Grey matter atrophy in patients with benign multiple sclerosis

Can International Classification of Functioning, Disability and Health (ICF) Be Used for Prediction of Work Capacity and Employment Status in Multiple Sclerosis?

Background: Multiple sclerosis (MS) affects many body functions and activities, including work capacity and ability to work. An evaluation of work-related parameters is important to understand the barriers to maintaining the job. The aim of this study was to evaluate if a Comprehensive International Classification of Functioning, Disability and Health (ICF) core set for MS can be used to predict work capacity and employment status. Methods: The cohort included 151 participants with MS (99 female/52 male, mean age 49 years) referred for a work capacity evaluation. Results: 71 (47.0%) were employed and a major part (131, 86.7%) had a work capacity between 20 and 40% with no difference between those who were employed and those who were unemployed. The analysis revealed that age and the following categories explained 68.8% of the work capacity: b770 Gait pattern functions; b730 Muscle power functions; b134 Sleep functions; d845 Acquiring, keeping and terminating a job; and b620 Urination functions. The following categories in 79.5% predicted ability to work: b164 Higher-level cognitive functions; d510 Washing oneself; d630; Preparing meals; and d870 Economic self-sufficiency. Conclusions: Here, we show that different functions/activities predicted work capacity in comparison with employment status in MS. Therefore, ICF should be implemented when assessing work ability.

Deciphering the shift from benign to active relapsing-remitting multiple sclerosis: Insights into T regulatory cell dysfunction and apoptosis regulation

BACKGROUND

Relapsing-remitting multiple sclerosis (RRMS), a common demyelinating disease among young adults, follows a benign course in 10-15% of cases, where patients experience minimal neurological disability for a decade following disease onset. However, there is potential for these benign cases to transition into a clinically active, relapsing state.

OBJECTIVE

To elucidate the biological mechanisms underlying the transition from benign to active RRMS using gene expression analysis.

METHODS

We employed complementary-DNA microarrays to examine peripheral-blood gene expression patterns in patients with benign MS, defined as having a disease duration exceeding 10 years and an Expanded Disability Status Scale (EDSS) score of ≤3.0. We compared the gene expression pattern between patients who switched to active disease (Switching BMS) with those who maintained a benign state (Permanent-BMS) during an additional 5-year follow-up.

RESULTS

We identified two primary mechanisms linked to the transition from benign MS to clinically active disease. The first involves the suppression of regulatory T cell activity, and the second pertains to the dysfunction of nuclear receptor 4 A family-dependent apoptosis. These mechanisms collectively contribute to an augmented autoimmune response and increased disease activity.

CONCLUSIONS

The intricate gene regulatory networks that operate in switching-BMS are related to suppression of immune tolerance and aberrant apoptosis. These findings may lead to new therapeutic targets to prevent the escalation to active disease.

Early-stage volume losses in the corpus callosum and thalamus predict the progression of brain atrophy in patients with multiple sclerosis

BACKGROUND

A method that can be used in the early stage of multiple sclerosis (MS) to predict the progression of brain volume loss (BVL) has not been fully established.

METHODS

To develop a method of predicting progressive BVL in patients with MS (pwMS), eighty-two consecutive Japanese pwMS-with either relapsing-remitting MS (86%) or secondary progressive MS (14%)-and 41 healthy controls were included in this longitudinal retrospective analysis over an observational period of approximately 3.5 years. Using a hierarchical cluster analysis with multivariate imaging data obtained by FreeSurfer analysis, we classified the pwMS into clusters.

RESULTS

At baseline and follow-up, pwMS were cross-sectionally classified into three major clusters (Clusters 1, 2, and 3) in ascending order by disability and BVL. Among the patients included in Cluster 1 at baseline, approximately one-third of patients (12/52) transitioned into Cluster 2 at follow-up. The volumes of the corpus callosum, the thalamus, and the whole brain excluding the ventricles were significantly decreased in the transition group compared with the nontransition group and were found to be the most important predictors of transition.

CONCLUSION

Decreased volumes of the corpus callosum and thalamus in the relatively early stage of MS may predict the development of BVL.

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.

Automated magnetic resonance imaging quantification of cerebral parenchymal and ventricular volume following subarachnoid hemorrhage: associations with cognition

This study aims to investigate cerebral parenchymal and ventricular volume changes after subarachnoid hemorrhage (SAH) and their potential association with cognitive impairment. 17 patients with aneurysmal SAH (aSAH) and 21 patients with angiographically negative SAH (anSAH) without visually apparent parenchymal loss on conventional magnetic resonance imaging (MRI) were included, along with 76 healthy controls. Volumetric analyses were performed using an automated clinical segmentation and quantification tool. Measurements were compared to on-board normative reference database (n = 1923) adjusted for age, sex, and intracranial volume. Cognition was assessed with tests for psychomotor speed, attentional control, (working) memory, executive functioning, and social cognition. All measurements took place 5 months after SAH. Lower cerebral parenchymal volumes were most pronounced in the frontal lobe (aSAH: n = 6 [35%], anSAH n = 7 [33%]), while higher volumes were most substantial in the lateral ventricle (aSAH: n = 5 [29%], anSAH n = 9 [43%]). No significant differences in regional brain volumes were observed between both SAH groups. Patients with lower frontal lobe volume exhibited significantly lower scores in psychomotor speed (U = 81, p = 0.02) and attentional control (t = 2.86, p = 0.004). Additionally, higher lateral ventricle volume was associated with poorer memory (t = 3.06, p = 0.002). Regional brain volume changes in patients with SAH without visible parenchymal abnormalities on MRI can still be quantified using a fully automatic clinical-grade tool, exposing changes which may contribute to cognitive impairment. Therefore, it is important to provide neuropsychological assessment for both SAH groups, also including those with clinically mild symptoms.

Gaining Biological Insights through Supervised Data Visualization

Dimensionality reduction-based data visualization is pivotal in comprehending complex biological data. The most common methods, such as PHATE, t-SNE, and UMAP, are unsupervised and therefore reflect the dominant structure in the data, which may be independent of expert-provided labels. Here we introduce a supervised data visualization method called RF-PHATE, which integrates expert knowledge for further exploration of the data. RF-PHATE leverages random forests to capture intricate featurelabel relationships. Extracting information from the forest, RF-PHATE generates low-dimensional visualizations that highlight relevant data relationships while disregarding extraneous features. This approach scales to large datasets and applies to classification and regression. We illustrate RF-PHATE's prowess through three case studies. In a multiple sclerosis study using longitudinal clinical and imaging data, RF-PHATE unveils a sub-group of patients with non-benign relapsingremitting Multiple Sclerosis, demonstrating its aptitude for time-series data. In the context of Raman spectral data, RF-PHATE effectively showcases the impact of antioxidants on diesel exhaust-exposed lung cells, highlighting its proficiency in noisy environments. Furthermore, RF-PHATE aligns established geometric structures with COVID-19 patient outcomes, enriching interpretability in a hierarchical manner. RF-PHATE bridges expert insights and visualizations, promising knowledge generation. Its adaptability, scalability, and noise tolerance underscore its potential for widespread adoption.