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

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

Fatigue in patients with systemic lupus erythematosus and neuropsychiatric symptoms is associated with anxiety and depression rather than inflammatory disease activity

IntroductionWe aimed to investigate risk factors for fatigue in patients with systemic lupus erythematosus (SLE) and neuropsychiatric symptoms in order to identify potential interventional strategies.MethodsPatients visiting the neuropsychiatric SLE (NPSLE) clinic of the Leiden University Medical Center between 2007-2019 were included. In a multidisciplinary consensus meeting, SLE patients were classified as having neuropsychiatric symptoms of inflammatory origin (inflammatory phenotype) or other origin (non-inflammatory phenotype). Fatigue was assessed with the SF-36 vitality domain (VT) since 2007 and the multidimensional fatigue inventory (MFI) and visual analogue scale (VAS) since 2011. Patients with a score on the SF-36 VT >= 1 standard deviation (SD) away from the mean of age-related controls of the general population were classified as fatigued; patients >= 2 SD away were classified as extremely fatigued. Disease activity was measured using the SLE disease activity index-2000. The influence of the presence of an inflammatory phenotype, disease activity and symptoms of depression and anxiety as measured by the hospital anxiety and depression scale (HADS) was analyzed using multiple regression analyses corrected for age, sex and education.Results348 out of 371 eligible patients filled in questionnaires and were included in this study . The majority was female (87%) and the mean age was 43 +/- 14 years. 72 patients (21%) had neuropsychiatric symptoms of an inflammatory origin. Fatigue was present in 78% of all patients and extreme fatigue was present in 50% of patients with an inflammatory phenotype vs 46% in the non-inflammatory phenotype. Fatigue was similar in patients with an inflammatory phenotype compared to patients with a non-inflammatory phenotype on the SF-36 VT (beta: 0.8 (95% CI -4.8; 6.1) and there was less fatigue in patients with an inflammatory phenotype on the MFI and VAS (beta: -3.7 (95% CI: -6.9; -0.5) and beta: -1.0 (95% CI -1.6; -0.3)). There was no association between disease activity and fatigue, but symptoms of anxiety and depression (HADS) associated strongly with all fatigue measurements.ConclusionThis study suggests that intervention strategies to target fatigue in (NP)SLE patients may need to focus on symptoms of anxiety and depression rather than immunosuppressive treatment.Clinical epidemiolog

Self-reported work productivity in people with multiple sclerosis and its association with mental and physical health

Purpose This study aimed to identify mental health, physical health, demographic and disease characteristics relating to work productivity in people with multiple sclerosis (MS). Methods In this cross-sectional study, 236 employed people with MS (median age = 42 years, 78.8% female) underwent neurological and neuropsychological assessments. Additionally, they completed questionnaires inquiring about work productivity (presenteeism: reduced productivity while working, and absenteeism: loss of productivity due to absence from work), mental and physical health, demographic and disease characteristics. Multiple linear and logistic regression analyses were performed with presenteeism and absenteeism as dependent variables, respectively. Results A model with mental and physical health factors significantly predicted presenteeism F(11,202) = 11.33, p < 0.001, R-2 = 0.38; a higher cognitive (p < 0.001) and physical impact (p = 0.042) of fatigue were associated with more presenteeism. A model with only mental health factors significantly predicted absenteeism; chi(2)(11)=37.72, p < 0.001, with R-2 = 0.27 (Nagelkerke) and R-2 = 0.16 (Cox and Snell). Specifically, we observed that more symptoms of depression (p = 0.041) and a higher cognitive impact of fatigue (p = 0.011) were significantly associated with more absenteeism. Conclusions In people with MS, both cognitive and physical impact of fatigue are positively related to presenteeism, while symptoms of depression and cognitive impact of fatigue are positively related to absenteeism.Neurological Motor Disorder