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.

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.