Power spectral density and coherence analysis of Alzheimer’s EEG

Wang, Ruofan; Jiang, Weixiong; Yu, Haitao; Wei, Xile; Yang, Chen; Deng, Bin

doi:10.1007/s11571-014-9325-x

Cited by 156 publications

(93 citation statements)

References 74 publications

(88 reference statements)

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“…Power analysis of source-reconstructed MEG signals confirmed the characteristic changes in the oscillatory activity of AD subjects compared to HC subjects ( Fig. 2a) (53)(54)(55)(56). Significant alpha power decreases were more evident in the parietal and occipital regions (Z < −2.58), while significant delta power increases (Z > 2.58) were more localized in the frontal regions of the cortex (Fig.…”

Section: Resultssupporting

confidence: 53%

Loss of brain inter-frequency hubs in Alzheimer's disease

Guillon

Attal²,

Colliot

et al. 2017

Sci Rep

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Alzheimer’s disease (AD) causes alterations of brain network structure and function. The latter consists of connectivity changes between oscillatory processes at different frequency channels. We proposed a multi-layer network approach to analyze multiple-frequency brain networks inferred from magnetoencephalographic recordings during resting-states in AD subjects and age-matched controls. Main results showed that brain networks tend to facilitate information propagation across different frequencies, as measured by the multi-participation coefficient (MPC). However, regional connectivity in AD subjects was abnormally distributed across frequency bands as compared to controls, causing significant decreases of MPC. This effect was mainly localized in association areas and in the cingulate cortex, which acted, in the healthy group, as a true inter-frequency hub. MPC values significantly correlated with memory impairment of AD subjects, as measured by the total recall score. Most predictive regions belonged to components of the default-mode network that are typically affected by atrophy, metabolism disruption and amyloid-β deposition. We evaluated the diagnostic power of the MPC and we showed that it led to increased classification accuracy (78.39%) and sensitivity (91.11%). These findings shed new light on the brain functional alterations underlying AD and provide analytical tools for identifying multi-frequency neural mechanisms of brain diseases.

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Section: Resultssupporting

confidence: 53%

Loss of brain inter-frequency hubs in Alzheimer's disease

Guillon

Attal²,

Colliot

et al. 2017

Sci Rep

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“…Studies regarding EEG total power spectral densities, as well as spectral densities of individual frequency bands is a concept not strictly confined to epilepsy, but also widely applicable to studies of Alzheimer's disease and generalized anxiety disorder (32,33). The study about Alzheimer's disease has noted reduction in high-frequency and increase in low-frequency bands, much like the results this study yielded.…”

Section: Discussionsupporting

confidence: 55%

Basic characteristics of epileptiform discharges triggered by lindane in rats

Useinović¹,

Vorkapić²,

Leković³

et al. 2018

Medicinski podmladak

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Introduction: EEG is a widely used method of epilepsy examination. In order to quantitatively inspect ictal EEG findings, a number of mathematical models have been developed over the years, one of them being the Fast Fourier Transform (FFT). It transforms the signal from time domain into frequency domain, giving information about their power spectral densities (PSD). Lindane is a well-established neurotoxic agent often used in experimental studies as a model of generalized epilepsy. This study aims to quantitatively examine the characteristics of ictal EEG activity in rats on model of generalized epilepsy induced by lindane. Materials and Methods: Wistar albino rats were used for the study. Electrodes were surgically implanted over the frontal, parietal and occipital cortices of each animal for EEG recording purposes prior to lindane administration in convulsive dose. An 8-channel EEG apparatus was used, combined with a software developed in the Laboratory (NeuroSciLaBG). Ictal EEG epochs were extracted from the original signal and FFT analysis performed to obtain information considering PSD in predefined frequency bands. Amplitude histogram feature of the software was used to differentiate ictal spikes based on their voltage. Results: FFT analysis has yielded important information regarding spectral powers in frequency domain. Ictal EEG showed considerable stratification, theta frequency band (4-7 Hz) being markedly dominant. Amplitude histogram showed the majority of spikes to be in the voltage ranges up to 250 µV, while higher voltage spikes were rarely observed. Conclusion: FFT is capable of giving important information about ictal period characteristics. Ictal periods induced by lindane are characterized by dominancy of theta rhythm and spiking activity mostly in amplitude bins up to 250 µV. FFT and amplitude histograms can be of critical importance in the future pharmacological and toxicity studies. Medicinski podmladak / Medical Youth Useinović N. et al. Basic characteristics of epileptiform discharges triggered by lindane in rats. MedPodml 2018, 69(3):69-75 70Uvod: EEG se široko primenjuje prilikom ispitivanja epilepsija. U cilju kvantitativnog ispitivanja iktalnih EEG nalaza, veliki broj matematičkih modela je razvijen tokom godina, među kojima je i Brza Furijeova Transformacija (engl. Fast Fourier Transform -FFT). Ona transformiše signal iz vremenskog domena u frekventni domen, dajući informacije o gustini spektralne snage (engl. Power Spectral Densities -PSD). Lindan je dobro poznat neurotoksični agens koji se često koristi u studijama kao model generalizovane epilepsije. Cilj ove studije je da se kvantitativno ispitaju karakteristike iktalne EEG aktivnosti kod pacova na modelu generalizovane epilepsije indukovane lindanom. Materijal i metode: Albino pacovi soja Wistar su korišćeni u studiji. Elektrode su hirurški implantirane nad frontalnim, parijetalnim i okcipitalnim korteksima svake životinje u cilju registrovanja EEG-a pre administracije lindana u konvulzivnoj dozi. EEG aparat sa 8 kanala je koriš...

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“…Mutual information analysis also showed that connections between the frontal and antero-temporal regions are weaker in AD patients [41]. Recent coherence studies using complex network analysis also support collapsed connectivity in the θ and α frequency bands [42,43]. Interestingly, in contrast to the decreased connectivity observed in AD patients, coherence increased in the θ, high β, and γ frequency bands in PD patients [44,45].…”

Section: Discussionmentioning

confidence: 87%

Wavelet Energy and Wavelet Coherence as EEG Biomarkers for the Diagnosis of Parkinson’s Disease-Related Dementia and Alzheimer’s Disease

Jeong

Kim

Song

et al. 2015

Entropy

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Abstract:Parkinson's disease (PD) and Alzheimer's disease (AD) can coexist in severely affected; elderly patients. Since they have different pathological causes and lesions and consequently require different treatments; it is critical to distinguish PD-related dementia (PD-D) from AD. Conventional electroencephalograph (EEG) analysis has produced poor results. This study investigated the possibility of using relative wavelet energy (RWE) and wavelet coherence (WC) analysis to distinguish between PD-D patients; AD patients and healthy elderly subjects. In EEG signals; we found that low-frequency wavelet energy increased and high-frequency wavelet energy decreased in PD-D patients and AD patients relative to healthy subjects. This result suggests that cognitive decline in both diseases is potentially related to slow EEG activity; which is consistent with previous studies. More importantly; WC values were lower in AD patients and higher in PD-D patients compared with healthy subjects. In particular; AD patients exhibited decreased WC primarily in the γ band and in links related to frontal regions; while PD-D patients exhibited increased WC primarily in the α and β bands and in temporo-parietal links. Linear discriminant analysis (LDA) of RWE produced a maximum accuracy of 79.18% for diagnosing PD-D and 81.25% for diagnosing AD. The discriminant accuracy was 73.40% with 78.78% sensitivity and 69.47% specificity. In distinguishing between the two diseases; the maximum performance of LDA using WC was 80.19%. We suggest that using a wavelet approach to evaluate EEG results may facilitate discrimination between PD-D and AD. In particular; RWE is useful for differentiating individuals with and without dementia and WC is useful for differentiating between PD-D and AD.

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Power spectral density and coherence analysis of Alzheimer’s EEG

Cited by 156 publications

References 74 publications

Loss of brain inter-frequency hubs in Alzheimer's disease

Loss of brain inter-frequency hubs in Alzheimer's disease

Basic characteristics of epileptiform discharges triggered by lindane in rats

Wavelet Energy and Wavelet Coherence as EEG Biomarkers for the Diagnosis of Parkinson’s Disease-Related Dementia and Alzheimer’s Disease

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