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

Comparison of resting electroencephalogram coherence in patients with mild cognitive impairment and normal elderly subjects

Mild cognitive impairment (MCI) was a condition beginning before more serious deterioration, leading to Alzheimer’s dementia (AD). MCI detection was needed to determine the patient's therapeutic management. Analysis of electroencephalogram (EEG) coherence is one of the modalities for MCI detection. Therefore, this study investigated the inter and intra-hemispheric coherence over 16 EEG channels in the frequency range of 1-30 Hz. The simulation results showed that most of the electrode pair coherence in MCI patients have decreased compared to normal elderly subjects. In inter hemisphere coherence, significant differences (p<0.05) were found in the FP1-FP2 electrode pairs. Meanwhile, significant differences (p<0.05) were found in almost all pre-frontal area connectivity of the intra-hemisphere coherence pairs. The electrode pairs were FP2-F4, FP2-T4, FP1-F3, FP1-F7, FP1-C3, FP1-T3, FP1-P3, FP1-T5, FP1-O1, F3-O1, and T3-T5. The decreased coherence in MCI patients showed the disconnection of cortical connections as a result of the death of the neurons. Furthermore, the coherence value can be used as a multimodal feature in normal elderly subjects and MCI. It is hoped that current studies may be considered for early detection of Alzheimer’s in a larger population

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

Dissociating Alzheimer’s Disease from Amnestic Mild Cognitive Impairment using Time-Frequency Based EEG Neurometrics

This work explores the utility of using magnitude (ERSP), phase angle (ITPC), and cross-frequency coupling (PAC) indices derived from electroencephalogram (EEG) recording using spectral decomposition as unique biomarkers of Alzheimer’s Disease (AD) and amnestic mild cognitive impairment (aMCI), respectively. The experimental protocol was a visual oddball discrimination task conducted during a brief (approximately 20 minute) recording session. Participants were 60 older adults from an outpatient memory clinic diagnosed with either aMCI (n=29; M=73.0; SD=9.32) or AD (n=31; M=78.29; SD=8.28) according to NIA-AA criteria. Results indicate that ITPC values differ significantly between AD and MCI groups. Findings contribute to a growing body of literature seeking to document illness-related abnormalities in time-frequency EEG signatures that may serve as reliable indicators of the pathophysiological processes underlying the cognitive deficits observed in AD and aMCI-afflicted populations

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

Multimodal phenotyping of synaptic damage in Alzheimer’s disease : translational perspective with focus on quantitative EEG

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder and the most common form of dementia. Accumulation of AD-associated pathology in the brain may begin a decade or more before the appearance of the first symptoms of the disease. The pathological-clinical “continuum of AD” therefore encompasses time between the initial neuropathological changes and symptoms of advanced disease. Besides cognitively healthy individuals at risk, it includes subjects with subjective cognitive decline (SCD), mild cognitive impairment (MCI) and eventually dementia when the severity of cognitive impairment affects patients’ ability to carry out everyday activities. Timely detection of the disease would therefore recognize patients that are at risk for future cognitive deterioration and provide time window for the prevention and novel therapeutical interventions. Accumulating evidence suggests that degeneration and dysfunction of brain neuronal connections, i.e. synapses, is one of the earliest and best proxies of cognitive deficits in patients along AD continuum. Human electroencephalography (EEG) is a non-invasive and widely available diagnostic method that records real-time large-scale synaptic activity. The commonly used method in research settings is quantitative EEG (qEEG) analysis that provides objective information on EEG recorded at the level of the scalp. Quantitative EEG analysis unravels complex EEG signal and adds relevant information on its spectral components (frequency domain), temporal dynamics (time domain) and topographic estimates (space domain) of brain cortical activity. The general aim of the present thesis was to characterize different aspects of synaptic degeneration in AD, with the focus on qEEG and its relationship to both conventional and novel synaptic markers. In study I, global qEEG measures of power and synchronization were found to correlate with conventional cerebrospinal fluid (CSF) biomarkers of Aβ and tau pathology in patients diagnosed with SCD, MCI and AD, linking the markers of AD pathology to the generalized EEG slowing and reduced brain connectivity in fast frequency bands. In study II, qEEG analysis in the time domain (EEG microstates) revealed alterations in the organization and dynamics of large-scale brain networks in memory clinic patients compared to healthy elderly controls. In study III, topographical qEEG analysis of brain functional connectivity was associated with regionspecific cortical glucose hypometabolism ([18F]Fluorodeoxyglucose positron-emission tomography) in MCI and AD patients. Study IV provided evidence that qEEG measures of global power and synchronization correlate with CSF levels of synaptic marker neurogranin, both modalities being in combination independent predictors of progression to AD dementia in MCI patients. Study V and associated preliminary study introduced in the thesis assessed the translational potential of CSF neurogranin and qEEG as well as their direct relationship to AD neuropathology in App knock-in mouse models of AD. In study V, changes in CSF neurogranin levels and their relationship to conventional CSF markers in App knock-in mice corresponded to the pattern observed in clinical AD cohorts. These findings highlighted the potential use of mouse CSF biomarkers as well as App knock-in mouse models for translational investigation of synaptic dysfunction due to AD. In general, the results of the thesis invite for further clinical validation of multimodal synaptic markers in the context of early AD diagnosis, prognosis, and treatment monitoring in individual patients

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

Analysis of spontaneous MEG activity in mild cognitive impairment and Alzheimer's disease using spectral entropies and statistical complexity measures

Alzheimer's disease (AD) is the most common cause of dementia. Over the last few years, a considerable effort has been devoted to exploring new biomarkers. Nevertheless, a better understanding of brain dynamics is still required to optimize therapeutic strategies. In this regard, the characterization of mild cognitive impairment (MCI) is crucial, due to the high conversion rate from MCI to AD. However, only a few studies have focused on the analysis of magnetoencephalographic (MEG) rhythms to characterize AD and MCI. In this study, we assess the ability of several parameters derived from information theory to describe spontaneous MEG activity from 36 AD patients, 18 MCI subjects and 26 controls. Three entropies (Shannon, Tsallis and Rényi entropies), one disequilibrium measure (based on Euclidean distance ED) and three statistical complexities (based on Lopez Ruiz–Mancini–Calbet complexity LMC) were used to estimate the irregularity and statistical complexity of MEG activity. Statistically significant differences between AD patients and controls were obtained with all parameters (p < 0.01). In addition, statistically significant differences between MCI subjects and controls were achieved by ED and LMC (p < 0.05). In order to assess the diagnostic ability of the parameters, a linear discriminant analysis with a leave-one-out cross-validation procedure was applied. The accuracies reached 83.9% and 65.9% to discriminate AD and MCI subjects from controls, respectively. Our findings suggest that MCI subjects exhibit an intermediate pattern of abnormalities between normal aging and AD. Furthermore, the proposed parameters provide a new description of brain dynamics in AD and MCI

Sugihara Causality Analysis of Scalp EEG for Detection of Early Alzheimer\u27s Disease

Recently, Sugihara proposed an innovative causality concept, which, in contrast to statistical predictability in Granger sense, characterizes underlying deterministic causation of the system. This work exploits Sugihara causality analysis to develop novel EEG biomarkers for discriminating normal aging from mild cognitive impairment (MCI) and early Alzheimer\u27s disease (AD). The hypothesis of this work is that scalp EEG based causality measurements have different distributions for different cognitive groups and hence the causality measurements can be used to distinguish between NC, MCI, and AD participants. The current results are based on 30-channel resting EEG records from 48 age-matched participants (mean age 75.7 years) - 15 normal controls (NCs), 16 MCI, and 17 early-stage AD. First, a reconstruction model is developed for each EEG channel, which predicts the signal in the current channel using data of the other 29 channels. The reconstruction model of the target channel is trained using NC, MCI, or AD records to generate an NC-, MCI-, or AD-specific model, respectively. To avoid over fitting, the training is based on the leave-one-out principle. Sugihara causality between the channels is described by a quality score based on comparison between the reconstructed signal and the original signal. The quality scores are studied for their potential as biomarkers to distinguish between the different cognitive groups. First, the dimension of the quality scores is reduced to two principal components. Then, a three-way classification based on the principal components is conducted. Accuracies of 95.8%, 95.8%, and 97.9% are achieved for resting eyes open, counting eyes closed, and resting eyes closed protocols, respectively. This work presents a novel application of Sugihara causality analysis to capture characteristic changes in EEG activity due to cognitive deficits. The developed method has excellent potential as individualized biomarkers in the detection of pathophysiological changes in early-stage AD