Normal and pathological changes in alpha rhythms

Samson-Dollfus, D; Delapierre, G.; Marcolino, Clarice; Blondeau, Corinne

doi:10.1016/s0167-8760(97)00778-2

Cited by 26 publications

(15 citation statements)

References 35 publications

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“…49 On the other hand, we point out that although with lower (absolute) t-values, we found significant cortical reactivity sources for frequencies different from alpha in some of the studied subjects. Two examples in which this phenomenon is evident in all frequency bands are shown in Figure 3.…”

Section: Discussion and Conclusion Localization Of Cortical Sources mentioning

confidence: 42%

Source Analysis of Alpha Rhythm Reactivity Using LORETA Imaging with 64-Channel EEG and Individual MRI

Cuspineda¹,

Machado²,

Virués

et al. 2009

Clin EEG Neurosci

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Conventional EEG and quantitative EEG visual stimuli (close-open eyes) reactivity analysis have shown their usefulness in clinical practice; however studies at the level of EEG generators are limited. The focus of the study was visual reactivity of cortical resources in healthy subjects and in a stroke patient. The 64 channel EEG and T1 magnetic resonance imaging (MRI) studies were obtained from 32 healthy subjects and a middle cerebral artery stroke patient. Low Resolution Electromagnetic Tomography (LORETA) was used to estimate EEG sources for both close eyes (CE) vs. open eyes (OE) conditions using individual MRI. The t-test was performed between source spectra of the two conditions. Thresholds for statistically significant t values were estimated by the local false discovery rate (lfdr) method. The Z transform was used to quantify the differences in cortical reactivity between the patient and healthy subjects. Closed-open eyes alpha reactivity sources were found mainly in posterior regions (occipito-parietal zones), extended in some cases to anterior and thalamic regions. Significant cortical reactivity sources were found in frequencies different from alpha (lower t-values). Significant changes at EEG reactivity sources were evident in the damaged brain hemisphere. Reactivity changes were also found in the "healthy" hemisphere when compared with the normal population. In conclusion, our study of brain sources of EEG alpha reactivity provides information that is not evident in the usual topographic analysis.

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Section: Discussion and Conclusion Localization Of Cortical Sources mentioning

confidence: 42%

Source Analysis of Alpha Rhythm Reactivity Using LORETA Imaging with 64-Channel EEG and Individual MRI

Cuspineda¹,

Machado²,

Virués

et al. 2009

Clin EEG Neurosci

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“…A relaxed waking state is the optimal condition for the posterior alpha rhythm, so EEG can serve as a tool for the assessment of arousal (Niedermeyer, 1999). A reduction of the alpha frequency can indicate exogenous intoxication (e.g., alcohol) or occur in demented patients (Samson-Dollfus et al, 1997). Alpha frequency also systematically changes with age, as has been well documented since Smith (1941), which suggests that alpha frequency is an indicator of neural maturation or myelination.…”

Section: Introductionmentioning

confidence: 88%

“…As with the conventional alpha, which is blocked by visual stimuli, the mu rhythm is blocked by movements (Niedermeyer, 1997), and the tau rhythm is blocked by auditory stimuli. Thus in the traditional alpha rhythm range there could exist a number of peaks, each differently modulated by experimental conditions and associated with a particular region of the brain (Samson-Dollfus et al, 1997). It is possible for more than one alpha rhythm to be present in a single recording.…”

Section: Introductionmentioning

confidence: 99%

Automated characterization of multiple alpha peaks in multi-site electroencephalograms

Chiang

Rennie

Robinson

et al. 2008

Journal of Neuroscience Methods

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“…Therefore, the current results may be specific to the age groups tested here and further research would be required using other age groups for greater understanding of dynamics. Furthermore, with younger children, the delta and theta frequency bands are known to be greater contributors to the EEG [25,26] than for the age group tested here so the role of additional frequency should also be assessed.…”

Section: Alpha-wave Characteristicsmentioning

confidence: 99%

“…EEG frequency bands vary across different age groups in their dominance and persistence and consequently summate with evoked potential recordings to varying degrees [24]. For example, in infancy, the delta frequency bands (\4 Hz) dominate the EEG recorded at the occipital cortex (Oz) [25], the theta frequencies (4 to \8 Hz) become more prominent in slightly older children [26] and the alpha frequencies (8)(9)(10)(11)(12)(13) dominate in children aged 5-12 years. For the age group in the present study, alpha rhythms are a notable background EEG frequency band and therefore have the potential to contribute to electrophysiological VEP recordings.…”

Section: Introductionmentioning

confidence: 99%

EEG alpha rhythms and transient chromatic and achromatic pattern visual evoked potentials in children and adults

et al. 2011

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Transient chromatic pattern visual evoked potentials (VEPs) have been found to be less repeatable in morphology in children than in adults at low to moderate chromatic contrasts. The purpose of this study is to investigate whether low repeatability of VEP components can be associated with high alpha power, in a comparison of alpha activity in children and adults. Transient chromatic contrast and achromatic resolution VEPs were recorded in children (n = 14, mean 9.6 years) and adults (n = 12, mean 21.8 years) with normal vision and assessed for repeatability. Isoluminant chromatic (magenta-cyan) and luminance-modulated achromatic grating stimuli were presented at and above psychophysical threshold levels, in pattern onset-offset at 2 Hz temporal frequency. EEGs (eyes closed and open) were recorded as single sweeps (1 s long) over three 30 s periods while facing a uniform computer display. An index of VEP detectability by observation was developed based on VEP component repeatability. The index was examined for correlations with alpha-wave parameters. Alpha power was calculated as the sum of the powers of 8-13 Hz frequencies of the EEG sweeps (using the discrete Fourier transform). Alpha power variability was calculated using the standard deviation of the powers of each sweep in a 30 s time period. The children had significantly higher alpha powers than the adults for both the eyes-open and eyes-closed conditions. Alpha power variability was significantly higher for the eyes-open condition only. There was no relationship between alpha power parameters and index of VEP detectability by observation for both the chromatic and achromatic grating stimuli. Poor repeatability of transient pattern VEPs is not associated with high alpha power or its variability in EEG measurements in older children or young adults at Oz.

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Normal and pathological changes in alpha rhythms

Cited by 26 publications

References 35 publications

Source Analysis of Alpha Rhythm Reactivity Using LORETA Imaging with 64-Channel EEG and Individual MRI

Source Analysis of Alpha Rhythm Reactivity Using LORETA Imaging with 64-Channel EEG and Individual MRI

Automated characterization of multiple alpha peaks in multi-site electroencephalograms

EEG alpha rhythms and transient chromatic and achromatic pattern visual evoked potentials in children and adults

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