Elsevier

Neuroscience

Volume 360, 30 September 2017, Pages 146-154
Neuroscience

Review
State-dependent alpha peak frequency shifts: Experimental evidence, potential mechanisms and functional implications

https://doi.org/10.1016/j.neuroscience.2017.07.037Get rights and content
Under a Creative Commons license
open access

Highlights

  • Alpha oscillations have emerged as one of the most robust predictors of brain state.

  • Alpha frequency fluctuates at shorter time scales as function of behavioral demands.

  • Adaptation of alpha peak frequency is hypothesized to influence perception and access to memory.

  • Cortical connectivity implements a gain control mechanism regulating alpha frequency.

  • Alpha frequency transitions can be externally triggered (entrained) by non-invasive brain stimulation.

Abstract

Neural populations produce complex oscillatory patterns thought to implement brain function. The dominant rhythm in the healthy adult human brain is formed by alpha oscillations with a typical power peak most commonly found between 8 and 12 Hz. This alpha peak frequency has been repeatedly discussed as a highly heritable and stable neurophysiological “trait” marker reflecting anatomical properties of the brain, and individuals’ general cognitive capacity. However, growing evidence suggests that the alpha peak frequency is highly volatile at shorter time scales, dependent on the individuals’ “state”. Based on the converging experimental and theoretical results from numerous recent studies, here we propose that alpha frequency variability forms the basis of an adaptive mechanism mirroring the activation level of neural populations which has important functional implications. We here integrate experimental and computational perspectives to shed new light on the potential role played by shifts in alpha peak frequency and discuss resulting implications. We further propose a potential mechanism by which alpha oscillations are regulated in a noisy network of spiking neurons in presence of delayed feedback.

Key words

alpha peak frequency
activation state
frequency tuning
network oscillations
information gating
neural computation

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