Article

Quantitative EEG Markers Relate to Alzheimer’s Disease Severity in the Prospective Dementia Registry Austria (PRODEM)

July 2014
Clinical Neurophysiology

DOI:10.1016/j.clinph.2014.07.005

Authors:

Heinrich Garn

AIT Austrian Institute of Technology

Markus Waser

Staburo GmbH Munich Germany

Manfred Deistler

TU Wien

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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.

A Pilot Study on Visually Stimulated Cognitive Tasks for EEG-Based Dementia Recognition

Article

Full-text available

Oct 2021

In the status quo, dementia is yet to be cured. Precise diagnosis prior to the onset of the symptoms can prevent the rapid progression of the emerging cognitive impairment. Recent progress has shown that Electroencephalography (EEG) is the promising and cost-effective test to facilitate the detection of neurocognitive disorders. However, most of the existing works have been using only resting-state EEG. The efficiencies of EEG signals from various cognitive tasks, for dementia classification, have yet to be thoroughly investigated. In this study, we designed four cognitive tasks that engage different cognitive performances: attention, working memory, and executive function. We investigated these tasks by using statistical analysis on both time and frequency domains of EEG signals from three classes of human subjects: Dementia (DEM), Mild Cognitive Impairment (MCI), and Normal Control (NC). We also further evaluated the classification performances of two features extraction methods: Principal Component Analysis (PCA) and Filter Bank Common Spatial Pattern (FBCSP). We found that the working memory related tasks yielded good performances for dementia recognition in both cases using PCA and FBCSP. Moreover, FBCSP with features combination from four tasks revealed the best sensitivity of 0.87 and the specificity of 0.80. To our best knowledge, this is the first work that concurrently investigated several cognitive tasks for dementia recognition using both statistical analysis and classification scores. Our results yielded essential information to design and aid in conducting further experimental tasks to early diagnose dementia patients.

Characterising Alzheimer's Disease With EEG-Based Energy Landscape Analysis

Article

Full-text available

Aug 2021

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases, with around 50 million patients worldwide. Accessible and non-invasive methods of diagnosing and characterising AD are therefore urgently required. Electroencephalography (EEG) fulfils these criteria and is often used when studying AD. Several features derived from EEG were shown to predict AD with high accuracy, e.g. signal complexity and synchronisation. However, the dynamics of how the brain transitions between stable states have not been properly studied in the case of AD and EEG data. Energy landscape analysis is a method that can be used to quantify these dynamics. This work presents the first application of this method to both AD and EEG. Energy landscape assigns energy value to each possible state, i.e. pattern of activations across brain regions. The energy is inversely proportional to the probability of occurrence. By studying the features of energy landscapes of 20 AD patients and 20 healthy age-matched counterparts, significant differences were found. The dynamics of AD patients EEG were shown to be more constrained - with more local minima, less variation in basin size, and smaller basins. We show that energy landscapes can predict AD with high accuracy, performing significantly better than baseline models.

Characterising Alzheimer's Disease with EEG-based Energy Landscape Analysis

Preprint

Full-text available

Feb 2021

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases, with around 50 million patients worldwide. Accessible and non-invasive methods of diagnosing and characterising AD are therefore urgently required. Electroencephalography (EEG) fulfils these criteria and is often used when studying AD. Several features derived from EEG were shown to predict AD with high accuracy, e.g. signal complexity and synchronisation. However, the dynamics of how the brain transitions between stable states have not been properly studied in the case of AD and EEG data. Energy landscape analysis is a method that can be used to quantify these dynamics. This work presents the first application of this method to both AD and EEG. Energy landscape assigns energy value to each possible state, i.e. pattern of activations across brain regions. The energy is inversely proportional to the probability of occurrence. By studying the features of energy landscapes of 20 AD patients and 20 healthy age-matched counterparts, significant differences were found. The dynamics of AD patients' brain networks were shown to be more constrained - with more local minima, less variation in basin size, and smaller basins. We show that energy landscapes can predict AD with high accuracy, performing significantly better than baseline models.

Resting state EEG biomarkers of cognitive decline associated with Alzheimer's disease and mild cognitive impairment

Article

Full-text available

Feb 2021
PLOS ONE

In this paper, we explore the utility of resting-state EEG measures as potential biomarkers for the detection and assessment of cognitive decline in mild cognitive impairment (MCI) and Alzheimer's disease (AD). Neurophysiological biomarkers of AD derived from EEG and FDG-PET, once characterized and validated, would expand the set of existing diagnostic molecular biomarkers of AD pathology with associated biomarkers of disease progression and neural dysfunction. Since symptoms of AD often begin to appear later in life, successful identification of EEG-based biomarkers must account for age-related neurophysiological changes that occur even in healthy individuals. To this end, we collected EEG data from individuals with AD (n = 26), MCI (n = 53), and cognitively normal healthy controls stratified by age into three groups: 18-40 (n = 129), 40-60 (n = 62) and 60-90 (= 55) years old. For each participant, we computed power spectral density at each channel and spectral coherence between pairs of channels. Compared to age matched controls, in the AD group, we found increases in both spectral power and coherence at the slower frequencies (Delta, Theta). A smaller but significant increase in power of slow frequencies was observed for the MCI group, localized to temporal areas. These effects on slow frequency spectral power opposed that of normal aging observed by a decrease in the power of slow frequencies in our control groups. The AD group showed a significant decrease in the spectral power and coherence in the Alpha band consistent with the same effect in normal aging. However, the MCI group did not show any significant change in the Alpha band. Overall, Theta to Alpha ratio (TAR) provided the largest and most significant differences between the AD group and controls. However, differences in the MCI group remained small and localized. We proposed a novel method to quantify these small differences between Theta and Alpha bands' power using empirically derived distributions of spectral power across the time domain as opposed to averaging power across time. We defined Power Distribution Distance Measure (PDDM) as a distance measure between probability distribution functions (pdf) of Theta and Alpha power. Compared to average TAR, using PDDF enhanced the statistical significance, the effect size, and the spatial distribution of significant effects in the MCI group. We designed classifiers for differentiating individual MCI and AD participants from age-matched controls. The classification performance measured by the area under ROC curve after cross-validation were AUC = 0.85 and AUC = 0.6, for AD and MCI classifiers, respectively. Posterior probability of AD, TAR, and the proposed PDDM measure were all significantly correlated with MMSE score and neuropsychological tests in the AD group.

Machine Learning Algorithms and Statistical Approaches for Alzheimer’s Disease Analysis Based on Resting-State EEG Recordings: A Systematic Review

Article

Dec 2020

Alzheimer’s Disease (AD) is a neurodegenerative disorder and the most common type of dementia with a great prevalence in western countries. The diagnosis of AD and its progression is performed through a variety of clinical procedures including neuropsychological and physical examination, Electroencephalographic (EEG) recording, brain imaging and blood analysis. During the last decades, analysis of the electrophysiological dynamics in AD patients has gained great research interest, as an alternative and cost-effective approach. This paper summarizes recent publications focusing on (a) AD detection and (b) the correlation of quantitative EEG features with AD progression, as it is estimated by Mini Mental State Examination (MMSE) score. A total of 49 experimental studies published from 2009 until 2020, which apply machine learning algorithms on resting state EEG recordings from AD patients, are reviewed. Results of each experimental study are presented and compared. The majority of the studies focus on AD detection incorporating Support Vector Machines, while deep learning techniques have not yet been applied on large EEG datasets. Promising conclusions for future studies are presented.

Longitudinal changes in resting‐state EEG are correlated with cognitive decline in Alzheimer’s disease and related dementia: Biomarkers (non‐neuroimaging) / Longitudinal change over time

Article

Dec 2020

Background Resting‐state EEG measures such as spectral power in Theta (3‐7 Hz) and Alpha (8‐13 Hz) bands have been previously linked to cognitive decline in Alzheimer’s disease (AD) and other dementias. However, routine use of EEG in clinical settings has not been widely practiced. In this ongoing longitudinal study, we demonstrate how changes in EEG measures after 1‐year are correlated with actual changes in cognitive decline as measured by MMSE score of cognitive assessment. Method Five minutes of eyes‐closed resting‐state EEG were recorded during both an initial and one‐year follow‐up visits from Healthy Controls (n=67), individuals with Mild Cognitive Impairment (n=16), Alzheimer’s Disease (n=4), Dementia with Lewy‐body (n=1), Parkinson’s disease dementia (n=1), and subjects reporting memory problems without a clear diagnosis (n=7). A generalized‐linear‐model (GLM) was used to regress the relationship between predictors (MMSE and age at initial visit as well as longitudinal changes in 9 EEG measures that were selected a priori ) and the outcome variable (actual changes in MMSE score after 1‐year). EEG measures included Theta‐Alpha Ratio (TAR) and relative power at 5 Hz (theta 5 ) at temporal channels as well as relative power at 10Hz at POz channel. Result MMSE scores fell in 32% of participants and improved in 8% (Figure 1). A GLM using logarithmic link‐function with 11 predictors and 21 terms (EEG predictors and their interaction with age) was fitted to the data (F=4.24, p=2.44x10 ‐6 ). The most significant (p<0.01) predictors were age, theta 5 , TAR and their interaction with age, all at channel T6. Predicted and actual decline in MMSE were highly correlated (r=0.73, p=10 ‐5 ) (Figure 2). A simpler model with 12 terms (no interaction between predictors) resulted in (F=4.34, p=3.98x10 ‐5 ). Conclusion Predictive power of EEG in modeling cognitive decline was demonstrated in a cohort of patients with known or possible dementia diagnosis as well as controls. These results support the utility of EEG as a biomarker of cognitive decline. These markers could supplement other neuropathological biomarkers (such as beta‐amyloid and tau), particularly because changes in cognition may not necessarily happen at the same rate as pathological changes.

Systematic Review on Resting-State EEG for Alzheimer’s Disease Diagnosis and Progression Assessment

Article

Full-text available

Oct 2018
DIS MARKERS

Alzheimer’s disease (AD) is a neurodegenerative disorder that accounts for nearly 70% of the more than 46 million dementia cases estimated worldwide. Although there is no cure for AD, early diagnosis and an accurate characterization of the disease progression can improve the quality of life of AD patients and their caregivers. Currently, AD diagnosis is carried out using standardized mental status examinations, which are commonly assisted by expensive neuroimaging scans and invasive laboratory tests, thus rendering the diagnosis time consuming and costly. Notwithstanding, over the last decade, electroencephalography (EEG) has emerged as a noninvasive alternative technique for the study of AD, competing with more expensive neuroimaging tools, such as MRI and PET. This paper reports on the results of a systematic review on the utilization of resting-state EEG signals for AD diagnosis and progression assessment. Recent journal articles obtained from four major bibliographic databases were analyzed. A total of 112 journal articles published from January 2010 to February 2018 were meticulously reviewed, and relevant aspects of these papers were compared across articles to provide a general overview of the research on this noninvasive AD diagnosis technique. Finally, recommendations for future studies with resting-state EEG were presented to improve and facilitate the knowledge transfer among research groups.

Quantifying synchrony patterns in the EEG of Alzheimer’s patients with linear and non-linear connectivity markers

Article

Full-text available

Mar 2016
J NEURAL TRANSM

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.

Concurrent behavioral and electrophysiological longitudinal recordings for in vivo assessment of aging

Article

Full-text available

Jan 2023

Electrophysiological and behavioral alterations, including sleep and cognitive impairments, are critical components of age-related decline and neurodegenerative diseases. In preclinical investigation, many refined techniques are employed to probe these phenotypes, but they are often conducted separately. Herein, we provide a protocol for one-time surgical implantation of EMG wires in the nuchal muscle and a skull-surface EEG headcap in mice, capable of 9-to-12-month recording longevity. All data acquisitions are wireless, making them compatible with simultaneous EEG recording coupled to multiple behavioral tasks, as we demonstrate with locomotion/sleep staging during home-cage video assessments, cognitive testing in the Barnes maze, and sleep disruption. Time-course EEG and EMG data can be accurately mapped to the behavioral phenotype and synchronized with neuronal frequencies for movement and the location to target in the Barnes maze. We discuss critical steps for optimizing headcap surgery and alternative approaches, including increasing the number of EEG channels or utilizing depth electrodes with the system. Combining electrophysiological and behavioral measurements in preclinical models of aging and neurodegeneration has great potential for improving mechanistic and therapeutic assessments and determining early markers of brain disorders.

A multi-scale model explains oscillatory slowing and neuronal hyperactivity in Alzheimer’s disease

Article

Full-text available

Jan 2023
J R SOC INTERFACE

Alzheimer’s disease is the most common cause of dementia and is linked to the spreading of pathological amyloid- β and tau proteins throughout the brain. Recent studies have highlighted stark differences in how amyloid- β and tau affect neurons at the cellular scale. On a larger scale, Alzheimer’s patients are observed to undergo a period of early-stage neuronal hyperactivation followed by neurodegeneration and frequency slowing of neuronal oscillations. Herein, we model the spreading of both amyloid- β and tau across a human connectome and investigate how the neuronal dynamics are affected by disease progression. By including the effects of both amyloid- β and tau pathology, we find that our model explains AD-related frequency slowing, early-stage hyperactivation and late-stage hypoactivation. By testing different hypotheses, we show that hyperactivation and frequency slowing are not due to the topological interactions between different regions but are mostly the result of local neurotoxicity induced by amyloid- β and tau protein.

The Impact of Disease Registries on Advancing Knowledge and Understanding of Dementia Globally

Article

Full-text available

Jan 2022

To help address the increasing challenges related to the provision of dementia care, dementia registries have emerged around the world as important tools to gain insights and a better understanding of the disease process. Dementia registries provide a valuable source of standardized data collected from a large number of patients. This review explores the published research relating to different dementia registries around the world and discusses how these registries have improved our knowledge and understanding of the incidence, prevalence, risk factors, mortality, diagnosis, and management of dementia. A number of the best-known dementia registries with high research output including SveDem, NACC, ReDeGi, CREDOS and PRODEM were selected to study the publication output based on their data, investigate the key findings of these registry-based studies. Registries data contributed to understanding many aspects of the disease including disease prevalence in specific areas, patient characteristics and how they differ in populations, mortality risks, as well as the disease risk factors. Registries data impacted the quality of patients’ lives through determining the best treatment strategy for a patient based on previous patient outcomes. In conclusion, registries have significantly advanced scientific knowledge and understanding of dementia and impacted policy, clinical practice care delivery.

Neurophysiological Hallmarks of Neurodegenerative Cognitive Decline: The Study of Brain Connectivity as A Biomarker of Early Dementia

Article

Full-text available

Apr 2020

Neurodegenerative processes of various types of dementia start years before symptoms, but the presence of a “neural reserve”, which continuously feeds and supports neuroplastic mechanisms, helps the aging brain to preserve most of its functions within the “normality” frame. Mild cognitive impairment (MCI) is an intermediate stage between dementia and normal brain aging. About 50% of MCI subjects are already in a stage that is prodromal-to-dementia and during the following 3 to 5 years will develop clinically evident symptoms, while the other 50% remains at MCI or returns to normal. If the risk factors favoring degenerative mechanisms are modified during early stages (i.e., in the prodromal), the degenerative process and the loss of abilities in daily living activities will be delayed. It is therefore extremely important to have biomarkers able to identify—in association with neuropsychological tests—prodromal-to-dementia MCI subjects as early as possible. MCI is a large (i.e., several million in EU) and substantially healthy population; therefore, biomarkers should be financially affordable, largely available and non-invasive, but still accurate in their diagnostic prediction. Neurodegeneration initially affects synaptic transmission and brain connectivity; methods exploring them would represent a 1st line screening. Neurophysiological techniques able to evaluate mechanisms of synaptic function and brain connectivity are attracting general interest and are described here. Results are quite encouraging and suggest that by the application of artificial intelligence (i.e., learning-machine), neurophysiological techniques represent valid biomarkers for screening campaigns of the MCI population.

Early diagnosis of alzheimer’s disease: the role of biomarkers including advanced EEG signals analysis. An I.F.C.N.-sponsored panel of Experts

Article

Mar 2020
CLIN NEUROPHYSIOL

Alzheimer’s disease (AD) is the most common neurodegenerative disease among the elderly with a progressive decline in cognitive function significantly affecting quality of life. Both the prevalence and emotional and financial burdens of AD on patients, their families, and society are predicted to grow significantly in the near future, due to a prolongation of the lifespan. Several lines of evidence suggest that modifications of risk-enhancing life styles and initiation of pharmacological and non-pharmacological treatments in the early stage of disease, although not able to modify its course, helps to maintain personal autonomy in daily activities and significantly reduces the total costs of disease management. Moreover, many clinical trials with potentially disease-modifying drugs are devoted to prodromal stages of AD. Thus, the identification of markers of conversion from prodromal form to clinically AD may be crucial for developing strategies of early interventions. The current available markers, including volumetric magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebral spinal fluid (CSF) analysis are expensive, poorly available in community health facilities, and relatively invasive. Taking into account its low cost, widespread availability and non-invasiveness, electroencephalography (EEG) would represent a candidate for tracking the prodromal phases of cognitive decline in routine clinical settings eventually in combination with other markers. In this scenario, the present paper provides an overview of epidemiology, genetic risk factors, neuropsychological, fluid and neuroimaging biomarkers in AD and describes the potential role of EEG in AD investigation, trying in particular to point out whether advanced analysis of EEG rhythms exploring brain function has sufficient specificity/sensitivity/accuracy for the early diagnosis of AD.

EEG Window Length Evaluation for the Detection of Alzheimer’s Disease over Different Brain Regions

Article

Full-text available

Apr 2019
BSRCCS

Alzheimer’s Disease (AD) is a neurogenerative disorder and the most common type of dementia with a rapidly increasing world prevalence. In this paper, the ability of several statistical and spectral features to detect AD from electroencephalographic (EEG) recordings is evaluated. For this purpose, clinical EEG recordings from 14 patients with AD (8 with mild AD and 6 with moderate AD) and 10 healthy, age-matched individuals are analyzed. The EEG signals are initially segmented in nonoverlapping epochs of different lengths ranging from 5 s to 12 s. Then, a group of statistical and spectral features calculated for each EEG rhythm (δ, θ, α, β, and γ) are extracted, forming the feature vector that trained and tested a Random Forests classifier. Six classification problems are addressed, including the discrimination from whole-brain dynamics and separately from specific brain regions in order to highlight any alterations of the cortical regions. The results indicated a high accuracy ranging from 88.79% to 96.78% for whole-brain classification. Also, the classification accuracy was higher at the posterior and central regions than at the frontal area and the right side of temporal lobe for all classification problems.

Bayesian deep neural networks for low-cost neurophysiological markers of Alzheimer's disease severity

Preprint

Full-text available

Dec 2018

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.

Quantitative EEG Markers of Entropy and Auto Mutual Information in Relation to MMSE Scores of Probable Alzheimer’s Disease Patients

Article

Full-text available

Mar 2017
Entropy

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. View Full-Text

Predicting rapid cognitive decline in Alzheimer's disease patients using quantitative EEG markers and neuropsychological test scores

Conference Paper

Aug 2016

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.

The application of information theory for the research of aging and aging-related diseases

Article

Mar 2016

This article reviews the application of information-theoretical analysis, employing measures of entropy and mutual information, for the study of aging and aging-related diseases. The research of aging and aging-related diseases is particularly suitable for the application of information theory methods, as aging processes and related diseases are multi-parametric, with continuous parameters coexisting alongside discrete parameters, and with the relations between the parameters being as a rule non-linear. Information theory provides unique analytical capabilities for the solution of such problems, with unique advantages over common linear biostatistics. Among the age-related diseases, information theory has been used in the study of neurodegenerative diseases (particularly using EEG time series for diagnosis and prediction), cancer (particularly for establishing individual and combined cancer biomarkers), diabetes (mainly utilizing mutual information to characterize the diseased and aging states), and heart disease (mainly for the analysis of heart rate variability). Few works have employed information theory for the analysis of general aging processes and frailty, as underlying determinants and possible early preclinical diagnostic measures for aging-related diseases. Generally, the use of information-theoretical analysis permits not only establishing the (non-linear) correlations between diagnostic or therapeutic parameters of interest, but may also provide a theoretical insight into the nature of aging and related diseases by establishing the measures of variability, adaptation, regulation or homeostasis, within a system of interest. It may be hoped that the increased use of such measures in research may considerably increase diagnostic and therapeutic capabilities and the fundamental theoretical mathematical understanding of aging and disease.

Neurophysiological Assessment of Alzheimer's Disease Individuals by a Single Electroencephalographic Marker

Article

Full-text available

Oct 2015
J ALZHEIMERS DIS

Here we presented a single electroencephalographic (EEG) marker for a neurophysiological assessment of Alzheimer's disease (AD) patients already diagnosed by current guidelines. The ability of the EEG marker to classify 127 AD individuals and 121 matched cognitively intact normal elderly (Nold) individuals was tested. Furthermore, its relationship to AD patients' cognitive status and structural brain integrity was examined. Low-resolution brain electromagnetic tomography (LORETA) freeware estimated cortical sources of resting state eyes-closed EEG rhythms. The EEG marker was defined as the ratio between the activity of parieto-occipital cortical sources of delta (2-4 Hz) and low-frequency alpha (8-10.5 Hz) rhythms. Results showed 77.2% of sensitivity in the recognition of the AD individuals; 65% of specificity in the recognition of the Nold individuals; and 0.75 of area under the receiver-operating characteristic curve. Compared to the AD subgroup with the EEG maker within one standard deviation of the Nold mean (EEG-), the AD subgroup with EEG+ showed lower global cognitive status, as revealed by Mini-Mental State Evaluation score, and more abnormal values of white-matter and cerebrospinal fluid normalized volumes, as revealed by structural magnetic resonance imaging. We posit that cognitive and functional status being equal, AD patients with EEG+ should receive special clinical attention due to a neurophysiological "frailty". EEG+ label can be also used in clinical trials (i) to form homogeneous groups of AD patients diagnosed by current guidelines and (ii) as end-point to evaluate intervention effects.

Analysis of EEG Signal using nonextensive statistics

Article

Full-text available

May 2023
J ENG TECHNOL MANAGE

Pragati Patel

Electroencephalogram (EEG) signal is the most effective, quick, and abundant source of information in understanding the brain related phenomenon. New avenues for EEG-based research in non-medical streams can also be seen with the growing number of qualitative and affordable wearable EEG headsets. But it is extremely hard to assess the information from EEG signal. However, information-theoretical approaches have appeared as a potentially beneficial means to gauge variations in the EEG datasets. This article discusses one such approach: the 'measure of Tsallis entropy (TsEn)' to explore and investigate the available natural data. This study set out to critically review the renowned research papers on Tsallis entropy-based EEG signal processing to understand the trends in EEG signal processing research. It attempts to provide practitioners and researchers with insights and future directions for applicability of Tsallis entropy for EEG signal processing and with an emphasis on the suitability of EEG research for clinical studies. It reviews about 35 published papers dividing into medical and non-medical domains and discusses the crucial role of Tsallis parameter 'q' in studying complex EEG systems. The result shows Tsallis's non-extensive initiatives seem to be more discriminatory than its Shannon counterpart and all other entropy variants and hence, can preferably be used to study the brain. The paper also concludes that Tsallis entropy offers a comprehensive test of any theory and it proves the efficacy of EEG research in clinical detection and therefore is highly significant in biomedical signal processing.

Proteostasis failure exacerbates neuronal circuit dysfunction and sleep impairments in Alzheimer's disease

Article

Full-text available

Apr 2023
MOL NEURODEGENER

Failed proteostasis is a well-documented feature of Alzheimer’s disease, particularly, reduced protein degradation and clearance. However, the contribution of failed proteostasis to neuronal circuit dysfunction is an emerging concept in neurodegenerative research and will prove critical in understanding cognitive decline. Our objective is to convey Alzheimer’s disease progression with the growing evidence for a bidirectional relationship of sleep disruption and proteostasis failure. Proteostasis dysfunction and tauopathy in Alzheimer’s disease disrupts neurons that regulate the sleep–wake cycle, which presents behavior as impaired slow wave and rapid eye movement sleep patterns. Subsequent sleep loss further impairs protein clearance. Sleep loss is a defined feature seen early in many neurodegenerative disorders and contributes to memory impairments in Alzheimer’s disease. Canonical pathological hallmarks, β-amyloid, and tau, directly disrupt sleep, and neurodegeneration of locus coeruleus, hippocampal and hypothalamic neurons from tau proteinopathy causes disruption of the neuronal circuitry of sleep. Acting in a positive-feedback-loop, sleep loss and circadian rhythm disruption then increase spread of β-amyloid and tau, through impairments of proteasome, autophagy, unfolded protein response and glymphatic clearance. This phenomenon extends beyond β-amyloid and tau, with interactions of sleep impairment with the homeostasis of TDP-43, α-synuclein, FUS, and huntingtin proteins, implicating sleep loss as an important consideration in an array of neurodegenerative diseases and in cases of mixed neuropathology. Critically, the dynamics of this interaction in the neurodegenerative environment are not fully elucidated and are deserving of further discussion and research. Finally, we propose sleep-enhancing therapeutics as potential interventions for promoting healthy proteostasis, including β-amyloid and tau clearance, mechanistically linking these processes. With further clinical and preclinical research, we propose this dynamic interaction as a diagnostic and therapeutic framework, informing precise single- and combinatorial-treatments for Alzheimer’s disease and other brain disorders. Graphical Abstract

Electroencephalography Classification of Healthy, Mild Cognitive Impairment and Probable Alzheimer’s Disease Through Linear and Non-Linear Biomarkers

Article

Sep 2022

Alzheimer’s disease is one of the main challenges of modern medicine since no cure has been found yet, the scientific community still does not fully understand the reasoning behind it, and any interventions found can delay the progress for only a limited amount of time. Over the years, research has shifted from attempts for curing the disease to efforts towards understanding the mechanisms behind it as well as finding tools that will speed up diagnosis many years before its clinical manifestations, when the brain deterioration begins. One of the many promising tools towards this direction is electroencephalography. Electroencephalography employs a variety of different measures that can be used as biomarkers for early diagnosis and differentiation of Alzheimer’s disease from other neurodegenerative disorders. Literature has produced a number of methods that have established reliable correlation between electroencephalography signals and structural abnormalities in Alzheimer’s disease. To that end, the present work proposes the combination of Tsallis Entropy and Higuchi Fractal Dimension within a common classification framework using machine learning techniques for classification among healthy, Mild Cognitive Impairment, and probable Alzheimer’s disease. The proposed methodology is applied on 75 subjects with different feature utilisation scenarios, reaching to an accuracy of 98.03% when classifying a signal epoch, following a 10-fold cross validation, as compared with other similar studies. Nevertheless, in a leave-one-out scenario with the same approach, the average accuracy drops significantly, suggesting that this method could complement other diagnosis approaches but cannot be used on each own.

M/EEG Dynamics Underlying Reserve, Resilience, and Maintenance in Aging: A Review

Article

Full-text available

May 2022

Cognitive reserve and resilience refer to the set of processes allowing the preservation of cognitive performance in the presence of structural and functional brain changes. Investigations of these concepts have provided unique insights into the heterogeneity of cognitive and brain changes associated with aging. Previous work mainly relied on methods benefiting from a high spatial precision but a low temporal resolution, and thus the temporal brain dynamics underlying these concepts remains poorly known. Moreover, while spontaneous fluctuations of neural activity have long been considered as noise, recent work highlights its critical contribution to brain functions. In this study, we synthesized the current state of knowledge from magnetoencephalography (MEG) and electroencephalography (EEG) studies that investigated the contribution of maintenance of neural synchrony, and variability of brain dynamics, to cognitive changes associated with healthy aging and the progression of neurodegenerative disease (such as Alzheimer's disease). The reviewed findings highlight that compensations could be associated with increased synchrony of higher (>10 Hz) frequency bands. Maintenance of young-like synchrony patterns was also observed in healthy older individuals. Both maintenance and compensation appear to be highly related to preserved structural integrity (brain reserve). However, increased synchrony was also found to be deleterious in some cases and reflects neurodegenerative processes. These results provide major elements on the stability or variability of functional networks as well as maintenance of neural synchrony over time, and their association with individual cognitive changes with aging. These findings could provide new and interesting considerations about cognitive reserve, maintenance, and resilience of brain functions and cognition.

Complexity Measures for Quantifying Changes in Electroencephalogram in Alzheimers Disease

Preprint

Full-text available

May 2022

Alzheimers disease (AD) is a progressive disorder that affects cognitive brain functions and starts many years before its clinical manifestations. A biomarker that provides a quantitative measure of changes in the brain due to AD in the early stages would be useful for early diagnosis of AD, but this would involve dealing with large numbers of people because up to 50% of dementia sufferers do not receive a formal diagnosis. Thus, there is a need for accurate, low-cost, and easy-to-use biomarkers that could be used to detect AD in its early stages. Potentially, electroencephalogram (EEG) based biomarkers can play a vital role in early diagnosis of AD as they can fulfill these needs. This is a cross-sectional study that aims to demonstrate the usefulness of EEG complexity measures in early AD diagnosis. We have focused on the three complexity methods which have shown the greatest promise in the detection of AD, Tsallis entropy (TsEn), Higuchi Fractal Dimension (HFD), and Lempel-Ziv complexity (LZC) methods. Unlike previous approaches, in this study, the complexity measures are derived from EEG frequency bands (instead of the entire EEG) as EEG activities have significant association with AD and this has led to enhanced performance. The results show that AD patients have significantly lower TsEn, HFD, and LZC values for specific EEG frequency bands and for specific EEG channels and that this information can be used to detect AD with a sensitivity and specificity of more than 90%.

Global EEG coherence as a marker for cognition in older adults at risk for dementia

Article

Full-text available

Dec 2019

Quantitative electroencephalography (EEG) provides useful information about neurophysiological health of the aging brain. Current studies investigating EEG coherence and power for specific brain areas and frequency bands have yielded inconsistent results. This study assessed EEG coherence and power indices at rest measured over the whole skull and for a wide frequency range as global EEG markers for cognition in a sample at risk for dementia. Since global markers are more reliable and less error-prone than region- and frequency-specific indices they might help to overcome previous inconsistencies. Global EEG coherence (1-30 Hz) and an EEG slowing score were assessed. The EEG slowing score was calculated by low-frequency power (1-8 Hz) divided by high-frequency power (9-30 Hz). In addition, the prognostic value of the two EEG indices for cognition and cognitive decline was assessed in a 5-year follow-up pilot study. Baseline global coherence correlated positively with cognition at baseline, but not with cognitive decline or with cognition at the 5-year follow-up. The EEG slowing ratio showed no significant association, neither with cognition at baseline or follow-up, nor with cognitive decline over a period of 5 years. The results indicate that the resting state global EEG coherence might be a useful and easy to assess electrophysiological correlate for neurocognitive health in older adults at risk for dementia. Because of the small statistical power for the follow-up analyses, the prognostic value of global coherence could not be determined in the present study. Future studies should assess its prognostic value with larger sample sizes.

Electroencephalogram Based Biomarkers for Detection of Alzheimer ’s Disease

Chapter

Full-text available

Nov 2019

Alzheimer’s disease (AD) is an age-related progressive, neurodegenerative disorder which is characterized by loss of memory and cognitive decline. It is the main cause of disability among older people. The rapid increase in the number of people living with AD and other forms of dementia due to the ageing population represents a major challenge to health and social care systems worldwide. However, many dementia sufferers do not receive a formal diagnosis. Degeneration of brain cells due to AD starts many years before the clinical manifestations become clear. Early diagnosis of AD will contribute to the development of effective treatments that could slow, stop or prevent significant cognitive decline. Therefore, the early diagnosis of AD could also be useful for identifying dementia sufferers who have not received a formal early diagnosis and this may provide an opportunity for them to access appropriate health care services. A biomarker that can measure degeneration of brain cells due to AD at an early stage would be useful for its early diagnosis. Therefore, simple, non-invasive, low-cost, and reliable biomarkers for early diagnosis may be useful in early diagnosis of AD. Potentially, the electroencephalogram (EEG) can play a valuable role in the early diagnosis of AD. EEG is non-invasive, low-cost, has a high temporal resolution, and provides valuable information about brain dynamics in AD. Thus, EEG-based biomarkers may be used as a first line decision-support tool in AD diagnosis and could complement other AD biomarkers. AD causes changes in the features of the EEG and EEG analysis may provide valuable information about brain dynamics due to AD. These changes in the EEG can be quantified as a biomarker of AD. This chapter describes research into the development of EEG biomarkers that detect AD based on analysis of changes in the EEG. These changes can be quantified as a biomarker of AD. In this chapter, we identified characteristic EEG features that have a significant association with AD. The most promising EEG features were then used to develop EEG biomarkers that can exhibit high diagnostic performance.

Neuroimaging markers of global cognition in early Alzheimer's disease: A magnetic resonance imaging–electroencephalography study

Article

Full-text available

Dec 2018

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.

Associations of event-related brain potentials and Alzheimer’s disease severity: A longitudinal study

Article

Full-text available

Dec 2018

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).

Chronic pulmonary exposure to traffic-related fine particulate matter causes brain impairment in adult rats

Article

Full-text available

Nov 2018

Background: Effects of air pollution on neurotoxicity and behavioral alterations have been reported. The objective of this study was to investigate the pathophysiology caused by particulate matter (PM) in the brain. We examined the effects of traffic-related particulate matter with an aerodynamic diameter of < 1 μm (PM1), high-efficiency particulate air (HEPA)-filtered air, and clean air on the brain structure, behavioral changes, brainwaves, and bioreactivity of the brain (cortex, cerebellum, and hippocampus), olfactory bulb, and serum after 3 and 6 months of whole-body exposure in 6-month-old Sprague Dawley rats. Results: The rats were exposed to 16.3 ± 8.2 (4.7~ 68.8) μg/m3 of PM1 during the study period. An MRI analysis showed that whole-brain and hippocampal volumes increased with 3 and 6 months of PM1 exposure. A short-term memory deficiency occurred with 3 months of exposure to PM1 as determined by a novel object recognition (NOR) task, but there were no significant changes in motor functions. There were no changes in frequency bands or multiscale entropy of brainwaves. Exposure to 3 months of PM1 increased 8-isoporstance in the cortex, cerebellum, and hippocampus as well as hippocampal inflammation (interleukin (IL)-6), but not in the olfactory bulb. Systemic CCL11 (at 3 and 6 months) and IL-4 (at 6 months) increased after PM1 exposure. Light chain 3 (LC3) expression increased in the hippocampus after 6 months of exposure. Spongiosis and neuronal shrinkage were observed in the cortex, cerebellum, and hippocampus (neuronal shrinkage) after exposure to air pollution. Additionally, microabscesses were observed in the cortex after 6 months of PM1 exposure. Conclusions: Our study first observed cerebral edema and brain impairment in adult rats after chronic exposure to traffic-related air pollution.

A Pilot Study Investigating a Novel Non-Linear Measure of Eyes Open versus Eyes Closed EEG Synchronization in People with Alzheimer’s Disease and Healthy Controls

Article

Full-text available

Jul 2018
BSRCCS

Background: The incidence of Alzheimer disease (AD) is increasing with the ageing population. The development of low cost non-invasive diagnostic aids for AD is a research priority. This pilot study investigated whether an approach based on a novel dynamic quantitative parametric EEG method could detect abnormalities in people with AD. Methods: 20 patients with probable AD, 20 matched healthy controls (HC) and 4 patients with probable fronto temporal dementia (FTD) were included. All had detailed neuropsychology along with structural, resting state fMRI and EEG. EEG data were analyzed using the Error Reduction Ratio-causality (ERR-causality) test that can capture both linear and nonlinear interactions between different EEG recording areas. The 95% confidence intervals of EEG levels of bi-centroparietal synchronization were estimated for eyes open (EO) and eyes closed (EC) states. Results: In the EC state, AD patients and HC had very similar levels of bi-centro parietal synchronization; but in the EO resting state, patients with AD had significantly higher levels of synchronization (AD = 0.44; interquartile range (IQR) 0.41 vs. HC = 0.15; IQR 0.17, p < 0.0001). The EO/EC synchronization ratio, a measure of the dynamic changes between the two states, also showed significant differences between these two groups (AD ratio 0.78 versus HC ratio 0.37 p < 0.0001). EO synchronization was also significantly different between AD and FTD (FTD = 0.075; IQR 0.03, p < 0.0001). However, the EO/EC ratio was not informative in the FTD group due to very low levels of synchronization in both states (EO and EC). Conclusion: In this pilot work, resting state quantitative EEG shows significant differences between healthy controls and patients with AD. This approach has the potential to develop into a useful non-invasive and economical diagnostic aid in AD.

Complexity Measures for Quantifying Changes in Electroencephalogram in Alzheimer’s Disease

Article

Full-text available

Mar 2018
COMPLEXITY

Alzheimer’s disease (AD) is a progressive disorder that affects cognitive brain functions and starts many years before its clinical manifestations. A biomarker that provides a quantitative measure of changes in the brain due to AD in the early stages would be useful for early diagnosis of AD, but this would involve dealing with large numbers of people because up to 50% of dementia sufferers do not receive formal diagnosis. Thus, there is a need for accurate, low-cost, and easy to use biomarkers that could be used to detect AD in its early stages. Potentially, electroencephalogram (EEG) based biomarkers can play a vital role in early diagnosis of AD as they can fulfill these needs. This is a cross-sectional study that aims to demonstrate the usefulness of EEG complexity measures in early AD diagnosis.We have focused on the three complexity methods which have shown the greatest promise in the detection of AD, Tsallis entropy (TsEn), Higuchi Fractal Dimension (HFD), and Lempel-Ziv complexity (LZC) methods. Unlike previous approaches, in this study, the complexity measures are derived from EEG frequency bands (instead of the entire EEG) as EEG activities have significant association with AD and this has led to enhanced performance.The results show that AD patients have significantly lower TsEn, HFD, and LZC values for specific EEG frequency bands and for specific EEG channels and that this information can be used to detect AD with a sensitivity and specificity of more than 90%.

Comparison of Medical and Consumer Wireless EEG Systems for Use in Clinical Trials

Article

Full-text available

Aug 2017
FRONT HUM NEUROSCI

Objectives: To compare quantitative EEG signal and test-retest reliability of medical grade and consumer EEG systems. Methods: Resting state EEG was acquired by two medical grade (B-Alert, Enobio) and two consumer (Muse, Mindwave) EEG systems in five healthy subjects during two study visits. EEG patterns, power spectral densities (PSDs) and test/retest reliability in eyes closed and eyes open conditions were compared across the four systems, focusing on Fp1, the only common electrode. Fp1 PSDs were obtained using Welch's modified periodogram method and averaged for the five subjects for each visit. The test/retest results were calculated as a ratio of Visit 1/Visit 2 Fp1 channel PSD at each 1 s epoch. Results: B-Alert, Enobio, and Mindwave Fp1 power spectra were similar. Muse showed a broadband increase in power spectra and the highest relative variation across test-retest acquisitions. Consumer systems were more prone to artifact due to eye blinks and muscle movement in the frontal region. Conclusions: EEG data can be successfully collected from all four systems tested. Although there was slightly more time required for application, medical systems offer clear advantages in data quality, reliability, and depth of analysis over the consumer systems. Significance: This evaluation provides evidence for informed selection of EEG systemsappropriate for clinical trials.

Differential diagnosis between patients with probable Alzheimer’s disease, Parkinson’s disease dementia, or dementia with Lewy bodies and frontotemporal dementia, behavioral variant, using quantitative electroencephalographic features

Article

Full-text available

May 2017
J NEURAL TRANSM

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.

Symbolic Entropy of the Amplitude rather than the Instantaneous Frequency of EEG Varies in Dementia

Article

Full-text available

Feb 2015
Entropy

The dynamics of human electroencephalography (EEG) have been proved to be related to cognitive activities. This study separately assessed the two EEG components, amplitude and rhythm, aiming to capture their individual contributions to cognitive functions. We extracted the local peaks of EEGs under rest or photic stimulation and calculated the symbolic dynamics of their voltages (amplitude) and interpeak intervals (instantaneous frequency), individually. The sample consisted of 89 geriatric outpatients in three patient groups: 38 fresh cases of vascular dementia (VD), 22 fresh cases of Alzheimer's disease (AD) and 29 controls. Both sample entropy and number of forbidden words revealed significantly less regular symbolic dynamics in the whole EEG tracings of the VD than the AD and control groups. We found consistent results between groups with the symbolic dynamics in the local-peak voltage sequence rather than the interpeak interval sequence. Photic stimulation amplified the differences between groups. These results suggest that the EEG dynamics which relates to either cognitive functions or the underlying pathologies of dementia are embedded within the dynamics of the amount of but not the interval between each synchronized firing of adjacent cerebral neurons.

Classification of Alzheimers’ Dementia by Using Various Signal Decomposition Methods

Conference Paper

Nov 2021

Research on Early Diagnosis of Mild Cognitive Impairment Based on EEG Entropy Features

Conference Paper

Jul 2021

Optimal flickering light stimulation for entraining gamma waves in the human brain

Article

Full-text available

Aug 2021

Although light flickering at 40 Hz reduced Alzheimer’s disease (AD) pathologies in mice by entraining gamma waves, it failed to reduce cerebral amyloid burden in a study on six patients with AD or mild cognitive impairment. We investigated the optimal color, intensity, and frequency of the flickering light stimulus for entraining gamma waves in young adults. We compared the event-related synchronization (ERS) values of entrained gamma waves between four different light colors (white, red, green, and blue) in the first experiment and four different luminance intensities in the second experiment. In both experiments, we compared the ERS values of entrained gamma waves between 10 different flickering frequencies from 32 to 50 Hz. We also examined the severity of six adverse effects in both experiments. We compared the propagation of gamma waves in the visual cortex to other brain regions between different luminance intensities and flickering frequencies. We found that red light entrained gamma waves most effectively, followed by white light. Lights of higher luminance intensities (700 and 400 cd/m ² ) entrained stronger gamma waves than those of lower luminance intensities (100 and 10 cd/m ² ). Lights flickering at 34–38 Hz entrained stronger and more widely spread beyond the visual cortex than those flickering at 40–50 Hz. Light of 700 cd/m ² resulted in more moderate-to-severe adverse effects than those of other luminance intensities. In humans, 400 cd/m ² white light flickering at 34–38 Hz was most optimal for gamma entrainment.

Seizure susceptibility in Alzheimer's disease

Article

Full-text available

May 2021

Epileptic seizures in Alzheimer's disease (AD) patients are rare but still approximately 8 times more common than in the general age-matched population. Experimental and clinical studies have suggested the epileptogenic potential of Aβ, which might represent a principal responsible for the epileptic-like discharges and cognitive decline observed in AD. In addition, an increase in cortical excitability has been demonstrated in AD animal models that may be due to an imbalance of excitatory/inhibitory synaptic transmission. Cortical hyperexcitability has also been demonstrated in the human EEG by the presence of a high proportion of fast oscillatory activities. This review tries to show the mechanisms involved in the generation of the epileptic seizures observed in AD and have been widely studied in animal models. Unfortunately, the EEG analysis in AD is not a standard procedure in clinical practice. Nevertheless, seizures and other electroencephalographic abnormalities are commonly found in AD patients. We suggest that EEG studies in these patients could help to an early diagnosis and inform about the evolution of this disease and their possible cognitive deterioration.

Seizure susceptibility in Alzheimer’s disease

Article

Full-text available

Jan 2021

Epileptic seizures in Alzheimer’s disease (AD) patients are rare but still approximately 8 times more common than in the general age-matched population. Experimental and clinical studies have suggested the epileptogenic potential of Aβ, which might represent a principal responsible for the epileptic-like discharges and cognitive decline observed in AD. In addition, an increase in cortical excitability has been demonstrated in AD animal models that may be due to an imbalance of excitatory/inhibitory synaptic transmission. Cortical hyperexcitability has also been demonstrated in the human EEG by the presence of a high proportion of fast oscillatory activities. This review tries to show the mechanisms involved in the generation of the epileptic seizures observed in AD and have been widely studied in animal models. Unfortunately, the EEG analysis in AD is not a standard procedure in clinical practice. Nevertheless, seizures and other electroencephalographic abnormalities are commonly found in AD patients. We suggest that EEG studies in these patients could help to an early diagnosis and inform about the evolution of this disease and their possible cognitive deterioration.

Advanced Dementia: Brain-State Characteristics and Clinical Indicators of Early Mortality

Article

Apr 2021
J ALZHEIMERS DIS

There is an urgent need in advanced dementia for evidence-based clinical prognostic predictors that could positively influence ethical decisions allowing health provider and family preparation for early mortality. Accordingly, the authors review and discuss the prognostic utility of clinical assessments and objective measures of pathological brain states in advanced dementia patients associated with accelerated mortality. Overall, due to the paucity of brain-activity and clinical-comorbidity predictors of survival in advanced dementia, authors outline the potential prognostic value of brain-state electroencephalography (EEG) measures and reliable clinical indicators for forecasting early mortality in advanced dementia patients. In conclusion, two consistent risk-factors for predicting accelerated mortality in terminal-stage patients with advanced dementia were identified: pressure ulcers and paroxysmal slow-wave EEG parameters associated with cognitive impairment severity and organic disease progression. In parallel, immobility, malnutrition, and co-morbid systemic diseases are highly associated with the risk for early mortality in advanced dementia patients. Importantly, the authors’ conclusions suggest utilizing reliable quantitative-parameters of disease progression for estimating accelerated mortality in dementia patients entering the terminal disease-stages characterized by severe intellectual deficits and functional disability.

Mutual information analysis on MCI-AD EEG signal during resting and task conditions

Conference Paper

Oct 2019

Evaluation of Early Detection Methods for Alzheimer's Disease

Article

Full-text available

Feb 2020
Bioproc Biosystems Eng

Elyas Irankhah

Amnesia, commonly referred to as Alzheimer's, is a type of brain dysfunction that gradually dissipates the patient's mental abilities. Memory disorder usually develops gradually and progresses. At first, memory impairment is limited to recent events and lessons, but old memories are gradually damaged. In this disease, the connection between nerve cells by the formation of neurofibrillary nodes disappeared. Currently, treatment for the disease mainly involves symptomatic treatments, treatment of behavioral disorders and medication use. Although there is no cure for Alzheimer's disease yet, medications can slow the progression of the disease and reduce the severity of memory impairment and behavioral problems. Today, whit the spread of definitive treatment for this disease, in this study, new techniques for the treatment of this disease can be explored by examining the early detection methods of the disease through brain signal processing with classifiers and medical imaging such as MRI and CT Scan. Signal processing has included EEG and ERP brain signals and the use of classifiers such as SVM, LDA and Neural network. In medical image processing, a combination of Neural network and Wavelet is used to expedite the time of diagnosis according to the above method. Given the process under consideration, combining brain signals and medical imaging can provide valuable help in early detection of Alzheimer disease.

Cognitive workload across the spectrum of cognitive impairments: A systematic review of physiological measures

Article

Jul 2017
NEUROSCI BIOBEHAV R

Our objective was to identify the physiological measures that are sensitive to assessing cognitive workload across the spectrum of cognitive impairments. Three database searches were conducted: PubMed, PsychINFO, and Web of Science. Studies from the last decade that used physiological measures of cognitive workload in older adults (mean age >65 years-old) were reviewed. The cognitive workload of healthy older individuals was compared with the cognitive workload of younger adults, patients with mild cognitive impairment (MCI), and patients with Alzheimer’s diseases (AD). The most common measures of cognitive workload included: electroencephalography, magnetoencephalography, functional magnetic resonance imaging, pupillometry, and heart rate variability. These physiological measures consistently showed greater cognitive workload in healthy older adults compared to younger adults when performing the same task. The same was observed in patients with MCI compared to healthy older adults. Behavioral performance declined when the available cognitive resources became insufficient to cope with the cognitive demands of a task, such as in AD. These findings may have implications for clinical practice and future cognitive interventions.

Predictors for drug effects with brain disease: Shed new light from EEG parameters to brain connectomics

Article

Apr 2017

Though researchers spent a lot of effort to develop treatments for neuropsychiatric disorders, the poor translation of drug efficacy data from animals to human hampered the success of these therapeutic approaches in human. Pharmaceutical industry is challenged by low clinical success rates for new drug registration. To maximize the success in drug development, biomarkers are required to act as surrogate end points and predictors of drug effects. The pathology of brain disease could be in part due to synaptic dysfunction. Electroencephalogram (EEG), generating from the result of the postsynaptic potential discharge between cells, could be a potential measure to bridge the gaps between animal and human data. Here we discuss recent progress on using relevant EEG characteristics and brain connectomics as biomarkers to monitor drug effects and measure cognitive changes on animal models and human in real-time. It is expected that the novel approach, i.e. EEG connectomics, will offer a deeper understanding on the drug efficacy at a microcirculatory level, which will be useful to support the development of new treatments for neuropsychiatric disorders.

Neuroimaging and Treatments Perspectives

Chapter

Sep 2016

Michael Hoffmann

Brain imaging in the context of both pathology and normal functioning is increasingly moving toward imaging of complex networks such as resting state networks (RSN) or intrinsic connectivity network (ICN) imaging and positron emission tomography (PET) brain imaging. Although largely the domain of functional imaging, contributions from certain anatomical imaging techniques such as diffusion tensor imaging (DTI) also image brain networks at the macroscopic level. Traumatic brain injury and depression are two examples where standard magnetic resonance imaging (MRI) is usually normal but ICN has for the first time shown objective abnormalities.

EEG-Biomarker in der Früherkennung und Verlaufskontrolle der Alzheimer-Demenz

Article

Full-text available

Jan 2015

EEG in the diagnostics of Alzheimer’s disease

Article

Full-text available

Nov 2013

Dementia caused by Alzheimer’s disease (AD) is worldwide one of the main medical and social challenges for the next years and decades. An automated analysis of changes in the electroencephalogram (EEG) of patients with AD may contribute to improving the quality of medical diagnoses. In this paper, measures based on uni- and multi-variate spectral densities are studied in order to measure slowing and, in greater detail, reduced synchrony in the EEG signals. Hereby, an EEG segment is interpreted as sample of a (weakly) stationary stochastic process. The spectral density was computed using an indirect estimator. Slowing was considered by calculating the spectral power in predefined frequency bands. As measures for synchrony between single EEG signals, we analyzed coherences, partial coherences, bivariate and conditional Granger causality; for measuring synchrony between groups of EEG signals, we considered coherences, partial coherences, bivariate and conditional Granger causality between the respective first principal components of each group, and dynamic canonic correlations. As measure for local synchrony within a group, the amount of variance explained by the respective first principal component of static and dynamic principal component analysis was investigated. These measures were exemplarily computed for resting state EEG recordings from 83 subjects diagnosed with probable AD. Here, the severity of AD is quantified by the Mini Mental State Examination score.

Driving Cessation and Dementia: Results of the Prospective Registry on Dementia in Austria (PRODEM)

Article

Full-text available

Dec 2012
PLOS ONE

Objective: To assess the influence of cognitive, functional and behavioral factors, co-morbidities as well as caregiver characteristics on driving cessation in dementia patients. Methods: The study cohort consists of those 240 dementia cases of the ongoing prospective registry on dementia in Austria (PRODEM) who were former or current car-drivers (mean age 74.2 (±8.8) years, 39.6% females, 80.8% Alzheimer's disease). Reasons for driving cessation were assessed with the patients' caregivers. Standardized questionnaires were used to evaluate patient- and caregiver characteristics. Cognitive functioning was determined by Mini-Mental State Examination (MMSE), the CERAD neuropsychological test battery and Clinical Dementia Rating (CDR), activities of daily living (ADL) by the Disability Assessment for Dementia, behavior by the Neuropsychiatric Inventory (NPI) and caregiver burden by the Zarit burden scale. Results: Among subjects who had ceased driving, 136 (93.8%) did so because of "Unacceptable risk" according to caregiver's judgment. Car accidents and revocation of the driving license were responsible in 8 (5.5%) and 1(0.7%) participant, respectively. Female gender (OR 5.057; 95%CI 1.803-14.180; p = 0.002), constructional abilities (OR 0.611; 95%CI 0.445-0.839; p = 0.002) and impairment in Activities of Daily Living (OR 0.941; 95%CI 0.911-0.973; p<0.001) were the only significant and independent associates of driving cessation. In multivariate analysis none of the currently proposed screening tools for assessment of fitness to drive in elderly subjects including the MMSE and CDR were significantly associated with driving cessation. Conclusion: The risk-estimate of caregivers, but not car accidents or revocation of the driving license determines if dementia patients cease driving. Female gender and increasing impairment in constructional abilities and ADL raise the probability for driving cessation. If any of these factors also relates to undesired traffic situations needs to be determined before recommendations for their inclusion into practice parameters for the assessment of driving abilities in the elderly can be derived from our data.

Possible generalization of Boltzmann-Gibbs statistics

Article

Full-text available

Jul 1988

Constantino Tsallis

With the use of a quantity normally scaled in multifractals, a generalized form is postulated for entropy, namelyS q k [1 – i=1 W p i q ]/(q-1), whereq characterizes the generalization andp i are the probabilities associated withW (microscopic) configurations (W). The main properties associated with this entropy are established, particularly those corresponding to the microcanonical and canonical ensembles. The Boltzmann-Gibbs statistics is recovered as theq1 limit.

Graphs for Dependence and Causality in Multivariate Time Series

Chapter

Full-text available

Jan 2012

In this contribution we describe measures for dependence and causality between component processes in multivariate time series in a stationary context. Symmetric measures, such as the partial spectral coherence, as well as directed measures, such as the partial directed coherence and the conditional Granger causality index, are described and discussed. These measures are used for deriving undirected and directed graphs (where the vertices correspond to the one-dimensional component processes), showing the inner structure of a multivariate time series. Our interest in these graphs originates from the problem of detecting the focus of an epileptic seizure, based on the analysis of invasive EEG data. An example for such an analysis is given in the last section of this chapter.

Disease Tracking Markers for Alzheimer's Disease at the Prodromal (MCI) Stage

Article

Full-text available

Oct 2011
J ALZHEIMERS DIS

Older persons with Mild Cognitive Impairment (MCI) feature neurobiological Alzheimer's Disease (AD) in 50% to 70% of the cases and develop dementia within the next 5 to 7 years. Current evidence suggests that biochemical, neuroimaging, electrophysiological, and neuropsychological markers can track the disease over time since the MCI stage (also called prodromal AD). The amount of evidence supporting their validity is of variable strength. We have reviewed the current literature and categorized evidence of validity into three classes: Class A, availability of multiple serial studies; Class B a single serial study or multiple cross sectional studies of patients with increasing disease severity from MCI to probable AD; and class C, multiple cross sectional studies of patients in the dementia stage, not including the MCI stage. Several Class A studies suggest that episodic memory and semantic fluency are the most reliable neuropsychological markers of progression. Hippocampal atrophy, ventricular volume and whole brain atrophy are structural MRI markers with class A evidence. Resting-state fMRI and connectivity, and diffusion MR markers in the medial temporal white matter (parahippocampus and posterior cingulum) and hippocampus are promising but require further validation. Change in amyloid load in MCI patients warrant further investigations, e.g. over longer period of time, to assess its value as marker of disease progression. Several spectral markers of resting state EEG rhythms that might reflect neurodegenerative processes in the prodromal stage of AD (EEG power density, functional coupling, spectral coherence, and synchronization) suffer from lack of appropriately designed studies. Although serial studies on late event-related potentials (ERPs) in healthy elders or MCI patients are inconclusive, others tracking disease progression and effects of cholinesterase inhibiting drugs in AD, and cross-sectional including MCI or predicting development of AD offer preliminary evidence of validity as a marker of disease progression from the MCI stage. CSF Markers, such as Aβ 1-42, t-tau and p-tau are valuable markers which support the clinical diagnosis of Alzheimer's disease. However, these markers are not sensitive to disease progression and cannot be used to monitor the severity of Alzheimer's disease. For Isoprostane F2 some evidence exists that its increase correlates with the progression and the severity of AD.

Aligning strategies for using EEG as a surrogate biomarker: A review of preclinical and clinical research

Article

Full-text available

Oct 2010

Electroencephalography (EEG) and related methodologies offer the promise of predicting the likelihood that novel therapies and compounds will exhibit clinical efficacy early in preclinical development. These analyses, including quantitative EEG (e.g. brain mapping) and evoked/event-related potentials (EP/ERP), can provide a physiological endpoint that may be used to facilitate drug discovery, optimize lead or candidate compound selection, as well as afford patient stratification and Go/No-Go decisions in clinical trials. Currently, the degree to which these different methodologies hold promise for translatability between preclinical models and the clinic have not been well summarized. To address this need, we review well-established and emerging EEG analytic approaches that are currently being integrated into drug discovery programs throughout preclinical development and clinical research. Furthermore, we present the use of EEG in the drug development process in the context of a number of major central nervous system disorders including Alzheimer's disease, schizophrenia, depression, attention deficit hyperactivity disorder, and pain. Lastly, we discuss the requirements necessary to consider EEG technologies as a biomarker. Many of these analyses show considerable translatability between species and are used to predict clinical efficacy from preclinical data. Nonetheless, the next challenge faced is the selection and validation of EEG endpoints that provide a set of robust and translatable biomarkers bridging preclinical and clinical programs.

Network Excitability Dysfunction in Alzheimer's Disease: Insights from In Vitro and In Vivo Models

Article

Full-text available

Jan 2010

Unlabelled: Recent reports have drawn attention to dysfunctions of intrinsic neuronal excitability and network activity in Alzheimer disease (AD). Here we review the possible causes of these basic dysfunctions and implications for AD, based on in vitro and in vivo findings. We then review the current therapeutic approaches particularly linked to the issue of neuronal excitability in AD. Conclusion: AD is a complex, neurodegenerative disorder. Hippocampal synaptic dysfunction is an early feature of the degenerative process that is clearly linked to memory impairment, the first and major symptom of AD. A growing body of evidence points toward a dysfunction of neuronal networks. Intrinsic neuronal excitability, mainly through profound dysregulation of calcium homeostasis, appears to be largely affected. Consequently, neuronal communication is disturbed. Such cellular defects might underlie cognitive manifestations like fluctuations in cognitive impairment and might also explain several observations obtained with EEG, MEG, MRI, or PET studies, leading to the concept of a disconnection syndrome in AD.

A new rating scale for Alzheimer's disease Am J Psychiatry 141: 1356-1364

Article

Full-text available

Dec 1984

A new rating instrument, the Alzheimer's Disease Assessment Scale, was designed specifically to evaluate the severity of cognitive and noncognitive behavioral dysfunctions characteristic of persons with Alzheimer's disease. Item descriptions, administration procedures, and scoring are outlined. Twenty-seven subjects with Alzheimer's disease and 28 normal elderly subjects were rated on 40 items. Twenty-one items with significant intraclass correlation coefficients for interrater reliability (range, .650-.989) and significant Spearman rank-order correlation coefficients for test-retest reliability (range, .514-1) constitute the final scale. Subjects with Alzheimer's disease had significantly more cognitive and noncognitive dysfunction than the normal elderly subjects.

EEG correlates in the spectrum of cognitive decline

Article

Full-text available

Sep 2007
CLIN NEUROPHYSIOL

To investigate relations between EEG measures and performance on tests of global cognition, memory, language and executive functioning. Twenty-two controls, 18 patients with mild cognitive impairment (MCI) and 16 with probable Alzheimer's disease (AD) underwent neuropsychological and EEG investigations. We used the following EEG measures: theta relative power during eyes closed, alpha reactivity during memory activation (i.e. the percentual decrease in alpha power as compared to eyes closed) and alpha coherence during eyes closed and memory activation. Theta relative power was increased in AD patients as compared with controls (p<0.001) and MCI patients (p<0.01) and related to decreased performance in all cognitive domains. Alpha reactivity was decreased in AD patients as compared with controls (p<0.005) and related to decreased performance on tests of global cognition, memory and executive functioning. Alpha coherence did not differ between groups and was unrelated to cognition. EEG power measures were associated with decreased performance on tests of global cognition, memory, language and executive functioning, while coherence measures were not. The EEG yielded several power measures related to cognitive functions. These EEG power measures might prove useful in prospective studies aimed at predicting longitudinal cognitive decline and dementia.

Applied Regression Analysis.

Article

Jun 1967

“Mini-Mental State”. A practical method for grading the cognitive state of patients for the clinician

Book

Dec 1975
J PSYCHIATR RES

Mini-mental state-practical method for grading cognitive state of patients for clinicians

Article

Jan 1975
J PSYCHIATR RES

The estimation of factor loadings by the method of maximum likelihood

Article

Jan 2014

D.N. Lawley

Investigating Causal Relations by Econometric Models and Cross-Spectral Methods

Article

Jan 1969
ECONOMETRICA

Clive W. J. Granger

Time series. Data analysis and theory. Repr

Article

Jan 2001

David Brillinger

This book will be most useful to applied mathematicians, communication engineers, signal processors, statisticians, and time series researchers, both applied and theoretical. Readers should have some background in complex function theory and matrix algebra and should have successfully completed the equivalent of an upper division course in statistics.

Removing cardiac interference from the electroencephalogram using a modified Pan-Tompkins algorithm and linear regression

Conference Paper

Jul 2013

Cardiac interference can alter the results of quantitative electroencephalograms (qEEG) used for medical diagnoses. The methods currently employed for the automated removal of cardiac interference, which rely solely on the electroencephalogram (EEG), are susceptible to non-cardiac interference commonly encountered in EEGs. Methods that rely on the electrocardiogram (ECG) - besides being unreliable when non-cardiac artifacts corrupt the ECG - either assume periodicity of the cardiac (QRS) peaks or alter uncorrupted EEG segments. This paper proposes a robust method for the automated removal of cardiac interference from EEGs by identifying QRS peaks in the ECG without assuming periodicity. Artificial signals consisting only of QRS peaks and the zero-lines in between are computed. Linear regression of the EEG channels on the "QRS signals" removes cardiac interference without altering uncorrupted EEG segments. The QRS-based regression method was tested on 30 multi-channel EEGs exhibiting cardiac interference of elderly subjects (15 male, 15 female). Achieving a correction rate of 80%, the QRS-based regression method has proved effective in removing cardiac interference from the EEG even in presence of additional non-cardiac interference in the EEG.

In Applied Regression Analysis

Book

Jan 1966

A Mathematical Theory of Communication

Article

Jul 1948

Claude Elwood Shannon

Bell System Technical Journal, also pp. 623-656 (October)

Relations Between Two Sets of Variates

Article

Nov 1935
BIOMETRIKA

Harold Hotelling

Concepts of correlation and regression may be applied not only to ordinary one-dimensional variates but also to variates of two or more dimensions. Marksmen side by side firing simultaneous shots at targets, so that the deviations are in part due to independent individual errors and in part to common causes such as wind, provide a familiar introduction to the theory of correlation; but only the correlation of the horizontal components is ordinarily discussed, whereas the complex consisting of horizontal and vertical deviations may be even more interesting. The wind at two places may be compared, using both components of the velocity in each place. A fluctuating vector is thus matched at each moment with another fluctuating vector. The study of individual differences in mental and physical traits calls for a detailed study of the relations between sets of correlated variates. For example the scores on a number of mental tests may be compared with physical measurements on the same persons. The questions then arise of determining the number and nature of the independent relations of mind and body shown by these data to exist, and of extracting from the multiplicity of correlations in the system suitable characterizations of these independent relations. As another example, the inheritance of intelligence in rats might be studied by applying not one but s different mental tests to N mothers and to a daughter of each

Elements of Information Theory

Chapter

Oct 2001

Half-title pageSeries pageTitle pageCopyright pageDedicationPrefaceAcknowledgementsContentsList of figuresHalf-title pageIndex

Elements of information theory. 2nd ed

Chapter

Nov 1990

Information theory answers two fundamental questions in communication theory: what is the ultimate data compression (answer: the entropy H), and what is the ultimate transmission rate of communication (answer: the channel capacity C). For this reason some consider information theory to be a subset of communication theory. We will argue that it is much more. Indeed, it has fundamental contributions to make in statistical physics (thermodynamics), computer science (Kolmogorov complexity or algorithmic complexity), statistical inference (Occam's Razor: “The simplest explanation is best”) and to probability and statistics (error rates for optimal hypothesis testing and estimation). The relationship of information theory to other fields is discussed. Information theory intersects physics (statistical mechanics), mathematics (probability theory), electrical engineering (communication theory) and computer science (algorithmic complexity). We describe these areas of intersection in detail.

Analysis of Complex Statistical Variables into Principal Components

Article

Nov 1932

Hotelling HH

A Mathematical Theory of Communication

Article

Jan 2001

Claude E. Shannon

Time Series: Data Analysis and Theory

Chapter

Feb 1976

David Brillinger

Review of advanced techniques for the estimation of brain connectivity measured with EEG/MEG

Article

Jul 2011

Vangelis Sakkalis

Brain connectivity can be modeled and quantified with a large number of techniques. The main objective of this paper is to present the most modern and widely established mathematical methods for calculating connectivity that is commonly applied to functional high resolution multichannel neurophysiological signals, including electroencephalographic (EEG) and magnetoencephalographic (MEG) signals. A historical timeline of each technique is outlined along with some illustrative applications. The most crucial underlying assumptions of the presented methodologies are discussed in order to help the reader understand where each technique fits into the bigger picture of measuring brain connectivity. In this endeavor, linear, nonlinear, causality-assessing and information-based techniques are summarized in the framework of measuring functional and effective connectivity. Model based vs. data-driven techniques and bivariate vs. multivariate methods are also discussed. Finally, certain important caveats (i.e. stationarity assumption) pertaining to the applicability of the methods are also illustrated along with some examples of clinical applications.

Clinical diagnosis of Alzheimer's disease: Report of the NINCDS--ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease

Article

Jul 2011
NEUROLOGY

Clinical criteria for the diagnosis of Alzheimer's disease include insidious onset and progressive impairment of memory and other cognitive functions. There are no motor, sensory, or coordination deficits early in the disease. The diagnosis cannot be determined by laboratory tests. These tests are important primarily in identifying other possible causes of dementia that must be excluded before the diagnosis of Alzheimer's disease may be made with confidence. Neuropsychological tests provide confirmatory evidence of the diagnosis of dementia and help to assess the course and response to therapy. The criteria proposed are intended to serve as a guide for the diagnosis of probable, possible, and definite Alzheimer's disease; these criteria will be revised as more definitive information become available.

The cholinergic system, EEG and sleep

Article

Aug 2011

Acetylcholine is a potent excitatory neurotransmitter, crucial for cognition and the control of alertness and arousal. Vigilance-specific recordings of the electroencephalogram (EEG) potently reflect thalamo-cortical and brainstem-cortical cholinergic activity that drives theta rhythms and task-specific cortical (de-synchronisation. Additionally, cholinergic projections from the basal forebrain act as a relay centre for the brainstem-cortical arousal system, but also directly modulate cortical activity, and thus promote wakefulness or rapid-eye movement (REM) sleep. Disease states such as sleep disorders, dementia and certain types of epilepsy are a further reflection of the potent cholinergic impact on CNS physiology and function, and highlight the relevance and inter-dependence of sleep and EEG. With novel technologies and computational tools now becoming available, advanced mechanistic insights may be gained and new avenues explored for diagnostics and therapeutics.

Global synchronization index as a biological correlate of cognitive decline in Alzheimer's disease

Article

Dec 2009

The recently developed global synchronization index (GSI) quantifies synchronization between neuronal signals at multiple sites. This study explored the clinical significance of the GSI in Alzheimer's disease (AD) patients. Electroencephalograms were recorded from 25 AD patients and 22 age-matched healthy normal controls (NC). GSI values were computed both across the entire frequency band and separately in the delta, theta, alpha, beta1, beta2, beta3, and gamma bands. The Mini-Mental Status Examination (MMSE) and Clinical Dementia Rating scale (CDR) were used to assess the symptom severity. GSI values in the beta1, beta2, beta3, and gamma bands were significantly lower in AD patients than in NC. GSI values in the beta and gamma bands were positively correlated with the MMSE scores in all participants (AD and NC). In AD patients, GSI values were negatively correlated with MMSE scores in the delta bands, but positively correlated in the beta1 and gamma band. Also, GSI values were positively correlated with CDR scores in the delta bands, but negatively correlated in the gamma band. GSI values of mainly high-frequency bands were significantly lower in AD patients than in NC, they were significantly correlated with scores on symptom severity scales. Our results suggest that GSI values are a useful biological correlate of cognitive decline in AD patients.

A comparative study of synchrony measures for the early diagnosis of Alzheimer's disease based on EEG

Article

Jul 2009
NEUROIMAGE

It is well known that EEG signals of Alzheimer's disease (AD) patients are generally less synchronous than in age-matched control subjects. However, this effect is not always easily detectable. This is especially the case for patients in the pre-symptomatic phase, commonly referred to as mild cognitive impairment (MCI), during which neuronal degeneration is occurring prior to the clinical symptoms appearance. In this paper, various synchrony measures are studied in the context of AD diagnosis, including the correlation coefficient, mean-square and phase coherence, Granger causality, phase synchrony indices, information-theoretic divergence measures, state space based measures, and the recently proposed stochastic event synchrony measures. Experiments with EEG data show that many of those measures are strongly correlated (or anti-correlated) with the correlation coefficient, and hence, provide little complementary information about EEG synchrony. Measures that are only weakly correlated with the correlation coefficient include the phase synchrony indices, Granger causality measures, and stochastic event synchrony measures. In addition, those three families of synchrony measures are mutually uncorrelated, and therefore, they each seem to capture a specific kind of interdependence. For the data set at hand, only two synchrony measures are able to convincingly distinguish MCI patients from age-matched control patients, i.e., Granger causality (in particular, full-frequency directed transfer function) and stochastic event synchrony. Those two measures are used as features to distinguish MCI patients from age-matched control subjects, yielding a leave-one-out classification rate of 83%. The classification performance may be further improved by adding complementary features from EEG; this approach may eventually lead to a reliable EEG-based diagnostic tool for MCI and AD.

Electrophysiological Markers of Rapid Cognitive Decline in Mild Cognitive Impairment

Article

Feb 2009

Electroencephalography (EEG) is an easily accessible and low-cost modality that might prove to be a particularly powerful tool for the identification of subtle functional changes preceding structural or metabolic deficits in progressive mild cognitive impairment (PMCI). Most previous contributions in this field assessed quantitative EEG differences between healthy controls, MCI and Alzheimer's disease(AD) cases leading to contradictory data. In terms of MCI conversion to AD, certain longitudinal studies proposed various quantitative EEG parameters for an a priori distinction between PMCI and stable MCI. However, cross-sectional comparisons revealed a substantial overlap in these parameters between MCI patients and elderly controls. Methodological differences including variable clinical definition of MCI cases and substantial interindividual differences within the MCI group could partly explain these discrepancies. Most importantly, EEG measurements without cognitive demand in both cross-sectional and longitudinal designs have demonstrated limited sensitivity and generally do not produce significant group differences in spectral EEG parameters. Since the evolution of AD is characterized by the progressive loss of functional connectivity within neocortical association areas, event-modulated EEG dynamic analysis which makes it possible to investigate the functional activation of neocortical circuits may represent a more sensitive method to identify early alterations of neuronal networks predictive of AD development among MCI cases. The present review summarizes clinically significant results of EEG activation studies in this field and discusses future perspectives of research aiming to reach an early and individual prediction of cognitive decline in healthy elderly controls.

Changes in absolute power values of EEG spectra in the follow-up of Alzheimer's disease

Article

Mar 1991

We have studied the absolute and relative power and amplitude of EEG spectra (T6-02) of 24 patients with "probable" Alzheimer's disease at the early stage of the disease and 1 year later and also compared the values to those of normal elderly controls. A remarkable variability of the absolute values was evident both for the patients and for the controls. The AD patients had significantly higher absolute theta amplitude and power and the absolute beta values tended to decrease compared to controls. Absolute delta and alpha values did not differ from those of the controls. The relative delta, theta and alpha power and amplitude, and beta amplitude showed significant changes in AD patients, whereas the relative beta power was unchanged. In the follow-up of AD patients at 1 year, absolute alpha values decreased and delta values tended to increase. As to relative values, both the alpha and the delta significantly changed but the theta and the beta were unaltered. We conclude that both absolute and relative power and amplitude values should be considered in EEG studies of dementia patients. Absolute values are especially useful in follow-up.

A longitudinal EEG study of mild senile dementia of Alzheimer type

Article

Sep 1985
Electroencephalogr Clin Neurophysiol

This longitudinal study of resting EEGs compared patients with senile dementia of Alzheimer type (SDAT) and healthy controls at 3 times of testing over a 2.5 year period. Measures included the mean EEG frequency as well as the percentage of power in alpha, beta, theta, and delta frequency bands obtained from power spectral analysis. The values from occipital to vertex derivations were averaged for the left and right hemispheres. In healthy older adults delta increased, and both beta and mean frequency decreased over the study period; there was no significant change in theta or alpha. In the SDAT group, all 5 EEG measures changed significantly; there were increases in delta and theta, and decreases in beta, alpha and mean frequency. Theta percentage power distinguished between all 4 stages of dementia (control, mild, moderate and severe). Other EEG measures discriminated only at certain stages. In the mild stage of SDAT theta, beta and mean frequency were already different from control values. In the moderate stage, these differences persisted, and alpha became different. Delta was the last to change, and in the present small sample of those with severe SDAT the difference had not yet reached significance.

The Fourier approach to the identification of functional coupling between neuronal spike trains

Article

Feb 1989
PROG BIOPHYS MOL BIO

CAMDEX: A standardized instrument for the diagnosis of mental disorder in the elderly

Article

Jan 1987

A new interview schedule for the diagnosis and measurement of dementia in the elderly is described. The schedule named the Cambridge Mental Disorders of the Elderly Examination (CAMDEX), consists of three main sections: A structured clinical interview with the patient to obtain systematic information about the present state, past history and family history; a range of objective cognitive tests which constitute a mini-neuropsychological battery; a structured interview with a relative or other informant to obtain independent information about the respondent's present state, past history and family history. The CAMDEX is acceptable to patients, has a high inter-rater reliability and the cognitive section has been shown to have high sensitivity and specificity.

Clinical diagnosis of Alzheimer's disease: Report of the NINCDS-ADRDA Work Group* under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease

Article

Aug 1984

Article

Sep 2003
INT J PSYCHOPHYSIOL

Quantitative EEG Markers Relate to Alzheimer’s Disease Severity in the Prospective Dementia Registry Austria (PRODEM)

Abstract

No full-text available