Eelbrain, a Python toolkit for time-continuous analysis with temporal response functions

  1. Christian Brodbeck  Is a corresponding author
  2. Proloy Das
  3. Marlies Gillis
  4. Joshua P Kulasingham
  5. Shohini Bhattasali
  6. Phoebe Gaston
  7. Philip Resnik
  8. Jonathan Z Simon
  1. University of Connecticut, United States
  2. Stanford University, United States
  3. KU Leuven, Belgium
  4. Linköping University, Sweden
  5. University of Toronto, Canada
  6. University of Maryland, College Park, United States

Abstract

Even though human experience unfolds continuously in time, it is not strictly linear; instead, it entails cascading processes building hierarchical cognitive structures. For instance, during speech perception, humans transform a continuously varying acoustic signal into phonemes, words, and meaning, and these levels all have distinct but interdependent temporal structures. Time-lagged regression using temporal response functions (TRFs) has recently emerged as a promising tool for disentangling electrophysiological brain responses related to such complex models of perception. Here we introduce the Eelbrain Python toolkit, which makes this kind of analysis easy and accessible. We demonstrate its use, using continuous speech as a sample paradigm, with a freely available EEG dataset of audiobook listening. A companion GitHub repository provides the complete source code for the analysis, from raw data to group level statistics. More generally, we advocate a hypothesis-driven approach in which the experimenter specifies a hierarchy of time-continuous representations that are hypothesized to have contributed to brain responses, and uses those as predictor variables for the electrophysiological signal. This is analogous to a multiple regression problem, but with the addition of a time dimension. TRF analysis decomposes the brain signal into distinct responses associated with the different predictor variables by estimating a multivariate TRF (mTRF), quantifying the influence of each predictor on brain responses as a function of time(-lags). This allows asking two questions about the predictor variables: 1) Is there a significant neural representation corresponding to this predictor variable? And if so, 2) what are the temporal characteristics of the neural response associated with it? Thus, different predictor variables can be systematically combined and evaluated to jointly model neural processing at multiple hierarchical levels. We discuss applications of this approach, including the potential for linking algorithmic/representational theories at different cognitive levels to brain responses through computational models with appropriate linking hypotheses.

Data availability

The data analyzed here was originally released with DOI: 10.7302/Z29C6VNH and can be retrieved from https://deepblue.lib.umich.edu/data/concern/data_sets/bg257f92t. For the purpose of this tutorial, the data were restructured and rereleased with DOI: 10.13016/pulf-lndn at http://hdl.handle.net/1903/27591. The companion GitHub repository contains code and instructions for replicating all analyses presented in the paper (https://github.com/Eelbrain/Alice).

The following previously published data sets were used

Article and author information

Author details

  1. Christian Brodbeck

    University of Connecticut, Storrs, United States
    For correspondence
    christian.brodbeck@uconn.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8380-639X
  2. Proloy Das

    Stanford University, Stanford, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8807-042X
  3. Marlies Gillis

    KU Leuven, Leuven, Belgium
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3967-2950
  4. Joshua P Kulasingham

    Linköping University, Linköping, Sweden
    Competing interests
    The authors declare that no competing interests exist.
  5. Shohini Bhattasali

    University of Toronto, Toronto, Canada
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6767-6529
  6. Phoebe Gaston

    University of Connecticut, Storrs, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Philip Resnik

    University of Maryland, College Park, College Park, United States
    Competing interests
    The authors declare that no competing interests exist.
  8. Jonathan Z Simon

    University of Maryland, College Park, College Park, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0858-0698

Funding

National Science Foundation (BCS 1754284)

  • Christian Brodbeck

National Science Foundation (BCS 2043903)

  • Christian Brodbeck

National Science Foundation (IIS 2207770)

  • Christian Brodbeck

National Science Foundation (SMA 1734892)

  • Joshua P Kulasingham
  • Jonathan Z Simon

National Institutes of Health (R01 DC014085)

  • Joshua P Kulasingham
  • Jonathan Z Simon

National Institutes of Health (R01 DC019394)

  • Jonathan Z Simon

Fonds Wetenschappelijk Onderzoek (SB 1SA0620N)

  • Marlies Gillis

Office of Naval Research (MURI N00014-18-1-2670)

  • Shohini Bhattasali
  • Philip Resnik

National Institutes of Health (T32 DC017703)

  • Phoebe Gaston

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

Copyright

© 2023, Brodbeck 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.

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  1. Christian Brodbeck
  2. Proloy Das
  3. Marlies Gillis
  4. Joshua P Kulasingham
  5. Shohini Bhattasali
  6. Phoebe Gaston
  7. Philip Resnik
  8. Jonathan Z Simon
(2023)
Eelbrain, a Python toolkit for time-continuous analysis with temporal response functions
eLife 12:e85012.
https://doi.org/10.7554/eLife.85012

Share this article

https://doi.org/10.7554/eLife.85012

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