Alexey N. Pavlov

Sorting of neural spikes: When wavelet based methods outperform principal component analysis

Sorting of the extracellularly recorded spikes is a basic prerequisite for analysis of the cooperative neural behavior and neural code. Fundamentally the sorting performance is defined by the quality of discriminative features extracted from spike waveforms. Here we discuss two features extraction approaches: principal component analysis (PCA), and wavelet transform (WT). We show that only when properly tuned to the data, the WT technique may outperform PCA. We present a novel method for extraction of spike features based on a combination of PCA and continuous WT. The method automatically tunes its WT part to the data structure making use of knowledge obtained by PCA. We demonstrate the method on simulated and experimental data sets.

Wavelets in Neuroscience

MathematicalMethods of Signal Processing in Neuroscience.- Brief Tour of Wavelet Theory.- Analysis of Single Neuron Recordings.- Classification of Neuronal Spikes from Extracellular Recordings.- Wavelet Approach to the Study of Rhythmic Neuronal Activity.- Time-Frequency Analysis of EEG: From Theory to Practice.- Automatic Diagnostics and Processing of EEG.- Conclusion.- Index.

Nephron blood flow dynamics measured by laser speckle contrast imaging

Tubuloglomerular feedback (TGF) has an important role in autoregulation of renal blood flow and glomerular filtration rate (GFR). Because of the characteristics of signal transmission in the feedback loop, the TGF undergoes self-sustained oscillations in single-nephron blood flow, GFR, and tubular pressure and flow. Nephrons interact by exchanging electrical signals conducted electrotonically through cells of the vascular wall, leading to synchronization of the TGF-mediated oscillations. Experimental studies of these interactions have been limited to observations on two or at most three nephrons simultaneously. The interacting nephron fields are likely to be more extensive. We have turned to laser speckle contrast imaging to measure the blood flow dynamics of 50-100 nephrons simultaneously on the renal surface of anesthetized rats. We report the application of this method and describe analytic techniques for extracting the desired data and for examining them for evidence of nephron synchronization. Synchronized TGF oscillations were detected in pairs or triplets of nephrons. The amplitude and the frequency of the oscillations changed with time, as did the patterns of synchronization. Synchronization may take place among nephrons not immediately adjacent on the surface of the kidney.

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.

Scaling features of texts, images and time series

In the given paper, we consider the scaling features of long letter sequences like human writings, discretized images and discretized financial data. Using several approaches we show that the symbolic strings and time series being analyzed have a complex multiscale structure and demonstrate different scalings for large and small fluctuations. We discuss complex phenomena in the scaling behavior of partition functions in the case of high frequency DAX-future data.

Diagnostic of cardio-vascular disease with help of largest Lyapunov exponent of RR-sequences

Abstract We suggest to present a discrete sequence of cardiointervals in the form of a smooth time dependence and for the given time series compute the largest Lyapunov exponent. Processing the database with RR-intervals of patients suffering from coronary artery disease (CAD) has shown that the largest Lyapunov exponent can be a diagnostic criteria allowing one to distinguish between different groups of patients with more confidence than the standard methods for time series processing accepted in cardiology.

Optical monitoring of stress-related changes in the brain tissues and vessels associated with hemorrhagic stroke in newborn rats

Stress is a major factor for a risk of cerebrovascular catastrophes. Studying of mechanisms underlying stress-related brain-injures in neonates is crucial for development of strategy to prevent of neonatal stroke. Here, using a model of sound-stress-induced intracranial hemorrhages in newborn rats and optical methods, we found that cerebral veins are more sensitive to the deleterious effect of stress than arteries and microvessels. The development of venous insufficiency with decreased blood outflow from the brain accompanied by hypoxia, reduction of complexity of venous blood flow and high production of beta-arrestin-1 are possible mechanisms responsible for a risk of neonatal hemorrhagic stroke.

Stability of neural firing in the trigeminal nuclei under mechanical whisker stimulation

Sensory information handling is an essentially nonstationary process even under a periodic stimulation. We show how the time evolution of ridges in the wavelet spectrum of spike trains can be used for quantification of the dynamical stability of the neuronal responses to a stimulus. We employ this method to study neuronal responses in trigeminal nuclei of the rat provoked by tactile whisker stimulation. Neurons from principalis (Pr5) and interpolaris (Sp5i) show the maximal stability at the intermediate (50 ms) stimulus duration, whereas Sp5o cells “prefer” shorter (10 ms) stimulation. We also show that neurons in all three nuclei can perform as stimulus frequency filters. The response stability of about 33% of cells exhibits low-pass frequency dynamics. About 57% of cells have band-pass dynamics with the optimal frequency at 5 Hz for Pr5 and Sp5i, and 4 Hz for Sp5o, and the remaining 10% show no prominent dependence on the stimulus frequency. This suggests that the neural coding scheme in trigeminal nuclei is not fixed, but instead it adapts to the stimulus characteristics.

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.

Coexistence of intermittencies in the neuronal network of the epileptic brain.

Intermittent behavior occurs widely in nature. At present, several types of intermittencies are known and well-studied. However, consideration of intermittency has usually been limited to the analysis of cases when only one certain type of intermittency takes place. In this paper, we report on the temporal behavior of the complex neuronal network in the epileptic brain, when two types of intermittent behavior coexist and alternate with each other. We prove the presence of this phenomenon in physiological experiments with WAG/Rij rats being the model living system of absence epilepsy. In our paper, the deduced theoretical law for distributions of the lengths of laminar phases prescribing the power law with a degree of -2 agrees well with the experimental neurophysiological data.