Rehabilitation Modulates High-Order Interactions Among Large-Scale Brain Networks in Subacute Stroke.

The recovery of motor functions after stroke is fostered by the functional integration of large-scale brain networks, including the motor network (MN) and high-order cognitive controls networks, such as the default mode (DMN) and executive control (ECN) networks. In this paper, electroencephalography signals are used to investigate interactions among these three resting state networks (RSNs) in subacute stroke patients after motor rehabilitation. A novel metric, the O-information rate (OIR), is used to quantify the balance between redundancy and synergy in the complex high-order interactions among RSNs, as well as its causal decomposition to identify the direction of information flow. The paper also employs conditional spectral Granger causality to assess pairwise directed functional connectivity between RSNs. After rehabilitation, a synergy increase among these RSNs is found, especially driven by MN. From the pairwise description, a reduced directed functional connectivity towards MN is enhanced after treatment. Besides, inter-network connectivity changes are associated with motor recovery, for which the mediation role of ECN seems to play a relevant role, both from pairwise and high-order interactions perspective.

Comparison between directed causal flow metrics for the assessment of resting-state EEG motor network connectivity in subacute stroke patients.

Isolated effective coherence (iCoh) is a measure of neural causal functional connectivity from EEG signals that was proven to overperform the Generalized Partial Directed Coherence (gPDC). However, iCoh sensitivity in the identification of reliable functional neural connections with respect to random links was not investigated. This study aims to compare the sensitivity of iCoh and gPDC with a statistical surrogates' approach. The cerebral motor network topology of a cohort of subjects in sub-acute stage after stroke was investigated. iCoh showed enhanced statistical discriminative power of the relevant connections within the motor network with respect to gPDC. This property influenced the assessment of ipsilesional intra-hemispheric topographic variations occurring in the population after a physical rehabilitation program.

Prediction of visual function from automatically quantified optical coherence tomography biomarkers in patients with geographic atrophy using machine learning.

Geographic atrophy (GA) is a vision-threatening manifestation of age-related macular degeneration (AMD), one of the leading causes of blindness globally. Objective, rapid, reliable, and scalable quantification of GA from optical coherence tomography (OCT) retinal scans is necessary for disease monitoring, prognostic research, and clinical endpoints for therapy development. Such automatically quantified biomarkers on OCT are likely to further elucidate structure-function correlation in GA and thus the pathophysiological mechanisms of disease development and progression. In this work, we aimed to predict visual function with machine-learning applied to automatically acquired quantitative imaging biomarkers in GA. A post-hoc analysis of data from a clinical trial and routine clinical care was conducted. A deep-learning automated segmentation model was applied on OCT scans from 476 eyes (325 patients) with GA. A separate machine learning prediction model (Random Forest) used the resultant quantitative OCT (qOCT) biomarkers to predict cross-sectional visual acuity under standard (VA) and low luminance (LLVA). The primary outcome was regression coefficient (r2) and mean absolute error (MAE) for cross-sectional VA and LLVA in Early Treatment Diabetic Retinopathy Study (ETDRS) letters. OCT parameters were predictive of VA (r2 0.40 MAE 11.7 ETDRS letters) and LLVA (r2 0.25 MAE 12.1). Normalised random forest feature importance, as a measure of the predictive value of the three constituent features of GA; retinal pigment epithelium (RPE)-loss, photoreceptor degeneration (PDR), hypertransmission and their locations, was reported both on voxel-level heatmaps and ETDRS-grid subfields. The foveal region (46.5%) and RPE-loss (31.1%) had greatest predictive importance for VA. For LLVA, however, non-foveal regions (74.5%) and PDR (38.9%) were most important. In conclusion, automated qOCT biomarkers demonstrate predictive significance for VA and LLVA in GA. LLVA is itself predictive of GA progression, implying that the predictive qOCT biomarkers provided by our model are also prognostic.

Are intravitreal injections essential during the COVID-19 pandemic? Global preferred practice patterns and practical recommendations.

Tertiary outpatient ophthalmology clinics are high-risk environments for COVID-19 transmission, especially retina clinics, where regular follow-up is needed for elderly patients with multiple comorbidities. Intravitreal injection therapy (IVT) for chronic macular diseases, is one of the most common procedures performed, associated with a significant burden of care because of the vigorous treatment regimen associated with multiple investigations. While minimizing the risk of COVID-19 infection transmission is a priority, this must be balanced against the continued provision of sight-saving ophthalmic care to patients at risk of permanent vision loss. This review aims to give evidence-based guidelines on managing IVT during the COVID-19 pandemic in common macular diseases such as age-related macular degeneration, diabetic macula edema and retinal vascular disease and to report on how the COVID-19 pandemic has affected IVT practices worldwide.To illustrate some real-world examples, 18 participants in the International Retina Collaborative, from 15 countries and across four continents, were surveyed regarding pre- and during- COVID-19 pandemic IVT practices in tertiary ophthalmic centers. The majority of centers reported a reduction in the number of appointments to reduce the risk of the spread of COVID-19 with varying changes to their IVT regimen to treat various macula diseases. Due to the constantly evolving nature of the COVID-19 pandemic, and the uncertainty about the normal resumption of health services, we suggest that new solutions for eye healthcare provision, like telemedicine, may be adopted in the future when we consider new long-term adaptations required to cope with the COVID-19 pandemic.

Input for baroreflex analysis: which blood pressure signal should be used?

The baroreflex (BR) is an important physiological regulatory mechanism which reacts to blood pressure perturbations with reflex changes of target variables such as the heart period (electrocardiogram derived RR interval) or the peripheral vascular resistance (PVR). Evaluation of cardiac chronotropic (RR as a target variable) and vascular resistance (target PVR) BR arms was in previous studies mainly based on the use of the spontaneous variability of the systolic or diastolic blood pressure (SBP, DBP), respectively, as the input signals. The use of other blood pressure measures such as the mean blood pressure (MBP) as an input signal for BR analysis is still under investigation. Making the assumption that the strength of coupling along the BR indicates the more appropriate input signal for baroreflex analysis, we employ partial spectral decomposition to assess in the frequency domain the causal coupling from SBP, MBP or DBP to RR or PVR. Noninvasive beat-to-beat recording of RR, SBP, MBP and DBP and PVR was performed in 39 and 36 volunteers in whom orthostatic and cognitive loads were evoked respectively through head-up tilt and mental arithmetic task. At rest, the MBP was most tightly coupled with RR, in contrast to the analysis of the vascular resistance BR arm where the results showed similar importance of all blood pressure input signals. During orthostasis, the increased importance of SBP as the input signal for BR analysis along the cardiac chronotropic arm was demonstrated. In addition, the gain from MBP to RR was more sensitive to physiological state changes compared to gains with SBP or DBP signal as inputs. We conclude that the coupling strength depends not only on the analysed baroreflex arm but also on the selection of the input blood pressure signal and the physiological state. The MBP signal should be more frequently used for the cardiac baroreflex analysis.

Vascular resistance arm of the baroreflex: methodology and comparison with the cardiac chronotropic arm.

Baroreflex response consists of cardiac chronotropic (effect on heart rate), cardiac inotropic (on contractility), venous (on venous return) and vascular (on vascular resistance) arms. Because of the simplicity of its measurement, the cardiac chronotropic arm is most often analyzed. The aim was to introduce a method to assess the vascular baroreflex arm and to characterize its changes during stress. We evaluated the effect of orthostasis and mental arithmetics (MA) in 39 (22 women, 17 men; median age: 18.7 yr) and 36 (21 women, 15 men; 19.2 yr) healthy volunteers, respectively. We recorded systolic (SBP) and mean (MBP) blood pressure by volume-clamp method and R-R interval (RR) by ECG. Cardiac output (CO) was recorded by impedance cardiography. From MBP and CO, peripheral vascular resistance (PVR) was calculated. The directional spectral coupling and gain of cardiac chronotropic (SBP to RR) and vascular (SBP to PVR) arms were quantified. The strength of the causal coupling from SBP to PVR was significantly higher than that of SBP to RR coupling over the whole protocol (P < 0.001). Along both arms, the coupling was higher during orthostasis compared with the supine position (P < 0.001 and P = 0.006); no MA effect was observed. No significant changes in the spectral gain (ratio of RR or PVR change to a unit SBP change) across all phases were found (0.111 ≤ P ≤ 0.907). We conclude that changes in PVR are tightly coupled with SBP oscillations via the baroreflex, providing an approach for baroreflex vascular arm analysis with the potential to reveal new aspects of blood pressure dysregulation.NEW & NOTEWORTHY Baroreflex response consists of several arms, but the cardiac chronotropic arm (blood pressure changes evoking heart rate response) is usually analyzed. This study introduces a method to assess the vascular baroreflex arm with the continuous noninvasive measurement of peripheral vascular resistance as an output considering causality in the interaction between oscillations and slower dynamics of vascular tone changes. We conclude that although vascular baroreflex arm involvement becomes dominant during orthostasis, gain of this interaction is relatively stable.

Quantification of Different Regulatory Pathways Contributing to Heartbeat Dynamics during Multiple Stimuli: a Proof of the Concept.

The dynamical interplay between brain and heart is mediated by several feedback mechanisms including the central autonomic network and baroreflex loop at a peripheral level, also for a short-term regulation. State of the art focused on the characterization of each regulatory pathway through a single stressor elicitation. However, no studies targeted the actual quantification of different mediating routes leading to the generation of heartbeat dynamics, particularly in case of combined exogenous stimuli. In this study, we propose a new approach based on computational modeling to quantify the contribution of multiple concurrent stimuli in modulating cardiovascular dynamics. In this preliminary attempt, the model estimates the high-frequency power of heartbeat dynamics, and derives disentangling coefficients quantifying the effect of multiple elicitations. Model evaluation is performed on healthy rate variability (HRV) series from fourteen healthy subjects undergoing physical (tilt-table) and mental stressors (aritmetics), as well as their combined administration. Results indicate that, at a group-wise level, in base of concurrent physical and mental elicitations, the physical stressor contributes for the 85% of the resulting heartbeat dynamics. These findings are in agreement with the current knowledge on heartbeat regulatory systems, providing valuable perspectives on the quantification of underlying generative mechanisms of HRV.

Information domain analysis of respiratory sinus arrhythmia mechanisms.

Ventilation related heart rate oscillations - respiratory sinus arrhythmia (RSA) - originate in human from several mechanisms. Two most important of them - the central mechanism (direct communication between respiratory and cardiomotor centers), and the peripheral mechanism (ventilation-associated blood pressure changes transferred to heart rate via baroreflex) have been described in previous studies. The major aim of this study was to compare the importance of these mechanisms in the generation of RSA non-invasively during various states by quantifying the strength of the directed interactions between heart rate, systolic blood pressure and respiratory volume signals. Seventy-eight healthy volunteers (32 male, age range: 16.02-25.77 years, median age: 18.57 years) participated in this study. The strength of mutual interconnections among the spontaneous beat-to-beat oscillations of systolic blood pressure (SBP), R-R interval (RR signal) and respiration (volume changes - RESP signal) was quantified during supine rest, orthostatic challenge (head-up tilt, HUT) and cognitive load (mental arithmetics, MA) using bivariate and trivariate measures of cardio-respiratory information transfer to separate baroreflex and nonbaroreflex (central) mechanisms. Our results indicate that both basic mechanisms take part in RSA generation in the intact cardiorespiratory control of human subjects. During orthostatic and mental challenges baroreflex based peripheral mechanism becomes more important.

Univariate and multivariate conditional entropy measures for the characterization of short-term cardiovascular complexity under physiological stress.

OBJECTIVE: A defining feature of physiological systems under the neuroautonomic regulation is their dynamical complexity. The most common approach to assess physiological complexity from short-term recordings, i.e. to compute the rate of entropy generation of an individual system by means of measures of conditional entropy (CE), does not consider that complexity may change when the investigated system is part of a network of physiological interactions. This study aims at extending the concept of short-term complexity towards the perspective of network physiology, defining multivariate CE measures whereby multiple physiological processes are accounted for in the computation of entropy rates. APPROACH: Univariate and multivariate CE measures are computed using state-of-the-art methods for entropy estimation and applied to time series of heart period (H), systolic (S) and diastolic (D) arterial pressure, and respiration (R) variability measured in healthy subjects monitored in a resting state and during conditions of postural and mental stress. MAIN RESULTS: Compared with the traditional univariate metric of short-term complexity, multivariate measures provide additional information with plausible physiological interpretation, such as (i) the dampening of respiratory sinus arrhythmia and activation of the baroreflex control during postural stress; (ii) the increased complexity of heart period and blood pressure variability during mental stress, reflecting the effect of respiratory influences and upper cortical centers; (iii) the strong influence of D on S, mediated by left ventricular ejection fraction and vascular properties; (iv) the role of H in reducing the complexity of D, related to cardiac run-off effects; and (v) the unidirectional role of R in influencing cardiovascular variability. SIGNIFICANCE: Our results document the importance of employing a network perspective in the evaluation of the short-term complexity of cardiovascular and respiratory dynamics across different physiological states.

Mutual information-based feature selection for low-cost BCIs based on motor imagery.

In the present study a feature selection algorithm based on mutual information (MI) was applied to electro-encephalographic (EEG) data acquired during three different motor imagery tasks from two dataset: Dataset I from BCI Competition IV including full scalp recordings from four subjects, and new data recorded from three subjects using the popular low-cost Emotiv EPOC EEG headset. The aim was to evaluate optimal channels and band-power (BP) features for motor imagery tasks discrimination, in order to assess the feasibility of a portable low-cost motor imagery based Brain-Computer Interface (BCI) system. The minimal sub set of features most relevant to task description and less redundant to each other was determined, and the corresponding classification accuracy was assessed offline employing linear support vector machine (SVM) in a 10-fold cross validation scheme. The analysis was performed: (a) on the original full Dataset I from BCI competition IV, (b) on a restricted channels set from Dataset I corresponding to available Emotiv EPOC electrodes locations, and (c) on data recorded with the EPOC system. Results from (a) showed that an offline classification accuracy above 80% can be reached using only 5 features. Limiting the analysis to EPOC channels caused a decrease of classification accuracy, although it still remained above chance level, both for data from (b) and (c). A top accuracy of 70% was achieved using 2 optimal features. These results encourage further research towards the development of portable low cost motor imagery-based BCI systems.

Extended Granger causality: a new tool to identify the structure of physiological networks.

Granger causality (GC) is a very popular tool for assessing the presence of directional interactions between two time series of a multivariate data set. In its original formulation, GC does not account for zero-lag correlations possibly existing between the observed time series. In the present study we compare the GC with a novel measure, termed extended GC (eGC), able to capture instantaneous causal relationships. We present a two-step procedure for the practical estimation of eGC based on first detecting the existence of zero-lag correlations, and then assigning them to one of the two possible causal directions using pairwise measures of non-Gaussianity. The proposed method was validated in a simulation study, showing that the estimation procedure based on the extended representation overcomes the limits of the classic computation of GC, correctly detecting the presence and direction of zero-lag interactions and providing a meaningful causal interpretation based on the eGC. Then, GC and eGC were computed on the physiological variability series of heart period (HP), mean arterial pressure (AP) and cerebral blood flow velocity (FV) in ten subjects with postural related syncope (PRS), during different epochs of an head-up tilt test protocol. We found that both measures reflect the baroreflex impairment and the loss of cerebral autoregulation during pre-syncope. Furthermore, eGC analysis suggests that fast, within-beat effects between AP and FV variability contribute substantially to the mutual regulation of these physiological variables, and may play an important role in the impairment of cerebrovascular regulation associated with PRS.

Linear and non-linear brain-heart and brain-brain interactions during sleep.

In this study, the physiological networks underlying the joint modulation of the parasympathetic component of heart rate variability (HRV) and of the different electroencephalographic (EEG) rhythms during sleep were assessed using two popular measures of directed interaction in multivariate time series, namely Granger causality (GC) and transfer entropy (TE). Time series representative of cardiac and brain activities were obtained in 10 young healthy subjects as the normalized high frequency (HF) component of HRV and EEG power in the δ, θ, α, σ, and ß bands, measured during the whole duration of sleep. The magnitude and statistical significance of GC and TE were evaluated between each pair of series, conditional on the remaining series, using respectively a linear model-based approach exploiting regression models, and a nonlinear model-free approach combining nearest-neighbor entropy estimation with a procedure for dimensionality reduction. The contribution of nonlinear dynamics to the TE was also assessed using surrogate data. GC and TE consistently detected structured networks of physiological interactions, with links directed predominantly from HRV to the EEG waves in the brain-heart network, and from the σ and ß EEG waves to the δ, θ, and α waves in the brain-brain network. While these common patterns supported the suitability of a linear model-based analysis, we also found a significant contribution of nonlinear dynamics, particularly involving the information transferred out of the δ node in the two networks. This suggested the importance of nonparametric TE estimation for evidencing the fine structure of the physiological networks underlying the autonomic regulation of cardiac and brain functions during sleep.

Blood flow velocity measured using the Retinal Function Imager predicts successful ranibizumab treatment in neovascular age-related macular degeneration: early prospective cohort study.

PURPOSE: Anti-VEGF treatment has a potent vasoconstrictive effect. Early changes of retinal blood flow velocity (RBFV) measured using the Retinal Function Imager (RFI) combined with indicators of vascular status may help in predicting the visual outcome 1 month post injection in patients with neovascular age-related macular degeneration (nvAMD) under ranibizumab treatment. To develop a simple prediction model based on the change in RBFV 3 days post injection and indicators of a patient's vascular status to assess the probability of a successful visual outcome 1 month post injection. METHODS: RBFV measured using RFI were prospectively collected pre-injection and 3 days post injection in 18 eyes of 15 patients. Indicators of vascular status (history of hypertension, diabetes mellitus without retinal affection, and smoking) were assessed by medical history. By univariate analyses, parameters associated with visual outcome were weighted (-1 to 6 points). A multivariate logistic regression model with the categorized visual outcome parameter (≥0 letters gained after 1 month) as the dependent variate and the sum score as the independent variate (continuous scale) was used to estimate the score value-specific probabilities of letters gained ≥0 1 month post injection. RESULTS: The indicators of vascular status negatively influenced the likelihood of a letter gain ≥0 whereas an increase in the arterial RBFV strongly increased it. The area under the receiver operating characteristics curve for these parameters investigated was 0.71 (95% CI: 0.43-1.00). CONCLUSION: Changes in the arterial RBFV following 3 days after ranibizumab injection combined with three indicators of the vascular status identified nvAMD patients with favorable visual outcome accurately.

Diagnostic accuracy of the Amsler grid and the preferential hyperacuity perimetry in the screening of patients with age-related macular degeneration: systematic review and meta-analysis.

OBJECTIVE: To clarify the screening potential of the Amsler grid and preferential hyperacuity perimetry (PHP) in detecting or ruling out wet age-related macular degeneration (AMD). EVIDENCE ACQUISITION: Medline, Scopus and Web of Science (by citation of reference) were searched. Checking of reference lists of review articles and of included articles complemented electronic searches. Papers were selected, assessed, and extracted in duplicate. EVIDENCE SYNTHESIS: Systematic review and meta-analysis. Twelve included studies enrolled 903 patients and allowed constructing 27 two-by-two tables. Twelve tables reported on the Amsler grid and its modifications, twelve tables reported on the PHP, one table assessed the MCPT and two tables assessed the M-charts. All but two studies had a case-control design. The pooled sensitivity of studies assessing the Amsler grid was 0.78 (95% confidence intervals; 0.64-0.87), and the pooled specificity was 0.97 (95% confidence intervals; 0.91-0.99). The corresponding positive and negative likelihood ratios were 23.1 (95% confidence intervals; 8.4-64.0) and 0.23 (95% confidence intervals; 0.14-0.39), respectively. The pooled sensitivity of studies assessing the PHP was 0.85 (95% confidence intervals; 0.80-0.89), and specificity was 0.87 (95% confidence intervals; 0.82-0.91). The corresponding positive and negative likelihood ratios were 6.7 (95% confidence intervals; 4.6-9.8) and 0.17 (95% confidence intervals; 0.13-0.23). No pooling was possible for MCPT and M-charts. CONCLUSION: Results from small preliminary studies show promising test performance characteristics both for the Amsler grid and PHP to rule out wet AMD in the screening setting. To what extent these findings can be transferred to a real clinic practice still needs to be established.

The Ca2+/Mn2+ ion-pump PMR1 links elevation of cytosolic Ca(2+) levels to α-synuclein toxicity in Parkinson's disease models.

Parkinson's disease (PD) is characterized by the progressive loss of dopaminergic neurons, which arises from a yet elusive concurrence between genetic and environmental factors. The protein α-synuclein (αSyn), the principle toxic effector in PD, has been shown to interfere with neuronal Ca(2+) fluxes, arguing for an involvement of deregulated Ca(2+) homeostasis in this neuronal demise. Here, we identify the Golgi-resident Ca(2+)/Mn(2+) ATPase PMR1 (plasma membrane-related Ca(2+)-ATPase 1) as a phylogenetically conserved mediator of αSyn-driven changes in Ca(2+) homeostasis and cytotoxicity. Expression of αSyn in yeast resulted in elevated cytosolic Ca(2+) levels and increased cell death, both of which could be inhibited by deletion of PMR1. Accordingly, absence of PMR1 prevented αSyn-induced loss of dopaminergic neurons in nematodes and flies. In addition, αSyn failed to compromise locomotion and survival of flies when PMR1 was absent. In conclusion, the αSyn-driven rise of cytosolic Ca(2+) levels is pivotal for its cytotoxicity and requires PMR1.

Assessing frequency domain causality in cardiovascular time series with instantaneous interactions.

BACKGROUND: The partial directed coherence (PDC) is commonly used to assess in the frequency domain the existence of causal relations between two time series measured in conjunction with a set of other time series. Although the multivariate autoregressive (MVAR) model traditionally used for PDC computation accounts only for lagged effects, instantaneous effects cannot be neglected in the analysis of cardiovascular time series. OBJECTIVES: We propose the utilization of an extended MVAR model for PDC computation, in order to improve the evaluation of frequency domain causality in the presence of zero-lag correlations among multivariate time series. METHODS: A procedure for the identification of a MVAR model combining instantaneous and lagged effects is introduced. The coefficients of the extended model are used to estimate an extended PDC (EPDC). EPDC is compared to the traditional PDC on a simulated MVAR process and on real cardiovascular variability series. RESULTS: Simulation results evidence that the presence of zero-lag correlations may produce misleading PDC profiles, while the correct causality patterns can be recovered using EPDC. Application on real data leads to spectral causality estimates which are better interpretable in terms of the known cardiovascular physiology using EPDC than PDC. CONCLUSIONS: This study emphasizes the necessity of including instantaneous effects in the MVAR model used for the computation of PDC in the presence of significant zero-lag correlations in multivariate time series.

Quantifying changes in EEG complexity induced by photic stimulation.

OBJECTIVES: This study aims to characterize EEG complexity, measured as the prediction error resulting from nonlinear prediction, in healthy humans during photic stimulation. METHODS: EEGs were recorded from 15 subjects with eyes closed (EC) and eyes open (EO), during the baseline condition and during stroboscopic photic stimulation (PS) at 5, 10, and 15 Hz. The mean squared prediction error (MSPE) resulting from nearest neighbor local linear prediction was taken as complexity index. Complexity maps were generated interpolating the MSPE index over a schematic scalp representation. RESULTS: Statistical analysis revealed that: i) EEG shows good predictability in all conditions and seems to be well explained by a linear stochastic process; ii) the complexity is lower with EC than with EO and increases significantly during PS, to a lesser extent during 10 Hz stimulation; iii) significant differences of EEG complexity are detectable between anterior-central and posterior scalp regions. CONCLUSIONS: Changes in EEG complexity during PS can be successfully assessed using nonlinear prediction. The observed modifications in the patterns of complexity seem to reflect neurophysiological behaviors and suggest future applicability of the method in clinical settings.

An integrated approach based on uniform quantization for the evaluation of complexity of short-term heart period variability: Application to 24 h Holter recordings in healthy and heart failure humans.

We propose an integrated approach based on uniform quantization over a small number of levels for the evaluation and characterization of complexity of a process. This approach integrates information-domain analysis based on entropy rate, local nonlinear prediction, and pattern classification based on symbolic analysis. Normalized and non-normalized indexes quantifying complexity over short data sequences ( approximately 300 samples) are derived. This approach provides a rule for deciding the optimal length of the patterns that may be worth considering and some suggestions about possible strategies to group patterns into a smaller number of families. The approach is applied to 24 h Holter recordings of heart period variability derived from 12 normal (NO) subjects and 13 heart failure (HF) patients. We found that: (i) in NO subjects the normalized indexes suggest a larger complexity during the nighttime than during the daytime; (ii) this difference may be lost if non-normalized indexes are utilized; (iii) the circadian pattern in the normalized indexes is lost in HF patients; (iv) in HF patients the loss of the day-night variation in the normalized indexes is related to a tendency of complexity to increase during the daytime and to decrease during the nighttime; (v) the most likely length L of the most informative patterns ranges from 2 to 4; (vi) in NO subjects classification of patterns with L=3 indicates that stable patterns (i.e., those with no variations) are more present during the daytime, while highly variable patterns (i.e., those with two unlike variations) are more frequent during the nighttime; (vii) during the daytime in HF patients, the percentage of highly variable patterns increases with respect to NO subjects, while during the nighttime, the percentage of patterns with one or two like variations decreases.

Causal transfer function analysis to describe closed loop interactions between cardiovascular and cardiorespiratory variability signals.

Although the concept of transfer function is intrinsically related to an input-output relationship, the traditional and widely used estimation method merges both feedback and feedforward interactions between the two analyzed signals. This limitation may endanger the reliability of transfer function analysis in biological systems characterized by closed loop interactions. In this study, a method for estimating the transfer function between closed loop interacting signals was proposed and validated in the field of cardiovascular and cardiorespiratory variability. The two analyzed signals x and y were described by a bivariate autoregressive model, and the causal transfer function from x to y was estimated after imposing causality by setting to zero the model coefficients representative of the reverse effects from y to x. The method was tested in simulations reproducing linear open and closed loop interactions, showing a better adherence of the causal transfer function to the theoretical curves with respect to the traditional approach in presence of non-negligible reverse effects. It was then applied in ten healthy young subjects to characterize the transfer functions from respiration to heart period (RR interval) and to systolic arterial pressure (SAP), and from SAP to RR interval. In the first two cases, the causal and non-causal transfer function estimates were comparable, indicating that respiration, acting as exogenous signal, sets an open loop relationship upon SAP and RR interval. On the contrary, causal and traditional transfer functions from SAP to RR were significantly different, suggesting the presence of a considerable influence on the opposite causal direction. Thus, the proposed causal approach seems to be appropriate for the estimation of parameters, like the gain and the phase lag from SAP to RR interval, which have a large clinical and physiological relevance.

Experimental approach for testing the uncoupling between cardiovascular variability series.

In cardiovascular variability analysis, the significance of the coupling between two series is commonly assessed by defining a zero level on the magnitude-squared coherence (MSC). Although the use of the conventional value of 0.5 does not consider the dependence of MSC estimates on the analysis parameters, a theoretical threshold Tt is available only for the weighted covariance (WC) estimator. In this study, an experimental threshold for zero coherence Te was derived by a statistical test from the sampling distribution of MSC estimated on completely uncoupled time series. MSC was estimated by the WC method (Parzen window, spectral bandwidth B = 0.015, 0.02, 0.025, 0.03 Hz) and by the parametric autoregressive (AR) method (model order M= 4, 8, 12, 16), on time series with length L = 180, 300, 420, 540 s. Te decreased with increasing B and L and with decreasing M (range: 0.11-0.54 for WC estimator, 0.06-0.46 for AR estimator). Values for the typical parameter settings of WC and AR estimation (B = 0.025 Hz; M = 8; L = 300 s) were, respectively, 0.24 and 0.17. Moreover, Tt was always higher (range: 0.12-0.65) and the results were less dependable than those for Te in defining the zero level of MSC. Thus, with the proposed method, the hypothesis of uncoupling is rejected by accounting for the parameters that affect the confidence of spectral and cross-spectral estimates. The broad applicability of this approach should favour its introduction for assessing the significance of the coupling between cardiovascular variability series.