Olga N. Pavlova

Multiresolution analysis of pathological changes in cerebral venous dynamics in newborn mice with intracranial hemorrhage: adrenorelated vasorelaxation

Intracranial hemorrhage (ICH) is the major problem of modern neonatal intensive care. Abnormalities of cerebral venous blood flow (CVBF) can play a crucial role in the development of ICH in infants. The mechanisms underlying these pathological processes remain unclear; however it has been established that the activation of the adrenorelated vasorelaxation can be an important reason. Aiming to reach a better understanding of how the adrenodependent relaxation of cerebral veins contributes to the development of ICH in newborns, we study here the effects of pharmacological stimulation of adrenorelated dilation of the sagittal sinus by isoproterenol on the cerebral venous hemodynamics. Our study is performed in newborn mice at different stages of ICH using the laser speckle contrast imaging and wavelet analysis of the vascular dynamics of CVBF. We show that the dilation of the sagittal sinus with the decreased velocity of blood flow presides to the stress-induced ICH in newborn mice. These morphofunctional vascular changes are accompanied by an increased variance of the wavelet-coefficients in the areas of endothelial and non-endothelial (KATP-channels activity of vascular muscle) sympathetic components of the CVBF variability. Changes in the cerebral venous hemodynamics at the latent stage of ICH are associated with a high responsiveness of the sagittal sinus to isoproterenol quantifying by wavelet-coefficients related to a very slow region of the frequency domain. The obtained results certify that a high activation of the adrenergic-related vasodilatory responses to severe stress in newborn mice can be one of the important mechanisms underlying the development of ICH. Thus, the venous insufficiency with the decreased blood outflow from the brain associated with changes in the endothelial and the sympathetic components of CVBF-variability can be treated as prognostic criteria for the risk of ICH during the first days after birth.

Characterizing multimode interaction in renal autoregulation

The purpose of this paper is to demonstrate how modern statistical techniques of non-stationary time-series analysis can be used to characterize the mutual interaction among three coexisting rhythms in nephron pressure and flow regulation. Besides a relatively fast vasomotoric rhythm with a period of 5-8 s and a somewhat slower mode arising from an instability in the tubuloglomerular feedback mechanism, we also observe a very slow mode with a period of 100-200 s. Double-wavelet techniques are used to study how the very slow rhythm influences the two faster modes. In a broader perspective, the paper emphasizes the significance of complex dynamic phenomena in the normal and pathological function of physiological systems and discusses how simulation methods can help to understand the underlying biological mechanisms. At the present there is no causal explanation of the very slow mode. However, vascular oscillations with similar frequencies have been observed in other tissues.

WAVELET-BASED ANALYSIS OF CEREBROVASCULAR DYNAMICS IN NEWBORN RATS WITH INTRACRANIAL HEMORRHAGES

Intracranial hemorrhage (IH) is a major problem of neonatal intensive care. The incidence of IH is typically asymptomatic and cannot be effectively detected by standard diagnostic methods. The mechanisms underlying IH are unknown but there is evidence that stress-induced disorders in adrenergic regulation of cerebral venous blood flow (CVBF) are among the main reasons. Quantitative and qualitative assessment of CVBF could significantly advance understanding of the nature of IH in newborns. In this work, we analyze variations of CVBF in newborn rats with an experimental model of stress-induced IH and adrenaline injection. Our analysis is based on the Doppler optical coherence tomography (DOCT) and a proposed adaptive wavelet-based approach that provides sensitive markers of abnormal reactions of the sagittal vein to external factors. The obtained results demonstrate that the incidence of IH in newborn rats is accompanied by a suppression of CVBF with the development of venous insufficiency and areactivity to adrenaline. We introduce a numerical measure θ, quantifying reactions of CVBF and show that the values θ < 1.23 estimated in the low-frequency (LF) spectral range corresponding to the sympathicus indicate abnormal reactions associated with the development of IH. We conclude that the revealed areactivity of the cerebral veins to adrenaline represents a possible mechanism responsible for pathological changes in CVBF.

Quantifying chaotic dynamics from integrate-and-fire processes

Characterizing chaotic dynamics from integrate-and-fire (IF) interspike intervals (ISIs) is relatively easy performed at high firing rates. When the firing rate is low, a correct estimation of Lyapunov exponents (LEs) describing dynamical features of complex oscillations reflected in the IF ISI sequences becomes more complicated. In this work we discuss peculiarities and limitations of quantifying chaotic dynamics from IF point processes. We consider main factors leading to underestimated LEs and demonstrate a way of improving numerical determining of LEs from IF ISI sequences. We show that estimations of the two largest LEs can be performed using around 400 mean periods of chaotic oscillations in the regime of phase-coherent chaos. Application to real data is discussed.

The effect of L-NAME on intra- and inter-nephron synchronization.

Kidney autoregulation involves complicated intra- and inter-nephron synchronization phenomena among oscillatory modes produced, respectively, by the tubuloglomerular feedback (TGF) mechanism and by the myogenic regulation of the afferent arteriolar blood flow. The present study aims at examining to what extent these phenomena are reflected in the overall blood flow to the kidney and how they are affected by intravenous administration of nitro-l-arginine-methyl-ester (L-NAME), a potent NO synthesis inhibitor. Wavelet analysis is applied to detect rhythmic activity at the level of the renal artery and compare the observed fluctuations with blood flow variations recorded from efferent arterioles of individual nephrons. We show that administration of L-NAME increases the gain in both the TGF and the myogenic oscillations, and that both normotensive and hypertensive rats demonstrate reduced stability of the various rhythms. This implies that L-NAME, besides strengthening the gain in the individual feedback mechanisms, also causes more frequent transitions among the various synchronization states. In a broader perspective the purpose of the study is to demonstrate the significance of complex dynamic phenomena in the normal regulation of physiological systems as well as in their response to drugs.

Characterizing scaling properties of complex signals with missed data segments using the multifractal analysis

The scaling properties of complex processes may be highly influenced by the presence of various artifacts in experimental recordings. Their removal produces changes in the singularity spectra and the Hölder exponents as compared with the original artifacts-free data, and these changes are significantly different for positively correlated and anti-correlated signals. While signals with power-law correlations are nearly insensitive to the loss of significant parts of data, the removal of fragments of anti-correlated signals is more crucial for further data analysis. In this work, we study the ability of characterizing scaling features of chaotic and stochastic processes with distinct correlation properties using a wavelet-based multifractal analysis, and discuss differences between the effect of missed data for synchronous and asynchronous oscillatory regimes. We show that even an extreme data loss allows characterizing physiological processes such as the cerebral blood flow dynamics.

Multifractal spectrum of physiological signals: a mechanism-related approach

In this paper we discuss an approach for mechanism-related analysis of physiological signals performed with the wavelet-based multifractal formalism. This approach assumes estimation of the singularity spectrum for the band-pass filtered processes at different physiological conditions in order to provide explanation of the occurred changes in the Hölder exponents and the multi-fractality degree. We illustrate the considered approach using two examples, namely, the dynamics of the cerebral blood flow (CBF) and the electrical activity of the brain.

Detrended fluctuation analysis of cerebral venous dynamics in newborn mice with intracranial hemorrhage

We study pathological changes in cerebral venous dynamics in newborn mice using the laser speckle contrast imaging and the detrended fluctuation analysis with a special attention to the latent stage of the development of the intracranial hemorrhage. We show that this stage is characterized by a high responsiveness of the sagittal sinus to pharmacological stimulations of adrenorelated dilation. We conclude that this effect can be considered as an important mechanism underlying the development of ICH in newborns.

Cerebral venous dynamics in newborn mice with intracranial hemorrhage studied using wavelets

We investigate the stress-induced development of the intracranial hemorrhage in newborn mice with the main attention to its latent stage. Our study is based on the laser speckle contrast imaging of the cerebral venous blood flow and the wavelet-based analysis of experimental data. We study responses of the sagittal sinus in different frequency ranges associated with distinct regulatory mechanisms and discuss significant changes of the spectral power in the frequency area associated with the NO-related endothelial function.

Power-law statistics of neurophysiological processes analyzed using short signals

We discuss the problem of quantifying power-law statistics of complex processes from short signals. Based on the analysis of electroencephalograms (EEG) we compare three interrelated approaches which enable characterization of the power spectral density (PSD) and show that an application of the detrended fluctuation analysis (DFA) or the wavelet-transform modulus maxima (WTMM) method represents a useful way of indirect characterization of the PSD features from short data sets. We conclude that despite DFA- and WTMM-based measures can be obtained from the estimated PSD, these tools outperform the standard spectral analysis when characterization of the analyzed regime should be provided based on a very limited amount of data.