Electromyographic Activity in the EEG in Alzheimer's Disease: Noise or Signal?

Many efforts have been directed at negating the influence of electromyographic (EMG) activity on the EEG, especially in elderly demented patients. We wondered whether these “artifacts” might reflect cognitive and behavioural aspects of dementia. In this pilot study, 11 patients with probable Alzheimer's disease (AD), 13 with amnestic mild cognitive impairment (MCI) and 13 controls underwent EEG registration. As EMG measures, we used frontal and temporal 50–70 Hz activity. We found that the EEGs of AD patients displayed more theta activity, less alpha reactivity, and more frontal EMG than controls. Interestingly, increased EMG activity indicated more cognitive impairment and more depressive complaints. EEG variables on the whole distinguished better between groups than EMG variables, but an EMG variable was best for the distinction between MCI and controls. Our results suggest that EMG activity in the EEG could be more than noise; it differs systematically between groups and may reflect different cerebral functions than the EEG

Small vessel disease burden and functional brain connectivity in mild cognitive impairment

Background: The role of small vessel disease in the development of dementia is not yet completely understood. Functional brain connectivity has been shown to differ between individuals with and without cerebral small vessel disease. However, a comprehensive measure of small vessel disease quantifying the overall damage on the brain is not consistently used and studies using such measure in mild cognitive impairment individuals are missing.Method: Functional brain connectivity differences were analyzed between mild cognitive impairment individuals with absent or low (n = 34) and high (n = 34) small vessel disease burden using data from the Parelsnoer Institute, a Dutch multicenter study. Small vessel disease was characterized using an ordinal scale considering: lacunes, microbleeds, perivascular spaces in the basal ganglia, and white matter hyperintensities. Resting state functional MRI data using 3 Tesla scanners was analyzed with group-independent component analysis using the CONN toolbox.Results: Functional connectivity between areas of the cerebellum and between the cerebellum and the thalamus and caudate nucleus was higher in the absent or low small vessel disease group compared to the high small vessel disease group.Conclusion: These findings might suggest that functional connectivity of mild cognitive impairment individuals with low or absent small vessel disease burden is more intact than in mild cognitive impairment individuals with high small vessel disease. These brain areas are mainly responsible for motor, attentional and executive functions, domains which in previous studies were found to be mostly associated with small vessel disease markers. Our results support findings on the involvement of the cerebellum in cognitive functioning

Small vessel disease burden and functional brain connectivity in mild cognitive impairment

Background: The role of small vessel disease in the development of dementia is not yet completely understood. Functional brain connectivity has been shown to differ between individuals with and without cerebral small vessel disease. However, a comprehensive measure of small vessel disease quantifying the overall damage on the brain is not consistently used and studies using such measure in mild cognitive impairment individuals are missing.Method: Functional brain connectivity differences were analyzed between mild cognitive impairment individuals with absent or low (n = 34) and high (n = 34) small vessel disease burden using data from the Parelsnoer Institute, a Dutch multicenter study. Small vessel disease was characterized using an ordinal scale considering: lacunes, microbleeds, perivascular spaces in the basal ganglia, and white matter hyperintensities. Resting state functional MRI data using 3 Tesla scanners was analyzed with group-independent component analysis using the CONN toolbox.Results: Functional connectivity between areas of the cerebellum and between the cerebellum and the thalamus and caudate nucleus was higher in the absent or low small vessel disease group compared to the high small vessel disease group.Conclusion: These findings might suggest that functional connectivity of mild cognitive impairment individuals with low or absent small vessel disease burden is more intact than in mild cognitive impairment individuals with high small vessel disease. These brain areas are mainly responsible for motor, attentional and executive functions, domains which in previous studies were found to be mostly associated with small vessel disease markers. Our results support findings on the involvement of the cerebellum in cognitive functioning

An Interpretable Machine Learning Model with Deep Learning-based Imaging Biomarkers for Diagnosis of Alzheimer's Disease

Machine learning methods have shown large potential for the automatic early diagnosis of Alzheimer's Disease (AD). However, some machine learning methods based on imaging data have poor interpretability because it is usually unclear how they make their decisions. Explainable Boosting Machines (EBMs) are interpretable machine learning models based on the statistical framework of generalized additive modeling, but have so far only been used for tabular data. Therefore, we propose a framework that combines the strength of EBM with high-dimensional imaging data using deep learning-based feature extraction. The proposed framework is interpretable because it provides the importance of each feature. We validated the proposed framework on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, achieving accuracy of 0.883 and area-under-the-curve (AUC) of 0.970 on AD and control classification. Furthermore, we validated the proposed framework on an external testing set, achieving accuracy of 0.778 and AUC of 0.887 on AD and subjective cognitive decline (SCD) classification. The proposed framework significantly outperformed an EBM model using volume biomarkers instead of deep learning-based features, as well as an end-to-end convolutional neural network (CNN) with optimized architecture.Comment: 11 pages, 5 figure

White matter hyperintensities associate with cognitive slowing in patients with systemic lupus erythematosus and neuropsychiatric symptoms

Objective- To compare cognitive function between patients with different phenotypes of neuropsychiatric systemic lupus erythematosus (NPSLE) and assess its association with brain and white matter hyperintensity (WMH) volumes. Methods- Patients attending the Leiden University Medical Centre NPSLE clinic between 2007 and 2015 without large brain infarcts were included (n=151; 42±13 years, 91% women). In a multidisciplinary consensus meeting, neuropsychiatric symptoms were attributed to systemic lupus erythematosus (SLE) (NPSLE, inflammatory (n=24) or ischaemic (n=12)) or to minor/non-NPSLE (n=115). Multiple regression analyses were performed to compare cognitive function between NPSLE phenotypes and to assess associations between brain and WMH volumes and cognitive function cross-sectionally. Results- Global cognitive function was impaired in 5%, learning and memory (LM) in 46%, executive function and complex attention (EFCA) in 39% and psychomotor speed (PS) in 46% of all patients. Patients with inflammatory NPSLE showed the most cognitive impairment in all domains (p≤0.05). Higher WMH volume associated with lower PS in the total group (B: −0.14 (95% CI −0.32 to −0.02)); especially in inflammatory NPSLE (B: −0.36 (95% CI −0.60 to −0.12). In the total group, lower total brain volume and grey matter volume associated with lower cognitive functioning in all domains (all: 0.00/0.01 (0.00;0.01)) and lower white matter volume associated with lower LM, EFCA and PS (all: 0.00/0.01 (0.00;0.01)). Conclusion- We demonstrated that an association between brain and WMH volumes and cognitive function is present in patients with SLE, but differs between (NP)SLE phenotypes. WMHs associated with PS especially in inflammatory NPSLE, which suggests a different, potentially more severe underlying pathophysiological mechanism of cognitive impairment in this phenotype

Cross-cohort generalizability of deep and conventional machine learning for MRI-based diagnosis and prediction of Alzheimer's disease

This work validates the generalizability of MRI-based classification of Alzheimer’s disease (AD) patients and controls (CN) to an external data set and to the task of prediction of conversion to AD in individuals with mild cognitive impairment (MCI).We used a conventional support vector machine (SVM) and a deep convolutional neural network (CNN) approach based on structural MRI scans that underwent either minimal pre-processing or more extensive pre-processing into modulated gray matter (GM) maps. Classifiers were optimized and evaluated using cross-validation in the Alzheimer’s Disease Neuroimaging Initiative (ADNI; 334 AD, 520 CN). Trained classifiers were subsequently applied to predict conversion to AD in ADNI MCI patients (231 converters, 628 non-converters) and in the independent Health-RI Parelsnoer Neurodegenerative Diseases Biobank data set. From this multi-center study representing a tertiary memory clinic population, we included 199 AD patients, 139 participants with subjective cognitive decline, 48 MCI patients converting to dementia, and 91 MCI patients who did not convert to dementia.AD-CN classification based on modulated GM maps resulted in a similar area-under-the-curve (AUC) for SVM (0.940; 95%CI: 0.924–0.955) and CNN (0.933; 95%CI: 0.918–0.948). Application to conversion prediction in MCI yielded significantly higher performance for SVM (AUC = 0.756; 95%CI: 0.720-0.788) than for CNN (AUC = 0.742; 95%CI: 0.709-0.776) (p<0.01 for McNemar’s test). In external validation, performance was slightly decreased. For AD-CN, it again gave similar AUCs for SVM (0.896; 95%CI: 0.855–0.932) and CNN (0.876; 95%CI: 0.836–0.913). For prediction in MCI, performances decreased for both SVM (AUC = 0.665; 95%CI: 0.576-0.760) and CNN (AUC = 0.702; 95%CI: 0.624-0.786). Both with SVM and CNN, classification based on modulated GM maps significantly outperformed classification based on minimally processed images (p=0.01).Deep and conventional classifiers performed equally well for AD classification and their performance decreased only slightly when applied to the external cohort. We expect that this work on external validation contributes towards translation of machine learning to clinical practice

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, R2 = 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; χ2(11)=37.72, p < 0.001, with R2 = 0.27 (Nagelkerke) and R2 = 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.Implications for rehabilitationMultiple sclerosis (MS) affects people of working age, significantly interfering with work productivity.Higher cognitive and physical impact of fatigue were associated with more presenteeism in workers with MS.A higher cognitive impact of fatigue and more depressive symptoms were associated with absenteeism in workers with MS.Occupational and healthcare professionals should be aware of the impact of both physical and mental health on work productivity in workers with MS

The MS@Work study:a 3-year prospective observational study on factors involved with work participation in patients with relapsing-remitting Multiple Sclerosis

Background: Multiple Sclerosis (MS) is the most common cause of neurological disability in young and middle-aged adults. At this stage in life most people are in the midst of their working career. The majority of MS patients are unable to retain employment within 10 years from disease onset. Leading up to unemployment, many may experience a reduction in hours or work responsibilities and increased time missed from work. The MS@Work study examines various factors that may influence work participation in relapsing-remitting MS patients, including disease-related factors, the working environment and personal factors. Methods/design: The MS@Work study is a multicenter, 3-year prospective observational study on work participation in patients with relapsing-remitting MS. We aim to include 350 patients through 15-18 MS outpatient clinics in the Netherlands. Eligible participants are 18 years and older, and either currently employed or within three years since their last employment. At baseline and after 1, 2 and 3 years, the participants are asked to complete online questionnaires (including questions on work participation, work problems and accommodations, cognitive and physical ability, anxiety, depression, psychosocial stress, quality of life, fatigue, empathy, personality traits and coping strategies) and undergo cognitive and neurological examinations. After six months, patients are requested to only complete online questionnaires. Patient perspectives on maintaining and improving work participation and reasons to stop working are gathered through semi-structured interviews in a sub-group of patients. Discussion: Prospective studies with long-term follow-up on work participation in MS are rare, or take into account a limited number of factors. The MS@Work study provides a 3-year follow-up on various factors that may influence work participation in patients with relapsing-remitting MS. We aim to identify factors that relate to job loss and to provide information about preventative measures for physicians, psychologists and other professionals working in the field of occupational health

Magnetization transfer imaging in ‘premanifest’ Huntington’s disease

To investigate whether magnetization transfer imaging (MTI) is a useful detector of diffuse brain abnormalities in ‘premanifest’ carriers of the Huntington’s disease (HD) gene mutation. Furthermore we examined the relations between MTI, clinical measures and CAG repeat length. Sixteen premanifest carriers of the HD gene without motor manifestation and 14 non-carriers underwent a clinical evaluation and a MRI scan. MTI analysis of whole brain, grey matter and white matter was performed producing magnetization transfer ratio (MTR) histograms. A lower peak height of the grey matter MTR histogram in carriers was significantly associated with more UHDRS motor abnormalities. Furthermore, a lower peak height of the whole brain, grey and white matter was strongly associated with a longer CAG repeat length. MTI measures themselves did not differ significantly between carriers and non-carriers. In premanifest HD mutation carriers, a lower MTR peak height, reflecting worse histological brain composition, was related to subtle motor abnormalities and higher CAG repeat length. Although we could not detect altered MTI characteristics in carriers of the HD gene mutation without clinical manifestations, we did provide evidence that the MTR peak height might reflect genetic and subclinical disease burden and may be of value in monitoring further disease progression and provide insight in clinical heterogeneity

Work Participation and Executive Abilities in Patients with Relapsing-Remitting Multiple Sclerosis.

The majority of patients with Multiple Sclerosis (MS) are unable to retain employment within 10 years from disease onset. Executive abilities, such as planning, working memory, attention, problem solving, inhibition and mental flexibility may have a direct impact on the ability to maintain a job. This study investigated differences in subjective and objective executive abilities between relapsing-remitting MS patients with and without a paid job. We included 55 relapsing-remitting MS patients from a community-based sample (47 females; mean age: 47 years; 36% employed). Patients underwent neurological, cognitive and psychological assessments at their homes, including an extensive executive test battery. We found that unemployed patients had a longer disease duration (t(53)=2.76, p=0.008) and reported more organising and planning problems (χ2(1)=6.3, p=0.012), higher distractibility (Kendall's tau-b= -0.24, p=0.03) and more cognitive fatigue (U=205.0, p=0.028, r=-0.30) than employed patients. Unemployed patients completed slightly less categories on the Wisconsin Card Sorting Test (U=243.5, p=0.042, r=-0.28). Possible influential factors such as age, educational level, physical functioning, depression and anxiety did not differ between groups. In conclusion, while relapsing-remitting MS patients without a paid job reported more executive problems and cognitive fatigue than patients with a paid job, little differences were found in objective executive abilities. Further research is needed to examine possible causal relations