Complexity of resting-state EEG activity in the patients with early-stage Parkinson’s disease

To investigate the abnormal brain activities in the early stage of Parkinson’s disease (PD), the electroencephalogram (EEG) signals were recorded with 20 channels from non-dementia PD patients (18 patients, 8 females) and age matched healthy controls (18 subjects, 8 females) during the resting state. Two methods based on the ordinal patterns of the recorded series, i.e., permutation entropy (PE) and order index (OI), were introduced to characterize the complexity of the cortical activities for two groups. It was observed that the resting-state EEG of PD patients showed lower PE and higher OI than healthy controls, which indicated that the early-stage PD caused the reduced complexity of EEG. We further applied two methods to determine the complexity of EEG rhythms in five sub-bands. The results showed that the gamma, beta and alpha rhythms of PD patients were characterized by lower PE and higher OI, i.e., reduced complexity, than healthy subjects. No significant differences were observed in theta or delta rhythms between two groups. The findings suggested that PE and OI were promising methods to detect the abnormal changes in the dynamics of EEG signals associated with early-stage PD. Further, such changes in EEG complexity may be the early markers of the cortical or subcortical dysfunction caused by PD.     

Age dependency and correlation of heart rate variability,blood pressure variability and baroreflex sensitivity

Simultaneous analysis of heart rate variability (HRV), blood pressure variability (BPV) and baroreflex sensitivity (BRS) with different types of measures may provide non-duplicative information about autonomic cardiovascular regulation. Therefore, a multiple signal analysis of cardiovascular time series will enhance the physiological understanding of neuro cardiovascular regulation with deconditioning in bedrest or related gravitational physiological studies. It has been shown that age is an important determinant of HRV and BRS in healthy subjects. Whereas in the case of BPV, the effect of aging seems to depend upon the activity status of the subjects. In view of the facts that most of the previous works were dealing with only the variability of one kind of cardiovascular parameters in one study with conventional time-domain and/or frequency-domain analysis, we therefore designed the present work to compare the HRV, BPV and BRS between young and middle-aged male healthy subjects in one study with the same subjects using various techniques, including the approximate entropy (ApEn) measurement, a statistic quantifying HRV "complexity" derived from non-linear dynamics.     

Effects of hypercapnia and hypocapnia on ventilatory variability and the chaotic dynamics of ventilatory flow in humans

In humans, lung ventilation exhibits breath-to-breath variability and dynamics that are nonlinear, complex, sensitive to initial conditions, unpredictable in the long-term, and chaotic. Hypercapnia, as produced by the inhalation of a CO(2)-enriched gas mixture, stimulates ventilation. Hypocapnia, as produced by mechanical hyperventilation, depresses ventilation in animals and in humans during sleep, but it does not induce apnea in awake humans. This emphasizes the suprapontine influences on ventilatory control. How cortical and subcortical commands interfere thus depend on the prevailing CO(2) levels. However, CO(2) also influences the variability and complexity of ventilation. This study was designed to describe how this occurs and to test the hypothesis that CO(2) chemoreceptors are important determinants of ventilatory dynamics. Spontaneous ventilatory flow was recorded in eight healthy subjects. Breath-by-breath variability was studied through the coefficient of variation of several ventilatory variables. Chaos was assessed with the noise titration method (noise limit) and characterized with numerical indexes [largest Lyapunov exponent (LLE), sensitivity to initial conditions; Kolmogorov-Sinai entropy (KSE), unpredictability; and correlation dimension (CD), irregularity]. In all subjects, under all conditions, a positive noise limit confirmed chaos. Hypercapnia reduced breathing variability, increased LLE (P = 0.0338 vs. normocapnia; P = 0.0018 vs. hypocapnia), increased KSE, and slightly reduced CD. Hypocapnia increased variability, decreased LLE and KSE, and reduced CD. These results suggest that chemoreceptors exert a strong influence on ventilatory variability and complexity. However, complexity persists in the quasi-absence of automatic drive. Ventilatory variability and complexity could be determined by the interaction between the respiratory central pattern generator and suprapontine structures.     

Increase in regularity of fetal heart rate variability with age.

It is generally assumed that fetal heart rate variability increases with gestation, reflecting prenatal development of the autonomic nervous system. We examined standard measures quantifying fetal heart rate variability, as well as a complexity measure, approximate entropy, in 66 fetal magnetocardiograms recorded from 22 healthy pregnant women between the 16th and 42nd week of gestation. In particular, regularity in the fetal RR interval time series was assessed on the basis of symbolic dynamics. The results showed that, beside an overall increase in fetal heart rate variability and complexity during pregnancy, there was also an increase in specific sets of binary patterns with low approximate entropy, i.e., a high degree of regularity. These sets were characterized by short epochs of heart rate acceleration and deceleration, and comparison with surrogate data confirmed that their random occurrence is rare. The results most likely reflect the influence of increasingly differentiated fetal behavioral states and transitions between them in association with fetal development.     

Measures of entropy and complexity in altered states of consciousness

Quantification of complexity in neurophysiological signals has been studied using different methods, especially those from information or dynamical system theory. These studies have revealed a dependence on different states of consciousness, and in particular that wakefulness is characterized by a greater complexity of brain signals, perhaps due to the necessity for the brain to handle varied sensorimotor information. Thus, these frameworks are very useful in attempts to quantify cognitive states. We set out to analyze different types of signals obtained from scalp electroencephalography (EEG), intracranial EEG and magnetoencephalography recording in subjects during different states of consciousness: resting wakefulness, different sleep stages and epileptic seizures. The signals were analyzed using a statistical (permutation entropy) and a deterministic (permutation Lempel–Ziv complexity) analytical method. The results are presented in complexity versus entropy graphs, showing that the values of entropy and complexity of the signals tend to be greatest when the subjects are in fully alert states, falling in states with loss of awareness or consciousness. These findings were robust for all three types of recordings. We propose that the investigation of the structure of cognition using the frameworks of complexity will reveal mechanistic aspects of brain dynamics associated not only with altered states of consciousness but also with normal and pathological conditions.     

Nonlinear Complexity Analysis of Brain fMRI Signals in Schizophrenia

We investigated the differences in brain fMRI signal complexity in patients with schizophrenia while performing the Cyberball social exclusion task, using measures of Sample entropy and Hurst exponent (H). 13 patients meeting diagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM IV) criteria for schizophrenia and 16 healthy controls underwent fMRI scanning at 1.5 T. The fMRI data of both groups of participants were pre-processed, the entropy characterized and the Hurst exponent extracted. Whole brain entropy and H maps of the groups were generated and analysed. The results after adjusting for age and sex differences together show that patients with schizophrenia exhibited higher complexity than healthy controls, at mean whole brain and regional levels. Also, both Sample entropy and Hurst exponent agree that patients with schizophrenia have more complex fMRI signals than healthy controls. These results suggest that schizophrenia is associated with more complex signal patterns when compared to healthy controls, supporting the increase in complexity hypothesis, where system complexity increases with age or disease, and also consistent with the notion that schizophrenia is characterised by a dysregulation of the nonlinear dynamics of underlying neuronal systems.     

Are Complexity Metrics Reliable in Assessing HRV Control in Obese Patients During Sleep?

Obesity is associated with cardiovascular mortality. Linear methods, including time domain and frequency domain analysis, are normally applied on the heart rate variability (HRV) signal to investigate autonomic cardiovascular control, whose imbalance might promote cardiovascular disease in these patients. However, given the cardiac activity non-linearities, non-linear methods might provide better insight. HRV complexity was hereby analyzed during wakefulness and different sleep stages in healthy and obese subjects. Given the short duration of each sleep stage, complexity measures, normally extracted from long-period signals, needed be calculated on short-term signals. Sample entropy, Lempel-Ziv complexity and detrended fluctuation analysis were evaluated and results showed no significant differences among the values calculated over ten-minute signals and longer durations, confirming the reliability of such analysis when performed on short-term signals. Complexity parameters were extracted from ten-minute signal portions selected during wakefulness and different sleep stages on HRV signals obtained from eighteen obese patients and twenty controls. The obese group presented significantly reduced complexity during light and deep sleep, suggesting a deficiency in the control mechanisms integration during these sleep stages. To our knowledge, this study reports for the first time on how the HRV complexity changes in obesity during wakefulness and sleep. Further investigation is needed to quantify altered HRV impact on cardiovascular mortality in obesity.     

Task-Driven Activity Reduces the Cortical Activity Space of the Brain: Experiment and Whole-Brain Modeling

How a stimulus or a task alters the spontaneous dynamics of the brain remains a fundamental open question in neuroscience. One of the most robust hallmarks of task/stimulus-driven brain dynamics is the decrease of variability with respect to the spontaneous level, an effect seen across multiple experimental conditions and in brain signals observed at different spatiotemporal scales. Recently, it was observed that the trial-to-trial variability and temporal variance of functional magnetic resonance imaging (fMRI) signals decrease in the task-driven activity. Here we examined the dynamics of a large-scale model of the human cortex to provide a mechanistic understanding of these observations. The model allows computing the statistics of synaptic activity in the spontaneous condition and in putative tasks determined by external inputs to a given subset of brain regions. We demonstrated that external inputs decrease the variance, increase the covariances, and decrease the autocovariance of synaptic activity as a consequence of single node and large-scale network dynamics. Altogether, these changes in network statistics imply a reduction of entropy, meaning that the spontaneous synaptic activity outlines a larger multidimensional activity space than does the task-driven activity. We tested this model’s prediction on fMRI signals from healthy humans acquired during rest and task conditions and found a significant decrease of entropy in the stimulus-driven activity. Altogether, our study proposes a mechanism for increasing the information capacity of brain networks by enlarging the volume of possible activity configurations at rest and reliably settling into a confined stimulus-driven state to allow better transmission of stimulus-related information.     

Heart Rate Dynamics after Combined Strength and Endurance Training in Middle-Aged Women: Heterogeneity of Responses

The loss of complexity in physiological systems may be a dynamical biomarker of aging and disease. In this study the effects of combined strength and endurance training compared with those of endurance training or strength training alone on heart rate (HR) complexity and traditional HR variability indices were examined in middle-aged women. 90 previously untrained female volunteers between the age of 40 and 65 years completed a 21 week progressive training period of either strength training, endurance training or their combination, or served as controls. Continuous HR time series were obtained during supine rest and submaximal steady state exercise. The complexity of HR dynamics was assessed using multiscale entropy analysis. In addition, standard time and frequency domain measures were also computed. Endurance training led to increases in HR complexity and selected time and frequency domain measures of HR variability (P<0.01) when measured during exercise. Combined strength and endurance training or strength training alone did not produce significant changes in HR dynamics. Inter-subject heterogeneity of responses was particularly noticeable in the combined training group. At supine rest, no training-induced changes in HR parameters were observed in any of the groups. The present findings emphasize the potential utility of endurance training in increasing the complex variability of HR in middle-aged women. Further studies are needed to explore the combined endurance and strength training adaptations and possible gender and age related factors, as well as other mechanisms, that may mediate the effects of different training regimens on HR dynamics.     

Visibly healthy corals exhibit variable pigment concentrations and symbiont phenotypes

Understanding the natural variability of photosynthetic pigment ranges and distributions in healthy corals is central to evaluating how useful these measurements are for assessing the health and bleaching status of endosymbiotic reef-building corals. This study examined the photosynthetic pigment variability in visibly healthy Porites lobata and Porites lutea corals from Kaneohe Bay, Hawaii and explored whether pigment variability was related to the genetic identity or phenotypic characteristics of the symbionts. Concentrations of the photosynthetic pigments chlorophyll a, peridinin, chlorophyll c ₂ , diadinoxanthin, diatoxanthin, β,β-carotene and dinoxanthin were quantified using high-performance liquid chromatography (HPLC). Pigment concentrations were found to range 1.5–10 fold in colonies of each species at similar depths (0–2, 2–4, 10–15 and 19–21 m). Despite the high pigment variability, pigment ratios for each species were relatively conserved over the 0–21 m depth gradient. The genetic identity of the symbiont communities was examined for each colony using 18S nuclear ribosomal DNA (nrDNA) restriction fragment length polymorphisms. All colonies contained symbionts belonging to clade C. The density and phenotypic characteristics of the symbionts were explored using flow cytometry, and fluorescence and side scatter (cell size) properties revealed phenotypically distinct symbiont subpopulations in every colony. The symbiont subpopulations displayed pigment trends that may be driven by acclimatization to irradiance microenvironments within the host. These results highlight the biological complexity of healthy coral–symbiont associations and the need for future research on pigments and symbiont subpopulation dynamics.     

Nonlinear analysis of continuous ECG during sleep II. Dynamical measures

The hypothesis that cardiac rhythms are associated with chaotic dynamics implicating a healthy flexibility has motivated the investigation of continuous ECG with methods of nonlinear system theory. Sleep is known to be associated with modulations of the sympathetic and parasympathetic control of cardiac dynamics. Thus, the differentiation of ECG signals recorded during different sleep stages can serve to determine the usefulness of nonlinear measures in discriminating ECG states in general. For this purpose the following six nonlinear measures were implemented: correlation dimension D2, Lyapunov exponent L1. Kolmogorov entropy K2, as well as three measures derived from the analysis of unstable periodic orbits. Results of this study show that continuous ECG signals can be differentiated from linear stochastic surrogates by each of the nonlinear measures. The most significant finding with respect to the sleep-related differentiation of ECG signals is an increase in dominant chaoticity assessed by L1 and a reduction in the degrees of freedom estimated by D2 during REM sleep compared to slow wave sleep. Our findings suggest that the increase in dominant chaoticity during REM sleep with regard to time-continuous nonlinear analysis is comparable to an increased heart rate variability. The reduction in the correlation dimension may be interpreted as an expression of the withdrawal of respiratory influences during REM sleep. Received: 7 June 1999 / Accepted in revised form: 10 December 1999     

Cancer of the gastrointestinal tract results in a restricted T-cell repertoire dependent upon tumor differentiation

We investigated the influence of tumor tissue differentiation on the diversity of TCR repertoire. CDR3 spectratypes of CD4⁺ and CD8⁺ T cell subsets were analyzed from 27 patients with gastrointestinal tract tumors exhibiting varying degrees of differentiation. A CDR3 spectratype complexity scoring system was used to quantify the diversity of TCR repertoire. Each patient was matched with an age-matched healthy group to control for age variability. Results show that the complexity scores (TCR repertoire diversity) have a significant correlation with the degree of tumor differentiation, which provides useful information for understanding immune response in cancer patients.     

Effects of exercise and passive head-up tilt on fractal and complexity properties of heart rate dynamics

tk;1Passive head-up tilt and exercise result in specific changes in the spectral characteristics of heart rate (HR) variability as a result of reduced vagal and enhanced sympathetic outflow. Recently analytic methods based on nonlinear system theory have been developed to characterize the nonlinear features in HR dynamics. This study was designed to assess the changes in the fractal and complexity measures of HR behavior during the passive head-up tilt and during exercise. Fractal exponent (alpha(1)) and approximate entropy (ApEn), measures of short-term correlation properties and overall complexity of HR, respectively, along with spectral components of HR variability were analyzed during a passive head-up tilt test (n = 10) and a low-intensity steady-state exercise (n = 20) in healthy subjects. We observed that alpha(1) increased during the tilt test (from 0.85 +/- 0.22 to 1.48 +/- 0.20; P < 0.001) and during the exercise (from 1.00 +/- 0.22 to 1.37 +/- 0. 14; P < 0.001). ApEn also increased during the exercise (from 1.04 +/- 0.11 to 1. 11 +/- 0.08; P < 0.05), but it did not change during the tilt test. The normalized high-frequency spectral component decreased and the low-frequency component increased similarly during both the exercise and the tilt test (P < 0.001 for all). Exercise and passive tilt result in an increase of short-term fractal correlation properties of HR dynamics, which is related to changes in the balance between the low- and high-frequency oscillations in controlled situations. Overall complexity of HR dynamics increases during exercise but not during passive tilt.     

Estimation of Instantaneous Complex Dynamics through Lyapunov Exponents: A Study on Heartbeat Dynamics

Measures of nonlinearity and complexity, and in particular the study of Lyapunov exponents, have been increasingly used to characterize dynamical properties of a wide range of biological nonlinear systems, including cardiovascular control. In this work, we present a novel methodology able to effectively estimate the Lyapunov spectrum of a series of stochastic events in an instantaneous fashion. The paradigm relies on a novel point-process high-order nonlinear model of the event series dynamics. The long-term information is taken into account by expanding the linear, quadratic, and cubic Wiener-Volterra kernels with the orthonormal Laguerre basis functions. Applications to synthetic data such as the Hénon map and Rössler attractor, as well as two experimental heartbeat interval datasets (i.e., healthy subjects undergoing postural changes and patients with severe cardiac heart failure), focus on estimation and tracking of the Instantaneous Dominant Lyapunov Exponent (IDLE). The novel cardiovascular assessment demonstrates that our method is able to effectively and instantaneously track the nonlinear autonomic control dynamics, allowing for complexity variability estimations.     

Dynamics of seated computer work before and after prolonged constrained sitting

This laboratory study investigated seated computer work before and after prolonged constrained sitting. Discomfort ratings and kinetic and kinematics data were recorded in nine healthy males performing computer work for 5 min before and after 96 min of sitting. The displacement of the center of pressure (CoP) in anterior-posterior and medial-lateral directions and lumbar curvature (LC) were calculated. The root mean square, standard deviation, and sample entropy values were computed from the CoPs and LC signals to assess the magnitude, amount of variability, and regularity of sitting dynamics, respectively. The discomfort increased for the buttocks (p = .02).The standard deviation and sample entropy values of the CoPs and LC signals, respectively, increased (p < .04) and decreased (p < .004) whereas the root mean square remained unchanged (p > .15) after prolonged constrained sitting compared with before. This present study showed that during seated computer work, prolonged constrained sitting affected the amount of variability and the regularity of sitting postural control, whereas the magnitude was not affected. The importance of the dynamics of sitting control may challenge the idea of a static and ideal seated posture at work.     

Increased brain signal variability accompanies lower behavioral variability in development

As the brain matures, its responses become optimized. Behavioral measures show this through improved accuracy and decreased trial-to-trial variability. The question remains whether the supporting brain dynamics show a similar decrease in variability. We examined the relation between variability in single trial evoked electrical activity of the brain (measured with EEG) and performance of a face memory task in children (8–15 y) and young adults (20–33 y). Behaviorally, children showed slower, more variable response times (RT), and less accurate recognition than adults. However, brain signal variability increased with age, and showed strong negative correlations with intrasubject RT variability and positive correlations with accuracy. Thus, maturation appears to lead to a brain with greater functional variability, which is indicative of enhanced neural complexity. This variability may reflect a broader repertoire of metastable brain states and more fluid transitions among them that enable optimum responses. Our results suggest that the moment-to-moment variability in brain activity may be a critical index of the cognitive capacity of the brain.     

Extended phenotypes as signals

Animal signals may result from construction behaviour and can provide receivers with essential information in various contexts. Here we explore the potential benefits of extended phenotypes with a signalling function as compared to bodily ornaments and behavioural displays. Their independence of the body, their physical persistence and the morphological and cognitive conditions required for their construction allow unique communication possibilities. We classify various levels of information transfer by extended phenotype signals and explore the differences between secreted signals and signals resulting from collection and construction, which usually involve higher behavioural complexity. We examine evolutionary pathways of extended phenotypes with a signalling function with help of a comparative evaluation and conclude that often constructions first provide a direct fitness benefit, with a signalling function becoming more and more prominent during evolutionary progression. The abundance and variability of extended phenotypes as signals is impressive and provides unique possibilities for animal communication research.     

Pointwise Hölder exponents of a model for skin laser Doppler flowmetry signals based on six nonlinear coupled oscillators with linear and parametric couplings: Comparison with experimental data from young healthy subjects

We study pointwise Hölder exponents of experimental and numerically simulated skin laser Doppler flowmetry (LDF) data that give a peripheral view of the cardiovascular system. The experimental signals are recorded in the forearm of young healthy subjects. The numerically simulated LDF data are computed from a model containing six nonlinear coupled oscillators reflecting six almost periodic rhythmic activities present in experimental LDF signals. Simulated LDF signals are for the first time generated with both linear and parametric couplings in order to represent cardiovascular system behaviors. Moreover, we propose the use of a parametric generalised quadratic variation (GQV) based estimation method for the estimation of the pointwise Hölder exponents. The latter identify possible multifractal characteristics of data. The GQV method is first tested on a white noise measure and then applied on the LDF data. The results of our signal processing analysis show that experimental LDF signals recorded in the forearm are weakly multifractal for young healthy subjects at rest. Furthermore, our findings show that the simulated data have a complexity similar to the one of signal recorded in young healthy subjects. However, their pointwise Hölder exponents have differences that we explain. This paper provides useful information to go deeper into the modeling of LDF data, which could bring enlightenment for a better understanding of the peripheral cardiovascular system.     

Force variability is mostly not motor noise: Theoretical implications for motor control

Variability in muscle force is a hallmark of healthy and pathological human behavior. Predominant theories of sensorimotor control assume ‘motor noise’ leads to force variability and its ‘signal dependence’ (variability in muscle force whose amplitude increases with intensity of neural drive). Here, we demonstrate that the two proposed mechanisms for motor noise (i.e. the stochastic nature of motor unit discharge and unfused tetanic contraction) cannot account for the majority of force variability nor for its signal dependence. We do so by considering three previously underappreciated but physiologically important features of a population of motor units: 1) fusion of motor unit twitches, 2) coupling among motoneuron discharge rate, cross-bridge dynamics, and muscle mechanics, and 3) a series-elastic element to account for the aponeurosis and tendon. These results argue strongly against the idea that force variability and the resulting kinematic variability are generated primarily by ‘motor noise.’ Rather, they underscore the importance of variability arising from properties of control strategies embodied through distributed sensorimotor systems. As such, our study provides a critical path toward developing theories and models of sensorimotor control that provide a physiologically valid and clinically useful understanding of healthy and pathologic force variability.     

Age-Related Changes in Electroencephalographic Signal Complexity

The study of active and healthy aging is a primary focus for social and neuroscientific communities. Here, we move a step forward in assessing electrophysiological neuronal activity changes in the brain with healthy aging. To this end, electroencephalographic (EEG) resting state activity was acquired in 40 healthy subjects (age 16–85). We evaluated Fractal Dimension (FD) according to the Higuchi algorithm, a measure which quantifies the presence of statistical similarity at different scales in temporal fluctuations of EEG signals. Our results showed that FD increases from age twenty to age fifty and then decreases. The curve that best fits the changes in FD values across age over the whole sample is a parabola, with the vertex located around age fifty. Moreover, FD changes are site specific, with interhemispheric FD asymmetry being pronounced in elderly individuals in the frontal and central regions. The present results indicate that fractal dimension well describes the modulations of brain activity with age. Since fractal dimension has been proposed to be related to the complexity of the signal dynamics, our data demonstrate that the complexity of neuronal electric activity changes across the life span of an individual, with a steady increase during young adulthood and a decrease in the elderly population.