Validating genuine changes in heartbeat-evoked potentials using pseudotrials and surrogate procedures

The brain continuously receives interoceptive information about the state and function of our internal organs. For instance, each time the heart beats, the brain responds by generating time-locked activity, known as heartbeat-evoked potentials (HEP). When investigating HEPs, it is essential to adequately control for heartbeat-independent confounding activity to avoid false interpretations. In the present study, we highlight the pitfalls of uncontrolled analyses and advocate for the use of surrogate heartbeat analysis and pseudotrial correction, which are promising tools to control for spurious results. Surrogate heartbeat analysis involves shuffling the timing of heartbeats to verify the time-locking of HEP effects. Pseudotrial correction works by subtracting heartbeat-independent activity from HEPs. In this study, we employ both procedures, validate them in simulations, and apply them to real electroencephalography (EEG) data. Using EEG recordings obtained during the performance of an auditory novelty oddball task in a large population, we show that, without control analyses, pre-stimulus HEPs appear inversely related to task-related measures such as P300 event-related potential amplitudes and reaction time speed. However, these effects disappear after carefully controlling for heartbeat-unrelated EEG activity. Additionally, in real and simulated data, we show that pseudotrial correction has the potential to remove task-related confounds from HEPs, thereby uncovering real heartbeat-related effects that otherwise could be missed. This study, therefore, highlights issues that can arise when analyzing HEPs during tasks, provides solutions to overcome them, and gives recommendations for future studies to avoid pitfalls when analyzing and designing experiments involving HEPs.

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Publication Details

Journal
Imaging Neuroscience
Published
2025-01-01
DOI
https://doi.org/10.1162/imag.a.30
Citations
4
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
Field-Weighted Citation Impact
5.72

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article

Validating genuine changes in heartbeat-evoked potentials using pseudotrials and surrogate procedures

Christian Sander, Arno Villringer, Vadim V. Nikulin, Antonin Fourcade et al.
4 citations
Imaging Neuroscience
EEG and Brain-Computer Interfaces
5.72
article

Validating genuine changes in heartbeat-evoked potentials using pseudotrials and surrogate procedures

Christian Sander, Arno Villringer, Vadim V. Nikulin, Antonin Fourcade, Paul Steinfath, Nadine Herzog
article en
4 citations

Abstract

The brain continuously receives interoceptive information about the state and function of our internal organs. For instance, each time the heart beats, the brain responds by generating time-locked activity, known as heartbeat-evoked potentials (HEP). When investigating HEPs, it is essential to adequately control for heartbeat-independent confounding activity to avoid false interpretations. In the present study, we highlight the pitfalls of uncontrolled analyses and advocate for the use of surrogate heartbeat analysis and pseudotrial correction, which are promising tools to control for spurious results. Surrogate heartbeat analysis involves shuffling the timing of heartbeats to verify the time-locking of HEP effects. Pseudotrial correction works by subtracting heartbeat-independent activity from HEPs. In this study, we employ both procedures, validate them in simulations, and apply them to real electroencephalography (EEG) data. Using EEG recordings obtained during the performance of an auditory novelty oddball task in a large population, we show that, without control analyses, pre-stimulus HEPs appear inversely related to task-related measures such as P300 event-related potential amplitudes and reaction time speed. However, these effects disappear after carefully controlling for heartbeat-unrelated EEG activity. Additionally, in real and simulated data, we show that pseudotrial correction has the potential to remove task-related confounds from HEPs, thereby uncovering real heartbeat-related effects that otherwise could be missed. This study, therefore, highlights issues that can arise when analyzing HEPs during tasks, provides solutions to overcome them, and gives recommendations for future studies to avoid pitfalls when analyzing and designing experiments involving HEPs.

Imaging NeuroscienceVol. 3
Humboldt-Universität zu Berlin (DE), University Hospital Leipzig (DE), Max Planck Institute for Human Cognitive and Brain Sciences (DE), Leipzig University (DE)
European Commission, Universität Leipzig, Freistaat Sachsen, European Regional Development Fund, European Social Fund
Openalex Percentile: Top 4%
EEG and Brain-Computer Interfaces
5.72
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