Non-linear cortico–cortical interactions modulated by cholinergic afferences from the rat basal forebrain

Villa, Alessandro E. P.; Tetko, Igor V.; Dutoit, Pierre; Vantini, G.

doi:10.1016/s0303-2647(00)00126-x

Cited by 42 publications

(34 citation statements)

References 40 publications

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“…At rest, the afferent cholinergic inputs of these projection systems would synchronize long-range cortico-cortical connections that are also affected in AD due to loss of dendritic spines, neurons and to beta-amyloid plaque deposition. Accordingly, these cholinergic inputs would sustain the power of the alpha rhythm (Holschneider et al, 1998;Villa et al, 2000). This explanation agrees with the reduction of functional cortico-cortical coupling, revealed by EEG coherence studies, in AD patients compared to normal controls (Leocani et al, 2000) and VaD patients (Leuchter et al, 1992).…”

Section: Relative Power and Frequency Of Alpha Rhythms In Mild Dementiasupporting

confidence: 84%

Individual analysis of EEG frequency and band power in mild Alzheimer's disease

Moretti

Babiloni

Binetti

et al. 2004

Clinical Neurophysiology

327

251

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Objective: This EEG study investigates the role of the cholinergic system, cortico-cortical connections, and sub-cortical white matter on the relationship between individual EEG frequencies and their relative power bands.Methods: EEGs were recorded at rest in 30 normal elderly subjects (Nold), 60 mild Alzheimer disease (AD) and 20 vascular dementia (VaD) patients, comparable for Mini Mental State Evaluation scores . Individual EEG frequencies were indexed by the theta/alpha transition frequency (TF) and by the individual alpha frequency (IAF) with power peak in the extended alpha range (5 -14 Hz). Relative power was separately computed for delta, theta, alpha1, alpha2, and alpha3 bands, on the basis of the TF and IAF.Results: Using normal subjects as a reference, VaD patients showed 'slowing' of alpha frequency (TF-IAF) and lower alpha2 power; Mild AD patients showed lower alpha2 and alpha3 power; delta power was higher in both AD and VaD patients; Theta power was higher only in VaD patients.Conclusions: Individual analysis of the alpha frequency and power can discriminate mild AD from VaD and normal elderly subjects.Significance: This analysis may probe pathophysiological mechanisms causing AD and VaD.

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Section: Relative Power and Frequency Of Alpha Rhythms In Mild Dementiasupporting

confidence: 84%

Individual analysis of EEG frequency and band power in mild Alzheimer's disease

Moretti

Babiloni

Binetti

et al. 2004

Clinical Neurophysiology

327

251

View full text Add to dashboard Cite

show abstract

“…For the purpose of AD diagnosis, two branches of EEG signal analysis have emerged: spectral and nonlinear dynamics [5]. Pioneering spectral analysis studies showed that AD patients presented increased activity in the delta (0.1-4 Hz) and theta (4-8 Hz) frequency bands, as well as decreased activity in the alpha (8)(9)(10)(11)(12) and beta (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) Hz) bands [6][7][8][9][10][11], thus suggesting a slowing of the EEG signal. Moreover, reduced spectral coherence between the two hemispheres was shown between the alpha and beta frequency bands [12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

EEG amplitude modulation analysis for semi-automated diagnosis of Alzheimer’s disease

Falk

Fraga

Trambaiolli

et al. 2012

EURASIP J. Adv. Signal Process.

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Recent experimental evidence has suggested a neuromodulatory deficit in Alzheimer's disease (AD). In this paper, we present a new electroencephalogram (EEG) based metric to quantitatively characterize neuromodulatory activity. More specifically, the short-term EEG amplitude modulation rate-of-change (i.e., modulation frequency) is computed for five EEG subband signals. To test the performance of the proposed metric, a classification task was performed on a database of 32 participants partitioned into three groups of approximately equal size: healthy controls, patients diagnosed with mild AD, and those with moderate-to-severe AD. To gauge the benefits of the proposed metric, performance results were compared with those obtained using EEG spectral peak parameters which were recently shown to outperform other conventional EEG measures. Using a simple feature selection algorithm based on area-under-the-curve maximization and a support vector machine classifier, the proposed parameters resulted in accuracy gains, relative to spectral peak parameters, of 21.3% when discriminating between the three groups and by 50% when mild and moderate-to-severe groups were merged into one. The preliminary findings reported herein provide promising insights that automated tools may be developed to assist physicians in very early diagnosis of AD as well as provide researchers with a tool to automatically characterize cross-frequency interactions and their changes with disease.

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“…Several authors have suggested diverse mechanisms that can be responsible for these alterations in the electromagnetic brain dynamics. An animal model has indicated that acetylcholine loss produces a decrease of highfrequency and an increase of slow-frequency couplings (Villa et al 2000). Considering this finding, it may be hypothesized that the abnormalities found in EEG and MEG dynamics result from anatomical disconnections among different cortical regions, which are essential for interactions between brain regions (Jelles et al 2008), or reduced cholinergic coupling interactions between cortical neurons (Jeong 2004).…”

Section: Discussionmentioning

confidence: 99%

Nonlinear analysis of electroencephalogram and magnetoencephalogram recordings in patients with Alzheimer's disease

Hornero

Abásolo

Escudero

et al. 2008

Phil. Trans. R. Soc. A.

164

158

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The aim of the present study is to show the usefulness of nonlinear methods to analyse the electroencephalogram (EEG) and magnetoencephalogram (MEG) in patients with Alzheimer's disease (AD). The following nonlinear methods have been applied to study the EEG and MEG background activity in AD patients and control subjects: approximate entropy, sample entropy, multiscale entropy, auto-mutual information and Lempel-Ziv complexity. We discuss why these nonlinear methods are appropriate to analyse the EEG and MEG. Furthermore, the performance of all these methods has been compared when applied to the same databases of EEG and MEG recordings. Our results show that EEG and MEG background activities in AD patients are less complex and more regular than in healthy control subjects. In line with previous studies, our work suggests that nonlinear analysis techniques could be useful in AD diagnosis.

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Non-linear cortico–cortical interactions modulated by cholinergic afferences from the rat basal forebrain

Cited by 42 publications

References 40 publications

Individual analysis of EEG frequency and band power in mild Alzheimer's disease

Individual analysis of EEG frequency and band power in mild Alzheimer's disease

EEG amplitude modulation analysis for semi-automated diagnosis of Alzheimer’s disease

Nonlinear analysis of electroencephalogram and magnetoencephalogram recordings in patients with Alzheimer's disease

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