Modality-specific tracking of attention and sensory statistics in the human electrophysiological spectral exponent

  1. Leonhard Waschke  Is a corresponding author
  2. Thomas Donoghue
  3. Lorenz Fiedler
  4. Sydney Smith
  5. Douglas D Garrett
  6. Bradley Voytek
  7. Jonas Obleser
  1. MPI for Human Development, Germany
  2. University of California, San Diego, United States
  3. Eriksholm Research Centre, Oticon A/S, Denmark
  4. Max Planck Institute for Human Development, Germany
  5. University of Lübeck, Germany

Abstract

A hallmark of electrophysiological brain activity is its 1/f-like spectrum - power decreases with increasing frequency. The steepness of this 'roll-off' is approximated by the spectral exponent, which in invasively recorded neural populations reflects the balance of excitatory to inhibitory neural activity (E:I balance). Here, we first establish that the spectral exponent of non-invasive electroencephalography (EEG) recordings is highly sensitive to general (i.e., anaesthesia-driven) changes in E:I balance. Building on the EEG spectral exponent as a viable marker of E:I, we then demonstrate its sensitivity to the focus of selective attention in an EEG experiment during which participants detected targets in simultaneous audio-visual noise. In addition to these endogenous changes in E:I balance, EEG spectral exponents over auditory and visual sensory cortices also tracked auditory and visual stimulus spectral exponents, respectively. Individuals' degree of this selective stimulus-brain coupling in spectral exponents predicted behavioural performance. Our results highlight the rich information contained in 1/f-like neural activity, providing a window into diverse neural processes previously thought to be inaccessible in non-invasive human recordings.

Data availability

Data and code have been deposited on OSF (https://osf.io/wyzrg/).

The following data sets were generated
The following previously published data sets were used

Article and author information

Author details

  1. Leonhard Waschke

    MPI for Human Development, Berlin, Germany
    For correspondence
    waschke@mpib-berlin.mpg.de
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1248-9259
  2. Thomas Donoghue

    University of California, San Diego, San Diego, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5911-0472
  3. Lorenz Fiedler

    Eriksholm Research Centre, Oticon A/S, Snekkersten,, Denmark
    Competing interests
    No competing interests declared.
  4. Sydney Smith

    University of California, San Diego, San Diego, United States
    Competing interests
    No competing interests declared.
  5. Douglas D Garrett

    Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Max Planck Institute for Human Development, Berlin, Germany
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0629-7672
  6. Bradley Voytek

    Cognitive Science, University of California, San Diego, La Jolla, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1640-2525
  7. Jonas Obleser

    Department of Psychology, University of Lübeck, Lübeck, Germany
    Competing interests
    Jonas Obleser, Reviewing Editor for eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7619-0459

Funding

Deutsche Forschungsgemeinschaft (Emmy Noether Programme)

  • Leonhard Waschke
  • Douglas D Garrett

H2020 European Research Council (ERC-CoG-2014-646696)

  • Jonas Obleser

Max Planck UCL Centre for Computational Psychiatry and Ageing Research

  • Leonhard Waschke
  • Douglas D Garrett

Whitehall Foundation (2017-12-73)

  • Bradley Voytek

National Science Foundation (BCS-1736028)

  • Bradley Voytek

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Human subjects: All participants gave written informed consent, reported normalhearing and had normal or corrected to normal vision. All experimental procedures wereapproved by the institutional review board of the University of California, San Diego, Human Research Protections Program (UCSD IRB Protocol #150834. ).

Reviewing Editor

  1. Maria Chait, University College London, United Kingdom

Publication history

  1. Preprint posted: January 14, 2021 (view preprint)
  2. Received: May 5, 2021
  3. Accepted: October 18, 2021
  4. Accepted Manuscript published: October 21, 2021 (version 1)
  5. Version of Record published: November 11, 2021 (version 2)

Copyright

© 2021, Waschke et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 1,610
    Page views
  • 271
    Downloads
  • 11
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Leonhard Waschke
  2. Thomas Donoghue
  3. Lorenz Fiedler
  4. Sydney Smith
  5. Douglas D Garrett
  6. Bradley Voytek
  7. Jonas Obleser
(2021)
Modality-specific tracking of attention and sensory statistics in the human electrophysiological spectral exponent
eLife 10:e70068.
https://doi.org/10.7554/eLife.70068
  1. Further reading

Further reading

    1. Neuroscience
    Maria Cecilia Martinez, Camila Lidia Zold ... Mariano Andrés Belluscio
    Research Article

    The automatic initiation of actions can be highly functional. But occasionally these actions cannot be withheld and are released at inappropriate times, impulsively. Striatal activity has been shown to participate in the timing of action sequence initiation and it has been linked to impulsivity. Using a self-initiated task, we trained adult male rats to withhold a rewarded action sequence until a waiting time interval has elapsed. By analyzing neuronal activity we show that the striatal response preceding the initiation of the learned sequence is strongly modulated by the time subjects wait before eliciting the sequence. Interestingly, the modulation is steeper in adolescent rats, which show a strong prevalence of impulsive responses compared to adults. We hypothesize this anticipatory striatal activity reflects the animals’ subjective reward expectation, based on the elapsed waiting time, while the steeper waiting modulation in adolescence reflects age-related differences in temporal discounting, internal urgency states, or explore–exploit balance.

    1. Computational and Systems Biology
    2. Neuroscience
    Sergio Oscar Verduzco-Flores, Erik De Schutter
    Research Article Updated

    How dynamic interactions between nervous system regions in mammals performs online motor control remains an unsolved problem. In this paper, we show that feedback control is a simple, yet powerful way to understand the neural dynamics of sensorimotor control. We make our case using a minimal model comprising spinal cord, sensory and motor cortex, coupled by long connections that are plastic. It succeeds in learning how to perform reaching movements of a planar arm with 6 muscles in several directions from scratch. The model satisfies biological plausibility constraints, like neural implementation, transmission delays, local synaptic learning and continuous online learning. Using differential Hebbian plasticity the model can go from motor babbling to reaching arbitrary targets in less than 10 min of in silico time. Moreover, independently of the learning mechanism, properly configured feedback control has many emergent properties: neural populations in motor cortex show directional tuning and oscillatory dynamics, the spinal cord creates convergent force fields that add linearly, and movements are ataxic (as in a motor system without a cerebellum).