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Cited by 101Neuroscience of respiration and sleepHeart Rate Variability and Autonomic ControlNeural and Behavioral Psychology Studies

The DOI matches its Crossref record; 72 of the 76 references it cites were found in a registry or an index.

The influence of the respiratory cycle on reaction times in sensory-cognitive paradigms

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Johannknecht, Michelle, Kayser, Christoph·2022·Scientific Reports
DOI 10.1038/s41598-022-06364-8PMC PMC8850565

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Abstract

Behavioural and electrophysiological studies point to an apparent influence of the state of respiration, i.e., whether we inhale or exhale, on brain activity and cognitive performance. Still, the prevalence and relevance of such respiratory-behavioural relations in typical sensory-cognitive tasks remain unclear. We here used a battery of six tasks probing sensory detection, discrimination and short-term memory to address the questions of whether and by how much behaviour covaries with the respiratory cycle. Our results show that participants tend to align their respiratory cycle to the experimental paradigm, in that they tend to inhale around stimulus presentation and exhale when submitting their responses. Furthermore, their reaction times, but not so much their response accuracy, consistently and significantly covary with the respiratory cycle, differing between inhalation and exhalation. This effect is strongest when analysed contingent on the respiratory state around participants’ responses. The respective effect sizes of these respiration-behaviour relations are comparable to those seen in other typical experimental manipulations in sensory-cognitive tasks, highlighting the relevance of these effects. Overall, our results support a prominent relation between respiration and sensory-cognitive function and show that sensation is intricately linked to rhythmic bodily or interoceptive functions.

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Crossref · 10.1038/s41598-022-06364-8

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Figure 1 Detection of respiratory cycles and their alignment to paradigm events. (A) Respiratory cycles were automatically segmented into inhalation (blue) and exhalation (red) periods, while atypical cycles were excluded (black). The trace shows one example signal (z-scored) together with the timing of trials for one participant in the Sound paradigm. The right panel shows how respiratory variables were coded as either a continuous cyclic variable (to test for an alignment of respiration to the paradigm); or using 4 phase-bins during inhalation and exhalation respectively (to test for a relation between respiration and performance). (B) Duration of respiratory cycles, and the inspiration and expiration states separately per paradigm (color-coded) and participant (dots indicate the participant-wise median values). (C) Alignment of respiratory cycles to paradigm events (stimulus onset, response times). The upper panels show participant-wise phase-locking values (PLV), the lower panel the same data but z-scored to participant-specific randomization distributions (based on 2000 randomizations). Here positive values indicate stronger phase-locking than expected by chance. (D) Distribution of participant-wise average phase values at stimulus onset or response times (derived as circular mean across trials).
Figure 1 Detection of respiratory cycles and their alignment to paradigm events. (A) Respiratory cycles were automatically segmented into inhalation (blue) and exhalation (red) periods, while atypical cycles were excluded (black). The trace shows one example signal (z-scored) together with the timing of trials for one participant in the Sound paradigm. The right panel shows how respiratory variables were coded as either a continuous cyclic variable (to test for an alignment of respiration to the paradigm); or using 4 phase-bins during inhalation and exhalation respectively (to test for a relation between respiration and performance). (B) Duration of respiratory cycles, and the inspiration and expiration states separately per paradigm (color-coded) and participant (dots indicate the participant-wise median values). (C) Alignment of respiratory cycles to paradigm events (stimulus onset, response times). The upper panels show participant-wise phase-locking values (PLV), the lower panel the same data but z-scored to participant-specific randomization distributions (based on 2000 randomizations). Here positive values indicate stronger phase-locking than expected by chance. (D) Distribution of participant-wise average phase values at stimulus onset or response times (derived as circular mean across trials).CC BY · PubMed Central
Figure 2 Group-level test for a statistical relation of respiration and behaviour across paradigms. Bars indicate the Akaike information criterion (AIC)-weights for mixed linear models predicting the fraction of correct responses (FCR) or response times (RT) based on all respiratory predictors (full), a model excluding the interaction of respiratory state and respiratory phase (-S*P; c.f. Figure 1A for the definition of state and phase), models excluding the interaction and either state (-S) or phase (-P), and a model excluding all respiratory predictors (-SP). Models were fit separately based on the respiratory variables at stimulus onset or at response times and were fit across all six paradigms and n = 122 datasets (see Table 1 for model parameters).
Figure 2 Group-level test for a statistical relation of respiration and behaviour across paradigms. Bars indicate the Akaike information criterion (AIC)-weights for mixed linear models predicting the fraction of correct responses (FCR) or response times (RT) based on all respiratory predictors (full), a model excluding the interaction of respiratory state and respiratory phase (-S*P; c.f. Figure 1A for the definition of state and phase), models excluding the interaction and either state (-S) or phase (-P), and a model excluding all respiratory predictors (-SP). Models were fit separately based on the respiratory variables at stimulus onset or at response times and were fit across all six paradigms and n = 122 datasets (see Table 1 for model parameters).CC BY · PubMed Central
Figure 3 Behavioural performance for individual paradigms and participants. For each paradigm the panels show the fraction of correct response (FCR) or log-transformed reaction times (RT). The upper two panels show these as a function of respiratory state (inhalation, exhalation; color-coded) and stimulus level; the lower two panels as a function of respiratory state and phase bin (averaged over levels). (A) Shows the data based on the respiratory variables at stimulus onset. (B) Shows the data based on the respiratory variables at response times. Boxplots indicate the central quartiles and median (black circle), individual coloured-dots the participant-wise data.
Figure 3 Behavioural performance for individual paradigms and participants. For each paradigm the panels show the fraction of correct response (FCR) or log-transformed reaction times (RT). The upper two panels show these as a function of respiratory state (inhalation, exhalation; color-coded) and stimulus level; the lower two panels as a function of respiratory state and phase bin (averaged over levels). (A) Shows the data based on the respiratory variables at stimulus onset. (B) Shows the data based on the respiratory variables at response times. Boxplots indicate the central quartiles and median (black circle), individual coloured-dots the participant-wise data.CC BY · PubMed Central
Figure 4 Paradigm-wise mixed-linear modelling of behaviour against respiratory variables. (A) Statistical significance (t-value) of model coefficients for linear models fit for individual paradigms modelling the fraction of correct responses (FCR) or response times (RT) based on the respiratory variables at stimulus onset or at response times. Significant predictors are indicated in grey (p < 0.01; two-sided t-test, uncorrected). Model predictors were respiratory state (S; inhalation or exhalation), respiratory phase within each state (P) and their interaction (S*P). (B) Model comparison based on Akaike information criterion (AIC)-weights. Here the full model including all respiratory predictors (full) was compared to a model excluding the interaction of respiratory state and respiratory phase (-S*P), models excluding the interaction and state (-S) or phase (-P) and a model excluding all respiratory predictions (-SP).
Figure 4 Paradigm-wise mixed-linear modelling of behaviour against respiratory variables. (A) Statistical significance (t-value) of model coefficients for linear models fit for individual paradigms modelling the fraction of correct responses (FCR) or response times (RT) based on the respiratory variables at stimulus onset or at response times. Significant predictors are indicated in grey (p < 0.01; two-sided t-test, uncorrected). Model predictors were respiratory state (S; inhalation or exhalation), respiratory phase within each state (P) and their interaction (S*P). (B) Model comparison based on Akaike information criterion (AIC)-weights. Here the full model including all respiratory predictors (full) was compared to a model excluding the interaction of respiratory state and respiratory phase (-S*P), models excluding the interaction and state (-S) or phase (-P) and a model excluding all respiratory predictions (-SP).CC BY · PubMed Central
Figure 5 Estimates of effect sizes. For those paradigms with significant effects in linear models (c.f. Table 2) the figure shows the respective variation in reaction times (A-C) or the fraction of correct responses (D) along the respiratory cycle (inset in Panel A). For display purpose, the individual participant data were mean-subtracted. The group-level median data were fit using a rhythmic model (grey line), based on which we determined those bins with positive and negative RT (or FC). We then derived a measure of effect size as the participant-wise difference in the data between positive and negative bins. This procedure ensures that effect sizes are derived under the assumption of a fixed respiratory pattern across participants within each individual paradigm. The right-hand panels indicate the participant-wise (dots) effect sizes.
Figure 5 Estimates of effect sizes. For those paradigms with significant effects in linear models (c.f. Table 2) the figure shows the respective variation in reaction times (A-C) or the fraction of correct responses (D) along the respiratory cycle (inset in Panel A). For display purpose, the individual participant data were mean-subtracted. The group-level median data were fit using a rhythmic model (grey line), based on which we determined those bins with positive and negative RT (or FC). We then derived a measure of effect size as the participant-wise difference in the data between positive and negative bins. This procedure ensures that effect sizes are derived under the assumption of a fixed respiratory pattern across participants within each individual paradigm. The right-hand panels indicate the participant-wise (dots) effect sizes.CC BY · PubMed Central

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Metadata

Authors
Johannknecht, M., & Kayser, C.
Affiliations
  • Heinrich Heine University Düsseldorf (DE)
  • Bielefeld University (DE)
Corresponding author
Kayser, Christoph
ORCID
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2022-02-16
Volume
12
Issue
1
Pages
2586-2586
ISSN
2045-2322 (ISSN-L)
DOI
10.1038/s41598-022-06364-8
PMC
PMC8850565
OpenAlex
W4220780205
Cited by
101
References
76
Citations by year
2026: 26 · 2025: 33 · 2024: 15 · 2023: 15 · 2022: 10
FWCI
9.63
Type
journal article
Language
en
Open access
Licence
Locations
  • Scientific Reports (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Publikationen an der Universität Bielefeld (Universität Bielefeld) (submittedVersion, cc-by)
Funders
Universität Bielefeld
Topics
  • Life Sciences › Neuroscience › Endocrine and Autonomic Systems › Neuroscience of respiration and sleep
  • Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
Keywords
respiratory cycle, respiration, reaction time, inhalation and exhalation, sensory detection, sensory discrimination, short-term memory, interoception
MeSH terms
Adult, Inhalation, Brain, Cognition, Female, Humans, Male, Memory, Short-Term, Reaction Time, Respiratory Physiological Phenomena, Sensation, Exhalation, Young Adult, Respiratory Rate

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