Inter- and intra-individual variability in alpha peak frequency

By Saskia Haegens, Helena Cousijn, George Wallis, Paul J. Harrison, Anna Christina Nobre. NeuroImage, 2014

576 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/83ddc40e9f708458092c7ad76563ba243e869847

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Citations in the corpus

• Alpha-beta and gamma rhythms subserve feedback and feedforward influences among human visual cortical areas (2016, unknown): “Alpha- and beta-band rhythms vary in frequency across subjects and can be influenced by stimulus and task conditions (Haegens et al., 2014).”
• Parameterizing neural power spectra into periodic and aperiodic components (2020, methodology): “To summarize, periodic parameters such as frequency 11,12 , power 9,10 and potentially bandwidth, as well as the aperiodic parameters of broadband offset 13–15 and exponent 8,16–19 , can and do change in behaviorally and physiologically meaningful ways, with some emerging evidence suggesting that…”
• Resting-state periodic and aperiodic brain oscillations from birth to preschool years: Aperiodic maturity predicts developmental course (2026, unknown): “As the dominant oscillation periodic activity is primarily generated in parietal-occipital brain regions (Berger, 1929; Haegens, 2014; Sal-melin and Hari, 1994), assessment of periodic neural activity in the parietal-occipital ROI was of primary interest.”
• Resting-state EEG correlates of sustained attention in healthy ageing: Cross-sectional findings from the LEISURE study. (2024, background): “IAF variability has been observed during tasks that increase cognitive load and during memory consolidation (Haegens et al., 2014; Klimesch et al., 1993) and has been associated with a variety of cognitive and motor functions, such as processing speed (Klimesch et al., 1996), working memory…”
• Neurophysiological mechanisms of cognition in the developing brain: Insights from intracranial EEG studies (2023, background): “For example, peak alpha frequency, i.e., the center frequency of the largest peak in the scalp EEG, is associated with visual perception precision (Samaha and Postle, 2015), working memory load (Haegens et al., 2014), and general intelligence (Grandy et al., 2013).”
• Spontaneous α Brain Dynamics Track the Episodic “When” (2023, background): “Given this result, we explored iAPF (Haegens et al., 2014), which has been implicated in numerous perceptual timing experiments (Samaha and Postle, 2015; Cecere et al., 2017; Minami and Amano, 2017; Ronconi et al., 2018; Mioni et al., 2020).”
• Rhythmicity Revisited: Evidence from Robust Behavioral Oscillations (2025, unknown): “Theta rhythms, for example, support multi-36 item working memory maintenance, slowing to accommodate more items 9–11 , whereas alpha rhythms often accelerate under higher cognitive load 12 .”
• Closing the loop between brain and electrical stimulation: towards precision neuromodulation treatments (2023, methodology): “…approach for identi fi cation of optimal parameters (e.g., improving tES ef fi cacy by application of stimulation with the individual dominant frequency [144 – 146], phase [147, 148], or electrode placement [149] according to individual functional maps, as reported in previous studies).”
• Day‐to‐day individual alpha frequency variability measured by a mobile EEG device relates to anxiety (2023, background): “Although some participants have been previously observed to lack a systematic alpha peak (Haegens et al., 2014), all participants in this study had clear alpha peaks.”
• Modulation of Peak Alpha Frequency Oscillations During Working Memory Is Greater in Females Than Males (2021, background): “The alpha oscillatory activity of a neurotypical young to middleage adult, measured when they are awake in a resting state, will on average exhibit a peak of power near the frequency of 10 Hz (Posthuma et al., 2001; Clark et al., 2004; Haegens et al., 2014).”