Basic mechanisms of cerebral rhythmic activities

Steriade, Mircea; Gloor, P.; Llinás, R.; Silva, F.H. Lopes da; Mesulam, M-Marsel

doi:10.1016/0013-4694(90)90001-z

Cited by 1,057 publications

(399 citation statements)

References 124 publications

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“…The specificity of BOLD changes in relation to alpha fluctuations is easy to understand when considering the high tvalue of negative and positive correlation obtained in previous studies. These results confirm animal studies (Lopes da Silva and Storm van Leeuwen, 1997;Steriade et al, 1990) reporting two different components in the generation of the alpha rhythm: the rhythmic thalamic activity and a cortico-cortical component that contributes to the generation of a cortical domain of alpha and its propagation over the cortex. Positive correlation of BOLD in the thalami could be explained by the rhythmic depolarization leading to repetitive bursts of action potential.…”

Section: Cerebral Correlates Of Alphasupporting

confidence: 90%

Effects of fluctuating physiological rhythms during prolonged EEG-fMRI studies

Tyvaert¹,

LeVan²,

Grova³

et al. 2008

Clinical Neurophysiology

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Objective-We evaluated BOLD correlates of alertness fluctuations commonly seen during prolonged EEG-fMRI studies to better define the brain areas active at different phases of vigilance and to assess the contribution of these fluctuations to the BOLD signal.Methods-We evaluated BOLD changes specifically related to the main physiological EEG rhythms (alpha, beta, theta, delta, spindles) in 15 epilepsy patients with rare discharges (all the regressors were included in the same general linear model to improve specificity).Results-We found a consistent effect of spindles, alpha and theta. For alpha, BOLD was positively correlated in thalami and putamen, and negatively correlated in the occipital, parietal and frontal lobes. For theta, a negative correlation was found over the parietal, temporal and frontal lobes. Spindles were correlated with a positive BOLD in thalami and putamen. Rhythm regressors added as confounds in the fMRI analysis explained at least 5% of BOLD signal variance in 6.8 ± 8.9% of gray matter voxels, a contribution which is of the order of typical changes in fMRI studies.Conclusion-First, we found specific cerebral structures involved in each main EEG rhythm generation. Second, fluctuations of these rhythms following vigilance changes are responsible for noteworthy BOLD changes. Significance: Physiological EEG rhythms may be integrated to the analysis of EEG-fMRI in studies with fluctuation of alertness, to eliminate possible confounding factors.

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Section: Cerebral Correlates Of Alphasupporting

confidence: 90%

Effects of fluctuating physiological rhythms during prolonged EEG-fMRI studies

Tyvaert¹,

LeVan²,

Grova³

et al. 2008

Clinical Neurophysiology

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“…A lot of studies have been reported inconsistent results about pain and its effect on brain mechanisms. However, despite its strong theoretical basis, experimental results are not satisfactory and all of these papers studied only the existence or nonexistence of pain [1]- [10]. In this study, we have shown that brain mechanism has changed significantly comparing pain and no-pain levels.…”

Section: Discussionmentioning

confidence: 74%

“…In general, the coherence function defined as below; two zero-mean random process x and y have been considered, the coherence between x and y at frequency f is defined: (1) where is the cross-spectral between x and y. It can be shown that if is close to 1, then x can be linearly approximated of y.…”

Section: B Wavelet Coherencymentioning

confidence: 99%

“…Studying evoked brain potentials can access brain responses to phasic pain. Steriade et al has been studied the relation between brain status and cortical EEG activities [1], [2].…”

Section: Introductionmentioning

confidence: 99%

“…Lopes and Steriade et al studied the tonic experimental pain stimulus, its effect on EEG subbands in the cortical areas and reported decrease in alpha and increase in gamma amplitude [1]- [3]. A few EEG changes have been repeatedly observed during processing of tonic pain.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Pain Level Measurement Using Discrete Wavelet Transform

Vatankhah¹,

Toliyat²

2016

IJET

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Abstract-Experimental pain has extensively been used as a tool for investigating neural mechanisms and the psychological factors involved in pain processing. The detection of existence and/or level of pain is vital when verbal information is not present e.g. for infants, disabled persons, anesthetized patients and animals also. This study shows that there is a firm relation between Electroencephalogram (EEG) and chronic pain levels and EEG can be used as a reliable tool for detecting, measuring and diagnosing pain levels in humans.This paper proposed a use of wavelet coherency in order to estimate the three pain levels and its usage as an index for pain measurement. Besides, wavelet coefficients are studied to show consistencies with EEG dynamic were extracted to provide the feature vector. A Hidden Markov Model (HMM) and a support vector machine (SVM) scheme was used for pain levels classification. This study confirms the hypothesis that brain pattern under the chronic pain mental task is mapped on EEG and the dependency of brain patterns to EEG is possible and detectable.Index Terms-Chronic pain index, electroencephalogram, SVM, HMM.

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