Central Neuroplasticity and Decreased Heart Rate Variability after Particulate Matter Exposure in Mice

BackgroundEpidemiologic studies show that exposure to fine particulate matter [aerodynamic diameter < or = 2.5 microm (PM(2.5))] increases the total daily cardiovascular mortality. Impaired cardiac autonomic function, which manifests as reduced heart rate variability (HRV), may be one of the underlying causes. However, the cellular mechanism(s) by which PM(2.5) exposure induces decreased HRV is not known.ObjectivesWe tested the hypothesis that exposure to PM(2.5) impairs HRV by decreasing the excitability of the cardiac vagal neurons in the nucleus ambiguus. We also determined the effect of iron on PM-exposure-induced decrease in HRV.MethodsWe measured 24-hr HRV in time domains from electrocardiogram telemetry recordings obtained in conscious, freely moving mice after 3 days of exposure to PM(2.5) in the form of soot only or iron-soot. In parallel studies, we determined the intrinsic properties of identified cardiac vagal neurons, retrogradely labeled with a fluorescent dye applied to the sinoatrial node.ResultsSoot-only exposure decreased short-term HRV (root mean square of successive difference). With the addition of iron, all HRV parameters were significantly reduced. In nonexposed mice, vagal blockade significantly reduced all HRV parameters, suggesting that HRV is, in part, under vagal regulation in mice. Iron-soot exposure had no significant effect on resting membrane potential but decreased spiking responses of the identified cardiac vagal neurons to depolarizations (p < 0.05). The decreased spiking response was accompanied with a higher minimal depolarizing current required to evoke spikes and a lower peak discharge frequency.ConclusionsThe data suggest that PM-induced neuroplasticity of cardiac vagal neurons may be one mechanism contributing to the cardiovascular consequences associated with PM(2.5) exposure seen in humans

Effects of antidepressant treatment on heart rate variability in major depression: A quantitative review

<p>Abstract</p> <p>Background</p> <p>The literature measuring effects of antidepressant and electroconvulsive therapy (ECT) for major depression on heart rate variability (HRV) in medically well individuals was reviewed.</p> <p>Methods</p> <p>Fourteen studies evaluating HRV were included. Twenty three pre-post or within group comparisons were available. Treatment impact on measures of HRV was pooled over studies. We examined different classes of antidepressants, and for short and long electrocardiogram (ECG) recordings separately.</p> <p>Results</p> <p>Tricyclic antidepressants (TCAs) were associated with declines in most measures of HRV and significant increase in heart rate (HR) in studies with short recording intervals. No significant changes were found for longer recording times.</p> <p>Treatment effects with selective serotonin reuptake inhibitors (SSRIs) were more variable. Short-recording studies revealed a significant decrease in HR and an increase in one HRV measure. In two 24-hour recording studies no significant changes were observed. No relationship between ECT and HRV has been established in the literature. The effects of other drugs are reported.</p> <p>Limitations</p> <p>Few studies measure the effects of treatment of depression on HRV. Existing studies have generally used very small samples, employing a variety of measurements and methodologies.</p> <p>Conclusion</p> <p>We confirm that TCAs are associated with a large decrease in HRV and increase HR. However, data for SSRIs is not clear. Although the effect of SSRIs on HRV is weaker than for TCAs, evidence shows that SSRIs are associated with a small decrease in HR, and an increase in one measure of HRV. The use of TCAs in depression leads to changes in HRV that are associated with increased risk of mortality.</p

Assessment of pulse rate variability by the method of pulse frequency demodulation

BACKGROUND: Due to its easy applicability, pulse wave has been proposed as a surrogate of electrocardiogram (ECG) for the analysis of heart rate variability (HRV). However, its smoother waveform precludes accurate measurement of pulse-to-pulse interval by fiducial-point algorithms. Here we report a pulse frequency demodulation (PFDM) technique as a method for extracting instantaneous pulse rate function directly from pulse wave signal and its usefulness for assessing pulse rate variability (PRV). METHODS: Simulated pulse wave signals with known pulse interval functions and actual pulse wave signals obtained from 30 subjects with a trans-dermal pulse wave device were analyzed by PFDM. The results were compared with heart rate and HRV assessed from simultaneously recorded ECG. RESULTS: Analysis of simulated data revealed that the PFDM faithfully demodulates source interval function with preserving the frequency characteristics of the function, even when the intervals fluctuate rapidly over a wide range and when the signals include fluctuations in pulse height and baseline. Analysis of actual data revealed that individual means of low and high frequency components of PRV showed good agreement with those of HRV (intraclass correlation coefficient, 0.997 and 0.981, respectively). CONCLUSION: The PFDM of pulse wave signal provides a reliable assessment of PRV. Given the popularity of pulse wave equipments, PFDM may open new ways to the studies of long-term assessment of cardiovascular variability and dynamics

Reproducibility of Heart Rate Variability Indices in Children with Cystic Fibrosis

Fundamental to the potential utilisation of heart rate variability (HRV) indices as a prognostic tool is the reproducibility of these measures. The purpose of the present study was therefore to investigate the reproducibility of 24-hour derived HRV indices in a clinical paediatric population. Eighteen children (10 boys; 12.4 ± 2.8 years) with mild to moderate Cystic Fibrosis (CF; FVC: 83 ± 12% predicted; FEV1: 80 ± 9% predicted) and eighteen age- and sex-matched controls (10 boys; 12.5 ± 2.7 years) wore a combined ECG and accelerometer for two consecutive days. Standard time and frequency domain indices of HRV were subsequently derived. Reproducibility was assessed by Bland-Altman plots, 95% limits of agreement and intra-class correlation coefficients (ICC). In both groups, there was no systematic difference between days, with the variables demonstrating a symmetrical, homoscedastic distribution around the zero line. The time domain parameters demonstrated a good to excellent reproducibility irrespective of the population considered (ICC: 0.56 to 0.86). In contrast, whilst the frequency domain parameters similarly showed excellent reproducibility in the healthy children (ICC: 0.70 to 0.96), the majority of the frequency domain parameters illustrated a poor to moderate reproducibility in those with CF (ICC: 0.22 to 0.43). The exceptions to this trend were the normalised LF and HF components which were associated with a good to excellent reproducibility. These findings thereby support the utilisation of time and relative frequency domain HRV indices as a prognostic tool in children with CF. Furthermore, the present results highlight the excellent reproducibility of HRV in healthy children, indicating that this may be a useful tool to assess intervention effectiveness in this population

Complex systems and the technology of variability analysis

Characteristic patterns of variation over time, namely rhythms, represent a defining feature of complex systems, one that is synonymous with life. Despite the intrinsic dynamic, interdependent and nonlinear relationships of their parts, complex biological systems exhibit robust systemic stability. Applied to critical care, it is the systemic properties of the host response to a physiological insult that manifest as health or illness and determine outcome in our patients. Variability analysis provides a novel technology with which to evaluate the overall properties of a complex system. This review highlights the means by which we scientifically measure variation, including analyses of overall variation (time domain analysis, frequency distribution, spectral power), frequency contribution (spectral analysis), scale invariant (fractal) behaviour (detrended fluctuation and power law analysis) and regularity (approximate and multiscale entropy). Each technique is presented with a definition, interpretation, clinical application, advantages, limitations and summary of its calculation. The ubiquitous association between altered variability and illness is highlighted, followed by an analysis of how variability analysis may significantly improve prognostication of severity of illness and guide therapeutic intervention in critically ill patients

Chaotic Signatures of Heart Rate Variability and Its Power Spectrum in Health, Aging and Heart Failure

A paradox regarding the classic power spectral analysis of heart rate variability (HRV) is whether the characteristic high- (HF) and low-frequency (LF) spectral peaks represent stochastic or chaotic phenomena. Resolution of this fundamental issue is key to unraveling the mechanisms of HRV, which is critical to its proper use as a noninvasive marker for cardiac mortality risk assessment and stratification in congestive heart failure (CHF) and other cardiac dysfunctions. However, conventional techniques of nonlinear time series analysis generally lack sufficient sensitivity, specificity and robustness to discriminate chaos from random noise, much less quantify the chaos level. Here, we apply a ‘litmus test’ for heartbeat chaos based on a novel noise titration assay which affords a robust, specific, time-resolved and quantitative measure of the relative chaos level. Noise titration of running short-segment Holter tachograms from healthy subjects revealed circadian-dependent (or sleep/wake-dependent) heartbeat chaos that was linked to the HF component (respiratory sinus arrhythmia). The relative ‘HF chaos’ levels were similar in young and elderly subjects despite proportional age-related decreases in HF and LF power. In contrast, the near-regular heartbeat in CHF patients was primarily nonchaotic except punctuated by undetected ectopic beats and other abnormal beats, causing transient chaos. Such profound circadian-, age- and CHF-dependent changes in the chaotic and spectral characteristics of HRV were accompanied by little changes in approximate entropy, a measure of signal irregularity. The salient chaotic signatures of HRV in these subject groups reveal distinct autonomic, cardiac, respiratory and circadian/sleep-wake mechanisms that distinguish health and aging from CHF