No abstract

Synchronization or phase-locking between oscillating neuronal groups is considered to be important for coordination of information among cortical networks. Spectral coherence is a commonly used approach to quantify phase locking between neural signals. We systematically explored the validity of spectral coherence measures for quantifying synchronization among neural oscillators. To that aim, we simulated coupled oscillatory signals that exhibited synchronization dynamics using an abstract phase-oscillator model as well as interacting gamma-generating spiking neural networks. We found that, within a large parameter range, the spectral coherence measure deviated substantially from the expected phase-locking. Moreover, spectral coherence did not converge to the expected value with increasing signal-to-noise ratio. We found that spectral coherence particularly failed when oscillators were in the partially (intermittent) synchronized state, which we expect to be the most likely state for neural synchronization. The failure was due to the fast frequency and amplitude changes induced by synchronization forces. We then investigated whether spectral coherence reflected the information flow among networks measured by transfer entropy (TE) of spike trains. We found that spectral coherence failed to robustly reflect changes in synchrony-mediated information flow between neural networks in many instances. As an alternative approach we explored a phase-locking value (PLV) method based on the reconstruction of the instantaneous phase. As one approach for reconstructing instantaneous phase, we used the Hilbert Transform (HT) preceded by Singular Spectrum Decomposition (SSD) of the signal. PLV estimates have broad applicability as they do not rely on stationarity, and, unlike spectral coherence, they enable more accurate estimations of oscillatory synchronization across a wide range of different synchronization regimes, and better tracking of synchronization-mediated information flow among networks.

This study introduces singular spectrum decomposition (SSD), a new adaptive method for decomposing nonlinear and nonstationary time series in narrow-banded components. The method takes its origin from singular spectrum analysis (SSA), a nonparametric spectral estimation method used for analysis and prediction of time series. Unlike SSA, SSD is a decomposition method in which the choice of fundamental parameters has been completely automated. This is achieved by focusing on the frequency content of the signal. In particular, this holds for the choice of the window length used to generate the trajectory matrix of the data and for the selection of its principal components for the reconstruction of a specific component series. Moreover, a new definition of the trajectory matrix with respect to the standard SSA allows the oscillatory content in the data to be enhanced and guarantees decrease of energy of the residual. Through the numerical examples and simulations, the SSD method is shown to be able to accurately retrieve different components concealed in the data, minimizing at the same time the generation of spurious components. Applications on time series from both the biological and the physical domain are also presented highlighting the capability of SSD to yield physically meaningful components.

Furlan R. Lateralization of expression of neural sympathetic activity to the vessels and effects of carotid baroreceptor stimulation. Am J Physiol Heart Circ Physiol 296: H1758 -H1765, 2009. First published April 10, 2009 doi:10.1152/ajpheart.01045.2008.-Human studies suggest that cardiovascular neural sympathetic control is predominantly modulated by the right cerebral hemisphere. It is unknown whether post-ganglionic sympathetic activity [muscle sympathetic nerve activity (MSNA)] shows any functional asymmetry. Eight right-handed volunteers (3 women and 5 men, 32 Ϯ 2 yr of age) underwent ECG, beat-by-beat blood pressure, respiratory activity, and simultaneous right and left MSNA recordings during spontaneous and controlled breathing (CB, 15 breaths/min, 0.25 Hz). Dynamic carotid baroreceptor stimulation was obtained by 0.1-Hz sinusoidal suction, from 0 to Ϫ50 mmHg, randomly applied to the right, left, and combined right and left sides of the neck during CB. Laterality was assessed by changes in the MSNA burst rate (in bursts/min, and bursts/100 beats), strength [amplitude (A) and area (AA)], and the oscillatory component at 0.1 Hz during baroreceptor stimulation. Amplitude parameters were normalized by CB burst mean amplitude and area of the same side. At rest, the right and left MSNA burst rate and total MSNA activity were similar. Conversely, the right MSNA normalized burst AN (1.36 Ϯ 0.18) and AAN (1.31 Ϯ 0.16) were larger than the left MSNA AN (1.04 Ϯ 0.09) and AAN (1.02 Ϯ 0.08). Unilateral and bilateral carotid baroreflex stimulation abolished the right prevalence of AN and AAN. In conclusion, the right lateralization of sympathetic activity to the vessels is indicated by normalized burst strength parameters of bilateral MSNA recordings at rest during spontaneous breathing. Carotid baroreceptor stimulation disrupted such expression of MSNA lateralization possibly by disturbing the synchronizing action of right cerebral hemisphere. sympathetic control of circulation; muscle sympathetic nerve activity recording; burst amplitude; area; laterality SEVERAL HUMAN STUDIES support the concept that the neural sympathetic activity regulating the cardiovascular system undergoes a predominant modulation exerted by the right cerebral hemisphere (2, 4, 10, 17-19, 33, 34). The right side of the medulla was also found to be involved in cardioacceleration in vagotomized animals (16). However, it has to be pointed out that the large majority of these investigations focused on functional modifications of the sympathetic target organs, i.e., on changes of heart rate and blood pressure and their variability, and did not focus on a direct measure of right and left efferent sympathetic activity.Two studies (27, 28) addressed the problem of the potential dissimilarity between the right and left neural efferent sympathetic activity by recording simultaneously muscle sympathetic nerve activity (MSNA) in both peroneal nerves in humans. Sundlof and Wallin (27) focused on burst rate, which was found to be similar in the right and le...

Background/Objective: Current severe traumatic brain injury (TBI) outcome prediction models calculate the chance of unfavourable outcome after 6 months based on parameters measured at admission. We aimed to improve current models with the addition of continuously measured neuromonitoring data within the first 24 h after intensive care unit neuromonitoring. Methods: Forty-five severe TBI patients with intracranial pressure/cerebral perfusion pressure monitoring from two teaching hospitals covering the period May 2012 to January 2019 were analysed. Fourteen high-frequency physiological parameters were selected over multiple time periods after the start of neuromonitoring (0-6 h, 0-12 h, 0-18 h, 0-24 h). Besides systemic physiological parameters and extended Corticosteroid Randomisation after Significant Head Injury (CRASH) score, we added estimates of (dynamic) cerebral volume, cerebral compliance and cerebrovascular pressure reactivity indices to the model. A logistic regression model was trained for each time period on selected parameters to predict outcome after 6 months. The parameters were selected using forward feature selection. Each model was validated by leave-one-out cross-validation. Results: A logistic regression model using CRASH as the sole parameter resulted in an area under the curve (AUC) of 0.76. For each time period, an increased AUC was found using up to 5 additional parameters. The highest AUC (0.90) was found for the 0-6 h period using 5 parameters that describe mean arterial blood pressure and physiological cerebral indices. Conclusions: Current TBI outcome prediction models can be improved by the addition of neuromonitoring bedside parameters measured continuously within the first 24 h after the start of neuromonitoring. As these factors might be modifiable by treatment during the admission, testing in a larger (multicenter) data set is warranted.

A novel automated approach to quantitatively evaluate the degree of spatio-temporal organization in the atrial activity (AA) during atrial fibrillation (AF) from surface recordings, obtained from body surface potential maps (BSPM), is presented. AA organization is assessed by measuring the reflection of the spatial complexity and temporal stationarity of the wavefront patterns propagating inside the atria on the surface ECG, by means of principal component analysis (PCA). Complexity and stationarity are quantified through novel parameters describing the structure of the mixing matrices derived by the PCA of the different AA segments across the BSPM recording. A significant inverse correlation between complexity and stationarity is highlighted by this analysis. The discriminatory power of the parameters in identifying different groups in the set of patients under study is also analyzed. The obtained results present analogies with earlier invasive studies in terms of number of significant components necessary to describe 95% of the variance in the AA (four for more organized AF, and eight for more disorganized AF). These findings suggest that automated analysis of AF organization exploiting spatial diversity in surface recordings is indeed possible, potentially leading to an improvement in clinical decision making and AF treatment.

Electrocardiographic imaging (ECGI) is a technique to reconstruct non-invasively the electrical activity on the heart surface from body-surface potential recordings and geometric information of the torso and the heart. ECGI has shown scientific and clinical value when used to characterize and treat both atrial and ventricular arrhythmias. Regarding atrial fibrillation (AF), the characterization of the electrical propagation and the underlying substrate favoring AF is inherently more challenging than for ventricular arrhythmias, due to the progressive and heterogeneous nature of the disease and its manifestation, the small volume and wall thickness of the atria, and the relatively large role of microstructural abnormalities in AF. At the same time, ECGI has the advantage over other mapping technologies of allowing a global characterization of atrial electrical activity at every atrial beat and non-invasively. However, since ECGI is time-consuming and costly and the use of electrical mapping to guide AF ablation is still not fully established, the clinical value of ECGI for AF is still under assessment. Nonetheless, AF is known to be the manifestation of a complex interaction between electrical and structural abnormalities and therefore, true electro-anatomical-structural imaging may elucidate important key factors of AF development, progression, and treatment. Therefore, it is paramount to identify which clinical questions could be successfully addressed by ECGI when it comes to AF characterization and treatment, and which questions may be beyond its technical limitations. In this manuscript we review the questions that researchers have tried to address on the use of ECGI for AF characterization and treatment guidance (for example, localization of AF triggers and sustaining mechanisms), and we discuss the technological requirements and validation. We address experimental and clinical results, limitations, and future challenges for fruitful application of ECGI for AF understanding and management. We pay attention to existing techniques and clinical application, to computer models and (animal or human) experiments, to challenges of methodological and clinical validation. The overall objective of the study is to provide a consensus on valuable directions that ECGI research may take to provide future improvements in AF characterization and treatment guidance.

Assessment of AF complexity from 12-lead ECGs significantly improves prediction of successful PCV and progression to persistent AF compared with common clinical and echocardiographic predictors.