Resting State Cortical Electroencephalographic Rhythms in Alzheimer`s Disease

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Measures of Resting State EEG Rhythms for Clinical Trials in Alzheimer’s Disease:Recommendations of an Expert Panel

The Electrophysiology Professional Interest Area (EPIA) and Global Brain Consortium endorsed recommendations on candidate electroencephalography (EEG) measures for Alzheimer's disease (AD) clinical trials. The Panel reviewed the field literature. As most consistent findings, AD patients with mild cognitive impairment and dementia showed abnormalities in peak frequency, power, and "interrelatedness" at posterior alpha (8-12Hz) and widespread delta (<4Hz) and theta (4-8Hz) rhythms in relation to disease progression and interventions. The following consensus statements were subscribed: (1) Standardization of instructions to patients, resting state EEG (rsEEG) recording methods, and selection of artifact-free rsEEG periods are needed; (2) power density and "interrelatedness" rsEEG measures (e.g., directed transfer function, phase lag index, linear lagged connectivity, etc.) at delta, theta, and alpha frequency bands may be use for stratification of AD patients and monitoring of disease progression and intervention; and (3) international multisectoral initiatives are mandatory for regulatory purposes

Drug polyconsumption is associated with increased synchronization of brain electrical-activity at rest and in a counting task

Drug abusers typically consume not just one but several types of drugs, starting from alcohol and marijuana consumption, and then dramatically lapsing into addiction to harder drugs, such as cocaine, heroin, or amphetamine. The brain of drug abusers presents various structural and neurophysiological abnormalities, some of which may predate drug consumption onset. However, how these changes translate into modifications in functional brain connectivity is still poorly understood. To characterize functional connectivity patterns, we recorded Electroencephalogram (EEG) activity from 21 detoxified drug abusers and 20 age-matched control subjects performing a simple counting task and at rest activity. To evaluate the cortical brain connectivity network we applied the Synchronization Likelihood algorithm. The results showed that drug abusers had higher synchronization levels at low frequencies, mainly in the θ band (4–8 Hz) between frontal and posterior cortical regions. During the counting task, patients showed increased synchronization in the β (14–35 Hz), and γ (35–45 Hz) frequency bands, in fronto-posterior and interhemispheric temporal regions. Taken together 'slow-down' at rest and task-related 'over-exertion' could indicate that the brain of drug abusers is suffering from a premature form of ageing. Future studies will clarify whether this condition can be reversed following prolonged periods of abstinence

Functional cortical source connectivity of resting state electroencephalographic alpha rhythms shows similar abnormalities in patients with mild cognitive impairment due to Alzheimer's and Parkinson's diseases

Objective: This study tested the hypothesis that markers of functional cortical source connectivity of resting state eyes-closed electroencephalographic (rsEEG) rhythms may be abnormal in subjects with mild cognitive impairment due to Alzheimer's (ADMCI) and Parkinson's (PDMCI) diseases compared to healthy elderly subjects (Nold). Methods: rsEEG data had been collected in ADMCI, PDMCI, and Nold subjects (N = 75 for any group). eLORETA freeware estimated functional lagged linear connectivity (LLC) from rsEEG cortical sources. Area under receiver operating characteristic (AUROC) curve indexed the accuracy in the classification of Nold and MCI individuals. Results: Posterior interhemispheric and widespread intrahemispheric alpha LLC solutions were abnormally lower in both MCI groups compared to the Nold group. At the individual level, AUROC curves of LLC solutions in posterior alpha sources exhibited moderate accuracies (0.70-0.72) in the discrimination of Nold vs. ADMCI-PDMCI individuals. No differences in the LLC solutions were found between the two MCI groups. Conclusions: These findings unveil similar abnormalities in functional cortical connectivity estimated in widespread alpha sources in ADMCI and PDMCI. This was true at both group and individual levels. Significance: The similar abnormality of alpha source connectivity in ADMCI and PDMCI subjects might reflect common cholinergic impairment. (C) 2018 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved

Deep Learning of Resting-state Electroencephalogram Signals for 3-class Classification of Alzheimer’s Disease, Mild Cognitive Impairment and Healthy Ageing

Objective. This study aimed to produce a novel deep learning (DL) model for the classification of subjects with Alzheimer's disease (AD), mild cognitive impairment (MCI) subjects and healthy ageing (HA) subjects using resting-state scalp electroencephalogram (EEG) signals. Approach. The raw EEG data were pre-processed to remove unwanted artefacts and sources of noise. The data were then processed with the continuous wavelet transform, using the Morse mother wavelet, to create time-frequency graphs with a wavelet coefficient scale range of 0-600. The graphs were combined into tiled topographical maps governed by the 10-20 system orientation for scalp electrodes. The application of this processing pipeline was used on a data set of resting-state EEG samples from age-matched groups of 52 AD subjects (82.3 ± 4.7 years of age), 37 MCI subjects (78.4 ± 5.1 years of age) and 52 HA subjects (79.6 ± 6.0 years of age). This resulted in the formation of a data set of 16197 topographical images. This image data set was then split into training, validation and test images and used as input to an AlexNet DL model. This model was comprised of five hidden convolutional layers and optimised for various parameters such as learning rate, learning rate schedule, optimiser, and batch size. Main results. The performance was assessed by a tenfold cross-validation strategy, which produced an average accuracy result of 98.9 ± 0.4% for the three-class classification of AD vs MCI vs HA. The results showed minimal overfitting and bias between classes, further indicating the strength of the model produced. Significance. These results provide significant improvement for this classification task compared to previous studies in this field and suggest that DL could contribute to the diagnosis of AD from EEG recordings

Selection of Mother Wavelet Function for Multi-Channel EEG Signals Analysis during a Working Memory Task

We performed a comparative study to select the efficient mother wavelet (MWT) basis functions that optimally represent the signal characteristics of the electrical activity of the human brain during a working memory (WM) task recorded through electro-encephalography (EEG). Nineteen EEG electrodes were placed on the scalp following the 10–20 system. These electrodes were then grouped into five recording regions corresponding to the scalp area of the cerebral cortex. Sixty-second WM task data were recorded from ten control subjects. Forty-five MWT basis functions from orthogonal families were investigated. These functions included Daubechies (db1–db20), Symlets (sym1–sym20), and Coiflets (coif1–coif5). Using ANOVA, we determined the MWT basis functions with the most significant differences in the ability of the five scalp regions to maximize their cross-correlation with the EEG signals. The best results were obtained using “sym9” across the five scalp regions. Therefore, the most compatible MWT with the EEG signals should be selected to achieve wavelet denoising, decomposition, reconstruction, and sub-band feature extraction. This study provides a reference of the selection of efficient MWT basis functions

Measures of resting state EEG rhythms for clinical trials in alzheimer's disease patients : recommendations of an expert panel

Background and Aim: Eyes-closed resting state electroencephalographic (rsEEG) rhythms reflect neurophysiological oscillatory mechanisms of synchronization/desynchronization of activity within neural populations of ascending reticular activating brain systems and thalamus-cortical circuits involved in quite vigilance regulation. Currently, they are not considered as biomarkers of Alzheimer’s disease (AD) in the amyloid, tau and neurodegeneration (ATN) Framework of Alzheimer’s Association and National Institute of Aging (AA-NIA). The Electrophysiology Professional Interest Area (EPIA) of AA and Global Brain Consortium endorsed this article written by a multidisciplinary Expert Panel to provide recommendations on candidate rsEEG measures for AD clinical trials. Method: The Panel revised the field literature and reached consensus about the rsEEG measures consistently associated with clinical phenotypes and neuroimaging markers of AD in previous international multicentric clinical trials. Most consistent findings: AD patients with mild cognitive impairment and dementia displayed reduced peak frequency, power, and paired-electrode “interrelatedness” in posterior alpha (8-12 Hz) rhythms and topographically widespread increases in delta (< 4 Hz) and theta (4-8 Hz) rhythms. Recommendations: (i) Careful multi-center standardization of instructions to patients, rsEEG recordings, and selection of artifact-free rsEEG periods; (ii) extraction of rsEEG power density and paired-electrode “interrelatedness” (e.g., directed transfer function, phase lag index, linear lagged connectivity, etc.) rsEEG measures computed at delta, theta, and alpha frequency bands by validated open-access software platforms for replicability; (iii) valid use of those measures in stratification of AD patients and monitoring of disease progression and intervention; and iv) international initiatives to cross-validate rsEEG measures (including nonlinear) for disease monitoring and intervention

Early diagnosis of Alzheimer's disease: the role of biomarkers including advanced EEG signal analysis. Report from the IFCN-sponsored panel of experts

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

Backtranslation of EEG biomarkers of Alzheimer's disease from patients to mouse model

The present Ph.D. thesis has been mainly developed on the data of the project with the short name PharmaCog (2010-2015), granted by the European Framework Programme 7 with about 28 millions of Euro (i.e. Innovative Medicine Initiative, IMI, grant agreement n°115009; www.pharmacog.org). This project involved 15 academic institutions, 12 global pharmaceutical companies, and 5 small and medium sized enterprises (SMEs). The PharmaCog project aimed at improving the pathway of drug discovery in Alzheimer’s disease (AD), based on a major interest of pharma companies, namely the validation of electrophysiological, neuroimaging, and blood biomarkers possibly sensitive to the effect of disease-modifying drugs reducing Ab42 in the brain in AD patients at the prodromal stage of amnesic mild cognitive impairment (aMCI). The core concept of the PharmaCog project was that the pathway of drug discovery in AD may be enhanced by (1) the validation of biomarkers derived from blood, EEG, magnetic resonance imaging (MRI), and positron emission tomography (PET) in patients with aMCI due to AD diagnosed by in-vivo measurement of Ab42 and phospho-tau in the brain and (2) the evaluation of the translational value of those human biomarkers in wild type (WT) mice and animal models of AD including transgenic mice with the mutation of PS1 and/or APP (i.e. PDAPP and TASTPM strains). Those genetic factors induce an abnormal accumulation of Ab42 in the brain and related cognitive deficits. The expected results may be (1) the identification of a matrix of biomarkers sensitive to the prodromal AD (aMCI cognitive status) and its progression in patients and (2) the selection of similar biomarkers related to AD neuropathology and cognitive deficits in PDAPP and TASTPM strains. These biomarkers were expected to be very useful in clinical trials testing the efficacy and neurobiological impact of new disease-modifying drugs against prodromal AD. For the development of this Ph.D. thesis, the access to the experiments and the data of the PharmaCog project was allowed by Prof. Claudio Babiloni, leader of an Italian Unit (University of Foggia in 2010-2012 and Sapienza University of Rome in 2013-2015) of the PharmaCog Consortium and coordinator of study activities relative to biomarkers derived from electroencephalographic (EEG) signals recorded from human subjects and animals in that project. Specifically, Prof. Claudio Babiloni was in charge for the centralized qualification and analysis of EEG data recorded from aMCI patients (Work Package 5, WP5) and transgenic mouse models of AD such as PDAPP and TASTPM strains (WP6). The data of the present Ph.D. thesis mostly derived from the WP5 and WP6. This document illustrating the Ph.D. thesis is structured in three main Sections: ▪ An Introductive part illustrating concisely the AD neuropathology, the mouse models of AD used in this thesis, and basic concepts of EEG techniques useful to understand the present study results; ▪ An Experimental part describing the result of the four research studies led in the framework of this Ph.D. project. Two of these studies were published in international journals registered in ISI/PubMed with impact factor, while the other two are being currently under minor revisions in those journals; ▪ A Conclusion section