Multiway Array Decomposition Analysis of EEGs in Alzheimer’s Disease

Methods for the extraction of features from physiological datasets are growing needs as clinical investigations of Alzheimer’s disease (AD) in large and heterogeneous population increase. General tools allowing diagnostic regardless of recording sites, such as different hospitals, are essential and if combined to inexpensive non-invasive methods could critically improve mass screening of subjects with AD. In this study, we applied three state of the art multiway array decomposition (MAD) methods to extract features from electroencephalograms (EEGs) of AD patients obtained from multiple sites. In comparison to MAD, spectral-spatial average filter (SSFs) of control and AD subjects were used as well as a common blind source separation method, algorithm for multiple unknown signal extraction (AMUSE). We trained a feed-forward multilayer perceptron (MLP) to validate and optimize AD classification from two independent databases. Using a third EEG dataset, we demonstrated that features extracted from MAD outperformed features obtained from SSFs AMUSE in terms of root mean squared error (RMSE) and reaching up to 100% of accuracy in test condition. We propose that MAD maybe a useful tool to extract features for AD diagnosis offering great generalization across multi-site databases and opening doors to the discovery of new characterization of the disease

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

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&lt;0.05) were found in the FP1-FP2 electrode pairs. Meanwhile, significant differences (p&lt;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

EARLY DETECTION OF DEMENTIA USING THE HUMAN ELECTROENCEPHALOGRAM

Improved life expectancy has led to a significant increase in the number of people in the high-risk age groups that will develop Alzheimer's disease and other dementia. Efforts are being made to develop treatments that slow the progress of these diseases. However, unless a sufferer is diagnosed in the early stages the treatments cannot give the maximum benefit. Therefore, there is an urgent need for a practical, decision support tool that will enable the earliest possible detection of dementia within the large at-risk population. Current techniques such as Magnetic Resonance Imaging (MRI) that are used to diagnose and assess neurological disorders require specialist equipment and expert clinicians to interpret results. Such techniques are inappropriate as a method of detecting individual subjects with early dementia within the large at-risk population, because everyone within the at-risk group would need to be tested regularly and this would carry a very high cost. Therefore, it is desirable to develop a low cost method of assessment. This thesis describes research into the use of automated EEG analysis to provide the required testing for dementia. The research begins with a review of previous automated EEG analysis, particularly fractal dimension measures. Initial investigation into the nature of the fractal dimension of the EEG are conducted, including problems encountered when applying fractal measures in affine space. More appropriate fractal methods were evaluated and the most promising of these methods was blind tested using an independent clinical data set. This method was estimated to achieve 67% sensitivity to probable early Alzheimer's disease and 17% sensitivity to vascular dementia (as confirmed by a clinical neurophysiologist from the EEG) with a specificity of 99.9%.Department of Neurophysiology, Derriford Hospital, Plymout

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

Physiological complexity of EEG as a proxy for dementia risk prediction: a review and preliminary cross-section analysis

The aim of this work is to give the readers a review (perspective) of prior work on this kind of complexity-based detection from resting-state EEG and present our preliminary cross-section analysis results on how EEG complexity of supposedly healthy senior persons can serve as an early warning to clinicians. Together with the use of wearables for health, this approach to early detection can be done out of clinical setting improving the chances of increasing the quality of life in seniors.Comment: 19 pages, 1 figure, 1 tabl

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 (&lt;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

Understanding the temporal dynamics of visual hallucinations in Parkinson's Disease with dementia

PhD ThesisBackground Integrative models of visual hallucinations (VH) posit that the symptom requires disruptions to both bottom-up and top-down visual processing. Although many lines of evidence point to a mixture of aberrant processing and disconnection between key nodes in the visual system, in particular the dorsal and ventral attention networks, there have been no attempts to understand the dynamic behaviour of these systems in Parkinson’s disease with dementia (PDD) with VH. Aims The primary aim of this thesis was to explore the correlates of synaptic communication in the visual system and how spatio-temporal dynamics of the early visual system are altered in relation to the severity of VH. The secondary aim was to help understand the balance between the contributions of bottom-up and top-down processing for the experience of VH in PDD. Methods An assortment of investigative approaches, including resting state electroencephalography (EEG), visual evoked potentials (VEPs), and concurrent EEG and transcranial magnetic stimulation (TMS) were applied in a group of PDD patients with a range of VH severities (n = 26) and contrasted with a group of age matched healthy controls (n = 17). Results Latency of the N1 component was similar between groups, suggesting intact transfer between the retina and the cortex. However, PDD patients had an inherent reduction in the amplitude of the VEP components and displayed a pattern of declining P1 latencies in association with more frequent and severe VH. Evoked potentials arising from TMS of the striate cortex were similar in amplitude and latency for each of the components between PDD and controls. However, inter-component activity at several stages was altered in the PDD group, whilst the frequency and severity of VH was positively associated with the amplitudes of several components in the occipital and parietal regions. Finally, attentional modulation as measured by the alpha-band reactivity was also compromised in PDD patients. iv Conclusions These data provide neurophysiological evidence that both early bottom-up and top-down dysfunctions of the visual system occur in PDD patients who hallucinate, thus supporting integrative models of VH.National Institute for Health Research (NIHR) Biomedical Research Unit (BRU)

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