While research in this area is still in its infancy, it appears that conscious control of the autonomic nervous system is possible. To what extent it is possible is still unknown. However, if we are able to achieve at least some level of mastery over our own physiology, this could have immense implications for health, medicine and wellbeing.
Instead of relying on drugs or external sources, our health and wellbeing can be in our own hands. Disorders linked to autonomic dysfunction such as autoimmune diseases, high blood pressure and inflammation could be treated or supplemented with practices aimed at improving autonomic control.
One technique called biofeedback , for example, is a well established technique in gaining autonomic control. Biofeedback involves connecting electrical sensors to the body to provide real-time feedback on internal physiological states e. Biofeedback has shown to be helpful in treating a number of medical conditions including diabetes, hypertension and migraines, as well as improving mental wellbeing.
Alongside practices such as meditation, mindfulness and breathing exercises, these techniques targeted at improving autonomic control could have far reaching benefits. So instead of thinking physiological processes within your body being outside of your control, think again.
You might be surprised at what is possible. Back Workshops Research Tools. But how much control over our bodies do we really have? This modulation is known as respiratory sinus arrhythmia RSA 5 , which manifests itself through the number of heart beats per breath changing according to the respiration cycle, with the heart rate increasing during inspiration and decreasing during expiration. Understanding the origin of the cardiac and respiratory rhythms and their coupling, as well as the role of mechanical and neuronal regulatory mechanisms, is essential for a better characterization of RSA and other interactive effects reported in the literature 6 , 7 , 8 , 9.
It is known that cardiac and respiratory systems are characterized by their own rhythms, which are generated by different neural centres located in the medulla oblongata 2. In turn, interactions between the centres via coupling and feedback loops affect the individual cardio and respiratory rhythms; such interactions can be characterized through the analysis of rhythm alternations 10 , Cardio-respiratory interaction stronger than that during RSA is observed during cardio-respiratory synchronization CRS , with a specific number m heart beats per n respiration cycles locking ratio n : m 6 , 7 , 8.
During CRS the heartbeats are only observed for specific phases of the respiratory cycle. CRS is more pronounced in athletes and occurs irregularly 10 , The irregularity is often explained by the presence of noise, i. According to the phase description, RSA can be represented as a continuous increase and decrease of the heart rate with respect to the phase of respiratory cycle CRS assumes constant phase difference for different locking ratios n : m between the heart m and respiratory n rates 7 , 10 ; CRS is typically considered for respiratory rates slower than the heart rate, i.
Several experimental observations, for example by Koephen 14 , have suggested that cardio and respiratory systems are coupled peripherally as well as centrally. Both RSA and CRS are considered the product of peripheral coupling 15 , 16 ; such peripheral links are characterized by the time delays between the action of one system and the reaction of another, with the time scales of the delays being smaller than the slow changing respiration.
Furthermore, when the two rhythms are similar in pace, central neural coupling might also occur under certain conditions. Koephen postulated that for such central coupling to exist there must be a common central neuronal source by which the two rhythms are coordinated The central coupling has been considered in a series of experiments by Pokrovskii see 17 and references therein.
They proposed 18 the existence of a cardiac rhythm generator in the central nervous system and a direct connection between this centre and the respiratory centre, both located in the medulla oblongata.
As a manifestation of this direct link, the phenomenon of CRS with locking ratio was discussed 19 , 20 , 21 suggesting that cardio-respiratory entrainment can be achieved by practising breathing at a rate greater than the resting heart rate RHR 21 , which is the heart rate when breathing rate is normal. A flashing light and auditory signal were used as a guide for the participants to keep their respiration rate above the RHR; participants were typically in a standing position.
The existence of synchronization was verified by a visual inspection of the recordings of each heartbeat and breath wave. Measurements were carried out for a large population. However in this research 19 , 20 , 21 no significance is placed upon length of synchronization episodes, nor are any statistical measures applied to verify the strength of the interconnection.
The methodology used by the group 19 , 20 , 21 to identify CRS was described as purely visual. The lack of rigorous data analysis does not allow for identification of coincidental equivalent rates versus true entrainment, as discussed previously 6. The aim of this work is to investigate whether phase-locking CRS can be observed for extended periods of time, and to describe the phenomenon in appropriate mathematical terms by designing an experiment to guide high-rate respiration from a basic rhythm corresponding to a slightly lower rate than the RHR, to higher rhythms, above the RHR.
If such entrainment exists, it would suggest that the respiratory centre in the medulla could become the dominant pacemaker controlling the heart rate and such an observation would suggest direct neuronal communication. To reduce the influence of the visual and auditory neural centres and mechanical cardio-respiratory coupling the light flash is replaced by a visual pattern and the measurements are taken with subjects in a lying position.
Furthermore, longer intervals of high-rate breathing was used that, in a combination with the developed methodology, allow robust identification of entrainment between the cardiac and respiratory systems by avoiding solely coincidental rate equivalency 6. We also aim to consider the adaptation of heart dynamics to a step change in respiration rhythm. The animation guiding breathing ran for complete breath cycles.
The total number of guided intervals for the 22 volunteers considered in this work is Instantaneous breathing rate blue and heart rate red are shown from data for volunteer Regions of guided breathing are shaded. Not all of the minute rest interval at the beginning is shown, as this data is not explicitly analysed in this article. Simultaneous recordings of ECG and respiratory signals were performed.
According to the data processing procedure described in Methods section, both the breathing and the heart rates were derived in Hertz; however, for illustrative purposes in this paper, the rates are presented in beats-per-minute BPM. The heart and breathing rates plotted together in Fig.
The heart rate demonstrates a response to a step change in breathing rates; these step responses will be discussed below. Owing to the design of the experiment, the breathing rate during guided intervals was intended to be constant. Additionally, swallowing or coughing were observed in a few cases.
However, the mean breathing rates matched the guided values set by the metronome. For this volunteer Fig.
The mean and standard deviation of the breathing rate for all intervals and volunteers are shown in Table SI1 of Supporting Information SI. The standard deviation of the breathing rate defines the minimal possible step increments between guided breathing rates.
The dashed black lines represent the standard deviation of the rate, while the solid black line is the mean breathing rate for that interval. Assuming a volunteer follows the metronome well, the range between standard deviation lines will be small. The normalisation demonstrates the proportional rate of breathing relative to RHR.
Data from volunteer The Shapiro-Wilk normality test showed that for 33 out of 66 guided intervals, the breathing rate is normally distributed. Thus, the stochastic component in guided breathing rate can be represented as a Gaussian random process, and the breathing signal itself corresponds to stochastic quasi-harmonic oscillations with a constant amplitude and a variable frequency see Fig.
SI1 in SI. The mean and standard deviation of the heart rate for all intervals and volunteers are shown in Table SI2 of SI. The variability of this data is significantly stronger than that of the breathing rate data. This can be explained by the nonstationary dynamics of heart rate.
Conversely to the guided breathing rate, the KPSS test demonstrated that for 63 out of 66 high-rate breathing intervals, instantaneous heart rate is non-stationary. Furthermore, the Shapiro-Wilk test showed that 49 out of 66 heart rate intervals are not normally distributed. The noticed non-stationarity is linked to transient adaptation periods which were observed for most guided intervals, with the heart rates rising to levels disproportionate to the prescribed breathing rate, forming a ramp response.
Adaptation was particularly visible during the first interval of high-rate breathing Fig. Regardless, assuming a volunteer relaxed and continued following the breathing metronome, their heart rate adjusted accordingly.
This transient period is less pronounced in the subsequent second and third intervals. To analyse the transient response, a slow trend of the heart rate was calculated via a moving average technique described in the Methods section. A variety of trend patterns was observed Fig. SI2 in SI and for some intervals there was no trend.
In the example presented in Fig. SI2 in SI. The patterns for the second and third intervals were more complex, but the majority included a transient increase of the rate. For some intervals, the heart rate seemed to begin to tend to a steady state value after the initial adaptation. However, there was no clear steady state observed and for the majority of cases, the heart rate continued to diffuse.
In fact, such wandering dynamics are a feature of heart rate 22 and ought to be considered when analysing synchronization.
Trends in heart rate during the intervals of guided breathing. Black curves correspond to the trends. Red lines specify the mean value solid line and standard deviation dashed lines of the breathing rate for each interval. All data normalised by the mean breathing rate of interval 2.
The intended heart rate response should mean the black curve falls within the red dashed lines for as much of the interval as possible.
An example of a synchrogram 6 encompassing all guided respiration intervals and spontaneous rest periods is shown in Fig. During this episode, wandering of the heart rate is limited and the heart rate fluctuates around a particular value Fig. Before and after this episode the heart rate shows a diffusive behaviour. Synchrogram for volunteer Shaded regions correspond to the regions of guided breathing. For 18 of the 22 volunteers, CRS occurred within the third interval, when the guided breathing rate was higher than RHR.
For four volunteers number 2, 10, 20 and 21 , episodes of synchronization were observed for the second interval when the breathing rate was intended to be equal to the RHR.
An additional analysis of heart rate during the minute rest interval prior to guided breathing suggests that the RHR value calculated for these was potentially too high, thus for this second interval breathing rate was above the actual RHR.
In many cases, more than one episode of CRS was observed within the same time interval. The longest episode was singled out and the total duration of all episodes in the given interval was calculated.
All results are summarised in Table 1 with times given to the nearest second. The CRS durations calculated by the two methods produced close values.
One volunteer number 3 had very short CRS episodes. The dynamics of the phase difference and rates for the third interval for this volunteer and volunteer 2 are shown in Fig. SI3 in SI. Interpretation of these plots allows for visualisation of the durations specified in Table 1. The top panel plot a and e in Fig. The duration of synchronization episodes for different volunteers are shown in Table 1. The second panel plot b and f shows time dependence of the synchronization index.
A value of the index close to one represents synchronization between two oscillating signals. Extended episodes above the experimentally-justified threshold of 0. The third panel plots c and g shows the synchrogram for the entire interval of high-rate breathing. During the phase synchronization points on synchrogram demonstrate a plateau. The final panel plots d and h are a representation of the heart and respiratory rates for a comparison of instantaneous rates during episodes of synchronization with dynamics of phases.
The dashed red lines represent the high variability of breathing rate even for controlled breathing- the larger this range, the more variable the breathing rate and thus the worse a volunteer maintained a constant rate. The solid red line is the average breathing rate, and the blue line demonstrates the dynamics of the instantaneous breathing rate throughout the interval. The black line in plots d and h corresponds to heart rate with removed high-frequency oscillations via applying moving average techniques.
During episodes of phase synchronization, the black line is expected to fall wholly between the dashed red lines, representing the fact that the variability of heart rate is contained within the variability of breathing rate. Synchronization measures for volunteer 2 left and volunteer 3 right. Figures a , e show the phase difference, figures b , f show the synchronization index, figures c , g show the synchrogram, and figures d , h show smoothed heart black line and respiratory blue line rates.
In figures d , h red lines specify the mean value solid line and standard deviation dashed lines of the breathing rate for each interval. In Fig. Story Source: Materials provided by Cell Press. Parra, Jacobo D. Conscious processing of narrative stimuli synchronizes heart rate between individuals.
Cell Reports , ; 36 11 : DOI: ScienceDaily, 14 September Cell Press. People synchronize heart rates while listening attentively to stories. Retrieved November 11, from www.
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