Association between cognitive impairment and motor dysfunction among patients with multiple sclerosis: a cross-sectional study

Does cognitive performance explain the gap between physiological and perceived fall-risk in people with multiple sclerosis?

BACKGROUND

Cognitive impairment is linked with increased risk of falls in people with multiple sclerosis (pwMS), but it is not clear whether cognitive performance may help to account for the discordance between fall-risk due to actual physiological functioning and the individual's perception of their fall-risk. This study examined the relationship between cognitive performance and the concordance/discordance of physiological and perceived fall-risk in pwMS.

METHODS

In this single-center cross-sectional analysis of 201 pwMS, proxies for physiological (gait speed) and perceived (Modified Falls Efficacy Scale) fall-risk were collected. Participants were categorized into 4 groups using established cut-off values: high physiological - high perceived (Hphy-Hper), high physiological - low perceived (Hphy-Lper), low physiological - low perceived (Lphy-Lper), and low physiological - high perceived (Lphy-Hper) fall-risk. Cognitive performance was evaluated using the NeuroTrax computerized cognitive battery, which generates a global cognitive score (GCS) as well as scores for individual cognitive domains.

RESULTS

Overall, 27.4 % of participants exhibited a discordance between physiological and perceived fall-risk. Individuals with discordant fall-risk did not have worse cognitive scores than individuals with concordant fall-risk, whether GCS or individual cognitive domains. However, among concordant groups, participants in the Hphy-Hper group had worse cognitive scores (GCS) as well as information processing, attention, motor skills, executive function and visual spatial domain scores than participants in the Lphy-Lper group.

CONCLUSION

In this study, one in 4 pwMS had a discordance between their physiological and perceived fall-risk. This discordance was not explained by cognitive performance.

Mobilization in Neurocritical Care: Challenges and Opportunities

PURPOSE OF REVIEW

Mobilization in the Neurological Intensive Care Unit (NICU) significantly improves outcomes and functional recovery while preventing immobility-related complications. The heterogeneity of neurologic conditions necessitates tailored, interdisciplinary mobilization strategies. This article reviews recent research on enhancing the feasibility and effectiveness of mobilization interventions in NICU settings.

RECENT FINDINGS

Early mobilization improves functional outcomes, reduces complications like muscle atrophy and pressure ulcers, and can shorten ICU stays. Safe implementation involves individualized protocols and a multidisciplinary team, emphasizing that early mobilization benefits critically ill neurological patients. Development of evidenced-based protocols for interdisciplinary NICU patient mobilization enhances patient outcomes and quality of life. Use of outcome measures can facilitate mobility while preventing complications from immobility. Future research in embracing emerging technologies such as mobilization equipment and virtual/augmented reality will help determine optimal timing as well as dosage of mobility to improve long-term functional outcomes in the unique NICU population.

Decreased functional mobility in individuals with mild to moderate expanded disability status from relapsing multiple sclerosis: Analysis of the Glittre-ADL test

INTRODUCTION

Multiple sclerosis (MS) is a chronic inflammatory and autoimmune disease that significantly limits an individual's activities of daily living (ADLs) and negatively affects their social participation as it progresses. The impact of activities and participation must be continuously assessed, and the Glittre-ADL is a validated test for MS to assess functional capacity in tasks similar to ADLs.

OBJECTIVE

To evaluate whether the Glittre-ADL test is a valid method for assessing functional mobility in individuals with MS and moderate disability or those who use assistive devices.

METHODS

This cross-sectional study enrolled 30 individuals in two groups: 1) MS group (n = 15); and 2) healthy control group (n = 15). The MS group underwent three functional mobility tests: 1) Glittre-ADL; 2) Timed 25-Foot Walk (T25FWT); and 3) Timed Up and Go (TUG) while the healthy group underwent only the Glittre-ADL test.

RESULTS

An association was found between the Glittre-ADL time and T25FWT (r = 0.78, p < .001) and TUG (r = 0.56, p = .030) times. In the MS group, statistically significant differences were found in time (F = 2.88, p = .038) and speed (F = 5.17, p = .024) between laps. A statistically significant difference was observed between the total time in the MS and control groups (Area Under Curve - AUC: 0.982, p < .0001). A total time > 46.0s represents the reduction of functional performance during ADLs in individuals with MS (sensitivity: 93.3%; specificity: 92.2%).

CONCLUSION

The Glittre-ADL test is a valid tool for assessing functional mobility in individuals with MS and mild to moderate disability (EDSS score ≤ 6.5).

NINJ1: A new player in multiple sclerosis pathogenesis and potential therapeutic target

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system (CNS) characterized by demyelination. Current treatment options for MS focus on immunosuppression, but their efficacy can be limited. Recent studies suggest a potential role for nerve injury-induced protein 1 (NINJ1) in MS pathogenesis. NINJ1, a protein involved in cell death and inflammation, may contribute to the infiltration and activation of inflammatory cells in the CNS, potentially through enhanced blood-brain barrier crossing; enhancing plasma membrane rupture during cell death, leading to the release of inflammatory mediators and further tissue damage. This review explores the emerging evidence for NINJ1's involvement in MS. It discusses how NINJ1 might mediate the migration of immune cells across the blood-brain barrier, exacerbate neuroinflammation, and participate in plasma membrane rupture-related damage. Finally, the review examines potential therapeutic strategies targeting NINJ1 for improved MS management. Abbreviations: MS, Multiple sclerosis; CNS, Central nervous system; BBB, Blood-brain barrier; GSDMD, Gasdermin-D; EAE, Experimental autoimmune encephalitis; HMGB-1, High mobility group box-1 protein; LDH, Lactate dehydrogenase; PMR, Plasma membrane rupture; DMF, Dimethyl fumarate; DUSP1, Dual-specificity phosphatase 1; PAMPs, Pathogen-associated molecular patterns; DAMPs, Danger-associated molecular patterns; PRRs, Pattern recognition receptors; GM-CSF, Granulocyte-macrophage colony stimulating factor; IFN-γ, Interferon gamma; TNF, Tumor necrosis factor; APCs, Antigen-presenting cells; ECs, Endothelial cells; TGF-β, Transforming growth factor-β; PBMCs, Peripheral blood mononuclear cells; FACS, Fluorescence-activated cell sorting; MCP-1, Monocyte chemoattractant protein-1; NLRP3, Pyrin domain-containing 3; TCR, T cell receptor; ROS, Reactive oxygen species; AP-1, Activator protein-1; ANG1, Angiopoietin 1; BMDMs, Bone marrow-derived macrophages; Arp2/3, actin-related protein 2/3; EMT, epithelial-mesenchymal transition; FAK, focal adhesion kinase; LIMK1, LIM domain kinase 1; PAK1, p21-activated kinases 1; Rac1, Ras-related C3 botulinum toxin substrate 1; β-cat, β-caten; MyD88, myeloid differentiation primary response gene 88; TIRAP, Toll/interleukin-1 receptor domain-containing adapter protein; TLR4, Toll-like receptor 4; IRAKs, interleukin-1 receptor-associated kinases; TRAF6, TNF receptor associated factor 6; TAB2/3, TAK1 binding protein 2/3; TAK1, transforming growth factor-β-activated kinase 1; JNK, c-Jun N-terminal kinase; ERK1/2, Extracellular Signal Regulated Kinase 1/2; IKK, inhibitor of kappa B kinase; IκB, inhibitor of NF-κB; NF-κB, nuclear factor kappa-B; AP-1, activator protein-1; ASC, Apoptosis-associated Speck-like protein containing a CARD; NEK7, NIMA-related kinase 7; NLRP3, Pyrin domain-containing 3; CREB, cAMP response element-binding protein.

Predicting multiple sclerosis disease progression and outcomes with machine learning and MRI-based biomarkers: a review

Multiple sclerosis (MS) is a demyelinating neurological disorder with a highly heterogeneous clinical presentation and course of progression. Disease-modifying therapies are the only available treatment, as there is no known cure for the disease. Careful selection of suitable therapies is necessary, as they can be accompanied by serious risks and adverse effects such as infection. Magnetic resonance imaging (MRI) plays a central role in the diagnosis and management of MS, though MRI lesions have displayed only moderate associations with MS clinical outcomes, known as the clinico-radiological paradox. With the advent of machine learning (ML) in healthcare, the predictive power of MRI can be improved by leveraging both traditional and advanced ML algorithms capable of analyzing increasingly complex patterns within neuroimaging data. The purpose of this review was to examine the application of MRI-based ML for prediction of MS disease progression. Studies were divided into five main categories: predicting the conversion of clinically isolated syndrome to MS, cognitive outcome, EDSS-related disability, motor disability and disease activity. The performance of ML models is discussed along with highlighting the influential MRI-derived biomarkers. Overall, MRI-based ML presents a promising avenue for MS prognosis. However, integration of imaging biomarkers with other multimodal patient data shows great potential for advancing personalized healthcare approaches in MS.

Cognitive impairment in multiple sclerosis: from phenomenology to neurobiological mechanisms

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

Motor Learning in Robot-Based Haptic Dyads: A Review

Rehabilitation robots have the potential to alleviate the global burden of neurorehabilitation. Robot-based multiplayer gaming with virtual and haptic interaction may improve motivation, engagement, and implicit learning in robotic therapy. Over the past few years, there has been growing interest in robot mediated haptic dyads, or human-robot-robot-human interaction. The effect of such a paradigm on motor learning in general and specifically for individuals with motor and/or cognitive impairments is an open area of research. We reviewed the literature to investigate the effect of a robot-based haptic dyad on motor learning. Thirty-eight articles met the inclusion criteria for this review. We summarize study characteristics including device, haptic rendering, and experimental task. Our main findings indicate that dyadic training's impact on motor learning is inconsistent in that some studies show significant improvement of motor training while others show no influence. We also find that the relative skill level of the partner and interaction characteristics such as stiffness of connection and availability of visual information influence motor learning outcomes. We discuss implications for neurorehabilitation and conclude that additional research is needed to determine optimal interaction characteristics for motor learning and to extend this research to individuals with cognitive and motor impairments.