Reduced physiological complexity in robust elderly adults with the APOE epsilon4 allele

Recovery of cardiac electrophysiological alterations by heart rate complexity based on multiscale entropy following liver transplantation

Autonomic nervous dysfunction is a known cardiac sequalae in patients with end-stage liver disease and is associated with a poor prognosis. Heart rate analysis using nonlinear models such as multiscale entropy (MSE) or complexity may identify marked changes in these patients where conventional heart rate variability (HRV) measurements do not. To investigate the application of heart rate complexity (HRC) based on MSE in liver transplantation settings. Thirty adult recipients of elective living donor liver transplantation were enrolled. HRV parameters using conventional HRV analysis and HRC analysis were obtained at the following time points: (1) 1 day before surgery, (2) postoperative day (POD) 7, (3) POD 14, (4) POD 90, and (5) POD 180. Preoperatively, patients with MELD score ≥ 25 had significantly lower HRC compared to patients with lower MELD scores. This difference in HRC disappeared by POD 7 following liver transplantation and subsequent analyses at POD 90 and 180 continued to show no significant difference. Our results indicated a significant negative correlation between HRC based on MSE analysis and liver disease severity preoperatively, which may be more sensitive than conventional linear HRV analysis. HRC in patients with MELD score ≧ 25 improved over time and became comparable to those with MELD < 25 as early as in 7 days.

Resting-state brain signal complexity discriminates young healthy APOE e4 carriers from non-e4 carriers

It is well established that the e4 allele of the APOE gene is associated with impaired brain functionality and cognitive decline in humans at elder age. However, it is controversial whether and how the APOE e4 allele is associated with superior brain function among young healthy individuals, thus indicates a case of antagonistic pleiotropy of APOE e4 allele. Signal complexity is a critical aspect of brain activity that has been associated with brain function. In this study, the multiscale entropy (MSE) of resting-state EEG signals among a sample of young healthy adults (N = 260) as an indicator of brain signal complexity was investigated. It was of interest whether MSE differs across APOE genotype groups while age and education level were controlled for and whether the APOE genotype effect on MSE interacts with MSE time scale, as well as EEG recording condition. Results of linear mixed models indicate overall larger MSE in APOE e4 carriers. This genotype-dependent difference is larger at high as compared with low time scales. The interaction effect between APOE genotype and recording condition indicates increased between-state MSE change in young healthy APOE e4 carriers as compared with non-carriers. Because higher complexity is commonly taken to be associated with better cognitive functioning, the present results complement previous findings and therefore point to a pleiotropic spectrum of the APOE gene polymorphism.

Relationship between heart rate variability and cognitive function in patients with enlarged perivascular space

Objective

To explore the relationship between heart rate variability (HRV), the brain distribution of enlarged perivascular space (EPVS), and cognitive impairment in patients with EPVS.

Materials and methods

The clinical and imaging data of 199 patients with EPVS were retrospectively analyzed. EPVS load in the basal ganglia (BG) and centrum semiovale (CS) regions were assessed using the Potter’s method. Cognitive function was evaluated using the Montreal Cognitive Assessment Scale. A logistic regression model was used to analyze the relationship between HRV, the brain distribution of EPVS and cognitive function in patients with EPVS. A receiver operating characteristic curve was used to assess the predictive value of HRV for cognitive function in patients with EPVS.

Results

Of the 199 patients, 27 and 42 presented with severe BG-EPVS and cognitive impairment, respectively. Significant differences were observed in the root mean square of successive differences of normal-normal (NN) intervals for period of interest (rMSSD), the percentage of adjacent NN intervals greater than 50 ms (PNN50), and the ratio of low-frequency power (LF) to high-frequency power (HF) between the mild and severe BG-EPVS groups (P &lt; 0.05). Patients who presented with and without cognitive impairment differed significantly in the standard deviation of NN intervals (SDNN), rMSSD, PNN50, total power, LF, and LF/HF (P &lt; 0.05). rMSSD (odds ratio [OR] 0.871, 95% confidence interval [CI] 0.768–0.988) and LF/HF (OR 3.854, 95% CI 1.196–12.419) were independent influencing factors of BG-EPVS, and rMSSD (OR 0.936, 95% CI 0.898–0.976) was an independent influencing factor of cognitive impairment in patients with EPVS. The optimal cut-off point was 0.312, with an area under the curve of 0.795 (95% CI 0.719–0.872) for predicting cognitive impairment in patients with EPVS by rMSSD.

Conclusion

Reduced HRV is involved in the pathophysiological mechanisms of the formation and development of BG-EPVS and is associated with cognitive impairment in patients with EPVS, independent of CS-EPVS. For patients with HRV changes but without autonomic nervous system symptoms, positive intervention may slow the occurrence or progression of EPVS and cognitive impairment in patients with EPVS.

Decreased resting-state brain signal complexity in patients with mild cognitive impairment and Alzheimer's disease: a multiscale entropy analysis

Multiscale entropy (MSE) analysis is a novel entropy-based analysis method for quantifying the complexity of dynamic neural signals and physiological systems across multiple temporal scales. This approach may assist in elucidating the pathophysiologic mechanisms of amnestic mild cognitive impairment (aMCI) and Alzheimer's disease (AD). Using resting-state fNIRS imaging, we recorded spontaneous brain activity from 31 healthy controls (HC), 27 patients with aMCI, and 24 patients with AD. The quantitative analysis of MSE revealed that reduced brain signal complexity in AD patients in several networks, namely, the default, frontoparietal, ventral and dorsal attention networks. For the default and ventral attention networks, the MSE values also showed significant positive correlations with cognitive performances. These findings demonstrated that the MSE-based analysis method could serve as a novel tool for fNIRS study in characterizing and understanding the complexity of abnormal cortical signals in AD cohorts.

Genetic Influences on Functional Capacities in Aging

PURPOSE

Older populations are characterized by great heterogeneity in functional capacities and understanding the factors underlying these differences has been a major area of research for some decades. Genetic differences arguably play an important role in the heterogeneity observed for many outcomes among older individuals. However, the role of genes in the variation and trajectories of functional capacities in older age is poorly understood. This review was conducted to explore the evidence for genetic influences on physical functional capacities in aging.

DESIGN AND METHODS

This rapid review was conducted using the following criteria: journal articles retrieved from the PubMed, Embase, AgeLine, Scopus, and Web of Science electronic databases including the key words: genetics, genotype, polymorphism, physical or functional performance, functional capacity, activities of daily living, older, and elderly. In total, 118 articles were included for initial review.

RESULTS

The heritability of objective measures of physical function ranges from 30% to 60% in studies of older twins. There is a paucity of evidence about genetic influences on functional capacities, but some candidate genes related to functional capacity have been identified.

IMPLICATIONS

No strong candidate genes exist for functional capacities. Current methodologies are beginning to generate new evidence about genetic influences on overall physical function at older ages, but the variety of measures of functional capacity makes evidence difficult to compare.

APOE Genotype in the Ethnic Majority and Minority Groups of Laos and the Implications for Non-Communicable Diseases

Background

Increasing age is associated with elevated risk of non-communicable diseases, including dementia and Alzheimer’s disease (AD). The apolipoprotein E (APOE) ε4 allele is a risk factor not only for AD, but also for cognitive decline, depressive symptoms, stroke, hypertension, coronary heart disease, cardiovascular disease, and diabetes. The Lao People’s Democratic Republic (Laos) is undergoing development; consequently, life expectancy has risen. To evaluate the future risk of non-communicable diseases, we investigated APOE genotypes and anthropometric characteristics in the Laotian population.

Methodology/Principal Findings

Subjects were 455 members of the Lao Loum majority and 354 members of ethnic minorities. APOE genotypes, anthropometric characteristics, blood pressure, and blood glucose were recorded. To compare individual changes, health examination data collected 5 years apart were obtained from a subset of Lao Loum subjects. APOE ε4 allele frequencies were higher among minorities (31.3%) than among Lao Loum (12.6%). In Lao Loum, but not in minorities, mean waist circumference and blood pressure increased significantly across age groups. Comparisons of health conditions between the beginning and end of the 5-year period revealed significant increases in obesity and blood glucose levels in Lao Loum. APOE ε4 carriers exhibited significant increases in resting heart rate in both ethnic groups.

Conclusions/Significance

A higher ε4 allele frequency was observed in Laotian minorities than in the Laotian majority. Furthermore, higher obesity, blood pressure and blood glucose were observed in the middle-aged ethnic majority. Therefore, given these genetic and non-communicable disease risk factors, it seems likely that as the Laotian population ages, elevated rates of non-communicable aging-related diseases, such as dementia, will also become more prevalent.

Heart rate complexity in sinoaortic-denervated mice

NEW FINDINGS

What is the central question of this study? New measurements for cardiovascular complexity, such as detrended fluctuation analysis (DFA) and multiscale entropy (MSE), have been shown to predict cardiovascular outcomes. Given that cardiovascular diseases are accompanied by autonomic imbalance and decreased baroreflex sensitivity, the central question is: do baroreceptors contribute to cardiovascular complexity? What is the main finding and its importance? Sinoaortic denervation altered both DFA scaling exponents and MSE, indicating that both short- and long-term mechanisms of complexity are altered in sinoaortic denervated mice, resulting in a loss of physiological complexity. These results suggest that the baroreflex is a key element in the complex structures involved in heart rate variability regulation. Recently, heart rate (HR) oscillations have been recognized as complex behaviours derived from non-linear processes. Physiological complexity theory is based on the idea that healthy systems present high complexity, i.e. non-linear, fractal variability at multiple scales, with long-range correlations. The loss of complexity in heart rate variability (HRV) has been shown to predict adverse cardiovascular outcomes. Based on the idea that most cardiovascular diseases are accompanied by autonomic imbalance and a decrease in baroreflex sensitivity, we hypothesize that the baroreflex plays an important role in complex cardiovascular behaviour. Mice that had been subjected to sinoaortic denervation (SAD) were implanted with catheters in the femoral artery and jugular vein 5 days prior to the experiment. After recording the baseline arterial pressure (AP), pulse interval time series were generated from the intervals between consecutive values of diastolic pressure. The complexity of the HRV was determined using detrended fluctuation analysis and multiscale entropy. The detrended fluctuation analysis α1 scaling exponent (a short-term index) was remarkably decreased in the SAD mice (0.79 ± 0.06 versus 1.13 ± 0.04 for the control mice), whereas SAD slightly increased the α2 scaling exponent (a long-term index; 1.12 ± 0.03 versus 1.04 ± 0.02 for control mice). In the SAD mice, the total multiscale entropy was decreased (13.2 ± 1.3) compared with the control mice (18.9 ± 1.4). In conclusion, fractal and regularity structures of HRV are altered in SAD mice, affecting both short- and long-term mechanisms of complexity, suggesting that the baroreceptors play a considerable role in the complex structure of HRV.

Application of multiscale entropy in arterial waveform contour analysis in healthy and diabetic subjects

We applied multiscale entropy (MSE) to assess variation in crest time (CT), a parameter in arterial waveform analysis, in diagnosing patients with diabetes. Data on digital volume pulse were obtained from 93 individuals in three groups [Healthy young (Group 1, 20 < age ≤ 40, n = 30), healthy upper-middle-aged (Group 2, age > 40, n = 30), and diabetic (Group 3, n = 33) subjects]. Crest time, normalized crest time, crest time ratio (CTR), small- and large-scale MSE on CT [MSESS(CT) and MSELS(CT), respectively] were computed and correlated with anthropometric (i.e., body weight/height, waist circumference), hemodynamic (i.e., blood pressure), and biochemical parameters (i.e., serum triglyceride, high-density lipoprotein, fasting blood sugar, and glycosylated hemoglobin). The results demonstrated higher variability in CT in healthy subjects (Groups 1 and 2) compared with that in diabetic patients (Group 3) as reflected in significantly elevated MSESS(CT) and MSELS(CT) in the former (p < 0.003 and p < 0.001, respectively). MSELS(CT) also showed significant association with waist circumference and fasting blood sugar (i.e., two diagnostic criteria of metabolic syndrome) as well as glycosylated hemoglobin concentration. In conclusion, using MSE analysis for assessing CT variation successfully distinguished diabetic patients from healthy subjects. MSESS(CT) and MSELS(CT) therefore may serve as noninvasive tools for identifying subjects with diabetes and those at risk.

Cognitive and neuropsychiatric correlates of EEG dynamic complexity in patients with Alzheimer's disease

This study assessed the utility of multiscale entropy (MSE), a complexity analysis of biological signals, to identify changes in dynamics of surface electroencephalogram (EEG) in patients with Alzheimer's disease (AD) that was correlated to cognitive and behavioral dysfunction. A total of 108 AD patients were recruited and their digital EEG recordings were analyzed using MSE methods. We investigate the appropriate parameters and time scale factors for MSE calculation from EEG signals. We then assessed the within-subject consistency of MSE measures in different EEG epochs and correlations of MSE measures to cognitive and neuropsychiatric symptoms of AD patients. Increased severity of AD was associated with decreased MSE complexity as measured by short-time scales, and with increased MSE complexity as measured by long-time scales. MSE complexity in EEGs of the temporal and occipitoparietal electrodes correlated significantly with cognitive function. MSE complexity of EEGs in various brain areas was also correlated to subdomains of neuropsychiatric symptoms. MSE analysis revealed abnormal EEG complexity across short- and long-time scales that were correlated to cognitive and neuropsychiatric assessments. The MSE-based EEG complexity analysis may provide a simple and cost-effective method to quantify the severity of cognitive and neuropsychiatric symptoms in AD patients.

The APOE ɛ4 allele affects complexity and functional connectivity of resting brain activity in healthy adults

The apolipoprotein E (APOE) gene is associated with structural and functional brain changes. We have used multiscale entropy (MSE) analysis to detect changes in the complexity of resting blood oxygen level-dependent (BOLD) signals associated with aging and cognitive function. In this study, we further hypothesized that the APOE genotype may affect the complexity of spontaneous BOLD activity in younger and older adults, and such altered complexity may be associated with certain changes in functional connectivity. We conducted a resting-state functional magnetic resonance imaging experiment in a cohort of 100 younger adults (aged 20-39 years; mean 27.2 ± 4.3 years; male/female: 53/47) and 112 older adults (aged 60-79 years; mean 68.4 ± 6.5 years; male/female: 54/58), and applied voxelwise MSE analysis to assess the main effect of APOE genotype on resting-state BOLD complexity and connectivity. Although the main effect of APOE genotype on BOLD complexity was not observed in younger group, we observed that older APOE ɛ4 allele carriers had significant reductions in BOLD complexity in precuneus and posterior cingulate regions, relative to noncarriers. We also observed that reduced BOLD complexity in precuneus and posterior cingulate regions was associated with increased functional connectivity to the superior and inferior frontal gyrus in the older group. These results support the compensatory recruitment hypothesis in older APOE ɛ4 carriers, and confer the impact of the APOE genotype on the temporal dynamics of brain activity in older adults.

Multiscale cross-approximate entropy analysis as a measurement of complexity between ECG R-R interval and PPG pulse amplitude series among the normal and diabetic subjects

Physiological signals often show complex fluctuation (CF) under the dual influence of temporal and spatial scales, and CF can be used to assess the health of physiologic systems in the human body. This study applied multiscale cross-approximate entropy (MC-ApEn) to quantify the complex fluctuation between R-R intervals series and photoplethysmography amplitude series. All subjects were then divided into the following two groups: healthy upper middle-aged subjects (Group 1, age range: 41-80 years, n = 27) and upper middle-aged subjects with type 2 diabetes (Group 2, age range: 41-80 years, n = 24). There are significant differences of heart rate variability, LHR, between Groups 1 and 2 (1.94 ± 1.21 versus 1.32 ± 1.00, P = 0.031). Results demonstrated differences in sum of large scale MC-ApEn (MC-ApEn(LS)) (5.32 ± 0.50 versus 4.74 ± 0.78, P = 0.003). This parameter has a good agreement with pulse-pulse interval and pulse amplitude ratio (PAR), a simplified assessment for baroreflex activity. In conclusion, this study employed the MC-ApEn method, integrating multiple temporal and spatial scales, to quantify the complex interaction between the two physical signals. The MC-ApEn(LS) parameter could accurately reflect disease process in diabetics and might be another way for assessing the autonomic nerve function.

Is mental illness complex? From behavior to brain

A defining but elusive feature of the human brain is its astonishing complexity. This complexity arises from the interaction of numerous neuronal circuits that operate over a wide range of temporal and spatial scales, enabling the brain to adapt to the constantly changing environment and to perform various amazing mental functions. In mentally ill patients, such adaptability is often impaired, leading to either ordered or random patterns of behavior. Quantification and classification of these abnormal human behaviors exhibited during mental illness is one of the major challenges of contemporary psychiatric medicine. In the past few decades, attempts have been made to apply concepts adopted from complexity science to better understand complex human behavior. Although considerable effort has been devoted to studying the abnormal dynamic processes involved in mental illness, unfortunately, the primary features of complexity science are typically presented in a form suitable for mathematicians, physicists, and engineers; thus, they are difficult for practicing psychiatrists or neuroscientists to comprehend. Therefore, this paper introduces recent applications of methods derived from complexity science for examining mental illness. We propose that mental illness is loss of brain complexity and the complexity of mental illness can be studied under a general framework by quantifying the order and randomness of dynamic macroscopic human behavior and microscopic neuronal activity. Additionally, substantial effort is required to identify the link between macroscopic behaviors and microscopic changes in the neuronal dynamics within the brain.

Multiscale cross-approximate entropy analysis as a measure of complexity among the aged and diabetic

Complex fluctuations within physiological signals can be used to evaluate the health of the human body. This study recruited four groups of subjects: young healthy subjects (Group 1, n = 32), healthy upper middle-aged subjects (Group 2, n = 36), subjects with well-controlled type 2 diabetes (Group 3, n = 31), and subjects with poorly controlled type 2 diabetes (Group 4, n = 24). Data acquisition for each participant lasted 30 minutes. We obtained data related to consecutive time series with R-R interval (RRI) and pulse transit time (PTT). Using multiscale cross-approximate entropy (MCE), we quantified the complexity between the two series and thereby differentiated the influence of age and diabetes on the complexity of physiological signals. This study used MCE in the quantification of complexity between RRI and PTT time series. We observed changes in the influences of age and disease on the coupling effects between the heart and blood vessels in the cardiovascular system, which reduced the complexity between RRI and PTT series.

Novel application of a multiscale entropy index as a sensitive tool for detecting subtle vascular abnormalities in the aged and diabetic

Although previous studies have shown the successful use of pressure-induced reactive hyperemia as a tool for the assessment of endothelial function, its sensitivity remains questionable. This study aims to investigate the feasibility and sensitivity of a novel multiscale entropy index (MEI) in detecting subtle vascular abnormalities in healthy and diabetic subjects. Basic anthropometric and hemodynamic parameters, serum lipid profiles, and glycosylated hemoglobin levels were recorded. Arterial pulse wave signals were acquired from the wrist with an air pressure sensing system (APSS), followed by MEI and dilatation index (DI) analyses. MEI succeeded in detecting significant differences among the four groups of subjects: healthy young individuals, healthy middle-aged or elderly individuals, well-controlled diabetic individuals, and poorly controlled diabetic individuals. A reduction in multiscale entropy reflected age- and diabetes-related vascular changes and may serve as a more sensitive indicator of subtle vascular abnormalities compared with DI in the setting of diabetes.

Complexity of spontaneous BOLD activity in default mode network is correlated with cognitive function in normal male elderly: a multiscale entropy analysis

The nonlinear properties of spontaneous fluctuations in blood oxygen level-dependent (BOLD) signals remain unexplored. We test the hypothesis that complexity of BOLD activity is reduced with aging and is correlated with cognitive performance in the elderly. A total of 99 normal older and 56 younger male subjects were included. Cognitive function was assessed using Cognitive Abilities Screening Instrument and Wechsler Digit Span Task. We employed a complexity measure, multiscale entropy (MSE) analysis, and investigated appropriate parameters for MSE calculation from relatively short BOLD signals. We then compared the complexity of BOLD signals between the younger and older groups, and examined the correlation between cognitive test scores and complexity of BOLD signals in various brain regions. Compared with the younger group, older subjects had the most significant reductions in MSE of BOLD signals in posterior cingulate gyrus and hippocampal cortex. For older subjects, MSE of BOLD signals from default mode network areas, including hippocampal cortex, cingulate cortex, superior and middle frontal gyrus, and middle temporal gyrus, were found to be positively correlated with major cognitive functions, such as attention, orientation, short-term memory, mental manipulation, and language. MSE from subcortical regions, such as amygdala and putamen, were found to be positively correlated with abstract thinking and list-generating fluency, respectively. Our findings confirmed the hypothesis that complexity of BOLD activity was correlated with aging and cognitive performance based on MSE analysis, and may provide insights on how dynamics of spontaneous brain activity relates to aging and cognitive function in specific brain regions.

Multiscale entropy analysis of pulse wave velocity for assessing atherosclerosis in the aged and diabetic

This study proposed a dynamic pulse wave velocity (PWV)-based biomedical parameter in assessing the degree of atherosclerosis for the aged and diabetic populations. Totally, 91 subjects were recruited from a single medical institution between July 2009 and October 2010. The subjects were divided into four groups: young healthy adults (Group 1, n = 22), healthy upper middle-aged adults (Group 2, n = 28), type 2 diabetics with satisfactory blood sugar control (Group 3, n = 21), and unsatisfactory blood sugar control (Group 4, n = 20). A self-developed six-channel electrocardiography (ECG)-PWV-based equipment was used to acquire 1000 successive recordings of PWV(foot) values within 30 min. The data, thus, obtained were analyzed with multiscale entropy (MSE). Large-scale MSE index (MEI(LS)) was chosen as the assessment parameter. Not only did MEI(LS) successfully differentiate between subjects in Groups 1 and 2, but it also showed a significant difference between Groups 3 and 4. Compared with the conventional parameter of PWV(foot) and MEI on R-R interval [i.e., MEI(RRI)] in evaluating the degree of atherosclerotic change, the dynamic parameter, MEI(LS) (PWV), could better reflect the impact of age and blood sugar control on the progression of atherosclerosis.

Clustering heart rate dynamics is associated with β-adrenergic receptor polymorphisms: analysis by information-based similarity index

Background

Genetic polymorphisms in the gene encoding the β-adrenergic receptors (β-AR) have a pivotal role in the functions of the autonomic nervous system. Using heart rate variability (HRV) as an indicator of autonomic function, we present a bottom-up genotype–phenotype analysis to investigate the association between β-AR gene polymorphisms and heart rate dynamics.

Methods

A total of 221 healthy Han Chinese adults (59 males and 162 females, aged 33.6±10.8 years, range 19 to 63 years) were recruited and genotyped for three common β-AR polymorphisms: β₁-AR Ser49Gly, β₂-AR Arg16Gly and β₂-AR Gln27Glu. Each subject underwent two hours of electrocardiogram monitoring at rest. We applied an information-based similarity (IBS) index to measure the pairwise dissimilarity of heart rate dynamics among study subjects.

Results

With the aid of agglomerative hierarchical cluster analysis, we categorized subjects into major clusters, which were found to have significantly different distributions of β₂-AR Arg16Gly genotype. Furthermore, the non-randomness index, a nonlinear HRV measure derived from the IBS method, was significantly lower in Arg16 homozygotes than in Gly16 carriers. The non-randomness index was negatively correlated with parasympathetic-related HRV variables and positively correlated with those HRV indices reflecting a sympathovagal shift toward sympathetic activity.

Conclusions

We demonstrate a bottom-up categorization approach combining the IBS method and hierarchical cluster analysis to detect subgroups of subjects with HRV phenotypes associated with β-AR polymorphisms. Our results provide evidence that β₂-AR polymorphisms are significantly associated with the acceleration/deceleration pattern of heart rate oscillation, reflecting the underlying mode of autonomic nervous system control.

Acute increase of complexity in the neurocardiovascular dynamics following carotid stenting

OBJECTIVE

Percutaneous carotid angioplasty and stenting (CAS) has been used to improve cerebral circulation and autoregulation. However, whether CAS ameliorates the autonomic regulatory dynamics remains unclear. This prospective study examines the neurocardiovascular dynamics following carotid stenting.

METHODS

Thirty minutes electrocardiograms were recorded at three different time points (pre-operative, 1-h post-operative, 1-day post-operative) on twelve male patients (mean age 70.8 ± 9.6 years) receiving unilateral primary CAS. The HR data were analyzed by the conventional heart rate variability (HRV) and the multiscale entropy (MSE) methods; the former associates with autonomic activities and the latter quantifies the regulatory complexity of heart beat intervals. Loss of complexity at multiple scales has been associated with decoupled regulatory network in vivo.

RESULTS

Conventional HRV indices showed no difference after CAS. Complexity indices increased significantly on scales 2-8 at 1-h and on scales 2-3 1-day post-treatment. The lower scale MSE (1-5) correlated with the frequency components of conventional HRV indices. The increased complexity could imply a restoration of the neurocardiovascular dynamics on the path to a healthier state.

CONCLUSIONS

Primary CAS can induce a recovery in the neurocardiovascular regulatory dynamics in patients with high-grade carotid stenosis.