Inferring a simple mechanism for alpha-blocking by fitting a neural population model to EEG spectra

PLoS Comput Biol. 2020 Apr 30;16(4):e1007662. doi: 10.1371/journal.pcbi.1007662. eCollection 2020 Apr.

Abstract

Alpha blocking, a phenomenon where the alpha rhythm is reduced by attention to a visual, auditory, tactile or cognitive stimulus, is one of the most prominent features of human electroencephalography (EEG) signals. Here we identify a simple physiological mechanism by which opening of the eyes causes attenuation of the alpha rhythm. We fit a neural population model to EEG spectra from 82 subjects, each showing a different degree of alpha blocking upon opening of their eyes. Though it has been notoriously difficult to estimate parameters by fitting such models, we show how, by regularizing the differences in parameter estimates between eyes-closed and eyes-open states, we can reduce the uncertainties in these differences without significantly compromising fit quality. From this emerges a parsimonious explanation for the spectral differences between states: Changes to just a single parameter, pei, corresponding to the strength of a tonic excitatory input to the inhibitory cortical population, are sufficient to explain the reduction in alpha rhythm upon opening of the eyes. We detect this by comparing the shift in each model parameter between eyes-closed and eyes-open states. Whereas changes in most parameters are weak or negligible and do not scale with the degree of alpha attenuation across subjects, the change in pei increases monotonically with the degree of alpha blocking observed. These results indicate that opening of the eyes reduces alpha activity by increasing external input to the inhibitory cortical population.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Alpha Rhythm*
  • Attention
  • Brain Mapping
  • Electroencephalography*
  • Humans
  • Models, Neurological
  • Neurons / physiology
  • Normal Distribution
  • Signal Processing, Computer-Assisted*

Grants and funding

This work was supported in part by a Swinburne Postgraduate Research Award to AH and in part by an Australian Research Council (https://www.arc.gov.au/) grant FT140101104 to DGH. Computations were performed on the gSTAR/ozSTAR national facilities at Swinburne University of Technology funded by Swinburne and the Australian Government’s Education Investment Fund. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.