A physiology based model of heart rate variability

A multiscale predictive digital twin for neurocardiac modulation

Cardiac function is tightly regulated by the autonomic nervous system (ANS). Activation of the sympathetic nervous system increases cardiac output by increasing heart rate and stroke volume, while parasympathetic nerve stimulation instantly slows heart rate. Importantly, imbalance in autonomic control of the heart has been implicated in the development of arrhythmias and heart failure. Understanding of the mechanisms and effects of autonomic stimulation is a major challenge because synapses in different regions of the heart result in multiple changes to heart function. For example, nerve synapses on the sinoatrial node (SAN) impact pacemaking, while synapses on contractile cells alter contraction and arrhythmia vulnerability. Here, we present a multiscale neurocardiac modelling and simulator tool that predicts the effect of efferent stimulation of the sympathetic and parasympathetic branches of the ANS on the cardiac SAN and ventricular myocardium. The model includes a layered representation of the ANS and reproduces firing properties measured experimentally. Model parameters are derived from experiments and atomistic simulations. The model is a first prototype of a digital twin that is applied to make predictions across all system scales, from subcellular signalling to pacemaker frequency to tissue level responses. We predict conditions under which autonomic imbalance induces proarrhythmia and can be modified to prevent or inhibit arrhythmia. In summary, the multiscale model constitutes a predictive digital twin framework to test and guide high-throughput prediction of novel neuromodulatory therapy. KEY POINTS: A multi-layered model representation of the autonomic nervous system that includes sympathetic and parasympathetic branches, each with sparse random intralayer connectivity, synaptic dynamics and conductance based integrate-and-fire neurons generates firing patterns in close agreement with experiment. A key feature of the neurocardiac computational model is the connection between the autonomic nervous system and both pacemaker and contractile cells, where modification to pacemaker frequency drives initiation of electrical signals in the contractile cells. We utilized atomic-scale molecular dynamics simulations to predict the association and dissociation rates of noradrenaline with the β-adrenergic receptor. Multiscale predictions demonstrate how autonomic imbalance may increase proclivity to arrhythmias or be used to terminate arrhythmias. The model serves as a first step towards a digital twin for predicting neuromodulation to prevent or reduce disease.

Stress, Inflammation and Metabolic Biomarkers Are Associated with Body Composition Measures in Lean, Overweight, and Obese Children and Adolescents

The aim of this study was to examine the associations between multiple indices of stress, inflammation and metabolism vs. body composition parameters in 121 (43 boys, 78 girls) children and adolescents, aged 5–15 y. Subjects were divided into two groups: normal weight (N) (N = 40, BMI z-score = −0.1923 ± 0.6), and overweight/obese (OB) (N = 81, BMI z-score = 2.1947 ± 1.4). All subjects completed the State-Trait Anxiety Inventory for Children (STAIC) and Children’s Depression Inventory, and underwent cortisol measurements in hair, diurnal series of saliva, and morning serum. Circulating concentrations of high sensitivity C-reactive protein (hsCRP) and other inflammation biomarkers were also obtained. Body composition analysis was performed with a clinically validated, advanced bioimpedance apparatus (BIA), while heart rate variability (HRV) was measured as a stress biomarker by photoplethysmography (PPG). The OB group had a higher STAIC-state score, waist-to-hip ratio, skeletal muscle mass, and total and abdominal fat mass, and a lower percent fat-free mass (FFM) and bone density than the N group. HRV did not differ between the groups. In the entire population, percent fat mass correlated strongly with circulating hsCRP (r = 0.397, p = 0.001), ferritin, and other inflammatory biomarkers, as well as with indices of insulin resistance. A strong correlation between serum hsCRP and hair cortisol was also observed (r = 0.777, p < 0.001), suggesting interrelation of chronic stress and inflammation. Thus, body fat accumulation in children and adolescents was associated with an elevation in clinical and laboratory biomarkers of stress, inflammation, and insulin resistance. BIA-ACC and PPG can be utilized as a direct screening tool for assessing overweight- and obesity -related health risks in children and adolescents.

Getting Into the Zone: A Pilot Study of Autonomic-Cardiac Modulation and Flow State During Piano Performance

BACKGROUND

Music performance anxiety is a common experience among elite and professional musicians and impedes performers from achieving flow state, or a state of focused, sustained engagement that promotes optimal performance.

OBJECTIVE

The aim of this study was to use heart rate variability (HRV) to determine the psychophysiological underpinnings of optimal music performance.

METHODS

We assessed HRV to study how autonomic-cardiac modulation was associated with flow during piano performance. Twenty-two pianists (15-22 years) with at least a Grade 8 Royal Conservatory of Music certification prepared two standardized pieces and a self-selected piece. Performer heart rate data were measured with a Polar 800 watch in 5-min periods immediately before performances, during performances and post-performance. HRV was employed to assess autonomic modulation of cardiac intervals. HRV indices of sympathetic and parasympathetic modulation of the heart were analyzed in 2.5-min segments to monitor short-term autonomic adjustments using the Kubios HRV Software. Flow state was measured using the 36-item Flow State Scale (FSS). Relationships were analyzed using zero-order correlations and multiple linear regressions.

RESULTS

Our sample consisted of 22 RCM Grade 8 certified pianists. Participants achieved the highest level of flow during performance of the Bach piece. Decreased HRV was observed during performance, as indicated by a significant drop in total power. Flow state was positively associated with High Frequency (HF) power during the pre-performance phase, and inversely associated with Low Frequency (LF) power during performance.

CONCLUSION

Inverse association of flow with LF-HRV during performance affirms the importance of vagal-HR modulation for achievement of flow state. Increased HF-HRV and reduced LF-HRV immediately prior to performance suggests that flow state may be shaped as much by physiological preparation during pre-performance as it is by physiologic responses during performance. Further research is required to validate the correlation between autonomic modulation of the heart and flow state. Evidence of this correlation between autonomic modulation of the heart and achievement of flow state may pave the way for further research on enhancing musical performance and targeting MPA through HRV-based interventions.

Changes in physiological network connectivity of body system in narcolepsy during REM sleep

BACKGROUND

Narcolepsy is marked by pathologic symptoms including excessive daytime drowsiness and lethargy, even with sufficient nocturnal sleep. There are two types of narcolepsy: type 1 (with cataplexy) and type 2 (without cataplexy). Unlike type 1, for which hypocretin is a biomarker, type 2 narcolepsy has no adequate biomarker to identify the causality of narcoleptic phenomenon. Therefore, we aimed to establish new biomarkers for narcolepsy using the body's systemic networks.

METHOD

Thirty participants (15 with type 2 narcolepsy, 15 healthy controls) were included. We used the time delay stability (TDS) method to examine temporal information and determine relationships among multiple signals. We quantified and analyzed the network connectivity of nine biosignals (brainwaves, cardiac and respiratory information, muscle and eye movements) during nocturnal sleep. In particular, we focused on the differences in network connectivity between groups according to sleep stages and investigated whether the differences could be potential biomarkers to classify both groups by using a support vector machine.

RESULT

In rapid eye movement sleep, the narcolepsy group displayed more connections than the control group (narcolepsy connections: 24.47 ± 2.87, control connections: 21.34 ± 3.49; p = 0.022). The differences were observed in movement and cardiac activity. The performance of the classifier based on connectivity differences was a 0.93 for sensitivity, specificity and accuracy, respectively.

CONCLUSION

Network connectivity with the TDS method may be used as a biomarker to identify differences in the systemic networks of patients with narcolepsy type 2 and healthy controls.