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The relation between cognitive functioning and work outcomes in patients with Multiple Sclerosis: a systematic literature review

Background: Cognitive dysfunction is increasingly being recognized as an important limiting factor in work participation in patients with multiple sclerosis. This literature review provides a description, synthesis and interpretation of the existing literature and identifies gaps in current knowledge.Methods: Papers published between 1970 and April 2017 were included. Clinical trials, randomized controlled trials or observational quantitative studies in which data on cognitive factors associated with employment, work functioning or work-related problems were reported. Papers were manually double checked by two blinded reviewers.Results: A total of 41 papers were included of which 4 described prospective, longitudinal studies. The majority of studies reported positive associations between cognitive functioning and work outcomes (38/41 studies; 93%). Positive associations were found between work outcomes and global cognitive functioning (8/9 studies; 89%), language (8/17 studies; 47%), processing speed/ working memory (23/26 studies; 88%),new learning and memory (12/22 studies; 55%), executive functioning (10/17; 59%), intelligence (1/6; 17%) and self-reported cognitive functioning (14/15; 93%). None of the reviewed studies found a relation between visuospatial processing and work outcomes (0/8; 0%). Models including cognitive measures as well as demographic (age, education), neurological (disability, fine motor coordination, disease course), and psychological variables (depression, personality, fatigue) best predicted work outcomes.Conclusion: By conducting this review we found ample evidence that objective cognitive functioning (specifically processing speed/ working memory and executive functioning) and self-reported cognitive functioning are important independent associates of work outcomes and these factors should be addressed by healthcare professionals. Future research could benefit from a focus on longitudinal changes in cognition and its relation to work outcomes.Health and self-regulatio

Vigilance: discussion of related concepts and proposal for a definition

We reviewed current definitions of vigilance to propose a definition, applicable in sleep medicine. As previous definitions contained terms such as attention, alertness, and arousal, we addressed these concepts too. We defined alertness as a quantitative measure of the mind state governing sensitivity to stimuli. Arousal comprises a stimulus-induced upward change in alertness, irrespective of the subsequent duration of the increased level of alertness. Vigilance is defined as the capability to be sensitive to potential changes in one's environment, ie the capability to reach a level of alertness above a threshold for a certain period of time rather than the state of alertness itself. It has quantitative and temporal dimensions. Attention adds direction towards a stimulus to alertness, requiring cognitive control: it involves being prepared to process stimuli coming from an expected direction. Sustained attention corresponds to a state in which some level of attention is purposefully maintained, adding a time factor to the definition of attention. Vigilance differs from sustained attention in that the latter in addition implies a direction to which attention is cognitively directed as well as a specification of duration. Attempts to measure vigilance, however, are often in fact measurements of sustained attention. (C) 2021 The Authors. Published by Elsevier B.V.Paroxysmal Cerebral Disorder

Machine learning for automated EEG-based biomarkers of cognitive impairment during Deep Brain Stimulation screening in patients with Parkinson's Disease

Objective: A downside of Deep Brain Stimulation (DBS) for Parkinson's Disease (PD) is that cognitive function may deteriorate postoperatively. Electroencephalography (EEG) was explored as biomarker of cognition using a Machine Learning (ML) pipeline.Methods: A fully automated ML pipeline was applied to 112 PD patients, taking EEG time-series as input and predicted class-labels as output. The most extreme cognitive scores were selected for class differentiation, i.e. best vs. worst cognitive performance (n = 20 per group). 16,674 features were extracted per patient; feature-selection was performed using a Boruta algorithm. A random forest classifier was modelled; 10-fold cross-validation with Bayesian optimization was performed to ensure generalizability. The predicted class-probabilities of the entire cohort were compared to actual cognitive performance.Results: Both groups were differentiated with a mean accuracy of 0.92; using only occipital peak frequency yielded an accuracy of 0.67. Class-probabilities and actual cognitive performance were negatively linearly correlated (b =-0.23 (95% confidence interval (-0.29,-0.18))).Conclusions: Particularly high accuracies were achieved using a compound of automatically extracted EEG biomarkers to classify PD patients according to cognition, rather than a single spectral EEG feature.Significance: Automated EEG assessment may have utility for cognitive profiling of PD patients during the DBS screening. (c) 2021 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).Neurological Motor Disorder

Machine learning for automated EEG-based biomarkers of cognitive impairment during Deep Brain Stimulation screening in patients with Parkinson’s Disease

Algorithms and the Foundations of Software technolog

Dissociation in SLE: a part of lupus fog?

Introduction Lupus fog is ill-defined. We aimed to study whether lupus fog is the result of dissociation by studying the prevalence of dissociation and dissociative fog in patients with SLE and neuropsychiatric manifestations of inflammatory and non-inflammatory origin. Methods Patients visiting the tertiary referral center for neuropsychiatric systemic lupus erythematosus (NPSLE) of the LUMC between 2007-2019 were included. Patients were classified as having neuropsychiatric symptoms of inflammatory or non-inflammatory origin. Dissociation was studied using the Dissociative Experience Scale-II (DES), in which the presence of 28 dissociative symptoms is rated (0-100% of the time), of which one question assesses the presence of a dissociative fog directly. Average scores are calculated and scores >= 25 are considered indicative of a dissociative disorder. A score of >= 30 on question 28 (dissociative fog) was considered indicative for the presence of a fog. Summary scores in the general adult population range from 4.4 to 14. Multiple regression analysis (MRA) was performed to study the association between inflammatory neuropsychiatric symptoms and dissociation. DES results are presented as median (range) and MRA as B and 95% confidence interval (CI). Results DES questionnaires were available for 337 patients, of which 69 had an inflammatory NPSLE phenotype (20%). Mean age in the total study population was 43 +/- 14 years and the majority was female (87%). The median dissociation score was 7.1 (0-75) and did not differ between patients with neuropsychiatric symptoms of inflammatory or non-inflammatory origin (B: -0.04 (95% CI: -0.17; 0.09)). 35 patients (10%) had a score indicative of a dissociative disorder. The most common type of dissociation was absorption/imagination. 43 patients (13%) reported a dissociative fog. Discussion In most patients with SLE and neuropsychiatric symptoms, dissociative symptoms are within normal range, regardless of underlying etiology. Dissociative fog is present, but uncommon. Lupus fog is most likely not associated with dissociation.Clinical epidemiolog

EEG markers of future cognitive performance in the elderly

FSW - Self-regulation models for health behavior and Psychopathology - Ou

Preoperative electroencephalography‐based machine learning predicts cognitive deterioration after subthalamic deepbrain stimulation

Algorithms and the Foundations of Software technolog

Eighteen-year disease progression and survival in CADASIL

Neuro Imaging Researc

Early magnetic resonance imaging and cognitive markers of hereditary cerebral amyloid angiopathy

FSW - Self-regulation models for health behavior and psychopathology - ou