Autonomic Nervous System Data Research Articles

Assessing Children's Autonomic Nervous System Activity During Structured Tasks: A Feasibility and Reliability Study in Ghana.

The significance of physiological regulation in relation to behavioral and emotional regulation is well documented, but primarily in economically advantaged contexts. Few studies have been conducted in low- and middle-income countries. We investigated the feasibility and reliability of measuring autonomic nervous system (ANS) activity and behavior during challenge tasks in 30 children aged 8-10 years in Ghana during two visits, 1 week apart. Completeness of ANS data ranged from 80% to 100% across all tasks. There was low-to-moderate test-retest reliability of video mood induction (VMI) emotion ratings and balloon analog risk task (BART) pumps (r=0.34-0.52). VMI elicited higher targeted emotion ratings in Visit 2 than Visit 1. Respiratory sinus arrhythmia (RSA) was higher, and pre-ejection period (PEP) was longer at Visit 2 than Visit 1 for baseline and both tasks. RSA was higher at baseline than during the VMI anger scene at Visit 1, whereas PEP was shorter at baseline than during all VMI emotion scenes at Visit 2. RSA was higher at baseline than during BART at both visits. In conclusion, ANS data collection within evocative and arousing challenge tasks was feasible in Ghana, and the tasks were generally reliable and effective in eliciting target emotions and risk-taking behavior in this sample.

Functional brain networks and neuroanatomy underpinning nausea severity can predict nausea susceptibility using machine learning.

Nausea is an adverse experience characterised by alterations in autonomic and cerebral function. Susceptibility to nausea is difficult to predict, but machine learning has yet to be applied to this field of study. The severity of nausea that individuals experience is related to the underlying morphology (shape) of the subcortex, namely of the amygdala, caudate and putamen; a functional brain network related to nausea severity was identified, which included the thalamus, cingulate cortices (anterior, mid- and posterior), caudate nucleus and nucleus accumbens. Sympathetic nervous system function and sympathovagal balance, by heart rate variability, was closely related to both this nausea-associated anatomical variation and the functional connectivity network, and machine learning accurately predicted susceptibility or resistance to nausea. These novel anatomical and functional brain biomarkers for nausea severity may permit objective identification of individuals susceptible to nausea, using artificial intelligence/machine learning; brain data may be useful to identify individuals more susceptible to nausea. Nausea is a highly individual and variable experience. The central processing of nausea remains poorly understood, although numerous influential factors have been proposed, including brain structure and function, as well as autonomic nervous system (ANS) activity. We investigated the role of these factors in nausea severity and if susceptibility to nausea could be predicted using machine learning. Twenty-eight healthy participants (15 males; mean age 24years) underwent quantification of resting sympathetic and parasympathetic nervous system activity by heart rate variability. All were exposed to a 10-min motion-sickness video during fMRI. Neuroanatomical shape differences of the subcortex and functional brain networks associated with the severity of nausea were investigated. A machine learning neural network was trained to predict nausea susceptibility, or resistance, using resting ANS data and detected brain features. Increasing nausea scores positively correlated with shape variation of the left amygdala, right caudate and bilateral putamen (corrected P=0.05). A functional brain network linked to increasing nausea severity was identified implicating the thalamus, anterior, middle and posterior cingulate cortices, caudate nucleus and nucleus accumbens (corrected P=0.043). Both neuroanatomical differences and the functional nausea-brain network were closely related to sympathetic nervous system activity. Using these data, a machine learning model predicted susceptibility to nausea with an overall accuracy of 82.1%. Nausea severity relates to underlying subcortical morphology and a functional brain network; both measures are potential biomarkers in trials of anti-nausea therapies. The use of machine learning should be further investigated as an objective means to develop models predicting nausea susceptibility.

How is autonomic nervous system activity in subjects who are sleepy but are unable to sleep in the daytime?

We investigated changes in the activity of the autonomic nervous system (ANS) in the relaxed condition in subjects who felt sleepy, but were unable to sleep. A total of 1021 subjects underwent daytime polysomnography. The sleep latency (SL) and the visual analog scale (VAS) were used to assess “immediate” objective and subjective sleepiness, respectively. The subjects were assigned to an “Alert-Alert” group (VAS ≤ 25 mm, SL ≥ 8 min), a “Sleepy-Alert” group (VAS ≥ 75 mm, SL ≥ 8 min), or a “Sleepy-Sleepy” group (VAS ≥ 75 mm, SL ≤ 4 min). In order to assess the ANS, the spectral analysis and the geometric method were used. The ANS data collected during the relaxed condition (after lights off, post-LO) was compared to that obtained during the control condition (before lights off, pre-LO). From the spectral analysis, a significant decrease of sympathetic function and an increase of parasympathetic function at post-LO in the Sleepy-Sleepy group, a tendency for sympathetic function decrease at post-LO in the Alert-Alert group, and no significant changes to sympathetic and parasympathetic function in the Sleepy-Alert group were observed. The results from the geometric method supported the results of the spectral analysis in the Alert-Alert group and the Sleepy-Sleepy group. The results of this study suggest that the ANS plays a role in individuals who are unable to sleep even though they feel sleepy and are given the opportunity to sleep.

A Bond Graph Model of the Cardiovascular System

The study of the autonomic nervous system (ANS) function has shown to provide useful indicators for risk stratification and early detection on a variety of cardiovascular pathologies. However, data gathered during different tests of the ANS are difficult to analyse, mainly due to the complex mechanisms involved in the autonomic regulation of the cardiovascular system (CVS). Although model-based analysis of ANS data has been already proposed as a way to cope with this complexity, only a few models coupling the main elements involved have been presented in the literature. In this paper, a new model of the CVS, representing the ventricles, the circulatory system and the regulation of the CVS activity by the ANS, is presented. The models of the vascular system and the ventricular activity have been developed using the Bond Graph formalism, as it proposes a unified representation for all energetic domains, facilitating the integration of mechanic and hydraulic phenomena. In order to take into account the electro-mechanical behaviour of both ventricles, an electrophysiologic model of the cardiac action potential, represented by a set of ordinary differential equations, has been integrated. The short-term ANS regulation of heart rate, cardiac contractility and peripheral vasoconstriction is represented by means of continuous transfer functions. These models, represented in different continuous formalisms, are coupled by using a multi-formalism simulation library. Results are presented for two different autonomic tests, namely the Tilt Test and the Valsalva Manoeuvre, by comparing real and simulated signals.