DOI: 10.5061/dryad.4mw6m90mz
title: Data from: Respiration shapes response speed and accuracy with a systematic time lag

description: Sensory-cognitive functions are intertwined with physiological processes
 such as the heartbeat or respiration. For example, we tend to align our
 respiratory cycle to expected events or actions. This happens during
 sports but also in computer-based tasks and systematically structures the
 respiratory phase around relevant events. However, studies also show that
 trial-by-trial variations in the respiratory phase shape brain activity
 and the speed or accuracy of individual responses. We show that both
 phenomena, the alignment of respiration to expected events and the
 explanatory power of the respiratory phase on behaviour co-exist. In fact,
 both the average respiratory phase of an individual relative to the
 experimental trials and trial-to-trial variations in the respiratory phase
 hold significant predictive power on behavioural performance, in
 particular for reaction times. This co-modulation of respiration and
 behaviour emerges regardless of whether an individual generally breathes
 faster or slower and is strongest for the respiratory phase about two
 seconds prior to the participant’s responses. The persistence of these
 effects across 12 datasets with 277 participants performing
 sensory-cognitive tasks confirms the robustness of these results and
 suggests a profound and time-lagged influence of structured respiration on
 sensory-motor responses.
description: Respiration was recorded using a temperature-sensitive resistor
 that was inserted into disposable clinical oxygen masks (Littelfuse
 Thermistor No. GT102B1K, Mouser Electronics). This effectively captures
 the continuous temperature changes resulting from the respiration-related
 airflow. The voltage drop across the thermistor was recorded via the
 analogue input of an ActiveTwo EEG system (BioSemi BV; Netherlands) at a
 sampling rate of 500 or 1000 Hz. We verified that the voltage drop of the
 temperature sensor follows the respiratory air flow without time lag. For
 this, we combined the temperature probe with two short-latency airflow
 sensors (F1031V, Mass Airflow Sensor, Winsen) and confirmed that the
 temperature change tightly aligns with the directional change in
 airflow. Compared to our previous work we improved the
 processing pipeline for respiratory data. The respiratory signals were
 filtered using 3-rd order Butterworth filters (high pass at 0.03 Hz, low
 pass at 6 Hz) and subsequently resampled at 100 Hz using the FieldTrip
 toolbox. The signals were then converted to z-scores to facilitate
 comparison across participants (Fig. 1C, D). To detect individual
 respiratory cycles, we applied the Hilbert transform to determine local
 peaks based on the respective phase. Individual respiratory cycles were
 determined based on the data in windows of 7 seconds around each peak,
 whereby individual peaks were only retained for further analysis if the
 z-scored trace exceeded a value of z=0.5. Note that alternative algorithms
 to detect individual respiratory cycles exist and in a previous study, we
 found little difference between these. The inhalation period was defined
 as the continuous period with a positive slope prior to the local peak
 (whereby interruptions of the positive slope shorter than 500ms were
 interpolated). The exhalation period was defined as the continuous period
 with a negative slope subsequent to the local peak (again interruptions
 shorter than 500ms were interpolated). This definition effectively splits
 the respiratory cycle effectively into the two main periods of inhalation
 and exhalation; though for some cycles short exhale pauses were classified
 as the third state and not analysed. In particular, compared to previous
 work this procedure assigned a defined inhalation/exhalation phase to more
 time points than in the previous work. To characterize atypical
 respiratory cycles, we compared the overall time courses of individual
 respiratory cycles using their mean-squared distances. We calculated the
 participant-wise distributions and excluded cycles with a distance larger
 than 3 standard deviations from the centroid as atypical. These cycles
 were excluded as they do not reflect the prototypical respiration under
 investigation here. Further individual trials were excluded from the
 analysis as noted below. From the full datasets, we retained only
 participants for which these procedures excluded less than 30% of the
 available trials for the final statistical analysis.
description: # Respiration shapes response speed and accuracy with a systematic time
 lag
 [https://doi.org/10.5061/dryad.4mw6m90mz](https://doi.org/10.5061/dryad.4mw6m90mz) ## Description of the data and file structure This repository contains the processed data and code required to reproduce the main results and figures.  ### Files and variables #### Data files **Dataset1.mat to Dataset_12.mat** The main processed behavioural and respiratory data for each of the 12 datasets analysed in this study. The numbers 1-12 map onto the 12 datasets explained in the Methods section:  PARNAMES = {'Pitch1','Time','Visual shape','Emotion1','Emotion2','Pitch2','Arithm','Visual dots','Pitch3','Pitch4','Sound','Emotion3'}; Each file contains the following: ARGglobal: multiple infos about dataset including information about preprocessing ARGglobal.Analysis ARGout: further details about prepro. ARGout.RespTraces{participant} epoch averaged respiratory trace for each participant. Visualizes the average respiratory curve. RespProp{participant}(cycle,:) for each respiratory cycle provides the durations of: [total duration, inhalation, exhalation] Alldata.data: trial-wise data from all participants and trials, with  the entries as defined in the variable Alldata.Varnames -&gt; PhaseStim, PhaseResp are respiratory phase at stimonset and response time (t=0s for each alignment presented in the paper) Perf and RT the main dependent variables used for analysis. RespPast: matrix with respiratory signal for each trial at time points relative to stimulus onset, defined in ARGout.RespPast Based on the respiratory signal aligned to stimulus onset one can re-compute the same aligned to response times (using the trial-wise RT in Alldata.data and the respiratory data in RespPast) **Overall_properties.mat**  Contains e.g. RespProp(Participant,:)  duration of the average respiratory cycles for each participant for [total duration, inhale, exhale] Traces(Participant,:)  the time course of the average respiratory trace **Alignment_data.mat** AllResult: phase locking for trials split by RT, accuracy, time on task in the following form: AllResult{alignment of data}{participant, split type, group within split,  time} alignment is either respiration relative to stimonset or response times Tax{alignment} Time axis of each alignment MeanPhase{alignment of data}{participant, time} trial-average phase for each participant Shuffle{alignment of data}{participant, time} Surrogate distribution of  phase locking without alignment of respiration and trials **Alldo_glms.mat** level{data_set}{Model for Accuracy, Model for RT} AIC difference of model without level - model with level. An effec ot level should lead to a  positive (!) difference respiration{data_set}: model comparison of models with and without respiration respiration{data_set}.Stim for stimulus aligned data, .Resp for response aligned data respiration{data_set}.Stim.tax : time axis respiration{data_set}.Stim.Aic(time,[Accuracy,RT]) AIC difference of model without level - model with level. respiration{data_set}.Stim.Pval(timepoint, [Accuracy,RT]) the p-value of the vector strength of the respiratory predictors compared to a surrogate distribution. time point here are discrete time points of -2.1, 0s and 2.1s Note that respiration is a one dimensional cell array and best indexed respiration{data_set}, rather than respiration{data_set,1}. **Alldo_glms_single.mat** contains the single participant modelling results used to split participants by respiration rate. respiration{dataset}.Stim.AIC contains the AIC difference of a model with respiration - a model without for each   (participant, time point, parameter). time point refers to teh time as indexed by the separate field .tax paramter refers to accuracy (1) and reaction time (2).  respiration{dataset}.Resp features the same, for the data aligned to response times.  Note that respiration is a one dimensional cell array and best indexed respiration{data_set}, rather than respiration{data_set,1}. **Disp_Sat.mat**  Data to show the phase-binned behavioural data. The file is also produced by Display_SAT_Fig5.m #### Code/software: **Matlab Code** **code_dryad.zip** Features a Howto file and the functions named according to the figures they produce.