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Inter- and intra-individual variability in alpha peak frequency
Open accessAbstract
Converging electrophysiological evidence suggests that the alpha rhythm plays an important and active role in cognitive processing. Here, we systematically studied variability in posterior alpha peak frequency both between and within subjects. We recorded brain activity using MEG in 51 healthy human subjects under three experimental conditions - rest, passive visual stimulation and an N-back working memory paradigm, using source reconstruction methods to separate alpha activity from parietal and occipital sources. We asked how alpha peak frequency differed within subjects across cognitive conditions and regions of interest, and looked at the distribution of alpha peak frequency between subjects. In both regions we observed an increase of alpha peak frequency from resting state and passive visual stimulation conditions to the N-back paradigm, with a significantly higher alpha peak frequency in the 2-back compared to the 0-back condition. There was a trend for a greater increase in alpha peak frequency during the N-back task in the occipital vs. parietal cortex. The average alpha peak frequency across all subjects, conditions, and regions of interest was 10.3 Hz with a within-subject SD of 0.9 Hz and a between-subject SD of 2.8 Hz. We also measured beta peak frequencies, and except in the parietal cortex during rest, found no indication of a strictly harmonic relationship with alpha peak frequencies. We conclude that alpha peak frequency in posterior regions increases with increasing cognitive demands, and that the alpha rhythm operates across a wider frequency range than the 8-12 Hz band many studies tend to include in their analysis. Thus, using a fixed and limited alpha frequency band might bias results against certain subjects and conditions.
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Nonparametric statistical testing of EEG- and MEG-data
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THE BERGER RHYTHM: POTENTIAL CHANGES FROM THE OCCIPITAL LOBES IN MAN
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Alpha frequency, cognitive load and memory performance
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Cited by
EEG frequency bands in psychiatric disorders: a review of resting state studies
Newson, J. J., & Thiagarajan, T. C. (2019) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2018.00521 · Cited by 863
Michalareas, G., Vezoli, J., van Pelt, S., Schoffelen, J.-M., Kennedy, H., & Fries, P. (2016) · Neuron · doi:10.1016/j.neuron.2015.12.018 · Cited by 696
Where Does EEG Come From and What Does It Mean?
Cohen, M. X. (2017) · Trends in Neurosciences · doi:10.1016/j.tins.2017.02.004 · Cited by 722
Methodological considerations for studying neural oscillations
Donoghue, T., Schaworonkow, N., & Voytek, B. (2022) · European Journal of Neuroscience · doi:10.1111/ejn.15361 · Cited by 309
Ketamine has distinct electrophysiological and behavioral effects in depressed and healthy subjects
Nugent, A. C., Ballard, E. D., Gould, T. D., Park, L. T., Moaddel, R., Brutsche, N. E., & Zarate, C. A. (2019) · Molecular Psychiatry · doi:10.1038/s41380-018-0028-2 · Cited by 250
Metadata
- Authors
- Haegens, S., Cousijn, H., Wallis, G., Harrison, P. J., & Nobre, A. C.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2014-05
- Volume
- 92
- Pages
- 46-55
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W1979330140
- Cited by
- 598
- References
- 6
- FWCI
- 12.43
- Type
- journal article
- Language
- en
- Open access
- hybrid
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