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

What Electrophysiology Tells Us About Alzheimer’s Disease::A Window into the Synchronization and Connectivity of Brain Neurons

Electrophysiology provides a real-time readout of neural functions and network capability in different brain states, on temporal (fractions of milliseconds) and spatial (micro, meso, and macro) scales unmet by other methodologies. However, current international guidelines do not endorse the use of electroencephalographic (EEG)/magnetoencephalographic (MEG) biomarkers in clinical trials performed in patients with Alzheimer’s disease (AD), despite a surge in recent validated evidence. This Position Paper of the ISTAART Electrophysiology Professional Interest Area endorses consolidated and translational electrophysiological techniques applied to both experimental animal models of AD and patients, to probe the effects of AD neuropathology (i.e., brain amyloidosis, tauopathy, and neurodegeneration) on neurophysiological mechanisms underpinning neural excitation/inhibition and neurotransmission as well as brain network dynamics, synchronization, and functional connectivity reflecting thalamocortical and cortico-cortical residual capacity. Converging evidence shows relationships between abnormalities in EEG/MEG markers and cognitive deficits in groups of AD patients at different disease stages. The supporting evidence for the application of electrophysiology in AD clinical research as well as drug discovery pathways warrants an international initiative to include the use of EEG/MEG biomarkers in the main multicentric projects planned in AD patients, to produce conclusive findings challenging the present regulatory requirements and guidelines for AD studies

Patients with Alzheimer's disease dementia show partially preserved parietal 'hubs' modeled from resting-state alpha electroencephalographic rhythms

IntroductionGraph theory models a network by its nodes (the fundamental unit by which graphs are formed) and connections. 'Degree' hubs reflect node centrality (the connection rate), while 'connector' hubs are those linked to several clusters of nodes (mainly long-range connections). MethodsHere, we compared hubs modeled from measures of interdependencies of between-electrode resting-state eyes-closed electroencephalography (rsEEG) rhythms in normal elderly (Nold) and Alzheimer's disease dementia (ADD) participants. At least 5 min of rsEEG was recorded and analyzed. As ADD is considered a 'network disease' and is typically associated with abnormal rsEEG delta (<4 Hz) and alpha rhythms (8-12 Hz) over associative posterior areas, we tested the hypothesis of abnormal posterior hubs from measures of interdependencies of rsEEG rhythms from delta to gamma bands (2-40 Hz) using eLORETA bivariate and multivariate-directional techniques in ADD participants versus Nold participants. Three different definitions of 'connector' hub were used. ResultsConvergent results showed that in both the Nold and ADD groups there were significant parietal 'degree' and 'connector' hubs derived from alpha rhythms. These hubs had a prominent outward 'directionality' in the two groups, but that 'directionality' was lower in ADD participants than in Nold participants. DiscussionIn conclusion, independent methodologies and hub definitions suggest that ADD patients may be characterized by low outward 'directionality' of partially preserved parietal 'degree' and 'connector' hubs derived from rsEEG alpha rhythms

Patients with Alzheimer’s disease dementia show partially preserved parietal ‘hubs’ modeled from resting-state alpha electroencephalographic rhythms

Introduction: Graph theory models a network by its nodes (the fundamental unit by which graphs are formed) and connections. ‘Degree’ hubs reflect node centrality (the connection rate), while ‘connector’ hubs are those linked to several clusters of nodes (mainly long-range connections). Methods: Here, we compared hubs modeled from measures of interdependencies of between-electrode resting-state eyes-closed electroencephalography (rsEEG) rhythms in normal elderly (Nold) and Alzheimer’s disease dementia (ADD) participants. At least 5 min of rsEEG was recorded and analyzed. As ADD is considered a ‘network disease’ and is typically associated with abnormal rsEEG delta (<4 Hz) and alpha rhythms (8–12 Hz) over associative posterior areas, we tested the hypothesis of abnormal posterior hubs from measures of interdependencies of rsEEG rhythms from delta to gamma bands (2–40 Hz) using eLORETA bivariate and multivariate-directional techniques in ADD participants versus Nold participants. Three different definitions of ‘connector’ hub were used. Results: Convergent results showed that in both the Nold and ADD groups there were significant parietal ‘degree’ and ‘connector’ hubs derived from alpha rhythms. These hubs had a prominent outward ‘directionality’ in the two groups, but that ‘directionality’ was lower in ADD participants than in Nold participants. Discussion: In conclusion, independent methodologies and hub definitions suggest that ADD patients may be characterized by low outward ‘directionality’ of partially preserved parietal ‘degree’ and ‘connector’ hubs derived from rsEEG alpha rhythms

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

MEG spectral analysis in subtypes of mild cognitive impairment

Mild cognitive impairment (MCI) has been described as an intermediate stage between normal aging and dementia. Previous studies characterized the alterations of brain oscillatory activity at this stage, but little is known about the differences between single and multidomain amnestic MCI patients. In order to study the patterns of oscillatory magnetic activity in amnestic MCI subtypes, a total of 105 subjects underwent an eyes-closed resting-state magnetoencephalographic recording: 36 healthy controls, 33 amnestic single domain MCIs (a-sd-MCI), and 36 amnestic multidomain MCIs (a-md-MCI). Relative power values were calculated and compared among groups. Subsequently, relative power values were correlated with neuropsychological tests scores and hippocampal volumes. Both MCI groups showed an increase in relative power in lower frequency bands (delta and theta frequency ranges) and a decrease in power values in higher frequency bands (alpha and beta frequency ranges), as compared with the control group. More importantly, clear differences emerged from the comparison between the two amnestic MCI subtypes. The a-md-MCI group showed a significant power increase within delta and theta ranges and reduced relative power within alpha and beta ranges. Such pattern correlated with the neuropsychological performance, indicating that the a-md-MCI subtype is associated not only with a "slowing" of the spectrum but also with a poorer cognitive status. These results suggest that a-md-MCI patients are characterized by a brain activity profile that is closer to that observed in Alzheimer disease. Therefore, it might be hypothesized that the likelihood of conversion to dementia would be higher within this subtype

Usefulness of EEG Techniques in Distinguishing Frontotemporal Dementia from Alzheimer's Disease and Other Dementias

The clinical distinction of frontotemporal dementia (FTD) and Alzheimer's disease (AD) may be difficult. In this narrative review we summarize and discuss the most relevant electroencephalography (EEG) studies which have been applied to demented patients with the aim of distinguishing the various types of cognitive impairment. EEG studies revealed that patients at an early stage of FTD or AD displayed different patterns in the cortical localization of oscillatory activity across different frequency bands and in functional connectivity. Both classical EEG spectral analysis and EEG topography analysis are able to differentiate the different dementias at group level. The combination of standardized low-resolution brain electromagnetic tomography (sLORETA) and power parameters seems to improve the sensitivity, but spectral and connectivity biomarkers able to differentiate single patients have not yet been identified. The promising EEG findings should be replicated in larger studies, but could represent an additional useful, noninvasive, and reproducible diagnostic tool for clinical practice

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

Dynamics of large-scale brain activity in health and disease

Tese de doutoramento em Engenharia Biomédica e Biofísica, apresentada à Universidade de Lisboa através da Faculdade de Ciências, 2008Cognition relies on the integration of information processed in widely distributed brain regions. Neuronal oscillations are thought to play an important role in the supporting local and global coordination of neuronal activity. This study aimed at investigating the dynamics of the ongoing healthy brain activity and early changes observed in patients with Alzheimer's disease (AD). Electro- and magnetoencephalography (EEG/MEG) were used due to high temporal resolution of these techniques. In order to evaluate the functional connectivity in AD, a novel algorithm based on the concept of generalized synchronization was improved by defining the embedding parameters as a function of the frequency content of interest. The time-frequency synchronization likelihood (TF SL) revealed a loss of fronto-temporal/parietal interactions in the lower alpha (8 10 Hz) oscillations measured by MEG that was not found with classical coherence. Further, long-range temporal (auto-) correlations (LRTC) in ongoing oscillations were assessed with detrended fluctuation analysis (DFA) on times scales from 1 25 seconds. Significant auto-correlations indicate a dependence of the underlying dynamical processes at certain time scales of separation, which may be viewed as a form of "physiological memory". We tested whether the DFA index could be related to the decline in cognitive memory in AD. Indeed, a significant decrease in the DFA exponents was observed in the alpha band (6 13 Hz) over temporo-parietal regions in the patients compared with the age-matched healthy control subjects. Finally, the mean level of SL of EEG signals was found to be significantly decreased in the AD patients in the beta (13 30 Hz) and in the upper alpha (10 13 Hz) and the DFA exponents computed as a measure of the temporal structure of SL time series were larger for the patients than for subjects with subjective memory complaint. The results obtained indicate that the study of spatio-temporal dynamics of resting-state EEG/MEG brain activity provides valuable information about the AD pathophysiology, which potentially could be developed into clinically useful indices for assessing progression of AD or response to medication