Silicon central pattern generators for cardiac diseases

In-vivo blood pressure sensing with bi-filler nanocomposite

Conductive elastomers present desirable qualities for sensing pressure in-vivo, such as high piezoresistance in tiny volumes, conformability and, biocompatibility. Many electrically conductive nanocomposites however, are susceptible to electrical drift following repeated stress cycles and chemical aging. Here we propose an innovative approach to stabilize nanocomposite percolation network against incomplete recovery to improve reproducibility and facilitate sensor calibration. We decouple the tunnelling-percolation network of highly-oriented pyrolytic graphite (HOPG) nanoparticles from the incomplete viscoelastic recovery of the polydimethylsiloxane (PDMS) matrix by inserting minute amounts of insulating SiO2 nanospheres. SiO2 nanospheres effectively reduce the number of nearest neighbours at each percolation node switching off the parallel electrical pathways that might become activated under incomplete viscoelastic relaxation. We varied the size of SiO2 nanospheres and their filling fraction to demonstrate nearly complete piezoresistance recovery when SiO2 and HOPG nanoparticles have equal diameters (≈400 nm) and SiO2 and HOPG volume fractions are 1 % and 29.5 % respectively. We demonstrate an in-vivo blood pressure sensor based on this bi-filler composite.

Remodeling in the AV block dog is essential for tolerating moderate treadmill activity

Background

A preclinical model standardized at different remodeling stages after AV block induction in awake state is suitable for the evaluation of improved cardiac devices. We studied exercise-induced cardiorespiratory parameters at three different timepoints after inducing AV block in dogs.

Methods

Mongrel dogs (n = 12) were placed on a treadmill with a 10% incline and performed a moderate exercise protocol (10-minute run at 6 km/h). Dogs ran at sinus rhythm (SR), at two days (AVB2d, initiation of remodeling), three weeks (CAVB3) and six weeks (CAVB6, completed remodeling) after AV block.

Results

All dogs completed the exercise protocol at SR, CAVB3 and CAVB6, while 6/12 dogs at AVB2d failed to complete the exercise protocol. The atrial rate was higher at all AV block timepoints (126 ± 20 to 141 ± 19 bpm at rest and 221 ± 10 to 231 ± 13 bpm during exercise) compared to SR (100 ± 29 bpm at rest and 162 ± 28 bpm during exercise, p < 0.05). Upon exercise, stroke volume increased from 66 ± 15 ml at SR, to 96 ± 21 ml at AVB2d (p < 0.05), 91 ± 13 ml at CAVB3 (p < 0.05) and 85 ± 24 ml at CAVB6 but failed to compensate for the AV block-induced bradycardia. Therefore, cardiac output was lower after AV block compared to SR. Exercising dogs at AVB2d showed most arrhythmic events, lowest VO₂, and signs of desaturation and acidification in venous blood.

Conclusion

Dogs with limited remodeling after AV block have a reduced exercise tolerance, which is reflected in changes in cardiorespiratory parameters.

Noise-activated barrier crossing in multiattractor dissipative neural networks

Noise-activated transitions between coexisting attractors are investigated in a chaotic spiking network. At low noise level, attractor hopping consists of discrete bifurcation events that conserve the memory of initial conditions. When the escape probability becomes comparable to the intrabasin hopping probability, the lifetime of attractors is given by a detailed balance where the less coherent attractors act as a sink for the more coherent ones. In this regime, the escape probability follows an activation law allowing us to assign pseudoactivation energies to limit cycle attractors. These pseudoenergies introduce a useful metric for evaluating the resilience of biological rhythms to perturbations.

Respiratory-cardiovascular interactions

Much of biology is rhythmical and comprises oscillators that can couple. These have optimized energy efficiency and have been preserved during evolution. The respiratory and cardiovascular systems contain numerous oscillators, and importantly, they couple. This coupling is dynamic but essential for an efficient transmission of neural information critical for the precise linking of breathing and oxygen delivery while permitting adaptive responses to changes in state. The respiratory pattern generator and the neural network responsible for sympathetic and cardiovagal (parasympathetic) tone generation interact at many levels ensuring that cardiac output and regional blood flow match oxygen delivery to the lungs and tissues efficiently. The most classic manifestations of these interactions are respiratory sinus arrhythmia and the respiratory modulation of sympathetic nerve activity. These interactions derive from shared somatic and cardiopulmonary afferent inputs, reciprocal interactions between brainstem networks and inputs from supra-pontine regions. Disrupted respiratory-cardiovascular coupling can result in disease, where it may further the pathophysiological sequelae and be a harbinger of poor outcomes. This has been well documented by diminished respiratory sinus arrhythmia and altered respiratory sympathetic coupling in animal models and/or patients with myocardial infarction, heart failure, diabetes mellitus, and neurological disorders as stroke, brain trauma, Parkinson disease, or epilepsy. Future research needs to assess the therapeutic potential for ameliorating respiratory-cardiovascular coupling in disease.

Optimal solid state neurons

Bioelectronic medicine is driving the need for neuromorphic microcircuits that integrate raw nervous stimuli and respond identically to biological neurons. However, designing such circuits remains a challenge. Here we estimate the parameters of highly nonlinear conductance models and derive the ab initio equations of intracellular currents and membrane voltages embodied in analog solid-state electronics. By configuring individual ion channels of solid-state neurons with parameters estimated from large-scale assimilation of electrophysiological recordings, we successfully transfer the complete dynamics of hippocampal and respiratory neurons in silico. The solid-state neurons are found to respond nearly identically to biological neurons under stimulation by a wide range of current injection protocols. The optimization of nonlinear models demonstrates a powerful method for programming analog electronic circuits. This approach offers a route for repairing diseased biocircuits and emulating their function with biomedical implants that can adapt to biofeedback.

Dual Mechanism for the Emergence of Synchronization in Inhibitory Neural Networks

During cognitive tasks cortical microcircuits synchronize to bind stimuli into unified perception. The emergence of coherent rhythmic activity is thought to be inhibition-driven and stimulation-dependent. However, the exact mechanisms of synchronization remain unknown. Recent optogenetic experiments have identified two neuron sub-types as the likely inhibitory vectors of synchronization. Here, we show that local networks mimicking the soma-targeting properties observed in fast-spiking interneurons and the dendrite-projecting properties observed in somatostatin interneurons synchronize through different mechanisms which may provide adaptive advantages by combining flexibility and robustness. We probed the synchronization phase diagrams of small all-to-all inhibitory networks in-silico as a function of inhibition delay, neurotransmitter kinetics, timings and intensity of stimulation. Inhibition delay is found to induce coherent oscillations over a broader range of experimental conditions than high-frequency entrainment. Inhibition delay boosts network capacity (ln2)^−N-fold by stabilizing locally coherent oscillations. This work may inform novel therapeutic strategies for moderating pathological cortical oscillations.

Utility of a Novel Biofeedback Device for Within-Breath Modulation of Heart Rate in Rats: A Quantitative Comparison of Vagus Nerve vs. Right Atrial Pacing

In an emerging bioelectronics era, there is a clinical need for physiological devices incorporating biofeedback that permits natural and demand-dependent control in real time. Here, we describe a novel device termed a central pattern generator (CPG) that uses cutting edge analog circuitry producing temporally controlled, electrical stimulus outputs based on the real time integration of physiological feedback. Motivated by the fact that respiratory sinus arrhythmia (RSA), which is the cyclical changes in heart rate every breath, is an essential component of heart rate variability (HRV) (an indicator of cardiac health), we have explored the versatility and efficiency of the CPG for producing respiratory modulation of heart rate in anesthetized, spontaneously breathing rats. Diaphragmatic electromyographic activity was used as the input to the device and its output connected to either the right cervical vagus nerve or the right atrium for pacing heart rate. We found that the CPG could induce respiratory related heart rate modulation that closely mimicked RSA. Whether connected to the vagus nerve or right atrium, the versatility of the device was demonstrated by permitting: (i) heart rate modulation in any phase of the respiratory cycle, (ii) control of the magnitude of heart rate modulation, and (iii) instant adaptation to changes in respiratory frequency. Vagal nerve pacing was only possible following transection of the nerve limiting its effective use chronically. Pacing via the right atrium permitted better flexibility and control of heart rate above its intrinsic level. This investigation now lays the foundation for future studies using this biofeedback technology permitting closer analysis of both the function and dysfunction of RSA.

Experimental observation of multistability and dynamic attractors in silicon central pattern generators

We report on the multistability of chaotic networks of silicon neurons and demonstrate how spatiotemporal sequences of voltage oscillations are selected with timed current stimuli. A three neuron central pattern generator was built by interconnecting Hodgkin-Huxley neurons with mutually inhibitory links mimicking gap junctions. By systematically varying the timing of current stimuli applied to individual neurons, we generate the phase lag maps of neuronal oscillators and study their dependence on the network connectivity. We identify up to six attractors consisting of triphasic sequences of unevenly spaced pulses propagating clockwise and anticlockwise. While confirming theoretical predictions, our experiments reveal more complex oscillatory patterns shaped by the ratio of the pulse width to the oscillation period. Our work contributes to validating the command neuron hypothesis.