Influence of cardiac tissue anisotropy on re-entrant activation in computational models of ventricular fibrillation

The aim of this study was to establish the role played by anisotropic diffusion in (i) the number of filaments and epicardial phase singularities that sustain ventricular fibrillation in the heart, (ii) the lifetimes of filaments and phase singularities, and (iii) the creation and annihilation dynamics of filaments and phase singularities. A simplified monodomain model of cardiac tissue was used, with membrane excitation described by a simplified 3-variable model. The model was configured so that a single re-entrant wave was unstable, and fragmented into multiple re-entrant waves. Re-entry was then initiated in tissue slabs with varying anisotropy ratio. The main findings of this computational study are: (i) anisotropy ratio influenced the number of filaments Sustaining simulated ventricular fibrillation, with more filaments present in simulations with smaller values of transverse diffusion coefficient, (ii) each re-entrant filament was associated with around 0.9 phase singularities on the surface of the slab geometry, (iii) phase singularities were longer lived than filaments, and (iv) the creation and annihilation of filaments and phase singularities were linear functions of the number of filaments and phase singularities, and these relationships were independent of the anisotropy ratio. This study underscores the important role played by tissue anisotropy in cardiac ventricular fibrillation

Fractal analysis of resting state functional connectivity of the brain

A variety of resting state neuroimaging data tend to exhibit fractal behavior where its power spectrum follows power-law scaling. Resting state functional connectivity is significantly influenced by fractal behavior which may not directly originate from neuronal population activities of the brain. To describe the fractal behavior, we adopted the fractionally integrated process (FIP) model instead of the fractional Gaussian noise (FGN) since the FIP model covers more general aspects of fractality than the FGN model. We also introduce a novel concept called the nonfractal connectivity which is defined as the correlation of short memory independent of fractal behavior, and compared it with the fractal connectivity which is an asymptotic wavelet correlation. We propose several wavelet-based estimators of fractal connectivity and nonfractal connectivity for a multivariate fractionally integrated noise (mFIN). The performance of these estimators was evaluated through simulation studies and the analyses of resting state functional MRI data of the rat brain.Comment: The 2012 International Joint Conference on Neural Network

Dynamical mechanism of atrial fibrillation: a topological approach

While spiral wave breakup has been implicated in the emergence of atrial fibrillation, its role in maintaining this complex type of cardiac arrhythmia is less clear. We used the Karma model of cardiac excitation to investigate the dynamical mechanisms that sustain atrial fibrillation once it has been established. The results of our numerical study show that spatiotemporally chaotic dynamics in this regime can be described as a dynamical equilibrium between topologically distinct types of transitions that increase or decrease the number of wavelets, in general agreement with the multiple wavelets hypothesis. Surprisingly, we found that the process of continuous excitation waves breaking up into discontinuous pieces plays no role whatsoever in maintaining spatiotemporal complexity. Instead this complexity is maintained as a dynamical balance between wave coalescence -- a unique, previously unidentified, topological process that increases the number of wavelets -- and wave collapse -- a different topological process that decreases their number.Comment: 15 pages, 14 figure

Flexible microelectrode arrays to interface epicardial electrical signals with intracardial calcium transients in zebrafish hearts

The zebrafish (Danio rerio) is an emerging genetic model for regenerative medicine. In humans, myocardial infarction results in the irreversible loss of cardiomyocytes. However, zebrafish hearts fully regenerate after a 20% ventricular resection, without either scarring or arrhythmias. To study this cardiac regeneration, we developed implantable flexible multi-microelectrode membrane arrays that measure the epicardial electrocardiogram signals of zebrafish in real-time. The microelectrode electrical signals allowed for a high level of both temporal and spatial resolution (~20 μm), and the signal to noise ratio of the epicardial ECG was comparable to that of surface electrode ECG (7.1 dB vs. 7.4 dB, respectively). Processing and analysis of the signals from the microelectrode array demonstrated distinct ECG signals: namely, atrial conduction (P waves), ventricular contraction (QRS), and ventricular repolarization (QT interval). The electrical signals were in synchrony with optically measured Calcium concentration gradients in terms of d[Ca^(2+)]/dt at both whole heart and tissue levels. These microelectrodes therefore provide a real-time analytical tool for monitoring conduction phenotypes of small vertebral animals with a high temporal and spatial resolution

Ventricular divergence correlates with epicardial wavebreaks and predicts ventricular arrhythmia in isolated rabbit hearts during therapeutic hypothermia

INTRODUCTION: High beat-to-beat morphological variation (divergence) on the ventricular electrogram during programmed ventricular stimulation (PVS) is associated with increased risk of ventricular fibrillation (VF), with unclear mechanisms. We hypothesized that ventricular divergence is associated with epicardial wavebreaks during PVS, and that it predicts VF occurrence. METHOD AND RESULTS: Langendorff-perfused rabbit hearts (n = 10) underwent 30-min therapeutic hypothermia (TH, 30°C), followed by a 20-min treatment with rotigaptide (300 nM), a gap junction modifier. VF inducibility was tested using burst ventricular pacing at the shortest pacing cycle length achieving 1:1 ventricular capture. Pseudo-ECG (p-ECG) and epicardial activation maps were simultaneously recorded for divergence and wavebreaks analysis, respectively. A total of 112 optical and p-ECG recordings (62 at TH, 50 at TH treated with rotigaptide) were analyzed. Adding rotigaptide reduced ventricular divergence, from 0.13±0.10 at TH to 0.09±0.07 (p = 0.018). Similarly, rotigaptide reduced the number of epicardial wavebreaks, from 0.59±0.73 at TH to 0.30±0.49 (p = 0.036). VF inducibility decreased, from 48±31% at TH to 22±32% after rotigaptide infusion (p = 0.032). Linear regression models showed that ventricular divergence correlated with epicardial wavebreaks during TH (p<0.001). CONCLUSION: Ventricular divergence correlated with, and might be predictive of epicardial wavebreaks during PVS at TH. Rotigaptide decreased both the ventricular divergence and epicardial wavebreaks, and reduced the probability of pacing-induced VF during TH

Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

Dissociation of Cerebral Blood Flow and Femoral Artery Blood Pressure Pulsatility After Cardiac Arrest and Resuscitation in a Rodent Model: Implications for Neurological Recovery.

Background Impaired neurological function affects 85% to 90% of cardiac arrest (CA) survivors. Pulsatile blood flow may play an important role in neurological recovery after CA. Cerebral blood flow (CBF) pulsatility immediately, during, and after CA and resuscitation has not been investigated. We characterized the effects of asphyxial CA on short-term (&lt;2&nbsp;hours after CA) CBF and femoral arterial blood pressure (ABP) pulsatility and studied their relationship to cerebrovascular resistance (CVR) and short-term neuroelectrical recovery. Methods and Results Male rats underwent asphyxial CA followed by cardiopulmonary resuscitation. A multimodal platform combining laser speckle imaging, ABP, and electroencephalography to monitor CBF, peripheral blood pressure, and brain electrophysiology, respectively, was used. CBF and ABP pulsatility and CVR were assessed during baseline, CA, and multiple time points after resuscitation. Neuroelectrical recovery, a surrogate for neurological outcome, was assessed using quantitative electroencephalography 90&nbsp;minutes after resuscitation. We found that CBF pulsatility differs significantly from baseline at all experimental time points with sustained deficits during the 2&nbsp;hours of postresuscitation monitoring, whereas ABP pulsatility was relatively unaffected. Alterations in CBF pulsatility were inversely correlated with changes in CVR, but ABP pulsatility had no association to CVR. Interestingly, despite small changes in ABP pulsatility, higher ABP pulsatility was associated with worse neuroelectrical recovery, whereas CBF pulsatility had no association. Conclusions Our results reveal, for the first time, that CBF pulsatility and CVR are significantly altered in the short-term postresuscitation period after CA. Nevertheless, higher ABP pulsatility appears to be inversely associated with neuroelectrical recovery, possibly caused by impaired cerebral autoregulation and/or more severe global cerebral ischemia

Presence and stability of rotors in atrial fibrillation: evidence and therapeutic implications

[EN] Rotor-guided ablation has opened new perspectives into the therapy of atrial fibrillation (AF). Analysis of the spatio-temporal cardiac excitation patterns in the frequency and phase domains has demonstrated the importance of rotors in research models of AF, however, the dynamics and role of rotors in human AF are still controversial. In this review, the current knowledge gained through research models and patient data that support the notion that rotors are key players in AF maintenance is summarized. We report and discuss discrepancies regarding rotor prevalence and stability in various studies, which can be attributed in part to methodological differences among mapping systems. Future research for validation and improvement of current clinical electrophysiology mapping technologies will be crucial for developing mechanistic-based selection and application of the best therapeutic strategy for individual AF patient, being it, pharmaceutical, ablative, or other approach.This work was supported in part by grants from the Instituto de Salud Carlos III (Ministry of Economy and Competitiveness, Spain: PI13-01882, PI13-00903, and PI14/00857), Spanish Society of Cardiology (Clinical Research Grant 2015), Generalitat Valenciana (ACIF/2013/021), Innovation (Red RIC, PLE2009-0152), and NHLBI (P01-HL039707, P01-HL087226, and R01-HL118304).Guillem Sánchez, MS.; Climent, AM.; Rodrigo Bort, M.; Fernandez-Aviles, F.; Atienza, F.; Berenfeld, O. (2016). Presence and stability of rotors in atrial fibrillation: evidence and therapeutic implications. Cardiovascular Research. 109(4):480-492. https://doi.org/10.1093/cvr/cvw011S480492109