Quantitative EEG has been shown to reflect neurodegenerative processes in Alzheimer's disease (AD) and may provide non-invasive and widely available biomarkers to enhance the objectivization of disease assessment. To address EEG's major drawback - its low spatial resolution - many studies have employed 3D source localization. However, none have investigated whether this complex mapping into 3D space actually adds value over standard surface derivation. In fact, we found no prior study - in any disease - that quantitatively compared the results of a 3D source localization method with those achieved by surface derivation. We analyzed data from one of the largest prospective AD EEG studies ever conducted (four study centers, 188 patients, 100 female). Thousands of distinct quantitative EEG markers of slowing, complexity, and functional connectivity were computed and regressed against disease severity, with rigorous control for multiple testing. We found highly significant associations between quantitative EEG markers and disease severity. However, standardized low-resolution electromagnetic tomography (sLORETA), a widely used 3D source localization method, did not improve results. Furthermore, a surface derivation marker (auto-mutual information of the left hemisphere during the eyes-closed condition) was the best performing marker across our entire sample. While our findings strongly support that quantitative EEG markers reflect neurodegenerative processes in AD, they do not demonstrate additional benefit from sLORETA. Importantly, our results are specific to AD and sLORETA. Therefore, they should not be generalized to other neurological or psychiatric disorders or to other 3D source localization methods without further validation. Finally, these findings do not diminish the value of 3D source localization for visual EEG inspection.

Background: Functional (un-)coupling (task-related change of functional connectivity) between different sites of the brain is a mechanism of general importance for cognitive processes. In Alzheimer's disease (AD), prior research identified diminished cortical connectivity as a hallmark of the disease. However, little is known about the relation between the amount of functional (un-)coupling and cognitive performance and decline in AD. Method: Cognitive performance (based on CERAD-Plus scores) and electroencephalogram (EEG)-based functional (un-)coupling measures (connectivity changes from rest to a Face-Name-Encoding task) were assessed in 135 AD patients (age: M = 73.8 years; SD = 9.0). Of these, 68 patients (M = 73.9 years; SD = 8.9) participated in a follow-up assessment of their cognitive performance 1.5 years later. Results: The amounts of functional (un-)coupling in left anterior-posterior and homotopic interhemispheric connections in beta1-band were related to cognitive performance at baseline (β = .340; p < .001; β = .274; P = .001, respectively). For both markers, a higher amount of functional coupling was associated with better cognitive performance. Both markers also were significant predictors for cognitive decline. However, while patients with greater functional coupling in left anterior-posterior connections declined less in cognitive performance (β = .329; P = .035) those with greater functional coupling in interhemispheric connections declined more (β = −.402; P = .010). Conclusion: These findings suggest an important role of functional coupling mechanisms in left anterior–posterior and interhemispheric connections in AD. Especially the complex relationship with cognitive decline in AD patients might be an interesting aspect for future studies.

In clinical practice, the quality of polysomnographic recordings in children and patients with neurodegenerative diseases may be affected by sensor displacement and diminished total sleep time due to stress during the recording. In the present study, we investigated if contactless three-dimensional (3D) detection of periodic leg movements during sleep was comparable to polysomnography. We prospectively studied a sleep laboratory cohort from two Austrian sleep laboratories. Periodic leg movements during sleep were classified according to the standards of the World Association of Sleep Medicine and served as ground truth. Leg movements including respiratory-related events (A1) and excluding respiratory-related events (A2 and A3) were presented as A1, A2 and A3. Three-dimensional movement analysis was carried out using an algorithm developed by the Austrian Institute of Technology. Fifty-two patients (22 female, mean age 52.2 +/- 15.1 years) were included. Periodic leg movement during sleep indexes were significantly higher with 3D detection compared to polysomnography (33.3 [8.1-97.2] vs. 30.7 [2.9-91.9]: +9.1%, p = .0055/27.8 [4.5-86.2] vs. 24.2 [0.00-88.7]: +8.2%, p = .0154/31.8 [8.1-89.5] vs. 29.6 [2.4-91.1]: +8.9%, p = .0129). Contactless automatic 3D analysis has the potential to detect restlessness mirrored by periodic leg movements during sleep reliably and may especially be suited for children and the elderly.

STUDY OBJECTIVES:The differentiation of isolated rapid eye movement (REM) sleep behavior disorder (iRBD) or its prodromal phase (prodromal RBD) from other disorders with motor activity during sleep is critical for identifying α-synucleinopathy in an early stage. Currently, definite RBD diagnosis requires video polysomnography (vPSG). The aim of this study was to evaluate automated 3D video analysis of leg movements during REM sleep as objective diagnostic tool for iRBD. METHODS:A total of 122 participants (40 iRBD, 18 prodromal RBD, 64 participants with other disorders with motor activity during sleep) were recruited among patients undergoing vPSG at the Sleep Disorders Unit, Department of Neurology, Medical University of Innsbruck. 3D videos synchronous to vPSG were recorded. Lower limb movements rate, duration, extent, and intensity were computed using a newly developed software. RESULTS:The analyzed 3D movement features were significantly increased in subjects with iRBD compared to prodromal RBD and other disorders with motor activity during sleep. Minor leg jerks with a duration < 2 seconds discriminated with the highest accuracy (90.4%) iRBD from other motor activity during sleep. Automatic 3D analysis did not differentiate between prodromal RBD and other disorders with motor activity during sleep. CONCLUSIONS:Automated 3D video analysis of leg movements during REM sleep is a promising diagnostic tool for identifying subjects with iRBD in a sleep laboratory population and is able to distinguish iRBD from subjects with other motor activities during sleep. For future application as a screening, further studies should investigate usefulness of this tool when no information about sleep stages from vPSG is available and in the home environment.

In the context of railway safety, it is crucial to know the positions of all trains moving along the infrastructure. In this contribution, we present an algorithm that extracts the positions of moving trains for a given point in time from Distributed Acoustic Sensing (DAS) signals. These signals are obtained by injecting light pulses into an optical fiber close to the railway tracks and measuring the Rayleigh backscatter. We show that the vibrations of moving objects can be identified and tracked in real-time yielding train positions every second. To speed up the algorithm, we describe how the calculations can partly be based on graphical processing units. The tracking quality is assessed by counting the inaccurate and lost train tracks for two different types of cable installations.

Introduction: Magnetic resonance imaging (MRI) and electroencephalography (EEG) are a promising means to an objectified assessment of cognitive impairment in Alzheimer's disease (AD). Individually, however, these modalities tend to lack precision in both AD diagnosis and AD staging. A joint MRI-EEG approach that combines structural with functional information has the potential to overcome these limitations. Materials and Methods: This cross-sectional study systematically investigated the link between MRI and EEG markers and the global cognitive status in early AD. We hypothesized that the joint modalities would identify cognitive deficits with higher accuracy than the individual modalities. In a cohort of 111 AD patients, we combined MRI measures of cortical thickness and regional brain volume with EEG measures of rhythmic activity, information processing and functional coupling in a generalized multiple regression model. Machine learning classification was used to evaluate the markers' utility in accurately separating the subjects according to their cognitive score. Results: We found that joint measures of temporal volume, cortical thickness, and EEG slowing were well associated with the cognitive status and explained 38.2% of ifs variation. The inclusion of the covariates age, sex, and education considerably improved the model. The joint markers separated the subjects with an accuracy of 84.7%, which was considerably higher than by using individual modalities. Conclusions: These results suggest that including joint MRI-EEG markers may be beneficial in the diagnostic workup, thus allowing for adequate treatment. Further studies in larger populations, with a longitudinal design and validated against functional-metabolic imaging are warranted to confirm the results.

As societies around the world are ageing, the number of Alzheimer's disease (AD) patients is rapidly increasing. To date, no low-cost, non-invasive biomarkers have been established to advance the objectivization of AD diagnosis and progression assessment. Here, we utilize Bayesian neural networks to develop a multivariate predictor for AD severity using a wide range of quantitative EEG (QEEG) markers. The Bayesian treatment of neural networks both automatically controls model complexity and provides a predictive distribution over the target function, giving uncertainty bounds for our regression task. It is therefore well suited to clinical neuroscience, where data sets are typically sparse and practitioners require a precise assessment of the predictive uncertainty. We use data of one of the largest prospective AD EEG trials ever conducted to demonstrate the potential of Bayesian deep learning in this domain, while comparing two distinct Bayesian neural network approaches, i.e., Monte Carlo dropout and Hamiltonian Monte Carlo.

Electroencephalogram (EEG) is a common tool in sleep medicine, but it is often compromised by non-neural artifacts. Excluding visually identified artifacts is time-consuming and removes relevant EEG information. Blind source separation (BSS) techniques, on the other hand, are capable of separating "brain" from "artifact source components". Existing algorithms for automated component labeling require either a priori morphological information or adaptation to individual recordings. We present a method for the automated identification of artifact components based on their autocorrelation and spectral properties. It requires no tuning for individual recordings. The method was tested on 100 one-minute EEG segments during rapid eye movement sleep. EEG source components were estimated by second order blind source identification and, as reference, manually labeled as "brain" or "artifact component". The algorithm identified electro-cardiogram components by autocorrelation peaks between 0.5-1.5 seconds and -oculogram components by linear discriminant analysis of spectral band-power. Using 5-fold cross-validation, we observed 97% accuracy (95% sensitivity, 98% specificity), as well as minimized correlation of artifacts and the EEG. The approach has demonstrated its potential as promising tool for a broad range of sleep medical applications.

Disrupted sleep is a contributing factor to cognitive ageing, while also being associated with neurodegenerative disorders. Little is known, however, about the relation of sleep and the gradual cognitive changes over the adult life course. Sleep electroencephalogram (EEG) patterns are potential markers of the cognitive progress. To test this hypothesis, we assessed sleep architecture and EEG of 167 men born in the Copenhagen Metropolitan Area in 1953, who, based on individual cognitive testing from early (~18 years) to late adulthood (~58 years), were divided into 85 subjects with negative and 82 with positive cognitive change over their adult life. Participants underwent standard polysomnography, including manual sleep scoring at age ~58 years. Features of sleep macrostructure were combined with a number of EEG features to distinguish between the two groups. EEG rhythmicity was assessed by spectral power analysis in frontal, central and occipital sites. Functional connectivity was measured by inter‐hemispheric EEG coherence. Group differences were assessed by analysis of covariance (p < 0.05), including education and severity of depression as potential covariates. Subjects with cognitive decline exhibited lower sleep efficiency, reduced inter‐hemispheric connectivity during rapid eye movement (REM) sleep, and slower EEG rhythms during stage 2 non‐REM sleep. Individually, none of these tendencies remained significant after multiple test correction; however, by combining them in a machine learning approach, the groups were separated with 72% accuracy (75% sensitivity, 67% specificity). Ongoing medical screenings are required to confirm the potential of sleep efficiency and sleep EEG patterns as signs of individual cognitive progress.

BACKGROUND:So far, no cost-efficient, widely-used biomarkers have been established to facilitate the objectivization of Alzheimer's disease (AD) diagnosis and monitoring. Research suggests that event-related potentials (ERPs) reflect neurodegenerative processes in AD and might qualify as neurophysiological AD markers. OBJECTIVES:First, to examine which ERP component correlates the most with AD severity, as measured by the Mini-Mental State Examination (MMSE). Then, to analyze the temporal change of this component as AD progresses. METHODS:Sixty-three subjects (31 with possible, 32 with probable AD diagnosis) were recruited as part of the cohort study Prospective Dementia Registry Austria (PRODEM). For a maximum of 18 months patients revisited every 6 months for follow-up assessments. ERPs were elicited using an auditory oddball paradigm. P300 and N200 latency was determined with regard to target as well as difference wave ERPs, whereas P50 amplitude was measured from standard stimuli waveforms. RESULTS:P300 latency exhibited the strongest association with AD severity (e.g., r = -0.512, p < 0.01 at Pz for target stimuli in probable AD subjects). Further, there were significant Pearson correlations for N200 latency (e.g., r = -0.407, p = 0.026 at Cz for difference waves in probable AD subjects). P50 amplitude, as measured by different detection methods and at various scalp sites, did not significantly correlate with disease severity - neither in probable AD, possible AD, nor in both subgroups of patients combined. ERP markers for the group of possible AD patients did not show any significant correlations with MMSE scores. Post-hoc pairwise comparisons between baseline and 18-months follow-up assessment revealed significant P300 latency differences (e.g., p < 0.001 at Cz for difference waves in probable AD subjects). However, there were no significant correlations between the change rates of P300 latency and MMSE score. CONCLUSIONS:P300 and N200 latency significantly correlated with disease severity in probable AD, whereas P50 amplitude did not. P300 latency, which showed the highest correlation coefficients with MMSE, significantly increased over the course of the 18 months study period in probable AD patients. The magnitude of the observed prolongation is in line with other longitudinal AD studies and substantially higher than in normal ageing, as reported in previous trials (no healthy controls were included in our study).

The objective of this work was to develop and evaluate a classifier for differentiating probable Alzheimer's disease (AD) from Parkinson's disease dementia (PDD) or dementia with Lewy bodies (DLB) and from frontotemporal dementia, behavioral variant (bvFTD) based on quantitative electroencephalography (QEEG). We compared 25 QEEG features in 61 dementia patients (20 patients with probable AD, 20 patients with PDD or probable DLB (DLBPD), and 21 patients with bvFTD). Support vector machine classifiers were trained to distinguish among the three groups. Out of the 25 features, 23 turned out to be significantly different between AD and DLBPD, 17 for AD versus bvFTD, and 12 for bvFTD versus DLBPD. Using leave-one-out cross validation, the classification achieved an accuracy, sensitivity, and specificity of 100% using only the QEEG features Granger causality and the ratio of theta and beta1 band powers. These results indicate that classifiers trained with selected QEEG features can provide a valuable input in distinguishing among AD, DLB or PDD, and bvFTD patients. In this study with 61 patients, no misclassifications occurred. Therefore, further studies should investigate the potential of this method to be applied not only on group level but also in diagnostic support for individual subjects.

Electroencephalogram (EEG) signal quality is often compromised by artifacts that corrupt quantitative EEG measurements used in clinical applications and EEG-related studies. Techniques such as filtering, regression analysis and blind source separation are often used to remove these artifacts. However, these preprocessing steps do not allow for complete artifact correction. We propose a method for the automated offline-detection of remaining artifacts after preprocessing in multi-channel EEG recordings. In contrast to existing methods it requires neither adaptive parameters varying between recordings nor a topography template. It is suited for short EEG segments and is flexible with regard to target applications. The algorithm was developed and tested on 60 clinical EEG samples of 20 seconds each that were recorded both in resting state and during cognitive activation to gain a realistic artifact set. Five EEG features were used to quantify temporal and spatial signal variations. Two distance measures for the single-channel and multi-channel variations of these features were defined. The global thresholds were determined by three-fold cross-validation and Youden's J statistic in conjunction with receiver operating characteristics (ROC curves). We observed high sensitivity of 95.5%±4.8 and specificity of 88.8%±2.1. The method has thus shown great potential and is promising as a possible tool for both EEG-based clinical applications and EEG-related research.

Analysis of nonlinear quantitative EEG (qEEG) markers describing complexity of signal in relation to severity of Alzheimer’s disease (AD) was the focal point of this study. In this study, 79 patients diagnosed with probable AD were recruited from the multi-centric Prospective Dementia Database Austria (PRODEM). EEG recordings were done with the subjects seated in an upright position in a resting state with their eyes closed. Models of linear regressions explaining disease severity, expressed in Mini Mental State Examination (MMSE) scores, were analyzed by the nonlinear qEEG markers of auto mutual information (AMI), Shannon entropy (ShE), Tsallis entropy (TsE), multiscale entropy (MsE), or spectral entropy (SpE), with age, duration of illness, and years of education as co-predictors. Linear regression models with AMI were significant for all electrode sites and clusters, where R 2 is 0.46 at the electrode site C3, 0.43 at Cz, F3, and central region, and 0.42 at the left region. MsE also had significant models at C3 with R 2 > 0.40 at scales τ = 5 and τ = 6 . ShE and TsE also have significant models at T7 and F7 with R 2 > 0.30 . Reductions in complexity, calculated by AMI, SpE, and MsE, were observed as the MMSE score decreased.

Event-related potentials (ERPs) have been shown to reflect neurodegenerative processes in Alzheimer's disease (AD) and might qualify as non-invasive and cost-effective markers to facilitate the objectivization of AD assessment in daily clinical practice. Lately, the combination of multivariate pattern analysis (MVPA) and Gaussian process classification (GPC) has gained interest in the neuroscientific community. Here, we demonstrate how a MVPA-GPC approach can be applied to electrophysiological data. Furthermore, in order to account for the temporal information of ERPs, we develop a novel method that integrates interregional synchrony of ERP time signatures. By using real-life ERP recordings of a prospective AD cohort study (PRODEM), we empirically investigate the usefulness of the proposed framework to build neurophysiological markers for single subject classification tasks. GPC outperforms the probabilistic reference method in both tasks, with the highest AUC overall (0.802) being achieved using the new spatiotemporal method in the prediction of rapid cognitive decline.

The diagnosis of Alzheimer’s disease (AD) in routine clinical practice is most commonly based on subjective clinical interpretations. Event-related potentials (ERPs) have been shown to reflect neurodegenerative processes in AD and might qualify as affordable and thereby widely available markers to facilitate the objectivization of AD assessment. Here, we present a novel method combining multivariate pattern analysis (MVPA) and Gaussian process classification (GP) and aim to develop ERP markers for two crucial AD classification problems, i.e., the prediction of rapid cognitive decline (RCD) and the distinction between carriers and non-carriers of the ✏4 allele of the apolipoprotein E gene, the main genetic risk factor for AD.

The diagnosis of Alzheimer's disease (AD) in routine clinical practice is most commonly based on subjective clinical interpretations. Quantitative electroencephalography (QEEG) measures have been shown to reflect neurodegenerative processes in AD and might qualify as affordable and thereby widely available markers to facilitate the objectivization of AD assessment. Here, we present a novel framework combining Riemannian tangent space mapping and elastic net regression for the development of brain atrophy markers. While most AD QEEG studies are based on small sample sizes and psychological test scores as outcome measures, here we train and test our models using data of one of the largest prospective EEG AD trials ever conducted, including MRI biomarkers of brain atrophy.

We analyzed the relation of several synchrony markers in the electroencephalogram (EEG) and Alzheimer's disease (AD) severity as measured by Mini-Mental State Examination (MMSE) scores. The study sample consisted of 79 subjects diagnosed with probable AD. All subjects were participants in the PRODEM-Austria study. Following a homogeneous protocol, the EEG was recorded both in resting state and during a cognitive task. We employed quadratic least squares regression to describe the relation between MMSE and the EEG markers. Factor analysis was used for estimating a potentially lower number of unobserved synchrony factors. These common factors were then related to MMSE scores as well. Most markers displayed an initial increase of EEG synchrony with MMSE scores from 26 to 21 or 20, and a decrease below. This effect was most prominent during the cognitive task and may be owed to cerebral compensatory mechanisms. Factor analysis provided interesting insights in the synchrony structures and the first common factors were related to MMSE scores with coefficients of determination up to 0.433. We conclude that several of the proposed EEG markers are related to AD severity for the overall sample with a wide dispersion for individual subjects. Part of these fluctuations may be owed to fluctuations and day-to-day variability associated with MMSE measurements. Our study provides a systematic analysis of EEG synchrony based on a large and homogeneous sample. The results indicate that the individual markers capture different aspects of EEG synchrony and may reflect cerebral compensatory mechanisms in the early stages of AD.

Alzheimer's Disease (AD) can take different courses: some patients remain relatively stable while others decline rapidly within a given period of time. Losing more than 3 Mini-Mental State Examination (MMSE) points in one year is classified as rapid cognitive decline (RCD). This study used neuropsychological test scores and quantitative EEG (QEEG) markers obtained at a baseline examination to identify if an AD patient will be suffering from RCD. Data from 68 AD patients of the multi-centric cohort study PRODEM-Austria were applied. 15 of the patients were classified into the RCD group. RCD versus non-RCD support vector machine (SVM) classifiers using QEEG markers as predictors obtained 72.1% and 77.9% accuracy ratings based on leave-one-out validation. Adding neuropsychological test scores of Boston Naming Test improved the classifier to 80.9% accuracy, 80% sensitivity, and 81.1% specificity. These results indicate that QEEG markers together with neuropsychological test scores can be used as RCD predictors.

Objective: To investigate which single quantitative electro-encephalographic (QEEG) marker or which combination of markers correlates best with Alzheimer's disease (AD) severity as measured by the Mini-Mental State Examination (MMSE).Methods: We compared quantitative EEG markers for slowing (relative band powers), synchrony (coherence, canonical correlation, Granger causality) and complexity (auto-mutual information, Shannon/Tsallis entropy) in 118 AD patients from the multi-centric study PRODEM Austria. Signal spectra were determined using an indirect spectral estimator. Analyses were adjusted for age, sex, duration of dementia, and level of education.Results: For the whole group (39 possible, 79 probable AD cases) MMSE scores explained 33% of the variations in relative theta power during face encoding, and 31% of auto-mutual information in resting state with eyes closed. MMSE scores explained also 25% of the overall QEEG factor. This factor was thus subordinate to individual markers. In probable AD, QEEG coefficients of determination were always higher than in the whole group, where MMSE scores explained 51% of the variations in relative theta power.Conclusions: Selected QEEG markers show strong associations with AD severity. Both cognitive and resting state should be used for QEEG assessments.Significance: Our data indicate theta power measured during face-name encoding to be most closely related to AD severity. (C) 2014 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.