Cardiac Coherence, Self-Regulation, Autonomic Stability (Involuntary), and Psychosocial Well-being

Summary
The capacity to regulate emotional responses constitutes a fundamental component of overall wellbeing and effective adaptation to life's demands. Among the core symptoms of traumatic events that exceed our coping and adaptive capacities is a transformation in our core internal reference framework, leading to recurrent reactivation of the traumatic experience. This can result in hypervigilance and heightened sensitivity to environmental cues, manifesting as inappropriate emotional responses and dysregulation in autonomic nervous system dynamics

This article examines the perspective that an individual's capacity to regulate the quality of emotions and feelings in their immediate experience is fundamentally interconnected with physiological functions, through reciprocal interactions between physiological, cognitive, and emotional systems. These interactions form the foundation of information processing networks where inter-system communication occurs through the generation and transmission of activity patterns.

Our discussion focuses on the communication pathways between the heart and brain, and how they relate to cognitive and emotional functions as well as self-regulatory capacity. We examine the hypothesis that self-generated positive emotions enhance coherence in physiological processes, reflected in heart rhythm patterns. This shift in cardiac rhythm in turn plays a vital role in:

Over time, this establishes a new core internal reference point—a form of implicit memory that regulates perception, emotions, and behavior. Without creating this new reference, individuals remain trapped in familiar yet unhealthy emotional and behavioral patterns, perceiving life through the filters of past traumatic experiences.

keywords:
"Coherence, trauma, heart rate variability, HRV, psychophysiological coherence, cardiac coherence, HeartMath, psychosocial well-being."

Introduction:
"The subjective experience of trauma varies from one individual to another depending on the type of trauma, but what remains constant is that trauma often constitutes a devastating disruption to the peaceful, calm, and fulfilling life one desires, accompanied by a fragmented sense of self and life in general. Most people agree that the lack of mental and emotional self-regulation is what typically characterizes states of stress, anxiety, and exhaustion."

"For some, the lack of self-regulation capacity stems from immaturity or underdeveloped skills, while for others it may result from trauma or dysfunction in the neural systems underlying self-regulatory ability. The degree of self-regulatory impairment characteristic of trauma often makes returning to a state of wholeness and wellbeing seem as distant as a fading memory."

"Yet most people have at some point known a state of balance, typically characterized by feelings of contentment, happiness, control, and harmony with oneself and others. Regardless of circumstances or demographic factors, they yearn to reclaim this state and experience fulfillment once more."

"The pursuit to understand the mechanisms and dynamics of this desired state permeates both scientific and popular literature, giving rise to theoretical and research approaches that span multiple disciplines. We broadly refer to this experience of internal and external interconnectedness as 'coherence.' Through our research at the HeartMath Institute, we have identified a specific physiological state associated with optimal cognitive function and emotional stability, and have developed the Psychophysiological Coherence Model."

The Psychophysiological Coherence Model is grounded in dynamic systems theory. It emphasizes the importance of healthy physiological variability, feedback systems, inhibition, and reciprocal interactions among the hierarchical, overlapping neural systems that constitute a complex psychophysiological system for maintaining stability and adaptability to complex, changing environments and social demands.

Similar to models proposed by Stephen Porges (2007) and Julian Thayer (2009), the Coherence Model also suggests that heart rate variability (HRV) mediated by efferent vagal fibers reflects self-regulatory capacity, and that age-adjusted low HRV indicates a functionally impaired system.

From our perspective, each of these models contributes critical insights into the neural systems involved in emotional experience and the self-regulation of emotions and behaviors. Collectively, they provide a comprehensive description of the evolution, structure, and functions of these psychophysiological control systems.
From our perspective, each of these models provides important insights into the neural systems involved in emotional experience and the self-regulation of feelings and behaviors. Collectively, they offer a more comprehensive description of the development, structure, and functions of these psychophysiological control systems. Since these models have been discussed in detail elsewhere and in other articles in this issue, they will not be addressed here.

A growing body of research indicates that vagally-mediated heart rate variability (HRV) is associated with self-regulatory capacity (Segerstrom & Nes, 2007; Geisler & Kubiak, 2009; Reynard et al., 2011), emotion regulation (Appelhans & Luecken, 2006; Geisler et al., 2010), social interactions (Smith et al., 2011; Geisler et al., 2013), an individual's sense of coherence (Miller et al., 1960), self-directed personality traits (Zohar et al., 2013), and coping patterns (Ramaekers et al., 1998).

We use the terms cardiac coherence and physiological coherence interchangeably to describe the measurement of regularity, stability, and synchronization in the oscillatory outputs of regulatory systems over any time period. For example, resting HRV data obtained from a group of returning soldiers diagnosed with post-traumatic stress disorder (PTSD) showed that those with PTSD had lower HRV levels and lower coherence compared to non-PTSD control group participants (Ginsberg et al., 2010). In a study examining the effects of violent versus non-violent video game play, players of violent games exhibited lower cardiac coherence levels and higher aggression levels compared to non-violent game players, with higher coherence levels being inversely correlated with aggression (Hasan et al., 2013).

The Psychophysiological Coherence Model predicts that distinct emotions produce specific state-dependent patterns in heart rhythms (HR; McCraty et al., 2009b) independent of HRV magnitude, although state-specific changes in HRV magnitude remain clinically significant. We can typically differentiate patterns associated with anxiety versus frustration or anger, for example, through analysis of HRV waveforms. Recent independent work has confirmed this with 75% accuracy in detecting discrete emotional states from HRV signals using neural network pattern recognition (Leon et al., 2010).

Numerous studies on healthy individuals, which have contributed to enriching the model, demonstrate that during positive emotional experiences, a naturally occurring sine wave-like pattern emerges in heart rhythm without any conscious changes in breathing (McCraty et al., 1995; Tiller et al., 1996). This phenomenon is likely attributable to more organized outputs from subcortical structures involved in emotional information processing, as described by Pribram and Melges (1969), Porges (2007), and Thayer et al. (2009) in their model of the central autonomic network, where subcortical structures modulate the oscillatory outputs of cardiac and respiratory centers in the brainstem.

Consequently, the term psychophysiological coherence is used in contexts where more regular heart rhythms emerge naturally through positive experiences or through the self-generation of positive emotions. This associates with a distinct subjective internal state achieved via techniques like paced breathing that enhance cross-coherence between respiratory and cardiac rhythms via brainstem medullary centers, without necessarily altering activity in higher-level subcortical structures that appear to mediate the architecture of distinct HRV waveform patterns and increased/decreased coherence associated with emotional states (McCraty et al., 2009b).

The crucial aspect of the Coherence Model (rather than specific HRV coherence measurements) focuses on targeted approaches to enhance people's self-regulatory capacity. In this context, HRV coherence measurement is intended for use as a skill-acquisition tool in self-regulation practices that produce measurable increases in HRV coherence.

The coherence model has led to the development of numerous mental and emotional self-regulation techniques, most designed for use either in moments when a person becomes emotionally triggered or stressed, or to better prepare for upcoming challenging events (Childre and Martin, 1999). The application of these techniques typically shifts the user's physiology into a more regular and balanced functional state, reflected in heart rhythm patterns.

We have found that regular practice of these simple self-regulation techniques - which primarily teach users to focus attention in the chest area while self-generating calm or positive emotions - can produce lasting increases in participants' self-regulatory capacity and emotional balance. Technique application also induces state-specific increases in HRV and vagal nerve activity (vagal tone; McCraty et al., 1995; Tiller et al., 1996), potentially leading to sustained HRV elevations over time. In a study of high school students practicing these techniques for three months, resting HRV increased significantly with substantially more regular HRV patterns. These resting HRV coherence improvements strongly correlated with higher test scores and behavioral improvements, suggesting that self-regulation practice stimulates more regular cardiac rhythms that enhance coupling in subcortical regulatory systems involved in matching more stable cardiovascular neural afferent rhythms with subjectively perceived positive emotions (Bradley et al., 2010).

By enhancing this natural coupling in subcortical regulatory systems, the self-generation of positive emotions can automatically initiate increased cardiac coherence, while simultaneously, the physiological shift induced by heart-focused breathing facilitates the experience of positive affect.

A pivotal aspect of the coherence model involves incorporating cardiovascular neural afferent inputs to subcortical and cortical structures, which can substantially influence cognitive resources and emotional states. We formally proposed the "Heart Rhythm Coherence Hypothesis," suggesting that information is transmitted through heart rhythm patterns reflecting current emotional states, and that patterns of neural afferent inputs (coherent vs. incoherent) to the brain shape emotional experience, regulate cortical function, and enhance self-regulatory capacity across extended time scales. Furthermore, we posit that intentional activation of positive emotions plays a critical role in augmenting cardiac coherence and strengthening self-regulatory capacity (McCraty et al., 2009b). Our findings expand upon extensive research documenting the benefits of positive emotional states for physical, mental, and emotional health (Isen, 1999; Fredrickson, 2001, 2002; Fredrickson & Joiner, 2002; Fredrickson et al., 2003; Wichers et al., 2007).

This paper provides a concise overview of the psychophysiological coherence model and its implications for enhancing mental/emotional health and self-regulation. Detailed discussions on the nature of coherence can be found in two foundational articles (McCraty et al., 2009b; McCraty & Childre, 2010). We also present a brief review of studies investigating coherence-based approaches for improving self-regulatory capacity, physical health, cognitive function, and psychosocial well-being across diverse populations. Trauma-specific applications are examined regarding mechanisms for mitigating trauma's impact, with particular emphasis on transforming core internal references – a form of implicit memory encoded in neural circuitry that helps regulate perception, emotion, and behavior. Central to this discussion is understanding the functional hierarchy of mental-emotional self-regulation and methods for shifting autonomic nervous system (ANS) activity toward increasingly balanced, harmonious functioning. As system regularity increases, it becomes possible to reintegrate the fragmented self-experience and life narrative common among trauma-exposed individuals.

Overview of Psychophysiological Coherence

The coherence model states that: (1) The functional state of fundamental psychophysiological systems determines the scope of an individual's ability to adapt to challenges, self-regulate, and engage in harmonious social relationships. Healthy physiological variations, feedback systems, and inhibition are key elements in the complex system for maintaining stability and the ability to respond appropriately and adapt to changing environments and social demands. (2) The oscillatory activity in heart rate reflects the state of a network of flexible relationships between interconnected dynamic neural structures in the central and autonomic nervous systems. (3) State-specific emotions are reflected in heart rate patterns regardless of changes in heart rate variability magnitude. (4) Subcortical structures continuously compare information from internal and external sensory systems through a matching/mismatching process that evaluates current inputs against past experiences to assess the environment in terms of risk or comfort and safety. (5) Physiological or cardiac coherence is reflected in a more regular, sine wave-like heart rhythm pattern associated with increased vagally-mediated heart rate variability, synchronization between respiratory, blood pressure, and cardiac rhythms, and increased synchronization between different rhythms in EEG and the cardiac cycle. (6) Vagally-mediated heart rate variability provides an indicator of the cognitive and emotional resources needed to function efficiently in challenging environments where response delay and behavioral inhibition are critical. (7) Information is encoded in the timing between intervals (action potentials, pulsatile hormone secretion, etc.). The information contained in interbeat intervals is communicated through multiple systems and helps synchronize the system as a whole. (8) Patterns of activity in cardiovascular afferent neurons can significantly influence cognitive performance, emotional experience, and self-regulatory capacity through inputs to the thalamus, amygdala, and other subcortical structures. (9) Increased "rate of change" in cardiac sensory neurons (which transform blood pressure, rhythm, etc.) during coherent states leads to increased vagal afferent activity that inhibits thalamic pain pathways at the spinal cord level. (10) Self-generated positive emotions can shift psychophysiological systems into a more globally coherent and harmonious mode associated with improved performance and overall well-being.

Psychophysiological Coherence and Well-being

As previously discussed, this model has been used to develop simple techniques that enable individuals to rapidly shift physiologically into a more coherent state. This shift leverages synchronized changes in neural afferent inputs to the brain, which are associated with enhanced self-regulatory capacity—thereby improving one's ability to navigate life's demands and challenges with greater ease and equilibrium. Consequently, individuals experience heightened interconnectedness, harmony, balance, and overall well-being across physical, emotional, and psychosocial dimensions.

Empirical studies conducted in laboratory, clinical, educational, and organizational settings with diverse population groups have demonstrated sustained reductions in stress levels and improvements across multiple dimensions of health, well-being, and performance. For example, a study of middle school students diagnosed with Attention Deficit Hyperactivity Disorder (ADHD) showed that participants achieved significant improvements across a wide range of cognitive functions, including short- and long-term memory, focus and attention, along with marked behavioral improvements both at home and in school (Lloyd et al., 2010). Additionally, a study involving 41 fighter pilots during flight simulation exercises found a strong correlation between higher performance levels and increased heart rhythm coherence, as well as reduced frustration levels (Li et al., 2013

Additional studies have demonstrated that using these self-regulation techniques increases parasympathetic nervous system activity (high-frequency power) (Tiller et al., 1996) and leads to significant reductions in cortisol alongside increases in DHEA over a 30-day period (McCraty et al., 1998). Research has also shown marked decreases in blood pressure and stress measures among populations diagnosed with hypertension (McCraty et al., 2003). A study of hypertensive patients revealed that those who used HRV coherence-enhancing techniques achieved significantly greater reductions in mean arterial pressure compared to patients relying solely on antihypertensive medications and relaxation techniques (Alabdulgader, 2012). A controlled trial with clergy members documented substantial improvements in stress and well-being measures, with an average $585 reduction in healthcare costs per participant, while the control group experienced a 9% cost increase. The most significant cost reduction was linked to decreased antihypertensive medication use (Bedell, 2010). Furthermore, a study of congestive heart failure patients showed clinically meaningful improvements in functional capacity and reduced depression compared to controls (Luskin et al., 1999

While general health benefits and well-being have been associated with increased coherence, more specific evidence exists regarding trauma and high-stress populations. A study conducted at the Columbia, South Carolina Armed Forces Hospital on recently returned Iraq veterans diagnosed with post-traumatic stress disorder (PTSD) found that relatively brief cardiac coherence training sessions combined with the Quick Coherence Technique® led to significant improvements in self-regulation capacity alongside marked enhancements across a broad range of cognitive functions—all correlated with increased cardiac coherence (Ginsberg et al., 2010). In a study of returning veterans with chronic pain, the treatment group demonstrated statistically significant increases in coherence (191%) along with substantial reductions in pain ratings (36%), perceived stress (16%), negative emotions (49%), and physical activity limitations (42%) (Berry et al., 2014). Furthermore, research on patients with severe traumatic brain injury revealed that emotional self-regulation training resulted in significantly higher coherence ratios and improved attention scores. Additionally, family assessments of participants' emotional control correlated with enhanced HRV metrics (Kim et al., 2013b)

A study on correctional officers reporting high work-related stress demonstrated significant reductions in systolic and diastolic blood pressure, total cholesterol, fasting glucose, overall stress, anger, fatigue, and hostility (McCraty et al., 2009a,b).

Similar results were obtained in multiple studies with police officers, demonstrating significant improvements in their ability to recognize and self-regulate stress responses in both professional and personal contexts. Officers exhibited measurable reductions in stress, negative emotions, and depression, along with increased calmness and vitality compared to control groups. Qualitative findings further revealed enhanced family relationships and improved workplace communication/collaboration (McCraty & Atkinson, 2012; Weltman et al., 2014).

Trauma

Just as coherence is characterized by synchronization and harmonious flow, the experience of trauma often involves dissociation, alienation, and dysregulation (Berntsen et al., 2003; Emerson & Hopper, 2011). Neurologist Robert Scaer (2012) examined trauma’s nature from a neuroscientific perspective, depicting a fragmented reality where traumatic experiences become frozen in time. Procedural and emotionally charged declarative memories become encapsulated fragments, repeatedly intruding into present awareness through internal or external triggers associated with the traumatic event. These recurrent intrusions feel inescapable and threatening, overwhelming an individual’s capacity for control. Submission to these temporal distortions disconnects the person from the present moment, resulting in a fragmented existence devoid of agency, wholeness, or meaningful connection with others.

Scaer (2012) further explored the bidirectional interactions between different brain regions during these episodes, highlighting both prefrontal cortex disruption and limbic system hyperactivity—particularly in the amygdala. Given the prefrontal cortex's role in self-regulation, strategic thinking, decision-making, empathy, and social bonding, it becomes evident that restoring prefrontal function and reducing amygdala overactivity are essential for achieving optimal personal functioning and psychosocial well-being.

While the extensive literature studying the trauma spectrum has produced numerous conceptions of the causes of trauma, various correlations of trauma in psychological and social function, and recommended therapeutic approaches to address the complex aspects and impact of trauma, there is a research consensus that trauma is characterized by a disruption in an individual's ability to respond appropriately to a perceived threat (Levine, 2008) and that physiological factors underlie cognitive, behavioral, and social function (Levine, 2010). The neural correlations of trauma are well-documented. Emerging research in the field of neurocardiology and physiological psychology provides an expanded understanding of the physiological role of trauma, especially with regard to self-regulation, heart rate variability (HRV), and how they relate to the restoration of optimal function along an integrated continuum (Porges, 2007; Thayer et al., 2009)

In the process of facilitating a return to optimal function, it is of utmost importance to recognize that trauma is associated with emotional dysregulation resulting from the persistent activation of trauma triggers and a corresponding inability to return to physiological equilibrium (Norte et al., 2013). The role of cardiac function has been clearly demonstrated in this mechanism, particularly concerning HRV (Frustaci et al., 2010). For example, studies have shown that elevated psychophysiological baseline scores and an increased physiological response to trauma triggers are hallmarks of PTSD that can be objectively measured through cardiac parameters (Keane et al., 1998). A recent twin study (Shah et al., 2013) found a significant relationship between autonomic nervous system function and PTSD, independent of potential confounding variables such as genetic, familial, and sociodemographic factors. After adjusting for cardiovascular risks, depression, and a history of substance abuse, the researchers stated: “...we were able to demonstrate a dose-response relationship between PTSD symptom severity and HRV. In contrast, we found a largely absent association between remitted PTSD and autonomic nervous system function, suggesting the potential for reversibility of autonomic nervous system dysregulation following the resolution of PTSD symptoms (p. 1106

A recent study funded by the U.S. Department of Defense (DoD) examined pre-deployment HRV as a predictor of post-deployment PTSD symptoms and compared resilience training using HRV biofeedback with a control group that received no additional training in a cohort of Army National Guard soldiers. Preliminary results showed that low pre-deployment HRV (SDNN) was a significant predictor of post-deployment PTSD symptoms. The HRV biofeedback resilience training group led to a reduction in the severity of PTSD symptoms for soldiers aged 26 and older (personal communication with Jeff Payne, Mental Health Outcomes Research Center at the Central Arkansas Veterans Healthcare System). Understanding these relationships between physiological fundamentals in relation to HRV is essential for developing effective treatment approaches for individuals suffering from trauma. To explore this topic on a deeper level, it becomes important to first examine the dynamics of self-regulation in the context of cardiac coherence

Feelings and Heart Rhythms

One of the most significant findings of particular relevance to this discussion concerns the relationship between the quality of emotional experience and the patterns reflected in HRV waveforms, including coherence. The nature of the emotional experience appears to be linked to the level of coherence of the heart rhythm pattern. 
McCraty et al.، 1995 ، 
2009b ...is shown by 
The form illustrates 
1 that emotions typically considered positive, such as appreciation and compassion, are associated with a more coherent heart rhythm pattern; whereas emotions typically considered negative are associated with a more incoherent pattern, suggesting that positive emotions may have a physiologically regenerative effect and negative emotions may have a physiologically depleting effect.

Emotions and Heartbeat Patterns. The heart rhythm diagrams on the left show patterns in heart rhythm waveforms that are typically observed in different psychological states. The power spectral density (PSD) analysis of each of these heart rhythms is shown on the left.

Although changes in heart rate often occur with changes in emotional state, we have found that it is the patterns reflected in the heart's rhythm that typically change in a state-specific manner, especially during emotions that do not elicit significant autonomic nervous system activations or inhibit parasympathetic outflow. "These changes in rhythmic patterns are independent of heart rate; that is, one can have a coherent or incoherent pattern at higher or lower heart rates." "Consequently, it is the rhythmic pattern (the ordering of rate changes over time), and not heart rate (at any given point in time), that reflects the more subtle autonomic nervous system and emotional dynamics as well as physiological synchronization." McCraty and Childre، 2010 ; p. 12). From a physiological perspective, a coherent heart rhythm differs from the heart rhythm that occurs during a relaxation response, which is associated with a decrease in heart rate, but not necessarily a more coherent rhythm.

Physiological coherence is reflected in more organized, sine wave-like HRV patterns at a frequency of approximately 0.1 Hz (a 10-second rhythm). A coherent rhythm can be defined as "a relatively harmonic (sine wave-like) signal with a very narrow and high-amplitude peak in the low-frequency (LF) region of the HRV power spectrum, with no major peaks in the very low frequency (VLF) or high-frequency (HF) regions. Coherence is assessed by identifying the maximum peak in the 0.04-0.26 Hz range of the HRV power spectrum, calculating the integral in a 0.03 Hz window, centered around the highest peak in that region, and then calculating the total power of the entire spectrum. The coherence ratio is formulated as follows: [Maximum Power/(Total Power – Maximum Power)]". McCraty and Childre، 2010 ; p. 14)

Heart-Brain Communication

The coherence hypothesis suggests that the coherent flow of information within and between physiological systems and processes in the central nervous system, autonomic nervous system, and the body plays a significant role in determining the quality of a person's emotions and feelings.

Therefore, heart rate variability analysis becomes an important tool that allows for a precise understanding of the activity occurring between the heart and the brain, as well as within the brain's regulatory centers. Heart rate variability arises largely from the interaction between the heart and the brain via neural signals flowing through the afferent (ascending) and efferent (descending) neural pathways of the sympathetic and parasympathetic branches of the autonomic nervous system. Malik and Camm, 1995. ؛ MacCrady et al., 1995. ؛ Kamath et al., 2013. ).

Specific heart rate variability (HRV) variables are used to assess changes in heart rate from beat to beat, associated with rhythms resulting from different physiological mechanisms. Various heart rate metrics can be used to gain insights into the complex interactions among the central nervous system, the autonomic nervous system (ANS), and the heart. McCraty et al., 2009b ... The appropriate level of physiological variability in regulatory systems reflects the organism's resilience and its ability to adapt coherently to stresses and challenges. Segerstrom and Nes, 2007 The total heart rate of a person, which is best assessed over 24 hours, is linked to age, with levels in older people being lower than in younger people. Umetani et al., 1998 ) A low age-adjusted heart rate, especially in the VLF and ULF ranges, has been shown to be associated with increased health risks in a wide range of clinical conditions and with all-cause mortality. Saul et al.، 1988 ؛ Arrone et al.، 1997 ؛ Levy et al.، 2002 ؛ Lindmark et al.، 2003 ، 2005 ، 2006 )... Heart rate, especially the HF range, provides an index of psychological resilience, behavioral flexibility, and an individual's ability to adapt to changing social demands. Beauchaine، 2001 Additionally, the model by Thayer and Lane, which describes a dynamic system of neural structures they call the central autonomic network, links cognitive performance with autonomic regulation and heart rate. It has been shown in a series of studies that resting heart rate levels can be predicted by individual differences in performance on tasks requiring the use of prefrontal structures that underlie executive functions. Thayer et al., 2009 ).

Cardiovascular Afferent Neurons

Porges (2007) states Porges (2007) also points to a bias in most textbooks to focus only on the efferent pathways in the autonomic nervous system and neglect the role of afferent neurons as part of the dynamic regulatory system. Therefore, it is not commonly understood that 85-90% of the fibers in the vagus nerve are afferent ( Cameron, 2002. )... and that the afferent neural traffic related to the cardiovascular system significantly influences activity in the majority of higher brain centers, as well as cognitive processes and emotional experience. MacCrady et al., 2009. ... as demonstrated in The form illustrates 2 ...cardiovascular afferent neurons have connections to many brain centers, including the thalamus, hypothalamus, and amygdala.

This diagram shows the main afferent inputs from the body to the dorsal vagal complex. Then, the afferent pathways connect directly to the amygdala, hypothalamus, thalamus, and others. There is increasing evidence for a direct connection between the dorsal vagal complex and the prefrontal cortex.

Lacey (1967). J. Lacey Lacey 1970 ، 1974 ...were the first to demonstrate a causal relationship between cognitive and sensorimotor performance and the activity of cardiovascular afferent neurons. He/They did... Fielding, J. M. (1987) updating their hypothesis after demonstrating that cognitive performance was indeed modulated by a rhythm of approximately 10 Hz. Essentially, they showed that afferent neural activity modifies cortical function by inhibiting or facilitating the synchronization of global cortical activity which is mediated through afferent pathways to the thalamus ( Fielding, J. M. (1987) ؛ Fielding, J. M. (1987) ، 1989 ) . And most importantly, they found that pattern واستقرار الإيقاع في المدخلات العصبية الواردة القلبية الوعائية بدلاً من معدل الانفجارات العصبية هو المهم.

Since then, numerous anatomical and neurophysiological studies in the field of neurocardiology have shown that the neural communication between the heart and the brain is far more complex than traditionally believed ( Armour and Ardell, 1994 ). This study found that complex patterns of afferent information are continuously sent to the brain (not just within the cardiac cycle), and are associated with mechanical and chemical factors over timescales ranging from milliseconds to minutes. Armour and Kember, 2004 ).

Therefore, the heart rhythm coherence hypothesis "postulates that the pattern and degree of stability in beat-to-beat changes in heart rate encode information on large time scales that can influence cognitive performance and emotional experience." McCraty et al.، 2009b ; p. 16). Several studies have demonstrated a relationship between interventions that increase coherence in heart rate and significant improvements in cognitive performance. Bradley et al.، 2010 ؛ Ginsberg et al.، 2010 ؛ Lloyd et al.، 2010 ...In a study using a novel auditory discrimination task with reaction times and error rates as measures of cognitive performance, participants used a technique to induce either a state of coherence or a state of relaxation for 5 minutes prior to the experimental protocol. In the coherence group, there was a persistent effect on subsequent performance compared to the relaxation group, and there was a significant correlation between pre-task heart coherence and performance across all participants. Additionally, the significant improvement in performance was six times greater than the improvement noted due to afferent neural effects occurring within a single cardiac cycle. McCraty et al., 2009b ).

vagal afferent activity

One of the characteristics of sensory neurons (baroreceptors) is that they are most responsive to increases in the rate of change in the function they are tuned to detect (heart rate, blood pressure, etc.). Armour and Ardell, 1994 During periods of increased heart coherence, there is usually an increased range of variability in both blood pressure and heart rate, which is detected as an increase in the rate of change by sensory neurons. This leads to increased firing rates, which in turn increases vagal afferent traffic. There is also a more organized pattern of activity. A recent study using heartbeat-evoked potential showed that using paced breathing at a coherence rhythm increased HRV range and coherence in rhythms as expected. It also increased the N200 amplitude of the heartbeat-evoked potential in the EEG, indicating increased afferent input. MacKinnon et al.، 2013 ). Conducted Format (1989). ، 1994 ، 1997 A series of anatomical and stimulation studies that showed that spinal thalamic pain pathways are inhibited by increases in the normal intrinsic levels of vagal afferent activity. Vagal afferent stimulation has also been shown to reduce migraines and cluster headaches. MacKenzie and others, 2005. . Furthermore, vagus nerve stimulation has also been shown to improve cognitive processing and memory. Hasegawa et al., 2004. . In recent years, there has also been an increasing number of studies using afferent vagus nerve stimulation in a wide range of clinical disorders such as epilepsy, obesity, depression, anxiety, autism, alcoholism, mood disorders, multiple sclerosis, and traumatic brain injuries. Thayer, 2003. ؛ Groves and Brown, 2005. ).

It is important to Lehrer et al., 2006. They showed that regular practice of heart rate biofeedback leads to lasting improvements in baroreflex gain, independent of cardiovascular and respiratory effects. This demonstrates neural plasticity within the baroreflex system, likely within the intrinsic cardiac nervous system. Consequently, repeated sessions of heart coherence can reset the gain of the baroreflex system, leading to increased afferent nerve activity in a non-invasive manner.

create a new baseline

In order to understand how increased heart coherence facilitates self-regulation and helps reset regulatory systems in cases of trauma, it becomes necessary to examine the emerging perspective in neuroscience that emotions reflect complex bodily states. Cameron, 2002. ؛ Damasio, 2003. ...which become "set points" in the neural structure that act as a type of implicit memory, or a baseline reference ( Pribram and Melges, 1969. ).

Pribram's (1970) theory proposes that ...that emotional information is transmitted through different internal rhythms, most notably heartbeats and facial expressions, in the form of low-frequency oscillations produced by these systems. He also suggests that high-frequency oscillations (EEG) are related to the cognitive interpretation of sensory stimuli in the environment. Specifically, Pribram considers the brain's role in monitoring information a fundamental element in this process. As the brain monitors these inputs, neural patterns are formed in overlapping feedback loops within the neural structure. This implicit memory acts as a baseline against which all sensory inputs are evaluated. Pribram (1970). ).

In other words, we establish physiological and behavioral set points, or default patterns, that the brain and nervous system, once they are established, seek to maintain. Although this is a more complex process, it is similar to setting the temperature to a specific setting on a thermostat that the heating system works to maintain. It is important to note that the established default patterns are adaptable, and while they may be appropriate in one context, they may not be healthy or ideal in another.

Once a stable pattern is formed and established in memory, all sensory inputs to the brain, from both the internal and external sensory systems, are compared to the reference patterns and programs. When the current input matches the baseline pattern, the brain recognizes it as familiar, and we feel comfortable and secure. It is important to note that this same process occurs even if the reference pattern is associated with anxiety, chaos, confusion, burnout, etc. It becomes comfortable because it is familiar.

In order to maintain stability and feelings of safety and comfort, we must be able to maintain a match between our current experience or "reality" and one of our pre-existing neural programs. Miller et al., 1960 In cases where we face a new experience or challenge, there can be a mismatch between the input patterns of the new experience and the lack of a familiar reference. Depending on the degree of the mismatch, it requires either an internal adjustment (self-regulation) or an external behavioral action to re-establish the match and the feeling of comfort. When a mismatch is detected by the external or internal sensory systems, a change in activity occurs in the central nervous system and the autonomic nervous system. If the response is short-term (one to three seconds), it's called an arousal or an orienting reaction. However, if the stimulus or event is repetitive, the brain eventually adapts and we get used to it by updating the memories that act as a reference. For example, people living in a noisy city adapt to the ambient noise and eventually ignore it. After this adaptation, the absence of noise doesn't seem strange or noticeable until we go on a trip to the quiet countryside. The discrepancy between the familiar noisy background and the quiet atmosphere leads to an excitatory reaction that grabs our attention. This divergence from the familiar is what creates a signaling function that generates an emotional experience, alerting us to the current state of discrepancy

In addition to monitoring and control processes for "in-the-moment" regulation, there are also evaluation processes that determine the degree of consistency or inconsistency between the current situation and an expected future. Assessments of future outcomes can be broadly divided into optimistic and pessimistic. Pribram (1970). . Evaluations that anticipate an inability to successfully handle a situation may lead to feelings of fear and anxiety. In line with recent research on attentional bias ( Olatunji et al., 2013 ... (Olatunji et al., 2013), this evaluation may not be accurate because it may be the result of a hypersensitivity to cues that resemble past painful experiences in the current situation. Alternatively, the inaccurate evaluation can be due to instability in the neural systems, or a lack of experience or insight on how to effectively deal with the expected future situation. Pribram (1970). While the evaluation may be inaccurate, familiarity with the inputs can be enough to trigger a pessimistic response. This means that we can easily "get stuck" in unhealthy emotional and behavioral patterns, and that lasting improvements in emotional experience or behaviors cannot be sustained in the absence of establishing a new baseline set point. If a change in behavior or an improvement in emotional states is desired, it is essential to focus on strategies that help create a new internal reference. When we successfully navigate new situations or challenges, the positive experience updates our internal reference. At its core, we mature through this process as we learn how to more effectively regulate our emotions and handle new situations and challenges. It is through this process that we can develop a new, healthier internal baseline reference against which to match inputs, so that our evaluations of benign inputs are more accurate and lead to a sense of safety and comfort rather than threat and anxiety.

Self-regulation and stability

Pribram and McGuinness (1975) conducted Pribram and McGuinness (1975) conducted numerous experiments that provide evidence that the higher brain centers that monitor the pattern-matching process can regulate themselves by inhibiting or "constraining" the information flowing into the brain. For example, where we focus our attention has a powerful effect on modulating inputs and thus on determining what is processed at higher levels. In a noisy room filled with many conversations, for example, we have the ability to ignore the noise and focus on a single conversation of interest. In the same way, we can modulate the pain from a stubbed toe or a headache or desensitize ourselves to sensations like tickling, and self-direct our emotions ( Pribram, 1971. ). Ultimately, when we achieve control through the process of self-regulation, it leads to feelings of satisfaction and fulfillment. In contrast, a failure to effectively self-regulate and regain control often leads to feelings of frustration, impatience, anxiety, overwhelm, hopelessness, or depression.

If the neural systems that maintain baseline reference patterns are unstable, we are more likely to experience turbulent emotions and atypical reactions. These neural systems can be destabilized as a result of trauma, stress, anxiety, or chemical stimuli, to name a few. Therefore, it is clear that responding in healthy and effective ways to the constant internal and external demands and conditions, such as daily life situations, depends to a large extent on the synchronization, sensitivity, and stability of our physiological systems. MacCrady et al., 2009. ؛ (McCraty and Childre, 2010) ).

Nervous inputs arise from many organs and muscles, especially the face. However, the heart and cardiovascular system have far more afferent input than other organs and are the primary source of consistent dynamic rhythms. Cameron, 2002. In addition to afferent nerve activity related to mechanical information such as pressure and the rate at which it occurs with each heartbeat, a continuous, dynamically changing pattern of afferent nerve activity related to chemical information is sent to the brain and other systems in the body. Regarding emotional experience, there are afferent pathways to the amygdala via the nucleus of the solitary tract, and activity in the central nucleus of the amygdala is synchronized with the cardiac cycle. Chang and others, 1986. ؛ Frysinger and Harper (1990) Therefore, afferent inputs from the cardiovascular system to the amygdala are important contributors in determining emotional experience and in setting the "set point" against which current inputs are compared.

In the context of this discussion, it is important to note that heart rhythm patterns and afferent neural signaling patterns change to a more organized and stable pattern when one uses heart-focused self-regulation techniques. Regular practice of these techniques, which involves shifting the focus of attention to the center of the chest (the heart area) accompanied by a conscious self-induction of a calm or positive emotional state, strengthens the association (pattern matching) between a more coherent rhythm and a calm or positive emotion. Thus, positive emotions automatically begin with increased heart coherence. The increased coherence initiated through heart-focused breathing tends to facilitate the tangible experience of positive emotion. Therefore, the practice facilitates a remodeling process. .

This is important in situations where there has been continuous exposure to high-risk environments or actual trauma in the past, but that context is no longer valid, and the patterns that were developed at that time no longer serve the individual in the current safe environments.

Through this feedforward process, regulatory capacity increases and new reference patterns are established, which the system strives to maintain. This makes it easier for individuals to maintain their stability and self-regulation during their daily activities, even in the most difficult situations. Without a change in the underlying baseline, behavioral change is extremely difficult to sustain, putting individuals at risk of living their lives through the automatic filters of familiar past experiences.

Social Coherence

In social interactions, we also have specific set points or familiar ways of perceiving and responding. In line with the coherence model, social coherence is reflected in the harmonious quality of the network of relationships shared by individuals. In a socially coherent system, relationships are aligned in a way that allows for optimal collective performance through effective communication and shared energy resources. (Bradley, 1987) If our familiar social set points reflect a pattern of harmony and support, optimal social performance in our interactions leads to an experience of safety, comfort, and well-being. Overall, social coherence depends on the ability of group members to remain aligned with the group and the group's ability to regulate and discipline itself according to mutually agreed-upon standards. (Bradley, 1987) ).

In most social contexts, individuals may sometimes feel incoherent emotions toward each other, such as preconceived notions or judgments, which are not expressed and can lead to disruptions in optimal social interactions through miscommunication or other harmful social dynamics.

Studies in the field of social incoherence suggest that, in addition to generating unpleasant emotions and relational dynamics, physiological processes are involved that have a direct impact on our state of health. For example, studies on individuals in socially incoherent situations, including social chaos or isolation, indicate that they are more prone to illness. Neser et al.، 1971 ؛ Marmot and Syme، 1976 ؛ Berkman and Syme، 1979 ؛ Ornstein and Sobel، 1987 ؛ Hermes et al.، 2009 Research by James Lynch on social isolation indicates that the risks of isolation far exceed the combined risks for heart disease caused by smoking, obesity, lack of exercise, and excessive alcohol consumption ( Lynch، 2000 ). This is particularly shocking and important for people who suffer from trauma. As we mentioned earlier, and aside from the experience of internal turmoil, one of the main symptoms of trauma is social alienation, which often stems from depersonalization.

In contrast, the protective value of close and meaningful relationships has also become clear. Based on studies that investigate the role of social connection in illness ( Cohen and Syme, 1985 ; Uchino et al., 1996 ; Ornish, 1998 In a follow-up to studies on the role of social connection in illness, James Coan and his colleagues at the University of Virginia are investigating the role of social interactions and networks in well-being. Coan calls this the Social Baseline Theory, and he suggests that the primary environments to which humans have adapted are other humans. The human brain implicitly assumes it is embedded within a relatively predictable social network characterized by familiarity, shared interest, common goals, and interdependence. In other words, social proximity is a "baseline" state, and when proximity is maintained or re-established, the brain is less vigilant for potential threats because it is familiar with the social environment. Thus, when we are in close proximity to our familiar social environment and are aligned with our baseline state, we expend less emotional energy. We also expend less energy on self-regulation. According to the Social Baseline Theory, being alone requires more effort, because a variety of activities require more energy expenditure due to reduced load-sharing and risk distribution. Beckes and Coan, 2011 ; Beckes et al., 2013 ; Maresh et al., 2013 Due to the prevailing fragmented psychological state in trauma, one could speculate that alienation has become the new norm for many. Although isolation is inherently stressful, the pattern of alienation under these circumstances can become familiar, such that social proximity may be perceived as a mismatch with the current baseline and thus add to the perceived stress load rather than alleviating it. This can put the individual in a downward spiral due to a self-sustaining, repetitive feedback loop of disconnection from one of the very resources proven to facilitate healing. This disconnection can be exacerbated by cultural patterns of marginalization in societies where people with presumed disabilities are avoided, judged, and even blamed, sometimes by the caregivers themselves. Thomas and Shours-Baum, 2011. ).

Re-establishing connection, both internally and externally, as in the case of trauma, is a complex process, yet it is one of the most important aspects that allows for reintegration. It talks about Peter A. Levine (2010). ... about the calming effect of social engagement and how the physiology of trauma affects an individual's ability to be present in the current moment and receive social support. He also speculates Skar (2012). He also speculates on the power of "face-to-face empathetic resonance" between the client and the therapist in a therapeutic environment to activate the limbic centers responsible for regulating the amygdala, which plays a pivotal role in the physiology of trauma. Skar notes that the therapist's skill in creating this container for reconnection to occur is of utmost importance. Therefore, it is essential that both the client and the therapist are able to be present and engage in an empathetic exchange.

Research indicates that when individuals learn how to maintain coherence while communicating with others, an increased physiological association occurs, and they become more sensitive to others, which enhances empathy and understanding, allowing the process of heart-to-heart connection to occur ( McCraty، 2004 ).

Trauma and Self-Regulation

Upon considering the importance of social reconnection, in addition to some of the core elements of trauma discussed earlier, such as emotional dysregulation, intrusive memories, difficulty returning to internal balance, inappropriate autonomic nervous system arousal, and reduced heart rate variability, it becomes clear that simple and direct techniques that effectively increase an individual's ability to self-regulate would be extremely helpful in achieving integration and harmony not only in their personal experience but also in their connection with others. Although the types of trauma and their treatment are a highly complex topic involving many interconnected approaches and modalities, some central elements emerge in the literature. A review conducted by Courtois and Ford (2013). for the principles of complex trauma and how it is expressed among individuals, that the core components for building a foundation of care include emotional regulation, health promotion, and stress management.

self-regulation techniques that increase heart coherence

HeartMath self-regulation techniques and assistive technologies provide a systematic process for self-regulating thoughts, emotions, and behaviors, and increasing physiological coherence ( Childre and Martin, 1999. ؛ Childre and Rozman, 2003. ، 2005 HeartMath self-regulation techniques and assistive technologies provide a systematic process for self-regulating thoughts, emotions, and behaviors, and increasing physiological coherence. Many of them are specifically designed to enable individuals to intervene the moment unhelpful stress reactions, thoughts, or emotions arise. Heart rate variability (HRV) coherence feedback technology often supports the skill acquisition of the tools and techniques (heart-focused breathing, freeze-frame, inner ease, quick coherence, heart lock-in, prep, shift and reset, heart rhythm, and coherent communication).

With practice, one can use one of these techniques to transition into a more coherent physiological state before, during, and after challenging or difficult situations, which improves mental clarity, calmness, and stability. As mentioned earlier, in this state, most people are able to quickly regain their focus, gain new perspectives, and confront ineffective and maladaptive thoughts, feelings, and behaviors.

Managing trauma and building a new internal reference point first requires increasing self-awareness and recognizing triggers and persistent emotional reactions (fear, negative thoughts, insecurity, anxiety, etc.). Once one becomes more aware, the next step is to consciously learn how to self-regulate and replace these feelings with more neutral or positive attitudes and perceptions.

The first step in most techniques is called heart-focused breathing, which involves focusing attention on the center of the chest (the heart area) and imagining that the breath is flowing in and out of that area while breathing slower and deeper than usual. Consciously regulating the breath at a 10-second rhythm (0.1 Hz) increases heart coherence and begins the process of transitioning into a more coherent state. In difficult situations or after a strong emotional trigger, heart-focused breathing is often the step most people remember and find helpful in "de-escalating" or "bringing down" the reaction. Since we have conscious control over breathing and can easily slow the rate and increase the depth of the breathing rhythm, we can leverage this physiological mechanism to modulate efferent vagal nerve activity and consequently heart rate variability. This, in turn, increases afferent vagal nerve traffic and increases the coherence (stability) in afferent vagal nerve patterns which influences the neural systems involved in regulating sympathetic outflow, informing emotional experience, and synchronizing the neural structures that underlie cognitive processes. McCraty et al.، 2009b While rhythmic breathing methods are an effective way to increase heart rate coherence, a cognitively-guided rhythmic breath is difficult for many people to sustain for more than about a minute before it becomes uncomfortable and distracting ( Alabdulgader، 2012 ).

We have found that self-generated positive emotions can initiate a shift into increased heart coherence without any conscious intention to change the breathing rhythm ( McCraty et al.، 1995 ؛ Tiller et al.، 1996 Normally, when people are able to activate a positive or calming feeling themselves instead of remaining focused on their breathing, they enjoy the shift in feeling and are able to sustain high levels of coherence for much longer periods. However, people who are just learning the techniques or who are experiencing strong emotional triggers may not be able to self-activate a calm or positive emotion. In these cases, the heart-focused breathing step can be used to initiate the process of regaining their composure and increasing their coherence. As their thoughts and emotions slow down and decrease in intensity, they can transition to the next step of the various techniques, depending on the situation. Remembering to use any self-regulation approach takes effort, and ongoing guidance or coaching can significantly help motivate clients to practice and sustain the use of the techniques. Bedell، 2010 ).

In addition to the techniques outlined above, other methods also increase heart rate coherence. For example, a study of Zen monks found that advanced monks tended to have coherent heart rhythms when they were recorded at rest, while this was not the case for those who had been monks for less than two years ( Lehrer et al.، 1999 A study on spontaneous meditation also showed an increase in heart rate coherence and found that heart coherence was closely related to alpha activity in the electroencephalogram (EEG). The authors suggested that heart coherence could be a general marker for the meditative state ( Kim et al.، 2013a While it does not suggest that all forms of meditation increase coherence, unless the state of coherence is driven by a focus on a 10-second rhythmic breath ( Peng et al.، 1999 ؛ Wu and Lo، 2008 ؛ Phongsuphap and Pongsupap، 2011 or a positive emotion. For example, a study that examined heart rate during rosary recitation, prayer beads, and yoga chants found that a coherent rhythm was produced through rhythmic breathing but not through random vocalization or unstructured breathing. The authors attributed the mechanisms of this finding to the changes in their breathing patterns to a rhythm of six cycles per minute and concluded that the rhythm of the chants and rosary prayers was deliberately designed to induce coherent heart rhythms by individuals who had an intuitive understanding of the benefits of this rhythm ( Bernardi et al., 2001. In a study on the effects of five different types of prayer on heart rate, all types of prayer were found to induce increased cardiac coherence; however, prayers of gratitude and prayers focused on genuine love led to definitively higher levels of coherence ( stanly 2009 There are also numerous studies demonstrating that practicing breathing at a rate of 6 breaths per minute, supported by HRV feedback, induces a coherent rhythm and has a wide range of benefits ( Lehrer et al.، 2003 ، 2006 ؛ Siepmann et al.، 2008 ؛ Hallman et al.، 2011 ؛ Henriques et al.، 2011 ؛ Ratanasiripong et al.، 2012 ؛ Beckham et al.، 2013 ؛ Li et al.، 2013 There is also evidence that rhythmically contracting the large muscles in the legs at a 10-second rate can induce a coherent heart rhythm ( Lehrer et al.، 2009 ).

Heart Rate Variability (HRV) Coherence Feedback

In addition to clinical applications, heart rate coherence training is often used to support the acquisition of self-regulation skills in educational, corporate, law enforcement, and military settings. Several systems are available that assess the degree of coherence in a user's heart rhythms. Most of these systems, such as emWavePro, Inner Balance for iOS devices (HeartMath Inc), Relaxing Rhythms (Wild Divine), and the Stress Resilience Training System (Ease Interactive), use a non-invasive pulse sensor on the earlobe or finger, display the user's heart rhythm, and provide information on its level of coherence.

Conclusion

The psychophysiological coherence model has contributed to the development of practical applications and approaches to increase the capacity for self-regulation and activate the vagus nerve in a wide range of populations, including individuals who have experienced trauma. Numerous studies have shown that coherence training, which involves the deliberate activation of positive and calming emotions, along with heart rate coherence feedback, facilitates significant improvements in health and well-being indicators across various groups.

The role of heart coherence in facilitating the resetting of adaptive response patterns by changing the fundamental physiological reference to a healthy pattern appropriate for current contexts has been highlighted as a key element in supporting the process of returning to optimal functioning. While the experience of trauma is associated with a sense of fragmentation and loss of control resulting from intrusive activations of the traumatic trigger, self-regulation dysfunctions, and difficulty returning to internal balance, practicing techniques that increase heart coherence is associated with an experience of internal and interactive synchronization, social harmony, and integration. This becomes especially significant in cases where quality of life significantly deteriorates, as is the case with trauma. The process of repatterning through the deliberate activation of positive emotions and the generation of an increasingly coherent state of psychophysiological coherence brings with it the potential to address the core components identified in the traumatic experience, thereby allowing individuals to move out of the "stalled state" of dysfunction and fragmentation into a state of harmonious, synchronous, healthy functioning on both individual and social levels.

Disclosures

Dr. Rollin McCraty works at the HeartMath Institute, a non-profit research center supported by grants and donations, in addition to some fee-for-service activities, such as providing self-regulation training courses and selling books and heart rate coherence technologies. The institute focuses on educational services, military personnel, veterans, and non-profit social service organizations. The HeartMath Institute does not manufacture any devices, and when used in research projects or resold, they are purchased from the manufacturer in the same way as any other institution.

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.

References

1. Abdul Qadir, A. (2012). Coherence: A new non-pharmacological method for lowering blood pressure in hypertensive patients. Advanced Global Journal of Health Medicine 1، 54-62 [ Digital Object Identifier (DOI) ] [ Free article from PMC ] [ PubMed ] [ Google Scholar ]
2. Appelhans, B. M., & Luecken, L. J. (2006). Heart rate variability as an index of regulated emotional responding. Journal of General Psychology, Volume 10، الصفحات 229-240، 10.1037/1089-2680، 10.3.229 [ DOI ] [ Google Scholar ]
3. Armor, J. A., & Ardell, J. L. (Eds.). (1994). Neurocardiology نيويورك: مطبعة جامعة أكسفورد [ Google Scholar ]
4. Armour, J. A., & Kember, G. C. (2004). "Cardiac sensory neurons." In Journal of Basic and Clinical Neurocardiology ، المحرران أرمور جيه إيه، أرديل جيه إل (نيويورك: مطبعة جامعة أكسفورد؛ ) ٧٩-١١٧ [ Google Scholar ]
5. Aronne, L. J., Mackintosh, R., Rosenbaum, M., Leibel, R. L., & Hirsch, J. (1997). Activity of the cardiac autonomic nervous system in obese and never-obese young adults. Obes. Res. 5 354–359 10.1002/j.1550-8528.1997.tb00564.x [ DOI ] [ PubMed ] [ Google Scholar ]
6. Porges, S. W. (2001). Vagal tone, development, and Gray's motivational theory: Toward an integrated model of autonomic nervous system functioning in psychopathology. Dave. Psychopathology. 13 183–214 10.1017/S0954579401002012 [ DOI ] [ PubMed ] [ Google Scholar ]
7. Beckes, L., & Coan, J. A. (2011). Social baseline theory: The role of social proximity in emotion and economy of action. Journal of Social and Personality Psychology, Volume 5، 976-988، 10.1111/j.1751-9004.2011.00400.x [ DOI ] [ Google Scholar ]
8. Beckes, L., Coan, J. A., & Morris, J. P. (2013). Implicit conditioning of faces via the social regulation of emotion: Evidence from event-related potential (ERP) on early attentional biases for security-conditioned faces. Physiological Psychology 50، 734-742، 10.1111/psyp.12056 [ DOI ] [ PubMed ] [ Google Scholar ]
9. Beeghly, A. J., Green, T. P., & Meltzer-Brody, S. (2013). A pilot study of heart rate variability biofeedback therapy in the treatment of perinatal depression in a specialized perinatal psychiatry inpatient unit. Journal of Women's Mental Health ، 16، 59-65، 10.1007/s00737-012-0318-7 [ Digital Object Identifier (DOI) ] [ Free article from PMC ] [ PubMed ] [ Google Scholar ]
10. Biddle, W. (2010). An actuarial study of healthcare costs correlation and root cause analysis of outcomes: A cost-effectiveness cohort study. Journal of Alternative Health Medicine 16-26-31 [ PubMed ] [ Google Scholar ]
11. Berkman, L. F., & Syme, S. L. (1979). Social networks, host resistance, and mortality: A nine-year follow-up study of Alameda County residents. American Journal of Epidemiology 109، 186-204 [ DOI ] [ PubMed ] [ Google Scholar ]
12. Bernardi, L., Sleight, P., Bandinelli, G., Cencetti, S., Fattorini, L., Wdowczyc-Szulc, J., et al. (2001). Effect of rosary prayer and yoga mantras on autonomic cardiovascular rhythms: Comparative study. British Medical Journal 323 1446–1449 10.1136/bmj.323.7327.1446 [ DOI ] [ Free article from PMC ] [ PubMed ] [ Google Scholar ]
13. Berntsen, D., Willert, M., & Rubin, D. C. (2003). Splintered memories or vivid landmarks? Qualities and organization of traumatic memories with and without PTSD. Applied Psychology 17، 675-693، 10.1002/acp.894 [ DOI ] [ Google Scholar ]
14. Berry, M. E., Chapple, I. T., Ginsberg, J. P., Gleichauf, K. J., Meyer, J. A., & Nagpal, M. L. (2014). Non-pharmacological intervention for chronic pain in veterans: A pilot study of heart rate variability biofeedback. Advanced Global Journal of Health Medicine ، 3، 28-33، 10.7453/gahmj.2013.075 [ Digital Object Identifier (DOI) ] [ Free article from PMC ] [ PubMed ] [ Google Scholar ]
15. Bradley, R. T. (1987). Bradley, R. T. (1987). Charisma and Social Structure: A Study in Love, Power, and Wholeness and Transformation. . نيويورك: دار باراغون [ Google Scholar ].
16. Bradley, R. T., McCraty, R., Atkinson, M., Tomasino, D., Daugherty, A., & Arguelles, L. (2010). Emotion self-regulation, psychophysiological coherence, and test anxiety: Results from an experiment using electrophysiological measures. Applied Psychophysiology and Biofeedback 35، 261-283، 10.1007/s10484-010-9134-x [ DOI ] [ PubMed ] [ Google Scholar ]
17. Cameron, O. G. (2002). Cameron, O. G. (2002). Visceral Sensory Neuroscience: Interoception. . نيويورك: مطبعة جامعة أكسفورد [ Google Scholar ]
18. Child, D., & Martin, H. (1999). "The HeartMath Solution" سان فرانسيسكو: HarperSanFrancisco [ Google Scholar ]
19. Childre, D., & Rozman, D. (2003). Childre, D., & Rozman, D. (2003). Transforming Anger: The HeartMath Solution for Letting Go of Rage, Frustration, and Irritation. . أوكلاند: منشورات نيو هاربينجر [ Google Scholar ]
20. Childre, D., & Rozman, D. (2005). Transforming Stress: The HeartMath Solution for Relieving Worry, Fatigue, and Tension . أوكلاند: منشورات نيو هاربينجر [ Google Scholar ]
21. Cohen, S., & Syme, S. (Eds.). (1985). Social Support and Health. أورلاندو: أكاديميك بريس [ Google Scholar ]
22. Courtois, C. A., & Ford, J. D. (2013). Treatment of Complex Trauma: A Sequenced, Relationship-Based Approach. . نيويورك: مطبعة جيلفورد [ Google Scholar ].
23. Damasio, A. (2003). Looking for Spinoza: Joy, Sorrow, and the Feeling Brain. . أورلاندو: هاركورت [ Google Scholar ]
24. Emerson, D., & Hopper, E. (2011). Overcoming Trauma through Yoga: Reclaiming Your Body. . بيركلي: كتب شمال الأطلسي [ Google Scholar ]
25. Forman, R. (1997). “Organization of Visceral Input,” in Anesthesia: Biological Foundations. ، تحرير ياكش تي إل، كارل لينش، زابول دبليو إم، وآخرون (فيلادلفيا: دار نشر ليبينكوت-رافين؛ ) ٦٦٣-٦٨٣ [ Google Scholar ]
26. Foreman, R. D. (1989). Organization of the spinothalamic pathway as a relay for sympathetic cardio-pulmonary afferent activity. Progress in Sensory Physiology, المجلد ٩، الصفحات ١-٥١، 10.1007/978-3-642-74058-9_1 [ DOI ] [ Google Scholar ]
27. Foreman, R. D. (1994). “Vagal Afferent Modulation of Cardiac Pain,” in Vagal Control of the Heart: Experimental Basis and Clinical Implications. ، تحرير ليفي م. ن.، شوارتز ب. ج. (دار نشر أرمونك: فوتورا للنشر)، الصفحات ٣٤٥-٣٦٨ [ Google Scholar ]
28. Fredrickson, B. L. (2001). The role of positive emotions in positive psychology: The broaden-and-build theory of positive emotions. American Psychologist, ٥٦ ٢١٨-٢٢٦، ١٠.١٠٣٧/٠٠٠٣-٠٦٦X.٥٦.٣.٢١٨ [ DOI ] [ Free article from PMC ] [ PubMed ] [ Google Scholar ]
29. Fredrickson, B. L. (2002). “Positive Emotions,” in Handbook of Positive Psychology. ، تحرير سنايدر سي آر، لوبيز إس جيه (نيويورك: مطبعة جامعة أكسفورد؛ ) ١٢٠-١٣٤ [ Google Scholar ]
30. Fredrickson, B. L., & Joiner, T. (2002). Positive emotions trigger upward spirals toward emotional well-being. Psychological Science, 13، 172-175، 10.1111/1467-9280.00431 [ DOI ] [ PubMed ] [ Google Scholar ]
31. Fredrickson, B. L., Tugade, M. M., Waugh, C. E., & Larkin, G. R. (2003). What good are positive emotions in crises? A prospective study of resilience and emotions following the terrorist attacks on the United States on September 11th, 2001. Journal of Social and Personality Psychology, Volume ، 84، 365-376، 10.1037/0022-3514.84.2.365 [ Digital Object Identifier (DOI) ] [ Free article from PMC ] [ PubMed ] [ Google Scholar ]
32. Frustaci, A., Lanza, G. A., Fernandez, E., Di Giannantonio, M., & Pozzi, G. (2010). Changes in psychological symptoms and heart rate variability during Eye Movement Desensitization and Reprocessing (EMDR) treatment: A case series of subthreshold posttraumatic stress disorder. Journal of EMDR Practice and Research, 4 3-12 10.1891/1933-3196.4.1.3 [ DOI ] [ Google Scholar ]
33. Freisenegger, R. C., & Harper, R. M. (1990). Cardiac and respiratory correlations with unit discharge in the human epileptic temporal lobe. Epilepsia, 31، المجلد 162-171، 10.1111/j.1528-1167.1990.tb06301.x [ DOI ] [ PubMed ] [ Google Scholar ]
34. Geisler, F., & Kubiak, T. (2009). Heart rate variability predicts self-control in goal pursuit. European Journal of Personality, 23، 623-633، 10.1002/لكل 727 [ DOI ] [ Google Scholar ]
35. Geisler, F., Vennewald, N., Kubiak, T., & Weber, H. (2010). The impact of heart rate variability on subjective well-being is mediated by emotion regulation. Journal of Individual Differences, Vol. 49، الصفحات 723-728، 10.1016/j.paid.2010.06.015 [ DOI ] [ Google Scholar ]
36. Geisler, F. C., Kubiak, T., Siewert, K., & Weber, H. (2013). Cardiac vagal tone is associated with social engagement and self-regulation. Biological Psychology, 93، 279-286، 10.1016/j.biopsycho.2013.02.013 [ DOI ] [ PubMed ] [ Google Scholar ]
37. Ginsberg, J. P., Berry, M. E., & Powell, D. A. (2010). Cardiac coherence and posttraumatic stress disorder in veterans. Journal of Alternative Health Medicine 16، 52-60 [ Psychological Reports, ] [ Google Scholar ]
38. Groves, D. A., & Brown, V. J. (2005). Vagal nerve stimulation: A review of its applications and potential mechanisms that mediate its clinical effects. Neuroscience & Biobehavioral Reviews, 29، 493-500، 10.1016/j.neubiorev.2005.01.004 [ DOI ] [ PubMed ] [ Google Scholar ]
39. Holman, D. M., Olsen, E. M., von Schéele, B., Melin, L., & Lisspers, J. (2011). The effects of heart rate variability biofeedback in patients with stress-related chronic neck pain: A pilot study. Applied Psychophysiology and Biofeedback 36، 71-80، 10.1007/s10484-011-9147-0 [ DOI ] [ PubMed ] [ Google Scholar ]
40. Hasan, Y., Bègue, L., & Bushman, B. J. (2013). Violent video games stress people out and make them more aggressive. Aggressive Behavior, 39 64-70 10.1002/ab.21454 [ DOI ] [ PubMed ] [ Google Scholar ]
41. Hassert, D. L., Miyashita, T., & Williams, C. L. (2004). The effects of peripheral vagus nerve stimulation with memory-modulated intensity on norepinephrine output in the basolateral amygdala. Behavioral Neuroscience, 118، 79-88، 10.1037/0735-7044، 118.1.79 [ DOI ] [ PubMed ] [ Google Scholar ]
42. Henriques, G., Keffer, S., Abrahamson, S., & Horst, S. J. (2011). Exploring the efficacy of a computer-based heart rate variability biofeedback program for reducing anxiety in college students. Applied Psychophysiology and Biofeedback 36، 101-112، 10.1007/s10484-011-9151-4 [ DOI ] [ PubMed ] [ Google Scholar ]
43. Hermes GL, Delgado B, Tretiakova M, Cavigelli SA, Krauss T, Kunzen SD, et al. (2009). Social isolation disrupts hormonal and behavioral stress regulation and increases the malignant burden of spontaneous mammary tumors. Proceedings of the National Academy of Sciences of the United States of America. ، 106 22393-22398، 10.1073/pnas.0910753106 [ DOI ] [ Free article from PMC ] [ PubMed ] [ Google Scholar ]
44. Eysen A. M. (1999). “The Positive Effect,” in ... book, “Handbook of Cognition and Emotion.” “، تحرير دالغليش ت.، وباور م. (نيويورك: جون وايلي وأولاده؛ ) ٥٢٢-٥٣٩ [ Google Scholar ]
45. "Kamath, M., Watanabe, M., & Upton, A. (Eds.) (2013)." ✅ "Heart Rate Variability Signal Analysis: Clinical Applications." بوكا راتون، فلوريدا: مطبعة سي آر سي [ Google Scholar ]
46. "Kinney, T. M., Kolb, L. C., Calubik, D. G., Orr, S. P., Blanchard, E. P., Thomas, R. J., et al. (1998). The utility of psychophysiological assessment in the diagnosis of post-traumatic stress disorder: Results from a multisite study conducted by the Department of Veterans Affairs. Journal" Journal of Clinical Psychology 66 914-923، 10.1037/0022-006X.66.6.914 [ DOI ] [ PubMed ] [ Google Scholar ]
47. Kim, D. K., Lee, K. M., Kim, J., Wang, M. K., Kang, S. O. (2013a). Dynamic correlations between heart and brain rhythms during self-reflection. Front. Hum. Neurosci. 7:414 10.3389/fnhum.2013.00414 [ DOI ] [ Free article from PMC ] [ PubMed ] [ Google Scholar ]
48. Kim, S., Zimon, F., Cavallo, M. M., Rath, J. F., McCraty, R., Foley, F. W. (2013b). Heart rate variability biofeedback, executive functions, and chronic brain injury. Journal of Neurosurgery 27، 209-222، 10.3109/02699052.2012.729292 [ DOI ] [ PubMed ] [ Google Scholar ]
49. Koseel, M., Schlaepfer, T. E. (2003). Beyond epilepsy treatment: Novel applications of vagus nerve stimulation in psychiatry. CNS Spectr. 8، 515-521 [ DOI ] [ PubMed ] [ Google Scholar ]
50. Lacey, J. I. (1967). "Patterns of bodily response and stress: Some reviews of arousal theory," in the book " Psychological Stress: Issues in Research" ، تحرير أبلي إم إتش، ترومبول ر. (نيويورك: أبلتون-سنتشري-كروفتس؛ ) ١٤-٤٢ [ Google Scholar ]
51. Lacey, J. I., & Lacey, B. C. (1970). "Some reciprocal relations between the autonomic and central nervous systems," in the book The Physiological Correlates of Emotion" “، تحرير بلاك ب. (نيويورك: أكاديميك بريس؛ ) ٢٠٥-٢٢٧ [ Google Scholar ]
52. Lacey, J. I., & Lacey, B. C. (1974). On heart rate responses and behavior: A reply to Elliot. Journal of Social and Personality Psychology, Volume ٣٠ ١-١٨ ١٠.١٠٣٧/h٠٠٣٦٥٥٩ [ DOI ] [ PubMed ] [ Google Scholar ]
53. Lehrer, P., Sasaki, Y., Saito, Y. (1999). Zazen and heart rate variability. Psychosom. Med. 61، 812-821، 10.1097/00006842-199911000-00014 [ DOI ] [ PubMed ] [ Google Scholar ]
54. Lehrer, P., Vaschillo, E., Lu, S. E., Eckberg, D., Vaschillo, B., Scardella, A., et al. (2006). Heart rate variability biofeedback: The influence of age on heart rate variability, baroreflex gain, and asthma. Chest 129، 278-284، 10.1378/chest.129.2.278 [ DOI ] [ PubMed ] [ Google Scholar ]
55. Lehrer, P., Vaschillo, E., Trost, Z., & Franzen, C. R. (2009). Effects of 0.1 Hz rhythmic muscle tension on cardiovascular resonance and baroreflex gain. Biological Psychology, 81، 24-30، 10.1016/j.biopsycho.2009.01.003 [ DOI ] [ PubMed ] [ Google Scholar ]
56. Lehrer, P. M., Vaschillo, E., Vaschillo, B., Lu, S. E., Eckberg, D. L., Edelberg, R., et al. (2003). Heart rate variability biofeedback increases baroreflex gain and peak expiratory flow. Psychosom. Med. 65، 796-805، 10.1097/01.PSY.0000089200.81962.19 [ DOI ] [ PubMed ] [ Google Scholar ]
57. Leon, E., Clark, J., Callaghan, F., & Doctor, F. (2010). Modeling conscious affective behavior and its regulation within a smart environment. Comprehensive Collective Computing, Vol. 6، الصفحات 559-574، 10.1016/j.pmcj.2009.12.002 [ DOI ] [ Google Scholar ]
58. Levin (2010). In an Unspoken Voice: How the Body Releases Trauma and Restores Itself . بيركلي: دار نشر نورث أتلانتيك بوكس [ Google Scholar ]
59. Levin (2008). The Body Keeps the Score: A Revolutionary Program to Restore Your Body's Wisdom بولدر، كولورادو: يبدو صحيحًا [ Google Scholar ]
60. Levy, B. R., Slade, M. D., Kunkel, S. R., & Kasl, S. V. (2002). Longevity increased through positive self-perceptions of aging. Journal of Social and Personality Psychology, Volume 83، 261-270، 10.1037/0022-3514.83.2.261 [ DOI ] [ PubMed ] [ Google Scholar ]
61. Lee W.-C., Chiu F.-C., Ko Y.-S., Wu K.-J. (2013). "A study of visual attention and workload between experts and novices in the cockpit," in Proceedings of the 10th International Conference on Engineering Psychology and Cognitive Work: Applications and Services ، تحرير هاريس د. (هايدلبرغ: سبرينغر برلين؛ ) 167-176 [ Google Scholar ]
62. Lindmark, S., Boren, J., & Eriksson, J. W. (2006). Insulin resistance, endocrine function, and adipokines in type 2 diabetes patients with varying blood glucose levels: Potential impact on in vivo glucose toxicity. Clin. Endocrinol. (Oxf.) 65 301–309 10.1111/j.1365-2265.2006.02593.x [ DOI ] [ PubMed ] [ Google Scholar ]
63. Lindmark, S., Lonn, L., Wiklund, U., Tavisson, M., Olsson, T., & Eriksson, J. W. (2005). Autonomic dysfunction may link visceral obesity and insulin resistance. Obes. Res. 13 717–728 10.1038/oby.2005.81 [ DOI ] [ PubMed ] [ Google Scholar ]
64. Lindmark, S., Wiklund, U., Pearl, B., & Eriksson, J. W. (2003). Does the autonomic nervous system play a role in the development of insulin resistance? A study of heart rate variability in first-degree relatives of type 2 diabetes patients and a control group. Diabetes Care 20، 399-405، 10.1046/j.1464-5491.2003.00920.x [ DOI ] [ PubMed ] [ Google Scholar ]
65. Lloyd, A., Brett, D., & Wisnes, K. (2010). Coherence training improves cognitive function and behavior in children with attention-deficit/hyperactivity disorder. Journal of Alternative Health Medicine المجلد 16، الصفحات 34-42 [ PubMed ] [ Google Scholar ]
66. Luskin, F., Ritz, M., Newell, K., Quinn, T., & Haskell, W. (1999). A randomized controlled trial of stress management training for elderly patients with congestive heart failure: An experimental study. J. Cardiovasc. Dis. Prev. المجلد 2، الصفحات 101-104 [ DOI ] [ PubMed ] [ Google Scholar ]
67. Lynch, J. J. (2000). A Cry Unheard: New Insights into the Medical Consequences of Loneliness بالتيمور: مطبعة بانكروفت [ Google Scholar ]
68. McKinnon, S., Givertz, R., McCraty, R., & Brown, M. (2013). Using pulse-evoked effort to assess cardiac regulation of vagal afferent activity during emotion and resonant breathing. Applied Psychophysiology and Biofeedback 38، 241-255، 10.1007/s10484-013-9226-5 [ DOI ] [ PubMed ] [ Google Scholar ]
69. Malek, M., & Kamm, A. J. (Eds.) (1995). Heart Rate Variability. أرمونك: شركة فوتورا للنشر [ Google Scholar ]
70. Marsh, E. L., Pecks, L., & Quan, J. A. (2013). Social regulation of threat-related attentional disengagement in highly anxious individuals. Front. Hum. Neurosci. 7:515 10.3389/fnhum.2013.00515 [ DOI ] [ Free article from PMC ] [ PubMed ] [ Google Scholar ]
71. Marmot, M. G., & Syme, S. L. (1976). Acculturation and coronary heart disease among Japanese Americans. American Journal of Epidemiology ١٠٤، ٢٢٥-٢٤٧ [ DOI ] [ PubMed ] [ Google Scholar ]
72. Mauskop, A. (2005). Vagus nerve stimulation alleviates treatment-resistant chronic migraine and cluster headache. Cephalalgia 25 82–86 10.1111/j.1468-2982.2005.00611.x [ DOI ] [ PubMed ] [ Google Scholar ]
73. McCraty, R. (2004). "The Energetic Heart: Bioelectromagnetic Communication Within and Between People," in Handbook of Human-Applicable Bioelectromagnetics ، تحرير روش ب.ج.، ماركوف م.س. (نيويورك: مارسيل ديكر؛ ) 541-562 [ Google Scholar ]
74. McCraty, R., & Atkinson, M. (2012). Resilience training program reduces psychological and physiological stress in police officers. Advanced Global Journal of Health Medicine ، 1، 44-66، 10.7453/gahmj.2012.1.5.013 [ Digital Object Identifier (DOI) ] [ Free article from PMC ] [ PubMed ] [ Google Scholar ]
75. McCraty, R., Atkinson, M., Lipsenthal, L., & Arguelles, L. (2009a). A new hope for correctional officers: An innovative program to reduce stress and health risks. Applied Psychophysiology and Biofeedback 34، 251-272، 10.1007/s10484-009-9087-0 [ DOI ] [ PubMed ] [ Google Scholar ]
76. McCraty, R., Atkinson, M., Tomasino, D., & Bradley, R. T. (2009b). The coherent heart: Heart–brain interactions, psychophysiological coherence, and the emergence of system-wide order. Integral. Rev. 5 10-115 [ Google Scholar ]
77. McCraty, R., Atkinson, M., Tyler, W. A., Rain, J., & Watkins, A. D. (1995). The effects of emotions on short-term heart rate variability using power spectrum analysis. The American Journal of Cardiology 76، 1089-1093، 10.1016/S0002-9149(99)80309-9 [ DOI ] [ PubMed ] [ Google Scholar ]
78. McCraty, R., Atkinson, M., & Tomasino, D. (2003). The effects of a workplace stress reduction program on blood pressure and emotional health in hypertensive employees. J. Altern. Complement. Med. المجلد 9، 355-369، 10.1089/107555303765551589 [ DOI ] [ PubMed ] [ Google Scholar ]
79. MacCraty R., Barrios-Choplin B., Rozman D., Atkinson M., Watkins A.D. (1998). The Effects of a Novel Self-Management Program on Stress, Emotions, Heart Rate Variability, Dehydroepiandrosterone, and Cortisol. Integrated Behavioral Physiology Science 33، 151-170، 10.1007/BF02688660 [ DOI ] [ PubMed ] [ Google Scholar ]
80. MacCraty R., Childre D. (2010). Coherence: Linking Personal, Social, and Global Health. Journal of Alternative Health Medicine 16-10-24 [ PubMed ] [ Google Scholar ]
81. Miller G. A., Galanter E., Pribram K. H. (1960). Plans and the Structure of Behavior. نيويورك: هنري هولت وشركاه [ Google Scholar ]
82. Nesler W.B., Tyroler H.A., Cassel J.C. (1971). Social Disorganization and Stroke Mortality in North Carolina Black Population. American Journal of Epidemiology 93، 166-175 [ Digital Object Identifier (DOI) ] [ Psychological Reports, ] [ Google Scholar ]
83. Nortey C. E., Sousa J. J., Feillet L., Marquez-Portella C., Coutinho E. S., Figueira I., et al. (2013). They learn their traumas by heart: Assessment of psychophysiological failure in recovery from post-traumatic stress disorder. Journal of Affective Disorders. 150، 136-141، 10.1016/j.jad.2012.11.039 [ DOI ] [ PubMed ] [ Google Scholar ]
84. Olatunji B. O., Armstrong T., McHugho M., Zald D. H. (2013). Enhanced attentional capture by threat in combat veterans with post-traumatic stress disorder. Journal of Abnormal Psychology. 122، 397-405، 10.1037/a0030440 [ Digital Object Identifier (DOI) ] [ Free article from PMC ] [ PubMed ] [ Google Scholar ]
85. Ornish D. (1998). Love and Survival: The Scientific Basis for the Healing Power of Intimacy. نيويورك: دار نشر هاربر كولينز [ Google Scholar ]
86. Ornstein R., Sobel D. (1987). The Healing Brain. نيويورك: سايمون وشوستر [ Google Scholar ]
87. Peng C. K., Mietus J. E., Liu Y., Khalsa G., Douglas P. S., Benson H., et al. (1999). Exaggerated Heart Rate Oscillations during Two Meditation Techniques. The American Journal of Cardiology 70، 101-107، 10.1016/S0167-5273(99)00066-2 [ DOI ] [ PubMed ] [ Google Scholar ]
88. Wongsopap S., Wongsopap Y. (2011). Heart Rate Variability Analysis During Meditation Using a Pattern Recognition Method. J. Cardiovasc. Dis. Prev. 38، 197-200 [ Google Scholar ]
89. Porges S. W. (2007). The polyvagal perspective. Biological Psychology, ٧٤، ١١٦-١٤٣، ١٠.١٠١٦/j.biopsycho.٢٠٠٦.٠٦.٠٠٩ [ DOI ] [ Free article from PMC ] [ PubMed ] [ Google Scholar ]
90. Pribram K. H. (1970). "Feelings as Monitors"، في Feelings and Emotions. ” ، تحرير أرنولد م.ب. (نيويورك: أكاديميك بريس؛ ) ٤١-٥٣ [ Google Scholar ]
91. Pribram K. H. (1971). Languages of the Brain: Experimental Paradoxes and Principles in Neuropsychology . نيويورك: براندون هاوس [ Google Scholar ]
92. Pribram K. H., McGuinness D. (1975). Arousal, activation, and effort in the control of attention. Psychological Review, 82(2), 116-149. 82، 116-149، 10.1037/h0076780 [ DOI ] [ PubMed ] [ Google Scholar ]
93. Pribram K. H., Melges F. T. (1969). "The Psychophysiological Basis of Emotion," in Handbook of Clinical Neurology “، المجلد ٣، الطبعة: فينكين ب.ج.، بروين ج.و. (أمستردام: دار نشر شمال هولندا؛ ) ٣١٦-٣٤١ [ Google Scholar ]
94. Ramackers D., Ector H., Demyttenaere K., Rubens A., Van de Werf F. (1998). The relationship between cardiac autonomic function and coping style in healthy subjects. Clinical Cardiac Electrophysiology. 21 1546-1552 10.1111/j.1540-8159.1998.tb00241.x [ DOI ] [ PubMed ] [ Google Scholar ]
95. Ratanasiripong P., Ratanasiripong N., Kathalae D. (2012). Biofeedback intervention for stress and anxiety among nursing students: A randomized controlled trial. Journal of the Medical Association 2012:827972، 10.5402/2012/827972 [ Digital Object Identifier (DOI) ] [ Free article from PMC ] [ PubMed ] [ Google Scholar ]
96. Reynard A., Gevirtz R., Berlow R., Brown M., Boutelle K. (2011). Heart rate variability as a marker of self-regulation. Applied Psychophysiology and Biofeedback 36، 209-215، 10.1007/s10484-011-9162-1 [ DOI ] [ PubMed ] [ Google Scholar ]
97. Saul J. P., Arai Y., Berger R. D., Lilly L. S., Colucci W. S., Cohen R. J. (1988). Assessment of autonomic regulation in chronic congestive heart failure by heart rate spectral analysis. The American Journal of Cardiology 61، 1292-1299، 10.1016/0002-9149(88)91172-1 [ DOI ] [ PubMed ] [ Google Scholar ]
98. scar .R.2012 Eight keys to achieving mind-body balance. نيويورك: نورتون وشركاه [ Google Scholar ]
99. Segerstrom S. C., Nes L. S. (2007). Heart rate variability reflects self-regulatory strength, effort, and fatigue. Psychological Science, 18، 275-281، 10.1111/j.1467-9280.2007.01888.x [ DOI ] [ PubMed ] [ Google Scholar ]
100. Shah A. J., Lampert R., Goldberg J., Veledar E., Bremner J. D., Vaccarino V. (2013). Posttraumatic stress disorder and impaired autonomic modulation in male twins. Journal of Psychiatry 73، 1103-1110، 10.1016/j.biopsych.2013.01.019 [ DOI ] [ Free article from PMC ] [ PubMed ] [ Google Scholar ]
101. Siepmann M., Aykac V., Unterdorfer J., Petrowski K., Mueck-Weymann M. (2008). A pilot study on the effects of heart rate variability biofeedback in patients with depression and in healthy subjects. Applied Psychophysiology and Biofeedback 33، 195-201، 10.1007/s10484-008-9064-z [ DOI ] [ PubMed ] [ Google Scholar ]
102. Smith T. W., Cribbet M. R., Nealey-Moore J. B., Uchino B. N., Williams P. G., MacKenzie J., et al. (2011). Matters of the variable heart: Respiratory sinus arrhythmia response to marital interaction and associations with marital quality. Journal of Social and Personality Psychology, Volume 100، 103-119، 10.1037/a0021136 [ DOI ] [ PubMed ] [ Google Scholar ]
103. Stanley R. (2009). Types of prayer, heart rate variability, and innate healing. Zhygun ٤٤، ٨٢٥-٨٤٦، ١٠.١١١١/j.١٤٦٧-٩٧٤٤.٢٠٠٩.٠١٠٣٦.x [ DOI ] [ Google Scholar ]
104. Thayer J. F., Hansen A. L., Saus-Rose E., Johnsen B. H. (2009). Heart rate variability, prefrontal neural function, and cognitive performance: The neurovisceral integration perspective on self-regulation, adaptation, and health. Annals of Behavioral Medicine, 37، 141-153، 10.1007/s12160-009-9101-z [ DOI ] [ PubMed ] [ Google Scholar ]
105. Thomas A. J., Shatzbaum S. E. (2011). Culture and Identity: Life Stories for Counselors and Therapists, الطبعة الثانية. ثاوزند أوكس: منشورات سيج [ Google Scholar ]
106. Tiller W. A., McCraty R., Atkinson M. (1996). Cardiac coherence: A new noninvasive measure of autonomic nervous system coordination. Journal of Alternative Health Medicine 2، 52-65 [ PubMed ] [ Google Scholar ]
107. Uchino B. N., Cacioppo J. T., Kiecolt-Glaser J. K. (1996). The relationship between social support and physiological processes: A review with emphasis on underlying mechanisms and implications for health. Psychological Bulletin, 119 488-531، 10.1037/0033-2909.119.3.488 [ DOI ] [ PubMed ] [ Google Scholar ]
108. Umetani K., Singer D. H., McCraty R., Atkinson M. (1998). Twenty-four hour time domain heart rate variability and heart rate: Relations to age and gender over nine decades. The American Journal of Cardiology 31، 593-601، 10.1016/S0735-1097(97)00554-8 [ DOI ] [ PubMed ] [ Google Scholar ]
109. Vildern M., Wolk S. (1987). Pain perception and cardiac activity under hypnosis and relaxation training. Journal of Architectural Psychology (Frankfurt) 139 107-114 [ PubMed ] [ Google Scholar ]
110. Wellman J., Lamon J., Fredi E., Chartrand D. (2014). Police management staff stress coping training: An institutional case study. Advanced Global Journal of Health Medicine 3، 72-79، 10.7453/gahmj.2014.015 [ Digital Object Identifier (DOI) ] [ Free article from PMC ] [ PubMed ] [ Google Scholar ]
111. Wichers M. C., Myin-Germeys I., Jacobs N., Peeters F., Kenis G., Derom C., et al. (2007). Evidence that moment-to-moment variation in positive emotions buffer genetic risk for depression: A momentary assessment twin study. Acta Psychiatrica Scandinavica, 115، 451-457، 10.1111/j.1600-0447.2006.00924.x [ DOI ] [ PubMed ] [ Google Scholar ]
112. Wolk C., Vildern M. (1987). Variability detection within the cardiac cycle: Towards a revision of the "baroreceptor hypothesis". Physiological Psychology ١٦١-٦٥ [ Google Scholar ]
113. Wolk C., Vildern M. (1989). "A revision of the baroreceptor hypothesis based on the new cardiac cycle effect," in Psychobiology: Issues and Applications ، تحرير بوند إن دبليو، سيدل دي إيه تي (أمستردام: دار نشر إلسفير ساينس بي في؛ ) ٣٧١-٣٧٩ [ Google Scholar ]
114. Wu S. D., Lo P. C. (2008). Inward-attention meditation increases parasympathetic activity: a study based on heart rate variability Biomed 29 245-250، 10.2220/biomedres.29.245 [ DOI ] [ PubMed ] [ Google Scholar ]
115. Zhang J. X., Harper R. M., Frysinger R. C. (1986). Respiratory modulation of neuronal discharge in the central nucleus of the amygdala during sleep and waking states Exp. Neurol. 91 193–207 10.1016/0014-4886(86)90037-3 [ DOI ] [ PubMed ] [ Google Scholar ]
116. Zohar A., Cloninger R., McCraty R. (2013). Personality and heart rate variability: Exploring pathways from personality to cardiac coherence and health. Open Social Science Journal ، 1، 32-39، 10.4236/jss.2013.16007 [ DOI ] [ Google Scholar

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