Changes of autonomic information flow due to idiopathic dilated cardiomyopathy

Spontaneous pain dynamics characterized by stochasticity in neural recordings of awake humans with chronic pain

ABSTRACT

Chronic pain is characterized by spontaneous fluctuations in pain intensity, a phenomenon that remains poorly understood. The aim of this study is to elucidate the neural mechanisms underlying pain fluctuations in patients with chronic pain undergoing deep brain stimulation surgery. We recorded local field potentials (LFPs) from pain-processing hub structures, including the ventral posteromedial nucleus of the thalamus, subgenual cingulate cortex, and periventricular and periaqueductal gray, while patients continuously reported their pain levels. Using novel auto-mutual information metrics to analyze LFP stochastic patterns, we found that pain intensity correlated with both increased regularity of spike-like events and greater past-dependency of neural oscillations in the 4- to 15-Hz frequency band. In addition, during periods of higher pain states, we observed enhanced functional connectivity between the examined hub structures and the prefrontal cortex, suggesting a more focused flow of pain-related information within the pain circuit. By characterizing the dynamic nature of pain fluctuations, this study bridges the gap in understanding moment-to-moment pain variations and their underlying neural mechanisms, paving the way for improved chronic pain management strategies.

NLDyn - An open source MATLAB toolbox for the univariate and multivariate nonlinear dynamical analysis of physiological data

BACKGROUND AND OBJECTIVE

We present NLDyn, an open-source MATLAB toolbox tailored for in-depth analysis of nonlinear dynamics in biomedical signals. Our objective is to offer a user-friendly yet comprehensive platform for researchers to explore the intricacies of time series data.

METHODS

NLDyn integrates approximately 80 distinct methods, encompassing both univariate and multivariate nonlinear dynamics, setting it apart from existing solutions. This toolbox combines state-of-the-art nonlinear dynamical techniques with advanced multivariate entropy methods, providing users with powerful analytical capabilities. NLDyn enables analyses with or without a sliding window, and users can easily access and customize default parameters.

RESULTS

NLDyn generates results that are both exportable and visually informative, facilitating seamless integration into research and presentations. Its ongoing development ensures it remains at the forefront of nonlinear dynamics analysis.

CONCLUSIONS

NLDyn is a valuable resource for researchers in the biomedical field, offering an intuitive interface and a wide array of nonlinear analysis tools. Its integration of advanced techniques empowers users to gain deeper insights from their data. As we continually refine and expand NLDyn's capabilities, we envision it becoming an indispensable tool for the exploration of complex dynamics in biomedical signals.

A comprehensive comparison of residue-level methylation levels with the regression-based gene-level methylation estimations by ReGear

MOTIVATION

DNA methylation is a biological process impacting the gene functions without changing the underlying DNA sequence. The DNA methylation machinery usually attaches methyl groups to some specific cytosine residues, which modify the chromatin architectures. Such modifications in the promoter regions will inactivate some tumor-suppressor genes. DNA methylation within the coding region may significantly reduce the transcription elongation efficiency. The gene function may be tuned through some cytosines are methylated.

METHODS

This study hypothesizes that the overall methylation level across a gene may have a better association with the sample labels like diseases than the methylations of individual cytosines. The gene methylation level is formulated as a regression model using the methylation levels of all the cytosines within this gene. A comprehensive evaluation of various feature selection algorithms and classification algorithms is carried out between the gene-level and residue-level methylation levels.

RESULTS

A comprehensive evaluation was conducted to compare the gene and cytosine methylation levels for their associations with the sample labels and classification performances. The unsupervised clustering was also improved using the gene methylation levels. Some genes demonstrated statistically significant associations with the class label, even when no residue-level methylation features have statistically significant associations with the class label. So in summary, the trained gene methylation levels improved various methylome-based machine learning models. Both methodology development of regression algorithms and experimental validation of the gene-level methylation biomarkers are worth of further investigations in the future studies. The source code, example data files and manual are available at http://www.healthinformaticslab.org/supp/.

Sympathetic nervous system activation and heart failure: Current state of evidence and the pathophysiology in the light of novel biomarkers

Heart failure (HF) is a complex clinical syndrome characterized by the activation of at least several neurohumoral pathways that have a common role in maintaining cardiac output and adequate perfusion pressure of target organs and tissues. The sympathetic nervous system (SNS) is upregulated in HF as evident in dysfunctional baroreceptor and chemoreceptor reflexes, circulating and neuronal catecholamine spillover, attenuated parasympathetic response, and augmented sympathetic outflow to the heart, kidneys and skeletal muscles. When these sympathoexcitatory effects on the cardiovascular system are sustained chronically they initiate the vicious circle of HF progression and become associated with cardiomyocyte apoptosis, maladaptive ventricular and vascular remodeling, arrhythmogenesis, and poor prognosis in patients with HF. These detrimental effects of SNS activity on outcomes in HF warrant adequate diagnostic and treatment modalities. Therefore, this review summarizes basic physiological concepts about the interaction of SNS with the cardiovascular system and highlights key pathophysiological mechanisms of SNS derangement in HF. Finally, special emphasis in this review is placed on the integrative and up-to-date overview of diagnostic modalities such as SNS imaging methods and novel laboratory biomarkers that could aid in the assessment of the degree of SNS activation and provide reliable prognostic information among patients with HF.

Amplitude- and Fluctuation-Based Dispersion Entropy

Dispersion entropy (DispEn) is a recently introduced entropy metric to quantify the uncertainty of time series. It is fast and, so far, it has demonstrated very good performance in the characterisation of time series. It includes a mapping step, but the effect of different mappings has not been studied yet. Here, we investigate the effect of linear and nonlinear mapping approaches in DispEn. We also inspect the sensitivity of different parameters of DispEn to noise. Moreover, we develop fluctuation-based DispEn (FDispEn) as a measure to deal with only the fluctuations of time series. Furthermore, the original and fluctuation-based forbidden dispersion patterns are introduced to discriminate deterministic from stochastic time series. Finally, we compare the performance of DispEn, FDispEn, permutation entropy, sample entropy, and Lempel–Ziv complexity on two physiological datasets. The results show that DispEn is the most consistent technique to distinguish various dynamics of the biomedical signals. Due to their advantages over existing entropy methods, DispEn and FDispEn are expected to be broadly used for the characterization of a wide variety of real-world time series. The MATLAB codes used in this paper are freely available at http://dx.doi.org/10.7488/ds/2326.

Sevoflurane preconditioning during myocardial ischemia-reperfusion reduces infarct size and preserves autonomic control of circulation in rats

PURPOSE

To investigate the myocardial ischemia-reperfusion with sevoflurane anesthetic preconditioning (APC) would present beneficial effects on autonomic and cardiac function indexes after the acute phase of a myocardial ischemia-reperfusion.

METHODS

Twenty Wistar rats were allocated in three groups: control (CON, n=10), myocardial infarction with sevoflurane (SEV, n=5) and infarcted without sevoflurane (INF, n=5). Myocardial ischemia (60 min) and reperfusion were performed by temporary coronary occlusion. Twenty-one days later, the systolic and diastolic function were evaluated by echocardiography; spectral analysis of the systolic arterial pressure (SAPV) and heart rate variability (HRV) were assessed. After the recording period, the infarct size (IS) was evaluated.

RESULTS

The INF group presented greater cardiac dysfunction and increased sympathetic modulation of the SAPV, as well as decreased alpha index and worse vagal modulation of the HRV. The SEV group exhibited attenuation of the systolic and diastolic dysfunction and preserved vagal modulation (square root of the mean squared differences of successive R-R intervals and high frequency) of HRV, as well as a smaller IS.

CONCLUSION

Sevoflurane preconditioning better preserved the cardiac function and autonomic modulation of the heart in post-acute myocardial infarction period.

Regularity of cardiac rhythm as a marker of sleepiness in sleep disordered breathing

Aim

The present study aimed to analyse the autonomic nervous system activity using heart rate variability (HRV) to detect sleep disordered breathing (SDB) patients with and without excessive daytime sleepiness (EDS) before sleep onset.

Methods

Two groups of 20 patients with different levels of daytime sleepiness -sleepy group, SG; alert group, AG- were selected consecutively from a Maintenance of Wakefulness Test (MWT) and Multiple Sleep Latency Test (MSLT) research protocol. The first waking 3-min window of RR signal at the beginning of each nap test was considered for the analysis. HRV was measured with traditional linear measures and with time-frequency representations. Non-linear measures -correntropy, CORR; auto-mutual-information function, AMIF- were used to describe the regularity of the RR rhythm. Statistical analysis was performed with non-parametric tests.

Results

Non-linear dynamic of the RR rhythm was more regular in the SG than in the AG during the first wakefulness period of MSLT, but not during MWT. AMIF (in high-frequency and in Total band) and CORR (in Total band) yielded sensitivity > 70%, specificity >75% and an area under ROC curve > 0.80 in classifying SG and AG patients.

Conclusion

The regularity of the RR rhythm measured at the beginning of the MSLT could be used to detect SDB patients with and without EDS before the appearance of sleep onset.

The autonomic nervous system and heart failure

The pathophysiology of heart failure (HF) is characterized by hemodynamic abnormalities that result in neurohormonal activation and autonomic imbalance with increase in sympathetic activity and withdrawal of vagal activity. Alterations in receptor activation from this autonomic imbalance may have profound effects on cardiac function and structure. Inhibition of the sympathetic drive to the heart through β-receptor blockade has become a standard component of therapy for HF with a dilated left ventricle because of its effectiveness in inhibiting the ventricular structural remodeling process and in prolonging life. Several devices for selective modulation of sympathetic and vagal activity have recently been developed in an attempt to alter the natural history of HF. The optimal counteraction of the excessive sympathetic activity is still unclear. A profound decrease in adrenergic support with excessive blockade of the sympathetic nervous system may result in adverse outcomes in clinical HF. In this review, we analyze the data supporting a contributory role of the autonomic functional alterations on the course of HF, the techniques used to assess autonomic nervous system activity, the evidence for clinical effectiveness of pharmacological and device interventions, and the potential future role of autonomic nervous system modifiers in the management of this syndrome.

Interpretation of the auto-mutual information rate of decrease in the context of biomedical signal analysis. Application to electroencephalogram recordings

The mutual information (MI) is a measure of both linear and nonlinear dependences. It can be applied to a time series and a time-delayed version of the same sequence to compute the auto-mutual information function (AMIF). Moreover, the AMIF rate of decrease (AMIFRD) with increasing time delay in a signal is correlated with its entropy and has been used to characterize biomedical data. In this paper, we aimed at gaining insight into the dependence of the AMIFRD on several signal processing concepts and at illustrating its application to biomedical time series analysis. Thus, we have analysed a set of synthetic sequences with the AMIFRD. The results show that the AMIF decreases more quickly as bandwidth increases and that the AMIFRD becomes more negative as there is more white noise contaminating the time series. Additionally, this metric detected changes in the nonlinear dynamics of a signal. Finally, in order to illustrate the analysis of real biomedical signals with the AMIFRD, this metric was applied to electroencephalogram (EEG) signals acquired with eyes open and closed and to ictal and non-ictal intracranial EEG recordings.

Approximate entropy and auto mutual information analysis of the electroencephalogram in Alzheimer's disease patients

We analysed the electroencephalogram (EEG) from Alzheimer's disease (AD) patients with two nonlinear methods: approximate entropy (ApEn) and auto mutual information (AMI). ApEn quantifies regularity in data, while AMI detects linear and nonlinear dependencies in time series. EEGs from 11 AD patients and 11 age-matched controls were analysed. ApEn was significantly lower in AD patients at electrodes O1, O2, P3 and P4 (p < 0.01). The EEG AMI decreased more slowly with time delays in patients than in controls, with significant differences at electrodes T5, T6, O1, O2, P3 and P4 (p < 0.01). The strong correlation between results from both methods shows that the AMI rate of decrease can be used to estimate the regularity in time series. Our work suggests that nonlinear EEG analysis may contribute to increase the insight into brain dysfunction in AD, especially when different time scales are inspected, as is the case with AMI.

Heart rate variability analysis based on time-frequency representation and entropies in hypertrophic cardiomyopathy patients

In hypertrophic cardiomyopathy (HCM) patients there is an increased risk of premature death, which can occur with little or no warning. Furthermore, classification for sudden cardiac death on patients with HCM is very difficult. The aim of our study was to improve the prognostic value of heart rate variability (HRV) in HCM patients, giving insight into changes of the autonomic nervous system. In this way, the suitability of linear and nonlinear measures was studied to assess the HRV. These measures were based on time-frequency representation (TFR) and on Shannon and Rényi entropies, and compared with traditional HRV measures. Holter recordings of 64 patients with HCM and 55 healthy subjects were analyzed. The HCM patients consisted of two groups: 13 high risk patients, after aborted sudden cardiac death (SCD); 51 low risk patients, without SCD. Five-hour RR signals, corresponding to the sleep period of the subjects, were considered for the analysis as a comparable standard situation. These RR signals were filtered in the three frequency bands: very low frequency band (VLF, 0-0.04 Hz), low frequency band (LF, 0.04-0.15 Hz) and high frequency band (HF, 0.15-0.45 Hz). TFR variables based on instantaneous frequency and energy functions were able to classify HCM patients and healthy subjects (control group). Results revealed that measures obtained from TFR analysis of the HRV better classified the groups of subjects than traditional HRV parameters. However, results showed that nonlinear measures improved group classification. It was observed that entropies calculated in the HF band showed the highest statistically significant levels comparing the HCM group and the control group, p-value < 0.0005. The values of entropy measures calculated in the HCM group presented lower values, indicating a decreasing of complexity, than those calculated from the control group. Moreover, similar behavior was observed comparing high and low risk of premature death, the values of the entropy being lower in high risk patients, p-value < 0.05, indicating an increase of predictability. Furthermore, measures from information entropy, but not from TFR, seem to be useful for enhanced risk stratification in HCM patients with an increased risk of sudden cardiac death.

On the application of the auto mutual information rate of decrease to biomedical signals

The auto mutual information function (AMIF) evaluates the signal predictability by assessing linear and non-linear dependencies between two measurements taken from a single time series. Furthermore, the AMIF rate of decrease (AMIFRD) is correlated with signal entropy. This metric has been used to analyze biomedical data, including cardiac and brain activity recordings. Hence, the AMIFRD can be a relevant parameter in the context of biomedical signal analysis. Thus, in this pilot study, we have analyzed a synthetic sequence (a Lorenz system) and real biosignals (electroencephalograms recorded with eyes open and closed) with the AMIFRD. We aimed at illustrating the application of this parameter to biomedical time series. Our results show that the AMIFRD can detect changes in the non-linear dynamics of a sequence and that it can distinguish different physiological conditions.