Medical Study: Meditation and The Heart

Relationships between heart coherence and EEG alpha band activities

In this study, relationships were assessed between the proposed heart coherence as a meditation index and EEG alpha band activities. The parietal peak alpha power is increased with increasing heart coherence during meditation but no such significant relationship was observed at baseline. Average lagged alpha coherence are increased with increasing heart coherence during meditation but reverse relationship was observed at baseline. Relative alpha power also increased with increasing heart coherence during both meditation and baseline while the regression coefficient still higher as similar in the other alpha variables during meditation than baseline.

There is a previous meditation-related study examining correlations between conventional HRV indices (LF, HF, LF/HF) and EEG variables (Takahashi et al., 2005); however, respiration rate in the study was fixed at predetermined rate that could give a sense of restraint to the participants. It would be more natural that the participant decides their respiration rate by themselves according to their own tempo for their successful meditative state.

There was a study exploring the correlation between average cardiac index changes and average EEG index changes during meditation (Kubota et al., 2001; Hamada et al., 2006) but there are few cases considering dynamic correlation changes between cardiac and EEG indices at within baseline and within meditation. One such exception was a study in which the correlation between heart rate and BOLD signals in the ACC region was higher in the meditative state than in a neutral state (Lutz et al., 2009).

In this study, the relationship between cardiac and EEG indices were explored at baseline and during meditation for 12 participants. The results show that our proposed cardiac index, heart coherence, has a significant positive correlation with every EEG alpha index (peak power, relative power, and average coherence) during meditation, but not during baseline.

At the same time, heart coherence had a stronger coupling, greater regression coefficient, with all EEG alpha variables during mediation than during baseline testing (Figure (Figure3).3). The regression coefficient, slope of the regression equation between heart coherence and EEG alpha variables, means to what extent of EEG alpha activities changes when heart coherence increase by 1. If we only observed average changes in various variables, the dynamic correlations between heart coherence and alpha peak power, for instance, would not have been detected. Many participants did not show positive heart coherence changes when the average changes were statistically assessed (Figure (Figure1).1). It means that coherent behavior of the heart rhythm was not so reliable for many of the participants. It means that many of the participants could not sustain heart coherent meditation for the entire duration of their meditation. However, it would be more natural that there were fluctuations in meditation quality within their entire meditation duration. Furthermore, all the participants only completed a basic course, and none were advanced meditator. Although heart coherence was not reliable in the most of the participants, there was strong coupling between heart coherence and EEG alpha variables within meditative state compared to the baseline. This could suggest that there are many moments in which heart coherence directly influence EEG alpha activities during meditation. In addition, alpha peak power did not also change significantly from baseline to during meditation in the analysis of group-based data. Regardless, the highest regression coefficient (Figure (Figure3)3) during meditation compared to baseline, was between heart coherence and alpha peak power, indicates that there may be many moments when heart coherence is strongly coupled with alpha peak power during meditation, even though the relationship may not be detected during an entire meditation duration (Figure (Figure2C2C).

An indication of meditation quality would be expected from the strength of the relationship, regression coefficient (Figure (Figure3),3), between heart coherence and EEG variables. Heart coherence may not only be a cardiac index but also an index of meditation if heart coherence is strongly correlated with EEG alpha variables especially in meditation. However, there is also an evidence that heart coherence usually increases in the early phase of meditation, accompanying slow and deep breathing, and shows quite different patterns in the deep phase of meditation in which the heart coherence cannot represent such a meditative state any more (McCraty et al., 2009).

Although heart coherence cannot cover all stages or types of meditation, we anticipate that a heart coherence index will become a simple tool for quick assessment of Autogenic meditative states, which is easily achievable during people’s daily lives, considering the fact that heart coherence can be implemented more easily than EEG index, based on the use of contemporary technologies.

In addition, all parameters observed in this study were indices reflecting a degree of ordering or self-organization; heart coherence, EEG alpha activities and the synchronizing relationship between heart coherence and EEG variables. Strengthening the degree of ordering by enhancing heart coherence, promoting EEG alpha activations and improving the relationship between heart and EEG variables would help in recovery of the homeostatic processes within our body. Interestingly, there were some researches showing harmonic frequency architecture in EEG study. Eleven Hertz alpha peak was clearly observed in vibrotactile discrimination task while harmonic peak at 22 Hz also emerged in a recent animal study (Haegens et al., 2011) and simultaneous appearance of 6 Hz frontal midline theta and 12 Hz alpha activity during retention period of a demanding working memory task in a human study (Jensen et al., 2002). Regarding the dynamic correlations between heart coherence and EEG alpha activities argued in this paper, we could assume that there could be a harmonic oscillation architecture in human body, connecting brain oscillating 10 Hz alpha frequency and heart oscillating 0.1 Hz respiration frequency (cardio-respiratory resonant frequency varies from person to person, the frequencies are generally around 0.1 Hz).

We still do not know how the heart coherence are coupled EEG alpha activity more actively in the meditation compared to the baseline. Further study will help to define this degree of ordering more clearly and a causality of the interactions between heart and brain more thoroughly and will eventually determine how we can achieve such a state more easily using physiological knowledge and biofeedback technologies.

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

If your really interested I recommend reading the full study here at NCBI 

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