Eelbrain, a Python toolkit for time-continuous analysis with temporal response functions
- University of Connecticut, United States
- Stanford University, United States
- KU Leuven, Belgium
- Linköping University, Sweden
- University of Toronto, Canada
- 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).
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EEG Datasets for Naturalistic Listening to "Alice in Wonderland"Deep Blue Data, DOI:10.7302/Z29C6VNH.
Article and author information
Author details
Christian Brodbeck
Shohini Bhattasali
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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Eelbrain, a Python toolkit for time-continuous analysis with temporal response functions
eLife 12:e85012.
https://doi.org/10.7554/eLife.85012
Further reading
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Recognizing and responding to threat cues is essential to survival. Freezing is a predominant threat behavior in rats. We have recently shown that a threat cue can organize diverse behaviors beyond freezing, including locomotion (Chu et al., 2024). However, that experimental design was complex, required many sessions, and had rats receive many foot shock presentations. Moreover, the findings were descriptive. Here, we gave female and male Long Evans rats cue light illumination paired or unpaired with foot shock (eight total) in a conditioned suppression setting using a range of shock intensities (0.15, 0.25, 0.35, or 0.50 mA). We found that conditioned suppression was only observed at higher foot shock intensities (0.35 mA and 0.50 mA). We constructed comprehensive temporal ethograms by scoring 22,272 frames across 12 behavior categories in 200-ms intervals around cue light illumination. The 0.50 mA and 0.35 mA shock-paired visual cues suppressed reward seeking, rearing, and scaling, as well as light-directed rearing and light-directed scaling. These shock-paired visual cues further elicited locomotion and freezing. Linear discriminant analyses showed that ethogram data could accurately classify rats into paired and unpaired groups. Using complete ethogram data produced superior classification compared to behavior subsets, including an immobility subset featuring freezing. The results demonstrate diverse threat behaviors – in a short and simple procedure – containing sufficient information to distinguish the visual fear conditioning status of individual rats.
