Investigating domain-specific cognitive impairment among patients with multiple sclerosis using touchscreen cognitive testing in routine clinical care

Cognitive dysfunction is present in up to 70% of patients with multiple sclerosis (MS) and has been reported at all stages and in all subtypes of the disease. These deficits have been reported across a variety of cognitive domains, but are generally under-recognized and incompletely evaluated in routine clinical practice. The aim of this study was to investigate the spectrum of cognitive impairment in patients with MS presenting to a specialist MS clinic using the Cambridge Neuropsychological Test Automated Battery (CANTAB), administered on a touchscreen platform. Ninety MS patients completed computerized CANTAB tasks assessing working memory, executive function, processing speed, attention, and episodic memory. Scores were adjusted for age, sex, and level of education and classified as normal or impaired based on comparison with a large normative data pool. We also investigated the impact of clinical and demographic variables which could potentially influence cognitive performance including patient educational level (a proxy for cognitive reserve), disease status (duration, course, and severity of MS), and depression. CANTAB testing detected cognitive impairment in 40 patients (44% of the sample). The most frequently impaired domain was executive function, present in 55% of cognitively impaired individuals. Disease duration and severity were significantly associated with performance across various cognitive domains. Patients with depressive symptoms were also more likely to exhibit impaired processing speed. Results from this study confirm that cognitive impairment is common and occurs across a range of domains among MS patients attending routine clinical visits. CANTAB tasks provide a sensitive and practical approach to cognitive testing in MS patients as part of a holistic patient assessment

Classification of Radiologically Isolated Syndrome and Clinically Isolated Syndrome with Machine-Learning Techniques

Background and purpose: The unanticipated detection by magnetic resonance imaging (MRI) in the brain of asymptomatic subjects of white matter lesions suggestive of multiple sclerosis (MS) has been named radiologically isolated syndrome (RIS). As the difference between early MS [i.e. clinically isolated syndrome (CIS)] and RIS is the occurrence of a clinical event, it is logical to improve detection of the subclinical form without interfering with MRI as there are radiological diagnostic criteria for that. Our objective was to use machine-learning classification methods to identify morphometric measures that help to discriminate patients with RIS from those with CIS. Methods: We used a multimodal 3-T MRI approach by combining MRI biomarkers (cortical thickness, cortical and subcortical grey matter volume, and white matter integrity) of a cohort of 17 patients with RIS and 17 patients with CIS for single-subject level classification. Results: The best proposed models to predict the diagnosis of CIS and RIS were based on the Naive Bayes, Bagging and Multilayer Perceptron classifiers using only three features: the left rostral middle frontal gyrus volume and the fractional anisotropy values in the right amygdala and right lingual gyrus. The Naive Bayes obtained the highest accuracy [overall classification, 0.765; area under the receiver operating characteristic (AUROC), 0.782]. Conclusions: A machine-learning approach applied to multimodal MRI data may differentiate between the earliest clinical expressions of MS (CIS and RIS) with an accuracy of 78%. Keywords: Bagging; Multilayer Perceptron; Naive Bayes classifier; clinically isolated syndrome; diffusion tensor imaging; machine-learning; magnetic resonance imaging; multiple sclerosis; radiologically isolated syndrome.Comment: 24 pages, 2 table

Cognitive rehabilitation, self-management, psychotherapeutic and caregiver support interventions in progressive neurodegenerative conditions: a scoping review

BACKGROUND: Despite their potentially significant impact, cognitive disability may be overlooked in a number of progressive neurodegenerative conditions, as other difficulties dominate the clinical picture. OBJECTIVE: We examined the extent, nature and range of the research evidence relating to cognitive rehabilitation, self-management, psychotherapeutic and caregiver support interventions in Parkinsonian disorders, multiple sclerosis (MS), frontotemporal dementias (FTD), motor neuron disease and Huntington’s disease. METHODS: Scoping review based on searches of MEDLINE and CINAHL up to 15 March 2016. RESULTS: We included 140 eligible papers. Over half of the studies, and almost all the randomised controlled trials, related to MS, while a number of single case studies described interventions for people with FTD. CR interventions addressed functional ability, communication and interaction, behaviour or memory. The majority of psychotherapy interventions involved cognitive behavioural therapy for depression or anxiety. Self-management interventions were mainly available for people with MS. There were few reports of interventions specific to caregivers. Numerous methodological challenges were identified. CONCLUSIONS: The limited range of studies for all conditions except MS suggests a need firstly to synthesise systematically the available evidence across conditions and secondly to develop well-designed studies to provide evidence about the effectiveness of CR and other psychological interventions

Biomarkers of Response to Ocrelizumab in Relapsing-Remitting Multiple Sclerosis

Objective: To ascertain the changes of serum neurofilament light chain (sNfL) and glial fibrillary acidic protein (sGFAP) values in relapsing-remitting multiple sclerosis (RRMS) patients treated with ocrelizumab and their association with treatment response. Methods: Multicenter prospective study including 115 RRMS patients initiating ocrelizumab treatment between February 2020 and March 2022 followed during a year. Serum samples were collected at baseline and every 3 months to measure sNfL and sGFAP levels using single-molecule array (SIMOA) technology. Based on age and body mass index, sNfL values were standardized using z-score. NEDA (non-evidence of disease activity)-3 status was defined for patients free of disease activity after a year of follow-up. Inflammation (INFL) was considered when new relapses occurred during follow-up or new MRI lesions were found at 1-year exploration. PIRA (progression independent of relapse activity) was defined as disability progression occurring in the absence of relapses or new MRI activity. Results: After a year on ocrelizumab, 85 patients (73.9%) achieved NEDA-3. Thirty patients did not achieve NEDA: 20 (17.4%) because of INFL and 10 (8.7%) because of PIRA. Of INFL patients, 6 (30.0%) had relapses, and 17 (85.0%) had at least one new MRI lesion at the 12-month examination. At baseline, INFL patients had higher sNfL (p = 0.0003) and sGFAP (p = 0.03) than the NEDA-3 group. PIRA patients mostly exhibited low sNfL and heterogeneous sGFAP levels. After a year, NEDA-3 and INFL patients showed similar decreases in sNfL (p 1.5 z-score 3 months after ocrelizumab initiation indicated a higher risk of inflammation (OR = 13.6; p < 0.0001). Decrease in sGFAP values occurred later in both groups, with significant reductions observed at 12 months for INFL and 6 and 12 months for NEDA-3. No significant changes in sNfL or sGFAP were observed in PIRA patients. Conclusion: Ocrelizumab induced normalization of sNfL and sGFAP in the majority of NEDA-3 and inflammatory patients but did not cause changes in the PIRA group. Our data suggest that normalization of sNfL and sGFAP is associated with the lack of inflammatory-associated disease progression but it may not affect non-inflammatory PIRA

Cognición y emoción: su impacto en la esclerosis múltiple

La primera parte del estudio, en el que se incluyeron 157 pacientes con esclerosis múltiple remitente-recurrente y 80 controles sanos, concluye que los pacientes con esclerosis múltiple presentan una regulación disfuncional de sus sentimientos de ira, ya que experimentan sentimientos de ira con más frecuencia y ante un mayor número de situaciones que la población general (Rasgo de Ira). Esta alteración emocional no es consecuencia de un proceso reactivo secundario a la discapacidad física inherente a la enfermedad, sino que más bien constituye, al igual que la depresión o la ansiedad, una parte integral del temperamento de estos pacientes. Además, el estudio demuestra que un estilo de Expresión Interna de ira, caracterizado por la tendencia a suprimir los sentimientos de irritación en lugar de expresarlos, muy habitual en los pacientes con Esclerosis Múltiple, tiene consecuencias negativas para el bienestar físico y mental de los pacientes con esclerosis múltiple. La segunda parte del estudio, en el que se incluyeron a 63 pacientes y sus respectivos cuidadores, concluye la presencia de estas alteraciones impactan de forma directa en la vida del cuidador, asociándose a una mayor presencia de síntomas depresivos y una peor calidad de vida

Classification of radiologically isolated syndrome and clinically isolated syndrome with machine-learning techniques

Versión final aceptada de: https://doi.org/10.1111/ene.13923[Abstract]: Introduction: The unanticipated magnetic resonance imaging (MRI) detection in the brain of asymptomatic subjects of white matter lesions suggestive of multiple sclerosis (MS) has been named as radiologically isolated syndrome (RIS). As the difference between early MS (i.e., clinically isolated syndrome [CIS]) and RIS is the occurrence of a clinical event, it should be logical to improve detection of subclinical form without interfering with MRI as there are radiological diagnostic criteria for that. Our objective was to use machine-learning classification methods to identify morphometric measures that help to discern patients with RIS from those with CIS. Methods: We used a multimodal 3T MRI approach by combining MRI biomarkers (cortical thickness, cortical and subcortical grey matter volume, and white matter integrity) of a cohort of 17 RIS and 17 CIS patients for single-subject level classification. Results: The best proposed models to predict the CIS and RIS diagnosis were based on the Naive Bayes, Bagging and Multilayer Perceptron classifiers using only three features: the left rostral middle frontal gyrus volume, and the fractional anisotropy values in the right amygdala and in the right lingual gyrus. The Naive Bayes obtained the highest accuracy (overall classification, 0.765 and AUROC, 0.782). Conclusions: A machine-learning approach applied to multimodal MRI data may differentiate between the earliest clinical expressions of MS (CIS and RIS), with an accuracy of 78%.This research was partially supported by “Collaborative Project in Genomic Data Integration (CICLOGEN) (PI17/01826), granted by the Spanish Health Research Agency from the National Plan for Scientific and Technical Research and Innovation 2013–2016 and FEDER Funds. Dr. Benito-León is supported by the National Institutes of Health, Bethesda, MD, USA (NINDS #R01 NS39422), the Commission of the European Union (grant ICT-2011-287739, NeuroTREMOR), the Ministry of Economy and Competitiveness (grant RTC-2015-3967-1, NetMD—platform for the tracking of movement disorder), and the Spanish Health Research Agency (grant PI12/01602 and grant PI16/00451).United States. National Institutes of Health; R01 NS3942

Understanding Tremor in Multiple Sclerosis: Prevalence, Pathological Anatomy, and Pharmacological and Surgical Approaches to Treatment

Background:&nbsp;Given that tremor is one of the most prevalent and disabling features of multiple sclerosis (MS), we will review the most significant milestones in tremor in this disease in recent years, focusing on prevalence, clinical features, anatomical basis, and treatment.Methods:&nbsp;Data for this review were identified by searching MEDLINE with the search terms &ldquo;multiple sclerosis&rdquo; and &ldquo;tremor&rdquo;. References were also identified from relevant articles published between January 1966 and May 2012.Results:&nbsp;The predominant type of MS tremor is a large-amplitude, postural, and kinetic tremor, which most commonly affects the arms, although tremor can also involve head, neck, vocal cords, and trunk. Involvement of the tongue, jaw, or palate has not been reported. Although the anatomical basis underlying tremor in MS is poorly understood, the link between the cerebellum and the MS-related tremor is supported by clinical and experimental studies. Currently available medication is often unsuccessful in most cases. Surgical treatment can be a satisfactory alternative to treat severe and disabling tremor.Discussion:&nbsp;Tremor in MS patients could be considered as an advanced consequence of the disease and its presence suggests a more aggressive course. MS tremor can be severe and very disabling for a small group of patients. Treatment of MS tremor remains a great challenge. Recent studies suggest that dissociating tremor from cerebellar dysfunction using selected clinical tests would be the key issue to successful surgical treatment. Understanding the pathophysiology and biochemistry of tremor production in MS may lead to new therapeutic approaches.</p