Brain event-related potentials: Diagnosing early-stage Alzheimer's disease

Chapman, Robert M.; Nowlis, Geoffrey H.; McCrary, John W.; Chapman, John A.; Sandoval, Tiffany C.; Guillily, Maria D.; Gardner, Margaret N.; Reilly, Lindsey A.

doi:10.1016/j.neurobiolaging.2005.12.008

Cited by 72 publications

(101 citation statements)

References 32 publications

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“…Structures in these regions have long been implicated in AD 31,32 and atrophy in these structures has been found to be predictive of disease progression. [33][34][35] The sensitivity and specificity of our results are comparable to studies using other ERP measures 8,36 as potential markers of preclinical AD. Thus, our analysis reveals a possible cognitive marker that may potentially aid early diagnosis, which needs to be confirmed with much larger population-based studies.…”

Section: Discussionsupporting

confidence: 76%

Event-related potential markers of brain changes in preclinical familial Alzheimer disease

Quiroz

Ally²,

Celone³

et al. 2011

Neurology

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Objectives: Event-related potentials (ERPs) can reflect differences in brain electrophysiology underlying cognitive functions in brain disorders such as dementia and mild cognitive impairment. To identify individuals at risk for Alzheimer disease (AD) we used high-density ERPs to examine brain physiology in young presymptomatic individuals (average age 34.2 years) who carry the E280A mutation in the presenilin-1 (PSEN1) gene and will go on to develop AD around the age of 45.Methods: Twenty-one subjects from a Colombian population with familial AD participated: 10 presymptomatic subjects positive for the PSEN1 mutation (carriers) and 11 siblings without the mutation (controls). Subjects performed a visual recognition memory test while 128-channel ERPs were recorded.Results: Despite identical behavioral performance, PSEN1 mutation carriers showed less positivity in frontal regions and more positivity in occipital regions, compared to controls. These differences were more pronounced during the 200-300 msec period. Discriminant analysis at this time interval showed promising sensitivity (72.7%) and specificity (81.8%) of the ERP measures to predict the presence of AD pathology. Conclusions:Presymptomatic PSEN1 mutation carriers show changes in brain physiology that can be detected by high-density ERPs. The relative differences observed showing greater frontal positivity in controls and greater occipital positivity in carriers indicates that control subjects may use frontally mediated processes to distinguish between studied and unstudied visual items, whereas carriers appear to rely more upon perceptual details of the items to distinguish between them. These findings also demonstrate the potential usefulness of ERP brain correlates as preclinical markers of AD. Neurology Recognition memory impairments in Alzheimer disease (AD) have been linked to neocortical association areas including temporal and parietal lobes.1 Event-related potentials (ERPs) are less expensive, more widely available, and more comfortable than many other imaging modalities (e.g., MRI, PET, SPECT). ERPs, along with other EEG measures, have proven to be a useful marker in neurodegenerative conditions. 2-5 ERP components of recognition memory are sensitive to decline in old age 6 and amnestic mild cognitive impairment (aMCI). 7 Studies have proposed ERPs as a sensitive method for early detection of AD, separating EEG activity related to AD pathology from normal aging. [8][9][10][11][12] Preclinical markers and early detection are increasingly

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Section: Discussionsupporting

confidence: 76%

Event-related potential markers of brain changes in preclinical familial Alzheimer disease

Quiroz

Ally²,

Celone³

et al. 2011

Neurology

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“…The EEG data used here have been analyzed in previous studies concerning early diagnosis of AD (Chapman et al, 2007;Cichocki et al, 2005;Hogan et al, 2003;Musha et al, 2002;Vialatte et al, 2005).…”

Section: Eeg Datamentioning

confidence: 99%

“…Finally, it should be noted that the MMSE scores of the MCI subjects studied here are quite high compared to a number of other studies. For example, in (Hogan et al, 2003) the inclusion criterion was MMSE = 20, with a mean value of 23.7, while in (Chapman et al, 2007), the criterion was MMSE = 22 (the mean value was not provided); thus, the disparity in cognitive ability between the MCI and control subjects is comparatively small, making the present classification task relatively difficult.…”

Section: Eeg Datamentioning

confidence: 99%

A comparative study of synchrony measures for the early diagnosis of Alzheimer's disease based on EEG

Dauwels

Vialatte

Musha³

et al. 2010

NeuroImage

366

384

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It is well known that EEG signals of Alzheimer's disease (AD) patients are generally less synchronous than in age-matched control subjects. However, this effect is not always easily detectable. This is especially the case for patients in the pre-symptomatic phase, commonly referred to as Mild Cognitive Impairment (MCI), during which neuronal degeneration is occurring prior to the clinical symptoms appearance. In this paper, various synchrony measures are studied in the context of AD diagnosis, including the correlation coefficient, mean-square and phase coherence, Granger causality, phase synchrony indices, information-theoretic divergence measures, state space based measures, and the recently proposed stochastic event synchrony measures. Experiments with EEG data show that many of those measures are strongly correlated (or anti-correlated) with the correlation coefficient, and hence, provide little complementary information about EEG synchrony. Measures that are only weakly correlated with the correlation coefficient include the phase synchrony indices, Granger causality measures, and stochastic-event synchrony measures. In addition, those three families of synchrony measures are mutually uncorrelated, and therefore, they each seem to capture a specific kind of interdependence. For the data set at hand, only two synchrony measures are able to convincingly distinguish AD patients from age-matched control patients, i.e., Granger causality (in particular, full-frequency directed transfer function) and stochastic event synchrony. Those two measures are used as features to distinguish MCI patients from age-matched control subjects, yielding a leave-one-out classification rate of 83%. The classification performance may be further improved by adding complementary features from EEG; this approach may eventually lead to a reliable EEG-based diagnostic tool for MCI and AD.

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“…Alzheimer's disease (AD) is the most common cause of dementia in people over the age of 65 years [1][2][3][4]. It is a neurodegenerative disease characterized by progressive cognitive deterioration, decline in the ability to perform daily activities, and behavioral changes.…”

Section: Introductionmentioning

confidence: 99%

“…Some of these methods work as preprocessing or de-noising, such as blind source separation (BSS) [9,10]. Some of these methods work as feature extraction, such as windowed signal power (WP) [10], bipolarity [11], coherence or synchronization [12,13], even-related potentials (ERP)-based feature [14], power frequency (PF) [15,16] and stochastic synchrony [17,18]. Other methods work as classifier, such as linear discriminant analysis (LDA) [9], sample entropy (SE) [5,6,[19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

The use of kurtosis de-noising for EEG analysis of patients suffering from Alzheimer’s disease

Gang

Shepherd

Beggs

et al. 2015

BME

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Abstract. The use of electroencephalograms (EEGs) to diagnose and analyses Alzheimer's disease (AD) has received much attention in recent years. The sample entropy (SE) has been widely applied to the diagnosis of AD. In our study, nine EEGs from 21 scalp electrodes in 3 AD patients and 9 EEGs from 3 age-matched controls are recorded. The calculations show that the kurtoses of the AD patients' EEG are positive and much higher than that of the controls. This finding encourages us to introduce a kurtosis-based de-noising method. The 21-electrode EEG is first decomposed using independent component analysis (ICA), and second sort them using their kurtoses in ascending order. Finally, the subspace of EEG signal using back projection of only the last five components is reconstructed. SE will be calculated after the above de-noising preprocess. The classifications show that this method can significantly improve the accuracy of SE-based diagnosis. The kurtosis analysis of EEG may contribute to increasing the understanding of brain dysfunction in AD in a statistical way.

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Brain event-related potentials: Diagnosing early-stage Alzheimer's disease

Cited by 72 publications

References 32 publications

Event-related potential markers of brain changes in preclinical familial Alzheimer disease

Event-related potential markers of brain changes in preclinical familial Alzheimer disease

A comparative study of synchrony measures for the early diagnosis of Alzheimer's disease based on EEG

The use of kurtosis de-noising for EEG analysis of patients suffering from Alzheimer’s disease

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