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Recently, it has been argued that entropy can be a direct measure of complexity, where the smaller value of entropy indicates lower system complexity, while its larger value indicates higher system complexity. We dispute this view and propose a universal measure of complexity that is based on Gell-Mann&rsquo, s view of complexity. Our universal measure of complexity is based on a non-linear transformation of time-dependent entropy, where the system state with the highest complexity is the most distant from all the states of the system of lesser or no complexity. We have shown that the most complex is the optimally mixed state consisting of pure states, i.e., of the most regular and most disordered which the space of states of a given system allows. A parsimonious paradigmatic example of the simplest system with a small and a large number of degrees of freedom is shown to support this methodology. Several important features of this universal measure are pointed out, especially its flexibility (i.e., its openness to extensions), suitability to the analysis of system critical behaviour, and suitability to study the dynamic complexity.

Electrophysiology provides a real-time readout of neural functions and network capability in different brain states, on temporal (fractions of milliseconds) and spatial (micro, meso, and macro) scales unmet by other methodologies. However, current international guidelines do not endorse the use of electroencephalographic (EEG)/magnetoencephalographic (MEG) biomarkers in clinical trials performed in patients with Alzheimer's disease (AD), despite a surge in recent validated evidence. This position paper of the ISTAART Electrophysiology Professional Interest Area endorses consolidated and translational electrophysiological techniques applied to both experimental animal models of AD and patients, to probe the effects of AD neuropathology (i.e., brain amyloidosis, tauopathy, and neurodegeneration) on neurophysiological mechanisms underpinning neural excitation/inhibition and neurotransmission as well as brain network dynamics, synchronization, and functional connectivity, reflecting thalamocortical and corticocortical residual capacity. Converging evidence shows relationships between abnormalities in EEG/MEG markers and cognitive deficits in groups of AD patients at different disease stages. The supporting evidence for the application of electrophysiology in AD clinical research as well as drug discovery pathways warrants an international initiative to include the use of EEG/MEG biomarkers in the main multicentric projects planned in AD patients, to produce conclusive findings challenging the present regulatory requirements and guidelines for AD studies. (C) 2019 Elsevier Inc. All rights reserved.

Noise has been recognized as an important factor in a range of physical and informational systems, including the elementary physics of life, such as cellular developments and functions. Here we use a stochastic differential equation to study the effects of noise on a typical system of antagonist actors, the Schlogl model. The phenomena of noise-induced bifurcation are observed. Detailed analysis demonstrates that the region revealing bistability can be modulated by the intensity of noise. This suggests that an external noise source can serve as an engineering tool for controlling antagonist inter-reacting systems in general, and in particular, for manipulating biochemical pathways.

Objective The head-up tilt (HUT) test, which provokes transient dynamical alterations in the regulation of cardiovascular system, provides insights into complex organization of this system. Based on signals with heart period intervals (RR-intervals) and/or systolic blood pressure (SBP), differences in the cardiovascular regulation between vasovagal patients (VVS) and the healthy people group (CG) are investigated. Approach Short-term relations among signal data represented symbolically by three-beat patterns allow to qualify and quantify the complexity of the cardiovascular regulation by Shannon entropy. Four types of patterns: permutation, ordinal, deterministic and dynamical, are used, and different resolutions of signal values in the the symbolization are applied in order to verify how entropy of patterns depends on a way in which values of signals are preprocessed. Main results At rest, in the physiologically important signal resolution ranges, independently of the type of patterns used in estimates, the complexity of SBP signals in VVS is different from the complexity found in CG. Entropy of VVS is higher than CG what could be interpreted as substantial presence of noisy ingredients in SBP of VVS. After tilting this relation switches. Entropy of CG occurs significantly higher than VVS for SBP signals. In the case of RR-intervals and large resolutions, the complexity after the tilt becomes reduced when compared to the complexity of RR-intervals at rest for both groups. However, in the case of VVS patients this reduction is significantly stronger than in CG. Significance Our observations about opposite switches in entropy between CG and VVS might support a hypothesis that baroreflex in VVS affects stronger the heart rate because of the inefficient regulation (possibly impaired local vascular tone alternations) of the blood pressure.

Biological regulation is sufficiently complex to pose an enduring challenge for characterization of both its equilibrium and transient non-equilibrium dynamics. Two univariate but coupled observables, heart rate and systolic blood pressure, are commonly characterized in the benchmark example of the human cardiovascular regulatory system. Asymmetric distributions of accelerations and decelerations of heart rate, as well as rises and falls in systolic blood pressure, recorded in humans during a head-up tilt test provide insights into the dynamics of cardiovascular response to a rapid, controlled deregulation of the system’s homeostasis. The baroreflex feedback loop is assumed to be the fundamental physiological mechanism for ensuring homeostatic blood supply to distant organs at rest and during orthostatic stress, captured in a classical beat-to-beat autoregressive model of baroreflex by de Boer et al. (1987). For model corroboration, a multistructure index statistic is proposed, seamlessly evaluating the size spectrum of magnitudes of neural reflexes such as baroreflex, responsible for maintaining the homeostatic dynamics. The multistructure index exposes a distinctly different dynamics of multiscale asymmetry between results obtained from real-life signals recorded from healthy subjects and those simulated using both the classical and perturbed versions of the model. Nonlinear effects observed suggest the pronounced presence of complex mechanisms resulting from baroreflex regulation when a human is at rest, which is aggravated in the system’s response to orthostatic stress. Using our methodology of multistructure index, we therefore show a marked difference between model and real-life scenarios, which we attribute to multiscale asymmetry of non-linear origin in real-life signals, which we are not reproducible by the classical model.

A network representation of time intervals between subsequent heart contractions is proposed as a way to qualify and quantify short-term dynamical patterns, obtained from ECG recordings. It allows the observation of the development of arrhythmia in a non-invasive and patient-oriented way. As an example, a network is constructed from the nocturnal Holter recordings of a male heart transplant patient, who had been in good overall health for 17 years after the surgery. The biatrial surgical technique applied to him led to interference between the recipient and donor pacemakers, which resulted in misleading information provided by standard heart rate variability measures. The noticeable differences in the network structure observed in the patient follow-up study serve to reveal and quantify changes in the leading mechanisms underlying the dynamics of heart contractions.

We provide evidence that catastrophic bifurcation breakdowns or transitions, preceded by early warning signs such as flickering phenomena, are present on notoriously unpredictable financial markets. For this we construct robust indicators of catastrophic dynamical slowing down and apply these to identify hallmarks of dynamical catastrophic bifurcation transitions. This is done using daily closing index records for the representative examples of financial markets of small and mid to large capitalisations experiencing a speculative bubble induced by the worldwide financial crisis of 2007-08.

There are evidences that the human right atrium and sinoatrial node (SAN) are functionally separated except at discrete SAN-atrial electrical junctions. We hypothesize that such anatomy could be a source of re-entry around the SAN. A model was developed which reconstructs the human right atrium anatomy. The activity of a myocyte was simulated by a timed automaton with continuous and discrete transitions reproducing the known stages of the action potential of the cellular membrane. A stochastic 2D network of timed automata was designed to model the grid of the right atrium. Discrete modeling allowed us to specify directly the rate of SAN to atrium pathways. Also, the influence of (1) atrial tissue fibrosis, via probability for transversal intercellular network connections, and of (2) impairment of individual cells via probability of a cell to excite, were controlled. The simulations provided a critical relationship between atrial anatomy and the rhythm of heart excitations. It occurred that at probability of 1/8 of SAN-atrium pathways (randomly chosen), the occurrence of the normal rhythm attained the highest probability - close to 1, in large intervals for the density of transversal intercellular connections, and for the levels of cellular impairment.

We hypothesize that increase with age of short-term measures of heart rhythm [a way in which accelerations and decelerations appear], especially dynamical pattern indices [Transition Rates (S T ) or self-Transfer Entropy (sTE)], provide indicates for unhealthy autonomic activity or cardiac tissue remodeling in elderly people. Hence they can help in early recognition of arrhythmogenesis processes. Based on properties of heart rhythm of 190 healthy persons, grouped into their age decade, we have found that both S T and sTE are efficient separators for discerning elderly people with erratic rhythm. The values in minimum in S T = 2.4 and minimum in sTE = 0.27, obtained by square function approximation, have been used to divide subjects of 70s and 80s into two groups. For all entropic measures these groups are different (p < 0.001, in t-test or Man-Whitney in case normality test failed). The fragmentation metrics [based on statistics of signs of heart rate changes] PPP and PAS also distingushed these groups though at greater p- value, and PSS yielded that the groups are identical. The minima of these function did not give satisfactory division of elderly into groups. Concluding, frequent changes in heart rate acceleration sign and size are the best signature for anomalous levels of short-term heart rate variability in elderly people.

Empirical time series of interevent or waiting times are investigated using a modified Multifractal Detrended Fluctuation Analysis operating on fluctuations of mean detrended dynamics. The core of the extended multifractal analysis is the nonmonotonic behavior of the generalized Hurst exponent h(q)-the fundamental exponent in the study of multifractals. The consequence of this behavior is the nonmonotonic behavior of the coarse Holder exponent α(q) leading to multibranchedness of the spectrum of dimensions. The Legendre-Fenchel transform is used instead of the routinely used canonical Legendre (single-branched) contact transform. Thermodynamic consequences of the multibranched multifractality are revealed. These are directly expressed in the language of phase transitions between thermally stable, metastable, and unstable phases. These phase transitions are of the first and second orders according to Mandelbrot's modified Ehrenfest classification. The discovery of multibranchedness is tantamount in significance to extending multifractal analysis.

We propose a novel semi-automatic approach to design biomarkers for capturing pharmacodynamic effects induced by pharmacological agents on the spectral power of electroencephalography (EEG) recordings. We apply this methodology to investigate the pharmacodynamic effects of methylphenidate (MPH) and atomoxetine (ATX) on attention deficit/hyperactivity disorder (ADHD), using rodent models. We inject the two agents into the spontaneously hypertensive rat (SHR) model of ADHD, the Wistar-Kyoto rat (WKY), and the Wistar rat (WIS), and record their EEG patterns. To assess individual EEG patterns quantitatively, we use an integrated methodological approach, which consists of calculating the mean, slope and intercept parameters of temporal records of EEG spectral power using a smoothing filter, outlier truncation, and linear regression. We apply Fisher discriminant analysis (FDA) to identify dominant discriminants to be heuristically consolidated into several new composite biomarkers. Results of the analysis of variance (ANOVA) and t-test show benefits in pharmacodynamic parameters, especially the slope parameter. Composite biomarker evaluation confirms their validity for genetic model stratification and the effects of the pharmacological agents used. The methodology proposed is of generic use as an approach to investigating thoroughly the dynamics of the EEG spectral power.

SummaryIntroduction: We present a software framework which enables the extension of current methods for the assessment of cognitive fitness using recent technological advances.Background: Screening for cognitive impairment is becoming more important as the world’s population grows older. Current methods could be enhanced by use of computers. Introduction of new methods to clinics requires basic tools for collection and communication of collected data.Objectives: To develop tools that, with minimal interference, offer new opportunities for the enhancement of the current interview based cognitive examinations.Methods: We suggest methods and discuss process by which established cognitive tests can be adapted for data collection through digitization by pen enabled tablets. We discuss a number of methods for evaluation of collected data, which promise to increase the resolution and objectivity of the common scoring strategy based on visual inspection. By involving computers in the roles of both instructing and scoring, we aim to increase the precision and reproducibility of cognitive examination.Results: The tools provided in Python framework CogExTools available at http://bsp.brain.riken.jp/cogextools/ enable the design, application and evaluation of screening tests for assessment of cognitive impairment. The toolbox is a research platform; it represents a foundation for further collaborative development by the wider research community and enthusiasts. It is free to download and use, and open-source.Conclusion: We introduce a set of open-source tools that facilitate the design and development of new cognitive tests for modern technology. We provide these tools in order to enable the adaptation of technology for cognitive examination in clinical settings. The tools provide the first step in a possible transition toward standardized mental state examination using computers.

Background We analyze the dynamics of rodent EEG amplitude in an experiment accompanied by video recordings. Brain activity of animals is commonly acquired together with a video of behavior, but recordings are rarely combined in analysis. The data acquired is most commonly analyzed separately. To our knowledge, no study has used behavior to improve the analysis of EEG waveforms, specifically for artifact removal – other than through manual editing. Comparison with existing method(s) We explore two approaches: a traditional approach that relies on data preprocessing and artifact rejection by an expert; and an alternative approach that combines analysis of EEG with behavior extracted from video recordings. New method We use the level of activity extracted from the behavioral video as a measure of confidence in the acquired EEG waveform, and as a weighting factor in averaging and statistical comparisons. Results We find in analysis of the EEG that the two approaches lead to similar conclusions, but the analysis leveraging behavioral data achieves this while avoiding many subjective choices often required for artifact rejection and data preprocessing. Conclusions The methods we describe allow for the inclusion of all recorded data in the analysis, thereby making statistical tests more friendly to interpretation, and making the data processing transparent and reproducible.

Glucose dynamics measured in ambulatory settings are fluid in nature and exhibit substantial complexity. We recently showed that a long-range negative correlation of glucose dynamics, which is considered to reflect blood glucose controllability over a substantial period, is absent in patients with diabetes mellitus. This was demonstrated using detrended fluctuation analysis (DFA), a modified random-walk analysis method for the detection of long-range correlations. In the present study, we further assessed the relationships between the established clinical indices of glycemic or insulinogenic control of hemoglobin A(1c) (HbA(1c)), glycated albumin (GA), 1,5-anhydroglucitol, and urine C-peptide immunoreactivity and the recently proposed DFA-based indices obtained from continuous glucose monitoring in 104 Japanese diabetic patients. Significant correlations between the following parameters were observed: (1) HbA(1c) and the long-range scaling exponent α(2) (r = 0.236, P.05), (2) GA and α(2) (r = 0.254, P.05), (3) GA and the short-range scaling exponent α(1) (r = 0.233, P.05), and (4) urine C-peptide immunoreactivity and the mean glucose fluctuations (r = -0.294, P.01). Therefore, we concluded that increases in the long-range DFA scaling exponent, which are indicative of the lack of a long-range negative correlation in glucose dynamics, reflected abnormalities in average glycemic control as clinically determined using HbA(1c) and GA parameters.