Entrainment of chaotic activities in brain and heart during MBSR mindfulness training

Gao, Junling; Fan, Jicong; Wu, Bian; Zhang, Zhiguo; Chang, Chunqi; Hung, Yeung-Sam; Fung, P. C. W.; Sik, Hin Hung

doi:10.1016/j.neulet.2016.01.001

Cited by 59 publications

(50 citation statements)

References 23 publications

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“…Contrarily, in line with QSEn, an increased spectral power in some EEG channels has been noticed for patients suffering from stressful situations, post-traumatic stress disorders and chronic hyperarousal [64]. Other nonlinear indices, like Lyapunov exponents and SEn, have also reported a complexity reduction in many brain time series for stressed subjects undergoing relaxing treatments based on meditation, restful music or foot reflexology [65,66]. However, analyzing the spatial distribution of the differences between the average values of QSEn and AAPEn for both emotional states, displayed in Figure 4, it could be observed that both kinds of dynamics are compatible.…”

Section: Discussionmentioning

confidence: 88%

Symbolic Analysis of Brain Dynamics Detects Negative Stress

García-Martínez

Martínez-Rodrigo

Zangróniz

et al. 2017

Entropy

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Abstract:The electroencephalogram (EEG) is the most common tool used to study mental disorders. In the last years, the use of this recording for recognition of negative stress has been receiving growing attention. However, precise identification of this emotional state is still an interesting unsolved challenge. Nowadays, stress presents a high prevalence in developed countries and, moreover, its chronic condition often leads to concomitant physical and mental health problems. Recently, a measure of time series irregularity, such as quadratic sample entropy (QSEn), has been suggested as a promising single index for discerning between emotions of calm and stress. Unfortunately, this index only considers repetitiveness of similar patterns and, hence, it is unable to quantify successfully dynamics associated with the data temporal structure. With the aim of extending QSEn ability for identification of stress from the EEG signal, permutation entropy (PEn) and its modification to be amplitude-aware (AAPEn) have been analyzed in the present work. These metrics assess repetitiveness of ordinal patterns, thus considering causal information within each one of them and obtaining improved estimates of predictability. Results have shown that PEn and AAPEn present a discriminant power between emotional states of calm and stress similar to QSEn, i.e., around 65%. Additionally, they have also revealed complementary dynamics to those quantified by QSEn, thus suggesting a synchronized behavior between frontal and parietal counterparts from both hemispheres of the brain. More precisely, increased stress levels have resulted in activation of the left frontal and right parietal regions and, simultaneously, in relaxing of the right frontal and left parietal areas. Taking advantage of this brain behavior, a discriminant model only based on AAPEn and QSEn computed from the EEG channels P3 and P4 has reached a diagnostic accuracy greater than 80%, which improves slightly the current state of the art. Moreover, because this classification system is notably easier than others previously proposed, it could be used for continuous monitoring of negative stress, as well as for its regulation towards more positive moods in controlled environments.

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Section: Discussionmentioning

confidence: 88%

Symbolic Analysis of Brain Dynamics Detects Negative Stress

García-Martínez

Martínez-Rodrigo

Zangróniz

et al. 2017

Entropy

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“…Please also refer to our previous method (Gao et al, 2016). The comparison results are shown in Figure 6 .…”

Section: Resultsmentioning

confidence: 99%

Repetitive Religious Chanting Modulates the Late-Stage Brain Response to Fear- and Stress-Provoking Pictures

Gao

Fan

et al. 2017

Front. Psychol.

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Chanting and praying are among the most popular religious activities, which are said to be able to alleviate people’s negative emotions. However, the neural mechanisms underlying this mental exercise and its temporal course have hardly been investigated. Here, we used event-related potentials (ERPs) to explore the effects of chanting the name of a Buddha (Amitābha) on the brain’s response to viewing negative pictures that were fear- and stress-provoking. We recorded and analyzed electroencephalography (EEG) data from 21 Buddhists with chanting experience as they viewed negative and neutral pictures. Participants were instructed to chant the names of Amitābha or Santa Claus silently to themselves or simply remain silent (no-chanting condition) during picture viewing. To measure the physiological changes corresponding to negative emotions, electrocardiogram and galvanic skin response data were also collected. Results showed that viewing negative pictures (vs. neutral pictures) increased the amplitude of the N1 component in all the chanting conditions. The amplitude of late positive potential (LPP) also increased when the negative pictures were viewed under the no-chanting and the Santa Claus condition. However, increased LPP was not observed when chanting Amitābha. The ERP source analysis confirmed this finding and showed that increased LPP mainly originated from the central-parietal regions of the brain. In addition, the participants’ heart rates decreased significantly when viewing negative pictures in the Santa Claus condition. The no-chanting condition had a similar decreasing trend although not significant. However, while chanting Amitābha and viewing negative pictures participants’ heart rate did not differ significantly from that observed during neutral picture viewing. It is possible that the chanting of Amitābha might have helped the participants to develop a religious schema and neutralized the effect of the negative stimuli. These findings echo similar research findings on Christian religious practices and brain responses to negative stimuli. Hence, prayer/religious practices may have cross-cultural universality in emotion regulation. This study shows for the first time that Buddhist chanting, or in a broader sense, repetition of religious prayers will not modulate brain responses to negative stimuli during the early perceptual stage, but only during the late-stage emotional/cognitive processing.

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“…Similarly, a low and regular neuronal activity during an emotional state of calm has also been previously reported by works dealing with different relaxing therapies. Indeed, nonlinear indices like Lyapunov exponents or SE have been able to notice significant decreases in brain activation during relaxing music, foot reflexology and meditation [66,67].…”

Section: Discussionmentioning

confidence: 99%

Application of Entropy-Based Metrics to Identify Emotional Distress from Electroencephalographic Recordings

García-Martínez

Martínez-Rodrigo

Cantabrana

et al. 2016

Entropy

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Recognition of emotions is still an unresolved challenge, which could be helpful to improve current human-machine interfaces. Recently, nonlinear analysis of some physiological signals has shown to play a more relevant role in this context than their traditional linear exploration. Thus, the present work introduces for the first time the application of three recent entropy-based metrics: sample entropy (SE), quadratic SE (QSE) and distribution entropy (DE) to discern between emotional states of calm and negative stress (also called distress). In the last few years, distress has received growing attention because it is a common negative factor in the modern lifestyle of people from developed countries and, moreover, it may lead to serious mental and physical health problems. Precisely, 279 segments of 32-channel electroencephalographic (EEG) recordings from 32 subjects elicited to be calm or negatively stressed have been analyzed. Results provide that QSE is the first single metric presented to date with the ability to identify negative stress. Indeed, this metric has reported a discriminant ability of around 70%, which is only slightly lower than the one obtained by some previous works. Nonetheless, discriminant models from dozens or even hundreds of features have been previously obtained by using advanced classifiers to yield diagnostic accuracies about 80%. Moreover, in agreement with previous neuroanatomy findings, QSE has also revealed notable differences for all the brain regions in the neural activation triggered by the two considered emotions. Consequently, given these results, as well as easy interpretation of QSE, this work opens a new standpoint in the detection of emotional distress, which may gain new insights about the brain's behavior under this negative emotion.

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Entrainment of chaotic activities in brain and heart during MBSR mindfulness training

Cited by 59 publications

References 23 publications

Symbolic Analysis of Brain Dynamics Detects Negative Stress

Symbolic Analysis of Brain Dynamics Detects Negative Stress

Repetitive Religious Chanting Modulates the Late-Stage Brain Response to Fear- and Stress-Provoking Pictures

Application of Entropy-Based Metrics to Identify Emotional Distress from Electroencephalographic Recordings

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