0.001). PVVC findings in patients treated with sc IFN β-1a suggested pseudo-atrophy that was ~ fivefold greater versus placebo in the first year of treatment (placebo 1.11%; qw 4.28%; tiw 6.76%; P < 001); similar findings were apparent for non-converting patients.

CONCLUSIONS

In patients with an FCDE, treatment with sc IFN β-1a tiw for 24 months reduced the number of new lesions evolving into black holes.

Correlation of brain segmental volume changes with clinical parameters: a longitudinal study in multiple sclerosis patients

OBJECTIVE

 To measure the cranial volume differences from 15 different parts in the follow-up of relapsing-remitting multiple sclerosis (RRMS) patients and correlate them with clinical parameters.

METHODS

 Forty-seven patients with RRMS were included in the study. Patients were grouped into two categories; low Expanded Disability Status Scale (EDSS) (< 3; group 1), and moderate-high EDSS (≥ 3; group 2). Patients were evaluated with Beck Depression Inventory (BDI), Montreal Cognitive Assessment (MOCA), Symbol Digit Modalities Test (SDMT), Fatigue Severity Scale (FSS), and calculated Annualized Relapse Rate (ARR) scores. Magnetic resonance imaging (MRI) was performed with a 1.5T MRI device (Magnetom AERA, Siemens, Erlangen, Germany) twice in a 1-year period. Volumetric analysis was performed by a free, automated, online MRI brain volumetry software. The differences in volumetric values between the two MRI scans were calculated and correlated with the demographic and clinical parameters of the patients.

RESULTS

 The number of attacks, disease duration, BDI, and FSS scores were higher in group 2; SDMT was higher in group 1. As expected, volumetric analyses have shown volume loss in total cerebral white matter in follow-up patients (p < 0.001). In addition, putaminal volume loss was related to a higher number of attacks. Besides, a negative relation between FSS with total amygdala volumes, a link between atrophy of globus pallidus and ARR, and BDI scores was found with the aid of network analysis.

CONCLUSIONS

 Apart from a visual demonstration of volume loss, cranial MRI with volumetric analysis has a great potential for revealing covert links between segmental volume changes and clinical parameters.

Early Predictors of Disability and Cognition in Multiple Sclerosis Patients: A Long-Term Retrospective Analysis

We conducted a retrospective analysis on multiple sclerosis (MS) patients with perceived cognitive decline and long disease duration to investigate early predictors of future cognitive impairment (CI) and motor disability. Sixty-five patients complaining of cognitive decline were assessed with an extensive neuropsychological battery at the last clinical follow-up and classified as mildly impaired, severely impaired, and cognitively spared based on the results. Motor disability was assessed with EDSS, MSSS, and ARMSS. Baseline demographic, clinical, and imaging parameters were retrospectively collected and inserted in separate multivariate regression models to investigate the predictive power of future impairment. Twenty-one patients (32.3%) showed no CI, seventeen (26.2%) showed mild CI, and twenty-seven (41.5%) showed severe CI. Older and less educated patients with higher EDSS, longer disease duration, and higher white matter lesion load (WMLL) at diagnosis (particularly with cerebellar involvement) were more likely to develop CI after a mean follow-up from diagnosis of 16.5 ± 6.9 years. DMT exposure was protective. The multivariate regression analyses confirmed WMLL, disease duration, and educational levels as the parameters with significant predictive value for future CI (R2 adjusted: 0.338 p: 0.001). Older patients with progressive phenotype both at diagnosis and T1 were more likely to be not fully ambulatory at T1 (R2 adjusted: 0.796 p: 0.0001). Our results further expand knowledge on early predictors of cognitive decline and evolution over time.

Paradigm shifts in multiple sclerosis management: Implications for daily clinical practice

Multiple sclerosis (MS) is the most common chronic inflammatory neurological disease. The emergence of disease-modifying therapies (DMTs) has greatly improved disease activity control and progression of disability in MS patients. DMTs differ in their mode of action, route of administration, efficacy, and safety profiles, offering multiple options for clinicians. Personalized medicine aims at tailoring the therapeutic strategy to patients' characteristics and disease activity but also patients' needs and preferences. New therapeutic options have already changed treatment paradigms for patients with active relapsing MS (RMS). The traditional approach consists in initiating treatment with moderate-efficacy DMTs and subsequently, escalating to higher-efficacy DMTs when there is evidence of clinical and/or radiological breakthrough activity. Recent real-world studies suggest that initiation of high-efficacy DMTs from disease onset can improve long-term outcomes for RMS patients. In this article, we review different treatment strategies and discuss challenges associated with personalized therapy.

Commercial volumetric MRI reporting tools in multiple sclerosis: a systematic review of the evidence

PURPOSE

MRI is integral to the diagnosis of multiple sclerosis (MS) and is important for clinical prognostication. Quantitative volumetric reporting tools (QReports) can improve the accuracy and objectivity of MRI-based assessments. Several QReports are commercially available; however, validation can be difficult to establish and does not currently follow a common pathway. To aid evidence-based clinical decision-making, we performed a systematic review of commercial QReports for use in MS including technical details and published reports of validation and in-use evaluation.

METHODS

We categorized studies into three types of testing: technical validation, for example, comparison to manual segmentation, clinical validation by clinicians or interpretation of results alongside clinician-rated variables, and in-use evaluation, such as health economic assessment.

RESULTS

We identified 10 companies, which provide MS lesion and brain segmentation and volume quantification, and 38 relevant publications. Tools received regulatory approval between 2006 and 2020, contextualize results to normative reference populations, ranging from 620 to 8000 subjects, and require T1- and T2-FLAIR-weighted input sequences for longitudinal assessment of whole-brain volume and lesions. In MS, six QReports provided evidence of technical validation, four companies have conducted clinical validation by correlating results with clinical variables, only one has tested their QReport by clinician end-users, and one has performed a simulated in-use socioeconomic evaluation.

CONCLUSION

We conclude that there is limited evidence in the literature regarding clinical validation and in-use evaluation of commercial MS QReports with a particular lack of clinician end-user testing. Our systematic review provides clinicians and institutions with the available evidence when considering adopting a quantitative reporting tool for MS.

MR volumetry in detection of brain atrophic changes in MS patients and its implication on disease prognosis: retrospective study

Background

Multiple sclerosis is a chronic demyelinating disease of the central nervous system. It may lead to disability and cognitive impairment. Our study aimed at evaluation of the role of MR volumetry technique in detection of brain atrophic changes in patients with multiple sclerosis and its impact on disease prognosis.

Results

This study was carried out on thirty healthy control with mean age 26.23 years and thirty patients with remitting relapsing multiple sclerosis, with a mean age of 28.18 years. Patients with multiple sclerosis were distributed across six subgroups based on the z-score cut-off of − 1.96 for regional and whole brain atrophy. We found that 2 patients (6.6%) showed no thalamic or brain atrophy, 28 patients (93.3%) showed whole brain atrophy only and 10 patients (33.3%) showed both, thalamic and BP atrophy. No patients showed only thalamic atrohy, 4 patients showed whole brain atrophy with other structure atrophy rather than thalamus (13.3%), 10 patients with whole brain and more than one structure atrophy (33.3%). Relation between subgroups and degree of increase in the Expanded Disability Status Scale (EDSS) as well as presence of cognitive decline were assessed. No significant relation were found between RRMS patients subgroups with whole brain atrophy, subgroup with isolated thalamic atrophy or subgroup with multiple structure atrophy and increase of EDSS or cognitive decline.

Conclusion

We found that MRI volumetry is a very useful technique in the assessment of the atrophic changes that occur as a consequence of multiple sclerosis affecting the whole brain, deep grey matter as well as corpus callosum. Although our study did not prove significant relation between presence of brain atrophic changes and disability or cognitive impairment, presence of atrophy warrants careful clinical evaluation of those patients to detect any possible further progression of disability or cognitive decline.

Individual differences in visual evoked potential latency are associated with variance in brain tissue volume in people with multiple sclerosis: An analysis of brain function-structure correlates

Visual evoked potentials (VEP) index visual pathway functioning, and are often used for clinical assessment and as outcome measures in people with multiple sclerosis (PwMS). VEPs may also reflect broader neural disturbances that extend beyond the visual system, but this possibility requires further investigation. In the present study, we examined the hypothesis that delayed latency of the P100 component of the VEP would be associated with broader structural changes in the brain in PwMS. We obtained VEP latency for a standard pattern-reversal checkerboard stimulus paradigm, in addition to Magnetic Resonance Imaging (MRI) measures of whole brain volume (WBV), gray matter volume (GMV), white matter volume (WMV), and T2-weighted fluid attenuated inversion recovery (FLAIR) white matter lesion volume (FLV). Correlation analyses indicated that prolonged VEP latency was significantly associated with lower WBV, GMV, and WMV, and greater FLV. VEP latency remained significantly associated with WBV, GMV, and WMV even after controlling for the variance associated with inter-ocular latency, age, time between VEP and MRI assessments, and other MRI variables. VEP latency delays were most pronounced in PwMS that exhibited low volume in both white and gray matter simultaneously. Furthermore, PwMS that had delayed VEP latency based on a clinically relevant cutoff (VEP latency ≥ 113 ms) in both eyes had lower WBV, GMV, and WMV and greater FLV in comparison to PwMS that had normal VEP latency in one or both eyes. The findings suggest that PwMS that have delayed latency in both eyes may be particularly at risk for exhibiting greater brain atrophy and lesion volume. These analyses also indicate that VEP latency may index combined gray matter and white matter disturbances, and therefore broader network connectivity and efficiency. VEP latency may therefore provide a surrogate marker of broader structural disturbances in the brain in MS.

Using personalized prognosis in the treatment of relapsing multiple sclerosis: A practical guide

The clinical course of multiple sclerosis (MS) is highly variable among patients, thus creating important challenges for the neurologist to appropriately treat and monitor patient progress. Despite some patients having apparently similar symptom severity at MS disease onset, their prognoses may differ greatly. To this end, we believe that a proactive disposition on the part of the neurologist to identify prognostic “red flags” early in the disease course can lead to much better long-term outcomes for the patient in terms of reduced disability and improved quality of life. Here, we present a prognosis tool in the form of a checklist of clinical, imaging and biomarker parameters which, based on consensus in the literature and on our own clinical experiences, we have established to be associated with poorer or improved clinical outcomes. The neurologist is encouraged to use this tool to identify the presence or absence of specific variables in individual patients at disease onset and thereby implement sufficiently effective treatment strategies that appropriately address the likely prognosis for each patient.

MSProDiscuss™ Clinical Decision Support Tool for Identifying Multiple Sclerosis Progression

This article describes the rationale for the development of the MSProDiscuss™ clinical decision support (CDS) tool, its development, and insights into how it can help neurologists improve care for patients with multiple sclerosis (MS). MS is a progressive disease characterized by heterogeneous symptoms and variable disease course. There is growing consensus that MS exists on a continuum, with overlap between relapsing–remitting and secondary progressive phenotypes. Evidence demonstrates that neuroaxonal loss occurs from the outset, that progression can occur independent of relapse activity, and that continuous underlying pathological processes may not be reflected by inflammatory activity indicative of the patient’s immune response. Early intervention can benefit patients, and there is a need for a tool that assists physicians in rapidly identifying subtle signs of MS progression. MSProDiscuss, developed with physicians and patients, facilitates a structured approach to patient consultations. It analyzes multidimensional data via an algorithm to estimate the likelihood of progression (the MSProDiscuss score), the contribution of various symptoms, and the impact of symptoms on daily living, enabling a more personalized approach to treatment and disease management. Data from CDS tools such as MSProDiscuss offer new insights into disease course and facilitate informed decision-making and a holistic approach to MS patient care.

Predicting Long-term Disability in Multiple Sclerosis: A Narrative Review of Current Evidence and Future Directions

The ability to reliably monitor disease progression in patients with multiple sclerosis (MS) is integral to patient care. The Expanded Disability Status Scale (EDSS) is a commonly used tool to assess the disability status of patients with MS; however, it has limited sensitivity in detecting subtle changes in disability levels and, as a result, does not consistently provide clinicians with accurate insight into disease progression. At the 2019 European Committee for Treatment and Research in Multiple Sclerosis meeting in Stockholm, Sweden, a panel of neurologists met to discuss the limitations of the EDSS as a short-term predictor of MS progression. Before this panel discussion, a targeted literature review was conducted to evaluate published evidence on prognostic measures such as fatigue, physical assessments, and measures that are more taxing for patients, all of which may be useful to clinicians at different stages of the course of MS. This article summarizes currently available evidence in support of these measures. In addition, this article highlights the current state of expert clinical consensus regarding the current approaches used to predict and monitor disease progression and offers insight for future studies to assist clinicians in accurately monitoring disease progression in patients with MS.

Brain volume loss and physical and cognitive impairment in naive multiple sclerosis patients treated with fingolimod: prospective cohort study in Buenos Aires, Argentina

BACKGROUND

The percentage of brain volume loss (PBVL) has been classically considered as a biomarker in multiple sclerosis (MS).

OBJECTIVE

The objective of the present study was to analyze if the PBVL during the 1^st year after the onset of the disease predicts physical and cognitive impairment (CI).

METHODS

Prospective study that included naïve patients without cognitive impairment who initiated MS treatment with fingolimod. Patients were followed for 3 years and relapses, expanded disability status scale (EDSS) progression (defined as worsening of 1 point on the EDSS), the annual PBVL (evaluated by structural image evaluation using normalization of atrophy [SIENA]), and the presence of CI were evaluated. Cognitive impairment was defined in patients who scored at least 2 standard deviations (SDs) below controls on at least 2 domains. The PBVL after 1 year of treatment with fingolimod was used as an independent variable, while CI and EDSS progression at the 3^rd year of follow-up as dependent variables.

RESULTS

A total of 71 patients were included, with a mean age of 35.4 ± 3 years old. At the 3^rd year, 14% of the patients were classified as CI and 6.2% had EDSS progression. In the CI group, the PBVL during the 1^st year was - 0.52 (±0.07) versus -0.42 (±0.04) in the no CI group (p < 0.01; odds ratio [OR] = 2.24; 95% confidence interval [CI]: 1.72-2.44). In the group that showed EDSS progression, the PBVL during the 1^st year was - 0.59 (±0.05) versus - 0.42 (±0.03) (p < 0.01; OR = 2.33; 95%CI: 1.60-2.55).

CONCLUSIONS

A higher PBVL during the 1^st year in naïve MS patients was independently associated with a significant risk of CI and EDSS progression.

Beneficial Effect of Melatonin Alone or in Combination with Glatiramer Acetate and Interferon β-1b on Experimental Autoimmune Encephalomyelitis

Experimental autoimmune encephalomyelitis (EAE) is a relevant animal model of multiple sclerosis (MS). Oxidative stress and chronic inflammation play a major role in the pathogenesis of MS and EAE. Melatonin, a neurohormone, has potent anti-inflammatory properties. The aim of our study was to assess the therapeutic properties of melatonin alone or in combination with interferon β-1b (IFNβ-1b) or glatiramer acetate (GA) on EAE. EAE was induced in male Sprague-Dawley rats with an intraperitoneal injection of a homogenate of spinal cord and pig brain. At day 10 post immunization, rats were euthanized, and their brains were immediately excised and processed to measure oxidative stress markers and membrane fluidity. In addition, proinflammatory cytokines were quantified in plasma. Melatonin alone or in combination with GA and IFNβ-1b inhibited the disease process of EAE and the synthesis of proinflammatory cytokines, caused a significant decrement in oxidative stress markers, and preserved the membrane fluidity in the motor cortex, midbrain, and spinal cord. The cumulative index score was significantly reduced in EAE rats treated with melatonin alone or in combination with GA and IFNβ-1b. In conclusion, our findings provide preclinical evidence for the use of melatonin as an adjuvant therapeutic treatment for MS.

Spatial distribution of multiple sclerosis iron rim lesions and their impact on disability

BACKGROUND

In multiple sclerosis (MS), iron rim lesions (IRLs) on magnetic resonance imaging (MRI) have been suggested as an imaging marker of disease progression. However, the exact mechanisms how they contribute to disability are yet not completely known. Strategic lesion location may be an important factor concerning the impact of focal lesions on clinical disability. Therefore, the aim of this study was to investigate the spatial distribution of IRLs compared to non-IRLs and their impact on disability.

METHODS

We retrospectively identified 67 patients with at least one IRL on MRI and 67 age- and sex-matched patients without IRLs. We compared the spatial distribution of lesions between both groups and between IRLs and non-IRLs in patients with IRLs. Furthermore, we assessed the relationship between lesion localisation and disability on a voxel-by-voxel basis and investigated the impact on structural network disruptions.

RESULTS

Patients with IRLs had higher disability scores (median Expanded Disability Status Scale score (range): 3.0 (0 - 8.5) versus 1.5 (0 - 6.5); p = 0.001; median pyramidal functional system score (range): 1.0 (0 - 5) versus 0 (0 - 4); p = 0.003), significantly lower brain volumes (mean normal-appearing grey matter volume: 749.66 ± 60.58 versus 785.83 ± 53.71 mL; mean normal-appearing white matter volume: 723.58 ± 60.13 versus 753.25 ± 69.61 mL; mean deep grey matter volume: 33.21 ± 4.19 versus 35.85 ± 4.89 mL; p < 0.05 for all comparisons) and a significantly higher total T2 lesion volume (mean: 9.96 ± 11.6 versus 4.31 ± 8.9 mL; p < 0.001). We found no neuroanatomical regions that were more often affected by IRLs. Furthermore, comparing the overall network disruption in the IRL group, IRLs caused less network disruption/mL lesion size compared to non-IRLs (1.54% / mL versus 2.0% / mL; p < 0.05).

CONCLUSION

IRLs are associated with higher disability scores. However, our results suggest that a higher disability is not explained by the sheer topography of IRLs or their network disruption.

Skeletonized mean diffusivity and neuropsychological performance in relapsing-remitting multiple sclerosis

BACKGROUND

Peak width of Skeletonized Mean Diffusivity (PSMD), as a novel marker of white matter (WM) microstructure damage, is associated with cognitive decline in several WM pathologies (i.e., small vessel disorders). We hypothesized that markers combining alterations in whole WM could be associated with cognitive dysfunction in relapsing-remitting multiple sclerosis (RRMS) patients.

METHODS

We used PSMD based on tract-based spatial statistics (TBSS) of diffusion tensor imaging (DTI) magnetic resonance (MR) scans. We investigated RRMS patients (n = 73) undergoing interferon beta (IFN-β) therapy. In this cross-sectional study, we investigated the association between neuropsychological data and clinical and MRI variables: PSMD, WM hypointensities, and normalized brain volume (NBV).

RESULTS

In our cohort, 37 (50.7%) patients were recognized as cognitively impaired (CI) and 36 (49.3%) patients were cognitively normal (CN). In regression analysis, PSMD was a statistically significant contributor in the California Verbal Learning Test (CVLT) list A (p = 0.04) and semantic fluency (p = 0.036). PSMD (p < 0.001, r²  = 0.35), NBV (p = 0.002, r²  = 2.6) and WM hypointensities (p < 0.001, r²  = 0.40) were major contributors to upper extremity disability (9HPT) in the CN subgroup. A significant contributor in the majority of neuropsychological measures was education attainment.

CONCLUSION

We investigated PSMD as a new parameter of WM microstructure damage that is a contributor in complex cognitive tasks, CVLT performance, and semantic fluency. PSMD was a statistically significant contributor to upper extremity disability (9HPT) together with WM hypointensities and NBV. Education attainment proved to be relevant in the majority of cognitive domains. Further studies are needed to estimate PSMD relevance as a marker of CI in MS.

MRI and laboratory monitoring of disease-modifying therapy efficacy and risks

PURPOSE OF REVIEW

Increasingly, therapeutic strategy in multiple sclerosis (MS) is informed by imaging and laboratory biomarkers, in addition to traditional clinical factors. Here, we review aspects of monitoring the efficacy and risks of disease-modifying therapy (DMT) with both conventional and emerging MRI and laboratory measures.

RECENT FINDINGS

The adoption of consensus-driven, stable MRI acquisition protocols and artificial intelligence-based, quantitative image analysis is heralding an era of precision monitoring of DMT efficacy. New MRI measures of compartmentalized inflammation, neuro-degeneration and repair complement traditional metrics but require validation before use in individual patients. Laboratory markers of brain cellular injury, such as neurofilament light, are robust outcomes in DMT efficacy trials; their use in clinical practice is being refined. DMT-specific laboratory monitoring for safety is critical and may include lymphocytes, immunoglobulins, autoimmunity surveillance, John Cunningham virus serology and COVID-19 vaccination seroresponse.

SUMMARY

A biomarker-enhanced monitoring strategy has immediate clinical application, with growing evidence of long-term reductions in disability accrual when both clinically symptomatic and asymptomatic inflammatory activity is fully suppressed; and amelioration of the risks associated with therapy. Emerging MRI and blood-based measures will also become important tools for monitoring agents that target the innate immune system and promote neuro-repair.

Diffusion magnetic resonance imaging of normal-appearing white matter in multiple sclerosis: correlation with brain volume and clinical disability

Background

Diffusion magnetic resonance imaging (MRI) abnormalities in multiple sclerosis (MS) are not limited to lesions, but have also been observed in the white matter that appears normal on conventional MRI sequences, known as normal-appearing white matter (NAWM). There is evidence of microstructural processes occurring in the NAWM.

Objective

To assess the correlation between NAWM apparent diffusion coefficient (ADC) and fractional anisotropy (FA) with brain volume and clinical disability in MS.

Methods

Brain MRI from 33 MS patients were included. ADC and FA measurements of the genu, body, and splenium of corpus callosum (CC) were done. ADC and FA values were analyzed to measure their correlation with brain volume from MR volumetry and clinical disability represented by Expanded Disability Status Scale (EDSS).

Results

The mean ADC of CC NAWM was .93 ×10⁻³ mm²/s (±.13 SD), and the mean FA .72 (±.12 SD). ADC and FA of CC NAWM were significantly correlated with the ratio of brain volume to intracranial volume (R = −0,70 and 0,78 respectively), and with EDSS (R = .52 and −.59 respectively).

Conclusion

There were significant correlations between ADC and FA of NAWM with brain volume and EDSS of MS patients. Further longitudinal studies were needed to evaluate the potential of diffusion MRI in the evaluation of MS.

Self-injectable DMTs in relapsing MS: NEDA assessment at 10 years in a real-world cohort

BACKGROUND

Multiple sclerosis (MS) is an immune-mediated disorder of the central nervous system. DMTs effectively reduce the annual relapse rate-thus reducing disease activity-and, to a lesser extent, some DMTs prevent disease progression in some people with MS. Monitoring the efficacy of DMTs with no evidence disease activity (NEDA) provides an objective perspective for evaluating treatment success.

OBJECTIVE

Our goal is to detect the prevalence of NEDA-3 in people with MS treated with self-injectable DMTs at two years and 10 years in a retrospective study.

METHODS

The treatment continuation rates and NEDA-3 parameters in the 2nd and 10th years were evaluated.

RESULTS

A total of 1032 patients diagnosed with RRMS were included in the study, and 613 patients (59.3%) continued with treatment after 10 years. In the first two years, NEDA-3 was detected in 321 patients (52.4%), and 112 of the 613 patients continued with self-injectable DMTs at the end of 10 years (18.3%). The rate of NEDA-3 in patients starting treatment over the age of 35 was 15.1% compared to that in the patient group starting treatment aged 34 or less at 20.2% (p = .004).

CONCLUSION

Our study includes the most comprehensive NEDA-3 data from real world evidence and supports the idea that NEDA-3 can be an effective early predictor of progression-free status at treatment follow-up of up to 10 years.

Effects of teriflunomide treatment on cognitive performance and brain volume in patients with relapsing multiple sclerosis: Post hoc analysis of the TEMSO core and extension studies

Background:

In post hoc analyses of Teriflunomide Multiple Sclerosis Oral study (TEMSO; NCT00134563), teriflunomide 14 mg significantly reduced brain volume loss (BVL) versus placebo in patients with relapsing multiple sclerosis (MS).

Objective:

In this post hoc analysis of TEMSO and its long-term extension (NCT00803049), we examined the relationship between teriflunomide’s effects on BVL and cognition.

Methods:

We analyzed data from 709 patients who received teriflunomide 14 mg in TEMSO or its extension. The change in cognitive performance, assessed using the Paced Auditory Serial Addition Test 3 (PASAT-3), was measured in subgroups stratified by BVL over 2 years (least BVL: ⩽ 0.52%; intermediate BVL: >0.52%–2.18%; most BVL: >2.18%). BVL, MRI lesions, and relapses over 2 years were evaluated as potential mediators of the effect of teriflunomide on cognition.

Results:

Teriflunomide 14 mg significantly improved PASAT-3 Z-scores versus placebo through year 2. In the least- and intermediate-BVL groups, significant improvements in PASAT-3 Z-score were demonstrated versus the most-BVL group over 3 years in the extension. According to the mediation analysis, 44% of the teriflunomide effect on cognition was due to effects on BVL at year 2.

Conclusion:

Teriflunomide improves cognition largely through its effects on BVL. Accelerated BVL earlier in the disease course may predict cognitive outcomes.

ClinicalTrials.gov identifier:

NCT00134563, NCT00803049

Exploring the Pro-Phagocytic and Anti-Inflammatory Functions of PACAP and VIP in Microglia: Implications for Multiple Sclerosis

Multiple sclerosis (MS) is a chronic neuroinflammatory and demyelinating disease of the central nervous system (CNS), characterised by the infiltration of peripheral immune cells, multifocal white-matter lesions, and neurodegeneration. In recent years, microglia have emerged as key contributors to MS pathology, acting as scavengers of toxic myelin/cell debris and modulating the inflammatory microenvironment to promote myelin repair. In this review, we explore the role of two neuropeptides, pituitary adenylate cyclase-activating polypeptide (PACAP) and vasoactive intestinal peptide (VIP), as important regulators of microglial functioning during demyelination, myelin phagocytosis, and remyelination, emphasising the potential of these neuropeptides as therapeutic targets for the treatment of MS.

Volumetric Brain Loss Correlates With a Relapsing MOGAD Disease Course

Background

Myelin oligodendrocyte glycoprotein antibody disorders (MOGAD) have evolved as a distinct group of inflammatory, demyelinating diseases of the CNS. MOGAD can present with a monophasic or relapsing disease course with distinct clinical manifestations.However, data on the disease course and disability outcomes of these patients are scarce. We aim to compare brain volumetric changes for MOGAD patients with different disease phenotypes and HCs.

Methods

Brain magnetic resonance imaging (MRI) scans and clinical data were obtained for 22 MOGAD patients and 22 HCs. Volumetric brain information was determined using volBrain and MDbrain platforms.

Results

We found decreased brain volume in MOGAD patients compared to HCs, as identified in volume of total brain, gray matter, white matter and deep gray matter (DGM) structures. In addition, we found significantly different volumetric changes between patients with relapsing and monophasic disease course, with significantly decreased volume of total brain and DGM, cerebellum and hippocampus in relapsing patients during the first year of diagnosis. A significant negative correlation was found between EDSS and volume of thalamus.

Conclusions

Brain MRI analyses revealed volumetric differences between MOGAD patients and HCs, and between patients with different disease phenotypes. Decreased gray matter volume during the first year of diagnosis, especially in the cerebrum and hippocampus of MOGAD patients was associated with relapsing disease course.

Associations of Alcohol Consumption and Smoking With Disease Risk and Neurodegeneration in Individuals With Multiple Sclerosis in the United Kingdom

IMPORTANCE

Understanding the effects of modifiable risk factors on risk for multiple sclerosis (MS) and associated neurodegeneration is important to guide clinical counseling.

OBJECTIVE

To investigate associations of alcohol use, smoking, and obesity with odds of MS diagnosis and macular ganglion cell layer and inner plexiform layer (mGCIPL) thickness.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional study analyzed data from the community-based UK Biobank study on health behaviors and retinal thickness (measured by optical coherence tomography in both eyes) in individuals aged 40 to 69 years examined from December 1, 2009, to December 31, 2010. Risk factors were identified with multivariable logistic regression analyses. To adjust for intereye correlations, multivariable generalized estimating equations were used to explore associations of alcohol use and smoking with mGCIPL thickness. Finally, interaction models explored whether the correlations of alcohol and smoking with mGCIPL thickness differed for individuals with MS. Data were analyzed from February 1 to July 1, 2021.

EXPOSURES

Smoking status (never, previous, or current), alcohol intake (never or special occasions only [low], once per month to ≤4 times per week [moderate], or daily/almost daily [high]), and body mass index.

MAIN OUTCOMES AND MEASURES

Multiple sclerosis case status and mGCIPL thickness.

RESULTS

A total of 71 981 individuals (38 685 women [53.7%] and 33 296 men [46.3%]; mean [SD] age, 56.7 [8.0] years) were included in the analysis (20 065 healthy control individuals, 51 737 control individuals with comorbidities, and 179 individuals with MS). Modifiable risk factors significantly associated with MS case status were current smoking (odds ratio [OR], 3.05 [95% CI, 1.95-4.64]), moderate alcohol intake (OR, 0.62 [95% CI, 0.43-0.91]), and obesity (OR, 1.72 [95% CI, 1.15-2.56]) compared with healthy control individuals. Compared with the control individuals with comorbidities, only smoking was associated with case status (OR, 2.30 [95% CI, 1.48-3.51]). High alcohol intake was associated with a thinner mGCIPL in individuals with MS (adjusted β = -3.09 [95% CI, -5.70 to -0.48] μm; P = .02). In the alcohol interaction model, high alcohol intake was associated with thinner mGCIPL in control individuals (β = -0.93 [95% CI, -1.07 to -0.79] μm; P < .001), but there was no statistically significant association in individuals with MS (β = -2.27 [95% CI, -4.76 to 0.22] μm; P = .07). Smoking was not associated with mGCIPL thickness in MS. However, smoking was associated with greater mGCIPL thickness in control individuals (β = 0.89 [95% CI, 0.74-1.05 μm]; P < .001).

CONCLUSIONS AND RELEVANCE

These findings suggest that high alcohol intake was associated with retinal features indicative of more severe neurodegeneration, whereas smoking was associated with higher odds of being diagnosed with MS.

Information processing speed as a prognostic marker of physical impairment and progression in patients with multiple sclerosis

BACKGROUND

Prediction of disability progression in patients with MS (pwMS) is challenging. So far, scarce evidence exists suggesting knowledge about how cognitive performance may potentially improve prediction of physical impairment and disability progression in MS. Therefore, we wanted to assess the prognostic value of cognitive performance regarding physical impairment and disability progression in pwMS.

METHODS

85 patients (64% female; 60% relapse-remitting MS; mean age=36.78 ± 9.63 years) underwent clinical, neuropsychological (Brief Repeatable Battery for Neuropsychological Test (BRB-N)) and brain MRI (T1-weighted and T2-weighted FLAIR images) assessment at baseline and after an average of 7 years (SD=3.75) at follow-up. We assessed physical impairment and annualized disability progression (disability progression divided by follow-up duration) using the Expanded Disability Status Scale (EDSS). To compare patients with no or mild physical impairment (EDSS≤2.5) and patients with moderate to severe physical impairment (EDSS≥3.0), we used an EDSS score ≥3.0 as cut-off. Silent progression was defined by an EDSS worsening of at least 0.5 in the absence of relapses and inflammation in relapsing-remitting MS.

RESULTS

In hierarchical regression models (method "STEPWISE", forward) performance in information processing speed was a significant and independent predictor of physical impairment (EDSS≥3.0) at follow-up (model R²=0.671, b=-1.46, OR=0.23, p=0.001) and annualized disability progression (adjusted model R²=0.257, β=-0.26, 95% CI: -0.066, -0.008, p=0.012), in addition to demographics (age, education, individual follow-up time), clinical (EDSS, disease duration, clinical phenotype, annualized-relapse-rate) and MRI measures (brain volumes and T2-lesion load). In a MANCOVA controlled for age, disease duration and individual follow-up time, worse baseline performance in information processing speed was found in patients with higher EDSS at follow-up (m=-1.91, SD=1.18, p<0.001) and silent progression (m=-2.19, SD=1.01, p=0.038).

CONCLUSION

Performance in information processing speed might help to identify patients at risk for physical impairment. Therefore, neuropsychological assessment should be integrated in clinical standard care to support disease management in pwMS.

Autologous Hematopoietic Stem Cell Transplantation in Multiple Sclerosis Patients: Monocentric Case Series and Systematic Review of the Literature

Multiple sclerosis (MS) is a chronic, inflammatory and immune-mediated disease of the central nervous system (CNS), commonly affecting young adults and potentially associated with life-long disability. About 14 disease-modifying treatments (DMTs) are currently approved for the treatment of MS. However, despite the use of highly effective therapies, some patients exhibit a highly active disease with an aggressive course from onset and a higher risk of long-term disability accrual. In the last few years, several retrospective studies, clinical trials, meta-analyses and systematic reviews have investigated autologous hematopoietic stem cell transplantation (AHSCT) as a possible therapeutic option in order to address this unmet clinical need. These studies demonstrated that AHSCT is a highly efficacious and relatively safe therapeutic option for the treatment of highly active MS. Particularly, over recent years, the amount of evidence has grown, with significant improvements in the development of patient selection criteria, choice of the most suitable transplant technique and clinical experience. In this paper, we present six patients who received AHSCT in our MS center and we systematically reviewed recent evidence about the long-term efficacy and safety of AHSCT and the placement of AHSCT in the rapidly evolving therapeutic armamentarium for MS.

The association between neurodegeneration and local complement activation in the thalamus to progressive multiple sclerosis outcome

The extent of grey matter demyelination and neurodegeneration in the progressive multiple sclerosis (PMS) brains at post‐mortem associates with more severe disease. Regional tissue atrophy, especially affecting the cortical and deep grey matter, including the thalamus, is prognostic for poor outcomes. Microglial and complement activation are important in the pathogenesis and contribute to damaging processes that underlie tissue atrophy in PMS. We investigated the extent of pathology and innate immune activation in the thalamus in comparison to cortical grey and white matter in blocks from 21 cases of PMS and 10 matched controls. Using a digital pathology workflow, we show that the thalamus is invariably affected by demyelination and had a far higher proportion of active inflammatory lesions than forebrain cortical tissue blocks from the same cases. Lesions were larger and more frequent in the medial nuclei near the ventricular margin, whilst neuronal loss was greatest in the lateral thalamic nuclei. The extent of thalamic neuron loss was not associated with thalamic demyelination but correlated with the burden of white matter pathology in other forebrain areas (Spearman r = 0.79, p < 0.0001). Only thalamic neuronal loss, and not that seen in other forebrain cortical areas, correlated with disease duration (Spearman r = −0.58, p = 0.009) and age of death (Spearman r = −0.47, p = 0.045). Immunoreactivity for the complement pattern recognition molecule C1q, and products of complement activation (C4d, Bb and C3b) were elevated in thalamic lesions with an active inflammatory pathology. Complement regulatory protein, C1 inhibitor, was unchanged in expression. We conclude that active inflammatory demyelination, neuronal loss and local complement synthesis and activation in the thalamus, are important to the pathological and clinical disease outcomes of PMS.

Magnetic Resonance Imaging as a Prognostic Disability Marker in Clinically Isolated Syndrome and Multiple Sclerosis: A Systematic Review and Meta-Analysis

To date, there are no definite imaging predictors for long-term disability in multiple sclerosis (MS). Magnetic resonance imaging (MRI) is the key prognostic tool for MS, primarily at the early stage of the disease. Recent findings showed that white matter lesion (WML) counts and volumes could predict long-term disability for MS. However, the prognostic value of MRI in the early stage of the disease and its link to long-term physical disability have not been assessed systematically and quantitatively. A meta-analysis was conducted using studies from four databases to assess whether MS lesion counts and volumes at baseline MRI scans could predict long-term disability, assessed by the expanded disability status scale (EDSS). Fifteen studies were eligible for the qualitative analysis and three studies for meta-analysis. T2 brain lesion counts and volumes after the disease onset were associated with disability progression after 10 years. Four or more lesions at baseline showed a highly significant association with EDSS 3 and EDSS 6, with a pooled OR of 4.10 and 4.3, respectively. The risk increased when more than 10 lesions were present. This review and meta-analysis confirmed that lesion counts and volumes could be associated with disability and might offer additional valid guidance in treatment decision making. Future work is essential to determine whether these prognostic markers have high predictive potential.

Fully automated detection of paramagnetic rims in multiple sclerosis lesions on 3T susceptibility-based MR imaging

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Paramagnetic rim lesions are an important subtype of multiple sclerosis lesion.

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Automated methods can accelerate the assessment of paramagnetic rim lesions.

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APRL automatically identifies and accurately classifies paramagnetic rim lesions.

Background and Purpose

The presence of a paramagnetic rim around a white matter lesion has recently been shown to be a hallmark of a particular pathological type of multiple sclerosis lesion. Increased prevalence of these paramagnetic rim lesions is associated with a more severe disease course in MS, but manual identification is time-consuming. We present APRL, a method to automatically detect paramagnetic rim lesions on 3T T2*-phase images.

Methods

T1-weighted, T2-FLAIR, and T2*-phase MRI of the brain were collected at 3T for 20 subjects with MS. The images were then processed with automated lesion segmentation, lesion center detection, lesion labelling, and lesion-level radiomic feature extraction. A total of 951 lesions were identified, 113 (12%) of which contained a paramagnetic rim. We divided our data into a training set (16 patients, 753 lesions) and a testing set (4 patients, 198 lesions), fit a random forest classification model on the training set, and assessed our ability to classify paramagnetic rim lesions on the test set.

Results

The number of paramagnetic rim lesions per subject identified via our automated lesion labelling method was highly correlated with the gold standard count per subject, r = 0.86 (95% CI [0.68, 0.94]). The classification algorithm using radiomic features classified lesions with an area under the curve of 0.82 (95% CI [0.74, 0.92]).

Conclusion

This study develops a fully automated technique, APRL, for the detection of paramagnetic rim lesions using standard T1 and FLAIR sequences and a T2*phase sequence obtained on 3T MR images.

Subcortical volumes as early predictors of fatigue in multiple sclerosis

OBJECTIVE

Fatigue is a frequent and severe symptom in multiple sclerosis (MS), but its pathophysiological origin remains incompletely understood. We aimed to examine the predictive value of subcortical gray matter volumes for fatigue severity at disease onset and after four years by applying structural equation modeling (SEM).

METHODS

This multi-center cohort study included 601 treatment-naive MS patients after the first demyelinating event. All patients underwent a standardized 3T MRI protocol. A subgroup of 230 patients with available clinical follow-up data after four years was also analyzed. Associations of subcortical volumes (included into SEM) with MS-related fatigue were studied regarding their predictive value. In addition, subcortical regions that have a central role in the brain network (hubs) were determined through structural covariance network (SCN) analysis.

RESULTS

Predictive causal modeling identified volumes of the caudate (s [standardized path coefficient]=0.763, p=0.003 [left]; s=0.755, p=0.006 [right]), putamen (s=0.614, p=0.002 [left]; s=0.606, p=0.003 [right]) and pallidum (s=0.606, p=0.012 [left]; s=0.606, p=0.012 [right]) as prognostic factors for fatigue severity in the cross-sectional cohort. Moreover, the volume of the pons was additionally predictive for fatigue severity in the longitudinal cohort (s=0.605, p=0.013). In the SCN analysis, network hubs in patients with fatigue worsening were detected in the putamen (p=0.008 [left]; p=0.007 [right]) and pons (p=0.0001).

INTERPRETATION

We unveiled predictive associations of specific subcortical gray matter volumes with fatigue in an early and initially untreated MS cohort. The colocalization of these subcortical structures with network hubs suggests an early role of these brain regions in terms of fatigue evolution. This article is protected by copyright. All rights reserved.