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

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

Multi-dimensional profiling of elderly at-risk for Alzheimer's disease in a differential framework

International audienceThe utility of EEG in Alzheimer’s disease (AD) research has been demonstrated over several decades in numerous studies. EEG markers have been employed successfully to investigate AD-related alterations in prodromal AD and AD dementia. Preclinical AD is a recent concept and a novel target for clinical research. This project tackles two issues: first, AD prediction at the preclinical sta ge, by exploiting the multimodal INSIGHT-preAD database, acquired at the Pitié-Salpetrière Hospital; second, an automatic AD diagnosis in a differential framework, by exploiting another large-scale EEG database, acquired at Charles-Foix Hospital. In this project, we will investigate AD predictors at preclinical stage, using EEG data of only subjective Memory Complainers in order to establish a cognitive profiling of elderly at-risk. We will also identify EEG markers for AD detection at early stages in a di fferential diagnosis context. The correlation between EEG markers and clinical biomarkers will be also assessed for a better characterization of the retrieved profiles and a better understanding on the severity of the cognitive disorder. The exploited larg e-scale complementary data offer the opportunity to investigate the full spectrum of the AD neuro-degeneration changes in the brain, using a big data approach and multimodal patient profiling based on resting-state EEG marker

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

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

Decision-based data fusion of complementary features for the early diagnosis of Alzheimer\u27s disease

As the average life expectancy increases, particularly in developing countries, the prevalence of Alzheimer\u27s disease (AD), which is the most common form of dementia worldwide, has increased dramatically. As there is no cure to stop or reverse the effects of AD, the early diagnosis and detection is of utmost concern. Recent pharmacological advances have shown the ability to slow the progression of AD; however, the efficacy of these treatments is dependent on the ability to detect the disease at the earliest stage possible. Many patients are limited to small community clinics, by geographic and/or financial constraints. Making diagnosis possible at these clinics through an accurate, inexpensive, and noninvasive tool is of great interest. Many tools have been shown to be effective at the early diagnosis of AD. Three in particular are focused upon in this study: event-related potentials (ERPs) in electroencephalogram (EEG) recordings, magnetic resonance imaging (MRI), as well as positron emission tomography (PET). These biomarkers have been shown to contain diagnostically useful information regarding the development of AD in an individual. The combination of these biomarkers, if they provide complementary information, can boost overall diagnostic accuracy of an automated system. EEG data acquired from an auditory oddball paradigm, along with volumetric T2 weighted MRI data and PET imagery representative of metabolic glucose activity in the brain was collected from a cohort of 447 patients, along with other biomarkers and metrics relating to neurodegenerative disease. This study in particular focuses on AD versus control diagnostic ability from the cohort, in addition to AD severity analysis. An assortment of feature extraction methods were employed to extract diagnostically relevant information from raw data. EEG signals were decomposed into frequency bands of interest hrough the discrete wavelet transform (DWT). MRI images were reprocessed to provide volumetric representations of specific regions of interest in the cranium. The PET imagery was segmented into regions of interest representing glucose metabolic rates within the brain. Multi-layer perceptron neural networks were used as the base classifier for the augmented stacked generalization algorithm, creating three overall biomarker experts for AD diagnosis. The features extracted from each biomarker were used to train classifiers on various subsets of the cohort data; the decisions from these classifiers were then combined to achieve decision-based data fusion. This study found that EEG, MRI and PET data each hold complementary information for the diagnosis of AD. The use of all three in tandem provides greater diagnostic accuracy than using any single biomarker alone. The highest accuracy obtained through the EEG expert was 86.1 ±3.2%, with MRI and PET reaching 91.1 +3.2% and 91.2 ±3.9%, respectively. The maximum diagnostic accuracy of these systems averaged 95.0 ±3.1% when all three biomarkers were combined through the decision fusion algorithm described in this study. The severity analysis for AD showed similar results, with combination performance exceeding that of any biomarker expert alone

Abnormal reactivity of resting-state EEG alpha rhythms during eyes open in patients with Alzheimer's and Lewy body diseases

Previous studies suggest that resting-state electroencephalographic (rsEEG) rhythms recorded in old patients with dementia due to different neurodegenerative diseases have a significant heuristic and clinical potential in identifying peculiar abnormalities of the ascending activating systems and reciprocal thalamocortical circuits in which oscillatory (de)synchronizing signals dynamically underpin cortical arousal in the regulation of quiet vigilance. In the present PhD program, a new methodological approach based on rsEEG cortical source estimation and individually-based frequency bands was used to test the hypothesis of significant abnormalities in the neurophysiological oscillatory mechanisms underlying the regulation of the quiet vigilance during the transition from an eyes-closed to an eyes-open condition in patients with the most prevalent neurodegenerative dementing disorders such as Alzheimer’s disease and Lewy Body and Parkinson’s diseases and initial abnormalities in the prodromal stage of ADD, characterized by mild cognitive impairment. Three rsEEG studies were performed for that purpose. In the first study, we tested if the reactivity of posterior rsEEG alpha rhythms from the eye- closed to the eyes-open condition may differ in patients with dementia due to Lewy Bodies (DLB) and Alzheimer’s disease (ADD) as a functional probe of the dominant neural synchronization mechanisms regulating the vigilance in posterior visual systems. We used clinical, demographical, and rsEEG datasets in 28 healthy elderly (Healthy) seniors, 42 DLB, and 48 ADD participants. The eLORETA freeware estimated rsEEG cortical sources at individual delta, theta, and alpha frequencies. Results showed a substantial (> -10%) reduction in the posterior alpha activities during the eyes-open condition in 24 Healthy, 26 ADD, and 22 DLB subjects. There were lower reductions in the posterior alpha activities in the ADD and DLB groups than in the Healthy group. The reduction in the occipital region was lower in the DLB than in the ADD group. These results suggest that DLB patients may suffer a greater alteration in the neural synchronization mechanisms regulating vigilance in occipital cortical systems compared to ADD patients. In the second study, we hypothesized that the vigilance dysregulation seen in PDD patients might be reflected by altered reactivity of posterior rsEEG alpha rhythms during the vigilance transition from an eyes-closed to an eyes-open condition. We used clinical, demographical, and rsEEG datasets in 28 healthy elderly (Healthy), 73 PDD, and 35 ADD participants. We have applied the same methodology used for the first study. Results showed substantial (> -10%) reduction (reactivity) in the posterior alpha source activities from the eyes-closed to the eyes-open condition in 88% of the Healthy seniors, 57% of the ADD patients, and only 35% of the PDD patients. In these alpha-reactive participants, there was lower reactivity in the parietal alpha source activities in the PDD group than in the Healthy and the ADD groups. These results suggest that PDD is characterized by poor reactivity of mechanisms desynchronizing posterior rsEEG alpha rhythms in response to visual inputs. This finding could be an interesting biomarker of impaired vigilance regulation in quiet wakefulness in PDD patients. Indeed, such biomarkers may provide endpoints for pharmacological intervention and brain electromagnetic stimulations to improve the PDD patients’ general ability to regulate vigilance and primary visual consciousness in the activities of daily living. In the third study, we tested the exploratory hypothesis that rsEEG alpha rhythms may predict and be sensitive to mild cognitive impairment due to AD (ADMCI) progression at a 6-month follow- up (a relevant feature for intervention clinical trials). Clinical, neuroimaging, and rsEEG datasets in 52 ADMCI and 60 Healthy seniors were used. We applied the same methodology used for the first and the second studies. Results showed a substantial (> -10%) reduction in the posterior alpha source activities during the eyes-open condition in about 90% and 70% of the Healthy and ADMCI participants, respectively. In the younger ADMCI patients (mean age of 64.3±1.1) with “reactive” rsEEG alpha source activities, posterior alpha source activities during the eyes closed condition predicted the global cognitive status at the 6-month follow-up. In all ADMCI participants with “reactive” rsEEG alpha source activities, posterior alpha source activities during the eyes-closed condition reduced in magnitude at that follow-up. These effects could not be explained by neuroimaging and neuropsychological biomarkers of AD. These results suggest that in ADMCI patients, the true (“reactive”) posterior rsEEG alpha rhythms, when present, predict (in relation to younger age) and are quite sensitive to the effects of the disease progression on neurophysiological mechanisms underpinning vigilance regulation. The results of the three studies unveiled the significant extent to which the well-known impairments in the cholinergic and dopaminergic neuromodulatory ascending systems could affect the brain neurophysiological oscillatory mechanisms underpinning the reactivity of rsEEG alpha rhythms during eyes open and, then, the regulation of quiet vigilance in ADD, PDD, and DLB patients, thus enriching the neurophysiological model underlying their known difficulties to remain awake in quiet environmental conditions during daytime

Ensemble of classifiers based data fusion of EEG and MRI for diagnosis of neurodegenerative disorders

The prevalence of Alzheimer\u27s disease (AD), Parkinson\u27s disease (PD), and mild cognitive impairment (MCI) are rising at an alarming rate as the average age of the population increases, especially in developing nations. The efficacy of the new medical treatments critically depends on the ability to diagnose these diseases at the earliest stages. To facilitate the availability of early diagnosis in community hospitals, an accurate, inexpensive, and noninvasive diagnostic tool must be made available. As biomarkers, the event related potentials (ERP) of the electroencephalogram (EEG) - which has previously shown promise in automated diagnosis - in addition to volumetric magnetic resonance imaging (MRI), are relatively low cost and readily available tools that can be used as an automated diagnosis tool. 16-electrode EEG data were collected from 175 subjects afflicted with Alzheimer\u27s disease, Parkinson\u27s disease, mild cognitive impairment, as well as non-disease (normal control) subjects. T2 weighted MRI volumetric data were also collected from 161 of these subjects. Feature extraction methods were used to separate diagnostic information from the raw data. The EEG signals were decomposed using the discrete wavelet transform in order to isolate informative frequency bands. The MR images were processed through segmentation software to provide volumetric data of various brain regions in order to quantize potential brain tissue atrophy. Both of these data sources were utilized in a pattern recognition based classification algorithm to serve as a diagnostic tool for Alzheimer\u27s and Parkinson\u27s disease. Support vector machine and multilayer perceptron classifiers were used to create a classification algorithm trained with the EEG and MRI data. Extracted features were used to train individual classifiers, each learning a particular subset of the training data, whose decisions were combined using decision level fusion. Additionally, a severity analysis was performed to diagnose between various stages of AD as well as a cognitively normal state. The study found that EEG and MRI data hold complimentary information for the diagnosis of AD as well as PD. The use of both data types with a decision level fusion improves diagnostic accuracy over the diagnostic accuracy of each individual data source. In the case of AD only diagnosis, ERP data only provided a 78% diagnostic performance, MRI alone was 89% and ERP and MRI combined was 94%. For PD only diagnosis, ERP only performance was 67%, MRI only was 70%, and combined performance was 78%. MCI only diagnosis exhibited a similar effect with a 71% ERP performance, 82% MRI performance, and 85% combined performance. Diagnosis among three subject groups showed the same trend. For PD, AD, and normal diagnosis ERP only performance was 43%, MRI only was 66%, and combined performance was 71%. The severity analysis for mild AD, severe AD, and normal subjects showed the same combined effect

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