Synaptic Communication in Diverse Astrocytic Connectivity: A Computational Model.

Astrocytes are recently considered active components in neural communication by modulating tripartite synaptic activity and the signaling mechanism facilitated by intercellular calcium wave (ICW) propagation. The heterogeneity in astrocytic connectivity produces diverse spatiotemporal signals equating to a diverse influence in synaptic communication. We developed a functional model of a neuron-astrocyte network consisting of tripartite synaptic interactions, gap-junction coupled astrocytic network, intra-, intercellular calcium diffusion, and varying topology to determine the effects of astrocytic connectivity to synaptic communication. The results suggest that the degree of astrocytic connectivity is vital in controlling the extrasynaptic glutamate to avoid disruption in synaptic communication.

Integrating nonlinear analysis and machine learning for human induced pluripotent stem cell-based drug cardiotoxicity testing.

Utilizing recent advances in human induced pluripotent stem cell (hiPSC) technology, nonlinear analysis and machine learning we can create novel tools to evaluate drug-induced cardiotoxicity on human cardiomyocytes. With cardiovascular disease remaining the leading cause of death globally it has become imperative to create effective and modern tools to test the efficacy and toxicity of drugs to combat heart disease. The calcium transient signals recorded from hiPSC-derived cardiomyocytes (hiPSC-CMs) are highly complex and dynamic with great degrees of response characteristics to various drug treatments. However, traditional linear methods often fail to capture the subtle variation in these signals generated by hiPSC-CMs. In this work, we integrated nonlinear analysis, dimensionality reduction techniques and machine learning algorithms for better classifying the contractile signals from hiPSC-CMs in response to different drug exposure. By utilizing extracted parameters from a commercially available high-throughput testing platform, we were able to distinguish the groups with drug treatment from baseline controls, determine the drug exposure relative to IC50 values, and classify the drugs by its unique cardiac responses. By incorporating nonlinear parameters computed by phase space reconstruction, we were able to improve our machine learning algorithm's ability to predict cardiotoxic levels and drug classifications. We also visualized the effects of drug treatment and dosages with dimensionality reduction techniques, t-distributed stochastic neighbor embedding (t-SNE). We have shown that integration of nonlinear analysis and artificial intelligence has proven to be a powerful tool for analyzing cardiotoxicity and classifying toxic compounds through their mechanistic action.

A multilayer-multiplexer network processing scheme based on the dendritic integration in a single neuron.

Advances in neuronal studies suggest that a single neuron can perform integration functions previously associated only with neuronal networks. Here, we proposed a dendritic abstraction employing a dynamic thresholding function that models the spatiotemporal dendritic integration process of a CA3 pyramidal neuron. First, we developed an input-output quantification process that considers the natural neuronal response and the full range of dendritic dynamics. We analyzed the IO curves and demonstrated that dendritic integration is branch-specific and dynamic rather than the commonly employed static nonlinearity. Second, we completed the integration model by creating a dendritic abstraction incorporating the spatiotemporal characteristics of the dendrites. Furthermore, we predicted the dendritic activity in each dendritic layer and the corresponding somatic firing activity by employing the dendritic abstraction in a multilayer-multiplexer information processing scheme comparable to a neuronal network. The subthreshold activity influences the suprathreshold regions via its dynamic threshold, a parameter that is dependent not only on the driving force but also on the number of activated synapses along the dendritic branch. An individual dendritic branch performs multiple integration modes by shifting from supralinear to linear then to sublinear. The abstraction includes synaptic input location-dependent voltage delay and decay, time-dependent linear summation, and dynamic thresholding function. The proposed dendritic abstraction can be used to create multilayer-multiplexer neurons that consider the spatiotemporal properties of the dendrites and with greater computational capacity than the conventional schemes.

Prediction of Cardiovascular Parameters With Supervised Machine Learning From Singapore "I" Vessel Assessment and OCT-Angiography: A Pilot Study.

Purpose: Assessment of cardiovascular risk is the keystone of prevention in cardiovascular disease. The objective of this pilot study was to estimate the cardiovascular risk score (American Hospital Association [AHA] risk score, Syntax risk, and SCORE risk score) with machine learning (ML) model based on retinal vascular quantitative parameters. Methods: We proposed supervised ML algorithm to predict cardiovascular parameters in patients with cardiovascular diseases treated in Dijon University Hospital using quantitative retinal vascular characteristics measured with fundus photography and optical coherence tomography - angiography (OCT-A) scans (alone and combined). To describe retinal microvascular network, we used the Singapore "I" Vessel Assessment (SIVA), which extracts vessel parameters from fundus photography and quantitative OCT-A retinal metrics of superficial retinal capillary plexus. Results: The retinal and cardiovascular data of 144 patients were included. This paper presented a high prediction rate of the cardiovascular risk score. By means of the Naïve Bayes algorithm and SIVA + OCT-A data, the AHA risk score was predicted with 81.25% accuracy, the SCORE risk with 75.64% accuracy, and the Syntax score with 96.53% of accuracy. Conclusions: Performance of these algorithms demonstrated in this preliminary study that ML algorithms applied to quantitative retinal vascular parameters with SIVA software and OCT-A were able to predict cardiovascular scores with a robust rate. Quantitative retinal vascular biomarkers with the ML strategy might provide valuable data to implement predictive model for cardiovascular parameters. Translational Relevance: Small data set of quantitative retinal vascular parameters with fundus and with OCT-A can be used with ML learning to predict cardiovascular parameters.

A Computational Study on Synaptic Plasticity Regulation and Information Processing in Neuron-Astrocyte Networks.

Postsynaptic ionotropic receptors critically shape synaptic currents and underpin their activity-dependent plasticity. In recent years, regulation of expression of these receptors by slow inward and outward currents mediated by gliotransmitter release from astrocytes has come under scrutiny as a potentially important mechanism for the regulation of synaptic information transfer. In this study, we consider a model of astrocyte-regulated synapses to investigate this hypothesis at the level of layered networks of interacting neurons and astrocytes. Our simulations hint that gliotransmission sustains the transfer function across layers, although it decorrelates the neuronal activity from the signal pattern. Overall, our results make clear how astrocytes could transform neuronal activity by inducing a lowfrequency modulation of postsynaptic activity.

Spatiotemporal model of tripartite synapse with perinodal astrocytic process.

Information transfer may not be limited only to synapses. Therefore, the processes and dynamics of biological neuron-astrocyte coupling and intercellular interaction within this domain are worth investigating. Existing models of tripartite synapse consider an astrocyte as a point process. Here, we extended the tripartite synapse model by considering the astrocytic processes (synaptic and perinodal) as compartments. The scattered extrinsic signals in the extracellular space and the presence of calcium stores in different astrocytic sites create local transient [Ca2+]. We investigated the Ca2+ dynamics and found that the increase in astrocytic intracellular [Ca2+] enhances the probability of neurotransmitter release. However, the period in which the extrasynaptic glutamate lingers in the extracellular space may cause excitotoxicity. We propose further biological investigation on intercellular communication, considering that unconventional sources (nonsynaptic) of glutamate may improve information processing in neuron-astrocyte networks.

Quantification of Contractile Dynamic Complexities Exhibited by Human Stem Cell-Derived Cardiomyocytes Using Nonlinear Dimensional Analysis.

Understanding the complexity of biological signals has been gaining widespread attention due to increasing knowledge on the nonlinearity that exists in these systems. Cardiac signals are known to exhibit highly complex dynamics, consisting of high degrees of interdependency that regulate the cardiac contractile functions. These regulatory mechanisms are important to understand for the development of novel in vitro cardiac systems, especially with the exponential growth in deriving cardiac tissue directly from human induced pluripotent stem cells (hiPSCs). This work describes a unique analytical approach that integrates linear amplitude and frequency analysis of physical cardiac contraction, with nonlinear analysis of the contraction signals to measure the signals' complexity. We generated contraction motion waveforms reflecting the physical contraction of hiPSC-derived cardiomyocytes (hiPSC-CMs) and implemented these signals to nonlinear analysis to compute the capacity and correlation dimensions. These parameters allowed us to characterize the dynamics of the cardiac signals when reconstructed into a phase space and provided a measure of signal complexity to supplement contractile physiology data. Thus, we applied this approach to evaluate drug response and observed that relationships between contractile physiology and dynamic complexity were unique to each tested drug. This illustrated the applicability of this approach in not only characterization of cardiac signals, but also monitoring and diagnostics of cardiac health in response to external stress.

Quantitatively characterizing drug-induced arrhythmic contractile motions of human stem cell-derived cardiomyocytes.

Quantification of abnormal contractile motions of cardiac tissue has been a noteworthy challenge and significant limitation in assessing and classifying the drug-induced arrhythmias (i.e., Torsades de pointes). To overcome these challenges, researchers have taken advantage of computational image processing tools to measure contractile motion from cardiomyocytes derived from human induced pluripotent stem cells (hiPSC-CMs). However, the amplitude and frequency analysis of contractile motion waveforms does not produce sufficient information to objectively classify the degree of variations between two or more sets of cardiac contractile motions. In this paper, we generated contractile motion data from beating hiPSC-CMs using motion tracking software based on optical flow analysis, and then implemented a computational algorithm, phase space reconstruction (PSR), to derive parameters (embedding, regularity, and fractal dimensions) to further characterize the dynamic nature of the cardiac contractile motions. Application of drugs known to cause cardiac arrhythmia induced significant changes to these resultant dimensional parameters calculated from PSR analysis. Integrating this new computational algorithm with the existing analytical toolbox of cardiac contractile motions will allow us to expand current assessments of cardiac tissue physiology into an automated, high-throughput, and quantifiable manner which will allow more objective assessments of drug-induced proarrhythmias.

Wolff-Parkinson-White (WPW) syndrome: the detection of delta wave in an electrocardiogram (ECG).

The delta wave remains an important indicator to diagnose the WPW syndrome. In this paper, a new method of detection of delta wave in an ECG signal is proposed. Firstly, using the continuous wavelet transform, the P wave, the QRS complex and the T wave are detected, then their durations are computed after determination of the boundary location (onsets and offsets of the P, QRS and T waves). Secondly, the PR duration, the QRS duration and the upstroke of the QRS complex are used to determine the presence or absence of the delta wave. This algorithm has been tested on the Physionel database (ptbdb) in order to evaluate its robustness. It has been applied to clinical signals from patients affected by WPW syndrome. This method can provide assistance to practitioners in order to detect the WPW syndrome.

In vitro arrhythmia generation by mild hypothermia: a pitchfork bifurcation type process.

The neurological damage after cardiac arrest presents a huge challenge for hospital discharge. Therapeutic hypothermia (34 °C - 32 °C) has shown its benefits in reducing cerebral oxygen demand and improving neurological outcomes after cardiac arrest. However, it can have many adverse effects, among them cardiac arrhythmia generation which represents an important part (up to 34%, according different clinical studies). A monolayer cardiac culture is prepared with cardiomyocytes from a newborn rat, directly on a multi-electrode array, which allows the acquisition of the extracellular potential of the culture. The temperature range is 37 °C - 30 °C-37 °C, representing the cooling and rewarming process of therapeutic hypothermia. Experiments showed that at 35 °C, the acquired signals are characterized by period-doubling phenomenon, compared with signals at other temperatures. Spiral waves, commonly considered to be a sign of cardiac arrhythmia, are observed in the reconstructed activation map. With an approach from nonlinear dynamics, phase space reconstruction, it is shown that at 35 °C, the trajectories of these signals formed a spatial bifurcation, even trifurcation. Another transit point is found between 30 °C-33 °C, which agreed with other clinical studies that induced hypothermia after cardiac arrest should not fall below 32 °C. The process of therapeutic hypothermia after cardiac arrest can be represented by a pitchfork bifurcation type process, which could explain the different ratios of arrhythmia among the adverse effects after this therapy. This nonlinear dynamic suggests that a variable speed of cooling/rewarming, especially when passing 35 °C, would help to decrease the ratio of post-hypothermia arrhythmia and then improve the hospital output.

Phase space reconstruction of an experimental model of cardiac field potential in normal and arrhythmic conditions.

Cardiac arrhythmias are one of the most important death causes in the world. Compared to the numerical models, the experimental ones provide a more realistic tool to study the mechanisms of cardiac arrhythmias. The in vitro culture of cardiac cells developed on the Multi-Electrodes Array (MEA) constitutes a suitable model in this context. The extracellular field potential (EFP) acquired from the MEA can be used to measure the electrophysiological parameters of action potential. In this article, the stability of this experimental model is investigated using the phase space reconstruction in normal and in arrhythmia conditions. The results show that the embedding dimension of signal EFP changed slightly in both cases (normal conditions and arrhythmia). The parameter time lag τ in the normal conditions is lower than in the arrhythmia. The shape of attractors remains similar but disturbed in case of arrhythmia compared to the normal conditions.

Impact of bipolar electrodes contact on fractionation index measurement.

Signals such as Complex Fractionated Atrial Electrograms (CFAE) are tracked during ablation procedures to locate the arrhythmical substrate regions. Most of CFAE classification tools use fractionation indexes. However, recordings from intracardiac catheter depend on electrode contact quality. This paper investigates the impact of electrode contact area on fractionation indexes. It is assessed through three kinds of arrhythmical activations resulting from a numerical simulation of a small piece of the cardiac tissue. Bipolar electrograms are extracted corresponding to 25 different contact areas and fractionation indexes (Shannon entropy, non linear energy operator and maximum peak ratio) are computed. Results yield that the Shannon entropy offers a good potential discrimination between arrhythmic scenarios and is less sensitive to the electrode contact variation.

Detection of complex fractionated atrial electrograms using recurrence quantification analysis.

Atrial fibrillation (AF) is the most common cardiac arrhythmia but its proarrhythmic substrate remains unclear. Reentrant electrical activity in the atria may be responsible for AF maintenance. Over the last decade, different catheter ablation strategies targeting the electrical substrate of the left atrium have been developed in order to treat AF. Complex fractionated atrial electrograms (CFAEs) recorded in the atria may represent not only reentry mechanisms, but also a large variety of bystander electrical wave fronts. In order to identify CFAE involved in AF maintenance as a potential target for AF ablation, we have developed an algorithm based on nonlinear data analysis using recurrence quantification analysis (RQA). RQA features make it possible to quantify hidden structures in a signal and offer clear representations of different CFAE types. Five RQA features were used to qualify CFAE areas previously tagged by a trained electrophysiologist. Data from these analyzes were used by two classifiers to detect CFAE periods in a signal. While a single feature is not sufficient to properly detect CFAE periods, the set of five RQA features combined with a classifier were highly reliable for CFAE detection.

Recurrence Quantification Analysis as a tool for complex fractionated atrial electrogram discrimination.

Atrial fibrillation is the most encountered pathology of the heart rate. The reasons of its occurrence and its particular characteristics remain unknown, resulting from complex phenomena interaction. From these interactions emerges Complex Fractionated Atrial Electrograms (CFAE) which are useful for the ablation procedure. This study presents a method based on nonlinear data analysis, the Recurrence Quantification Analysis (RQA) applied on intracardiac atrial electrograms to detect CFAE particularities. The results obtained on areas previously tagged by a cardilogist show a good sensitivity to CFAE. Combination of RQA features offers a larger discrimination potential for future automated detection.

Comparison of complex fractionated atrial electrograms at cellular scale using numerical and experimental models.

This study investigates the existence of the pseudo complex fractionated atrial electrogram (CFAE) at cellular level. Our assumptions are based on the fact that CFAEs are linked to the generation of the spiral waves. These are created using a numerical model and an experimental model of in vitro culture of neonatal rats cardiac cells. Pseudo bipolar electrograms resulting from these two models are compared qualitatively and some patterns could be identified as CFAE signature.

Spiral wave induced numerically using electrical stimulation and comparison with experimental results.

Experiments in vitro on a Microelectrode Array (MEA) platform show that electrical stimulation can provoke the generation of spiral waves in cardiac tissue. Nevertheless, the conditions leading to this artificial fibrillation state remain unclear. In order to have a better understanding of this phenomenon, a numerical simulation study has been conducted. The results obtained with a two-dimensional FitzHugh-Nagumo model proved that it is possible to create spiral waves by adding a stimulation current under certain conditions, which are made explicit.

Computation of the electrical potential inside the nerve induced by an electrical stimulus.

The aim is to investigate the activation conditions of the different nerves which control the bladder. The selective stimulation of the nerve fibers depends on electrode configuration and intensity of applied current. The goal of this study is to compute the electrical potential inside the nerve due to an applied boundary currents. A symmetrically cylindrical model, representing the geometry and electrical conductivity of a nerve surrounded by a connective tissue and a cuff is used. In the quasistatic approximation, the problem can be modeled by a Poisson equation with Neumann boundary conditions. A symmetric boundary integral formulation is discretized using mixed finite elements. We can thus compute an electrical potential distribution depending on the electrode configuration and the applied current inside a nerve. Our results show that the distribution of the electrical potential inside a nerve or a fascicle depends on the geometry of the electrode and the shape of the applied current.

Experimental study of electrical FitzHugh-Nagumo neurons with modified excitability.

We present an electronical circuit modelling a FitzHugh-Nagumo neuron with a modified excitability. To characterize this basic cell, the bifurcation curves between stability with excitation threshold, bistability and oscillations are investigated. An electrical circuit is then proposed to realize a unidirectional coupling between two cells, mimicking an inter-neuron synaptic coupling. In such a master-slave configuration, we show experimentally how the coupling strength controls the dynamics of the slave neuron, leading to frequency locking, chaotic behavior and synchronization. These phenomena are then studied by phase map analysis. The architecture of a possible neural network is described introducing different kinds of coupling between neurons.