Impact of light-adaptive mechanisms on mammalian retinal visual encoding at high light levels

A persistent change in illumination causes light-adaptive changes in retinal neurons. Light adaptation improves visual encoding by preventing saturation and by adjusting spatiotemporal integration to increase the signal-to-noise ratio (SNR) and utilize signaling bandwidth efficiently. In dim light, the visual input contains a greater relative amount of quantal noise, and vertebrate receptive fields are extended in space and time to increase SNR. Whereas in bright light, SNR of the visual input is high, the rate of synaptic vesicle release from the photoreceptors is low so that quantal noise in synaptic output may limit SNR postsynaptically. Whether and how reduced synaptic SNR impacts spatiotemporal integration in postsynaptic neurons remains unclear. To address this, we measured spatiotemporal integration in retinal horizontal cells and ganglion cells in the guinea pig retina across a broad illumination range, from low to high photopic levels. In both cell types, the extent of spatial and temporal integration changed according to an inverted U-shaped function consistent with adaptation to low SNR at both low and high light levels. We show how a simple mechanistic model with interacting, opponent filters can generate the observed changes in ganglion cell spatiotemporal receptive fields across light-adaptive states and postulate that retinal neurons postsynaptic to the cones in bright light adopt low-pass spatiotemporal response characteristics to improve visual encoding under conditions of low synaptic SNR.

A Bit-Encoding Based New Data Structure for Time and Memory Efficient Handling of Spike Times in an Electrophysiological Setup

Recent neuroscientific and technical developments of brain machine interfaces have put increasing demands on neuroinformatic databases and data handling software, especially when managing data in real time from large numbers of neurons. Extrapolating these developments we here set out to construct a scalable software architecture that would enable near-future massive parallel recording, organization and analysis of neurophysiological data on a standard computer. To this end we combined, for the first time in the present context, bit-encoding of spike data with a specific communication format for real time transfer and storage of neuronal data, synchronized by a common time base across all unit sources. We demonstrate that our architecture can simultaneously handle data from more than one million neurons and provide, in real time (< 25 ms), feedback based on analysis of previously recorded data. In addition to managing recordings from very large numbers of neurons in real time, it also has the capacity to handle the extensive periods of recording time necessary in certain scientific and clinical applications. Furthermore, the bit-encoding proposed has the additional advantage of allowing an extremely fast analysis of spatiotemporal spike patterns in a large number of neurons. Thus, we conclude that this architecture is well suited to support current and near-future Brain Machine Interface requirements.

Neuronal Mechanisms for Sleep/Wake Regulation and Modulatory Drive

Humans have been fascinated by sleep for millennia. After almost a century of scientific interrogation, significant progress has been made in understanding the neuronal regulation and functions of sleep. The application of new methods in neuroscience that enable the analysis of genetically defined neuronal circuits with unprecedented specificity and precision has been paramount in this endeavor. In this review, we first discuss electrophysiological and behavioral features of sleep/wake states and the principal neuronal populations involved in their regulation. Next, we describe the main modulatory drives of sleep and wakefulness, including homeostatic, circadian, and motivational processes. Finally, we describe a revised integrative model for sleep/wake regulation.

Electromechanical heterogeneity in the heart : A key to long QT syndrome?

In the healthy heart, physiological heterogeneities in structure and in electrical and mechanical activity are crucial for normal, efficient excitation and pumping. Alterations of heterogeneity have been linked to arrhythmogenesis in various cardiac disorders such as long QT syndrome (LQTS). This inherited arrhythmia disorder is caused by mutations in different ion channel genes and is characterized by (heterogeneously) prolonged cardiac repolarization and increased risk for ventricular tachycardia, syncope and sudden cardiac death. Cardiac electrical and mechanical function are not independent of each other but interact in a bidirectional manner by electromechanical and mechano-electrical coupling. Therefore, changes in either process will affect the other. Recent experimental and clinical evidence suggests that LQTS, which is primarily considered an "electrical" disorder, also exhibits features of disturbed mechanical function and heterogeneity, which in turn appears to correlate with the risk of arrhythmia in the individual patient. In this review, we give a short overview of the current knowledge about physiological and pathological, long QT-related electrical and mechanical heterogeneity in the heart. Also, their respective roles for future risk prediction approaches in LQTS are discussed.

Cardiac fibroblasts : Active players in (atrial) electrophysiology?

Fibrotic areas in cardiac muscle-be it in ventricular or atrial tissue-are considered as obstacles for conduction of the excitatory wave and can therefore facilitate re-entry, which may contribute to the sustenance of cardiac arrhythmias. Persistence of one of the most frequent arrhythmias, atrial fibrillation (AF), is accompanied by enhanced atrial fibrosis. Any kind of myocardial perturbation, whether via mechanical stress or ischemic damage, inflammation, or irregular and high-frequency electrical activity, activates fibroblasts. This leads to the secretion of paracrine factors and extracellular matrix proteins, especially collagen, and to the differentiation of fibroblasts into myofibroblasts. Excessive collagen production is the hallmark of fibrosis and impairs regular impulse propagation. In addition, direct electrical coupling between cardiomyocytes and nonmyocytes, such as fibroblasts and macrophages, via gap junctions affects conduction. Although fibroblasts are not electrically excitable, they express functional ion channels, in particular K⁺ channels and mechanosensitive channels, some of which could be involved in tissue remodeling. Here, we briefly review these aspects with special reference to AF.

Differences in the Electrophysiological Properties of Mouse Somatosensory Layer 2/3 Neurons In Vivo and Slice Stem from Intrinsic Sources Rather than a Network-Generated High Conductance State

Synaptic activity in vivo can potentially alter the integration properties of neurons. Using recordings in awake mice, we targeted somatosensory layer 2/3 pyramidal neurons and compared neuronal properties with those from slices. Pyramidal cells in vivo had lower resistance and gain values, as well as broader spikes and increased spike frequency adaptation compared to the same cells in slices. Increasing conductance in neurons using dynamic clamp to levels observed in vivo, however, did not lessen the differences between in vivo and slice conditions. Further, local application of tetrodotoxin (TTX) in vivo blocked synaptic-mediated membrane voltage fluctuations but had little impact on pyramidal cell membrane input resistance and time constant values. Differences in electrophysiological properties of layer 2/3 neurons in mouse somatosensory cortex, therefore, stem from intrinsic sources separate from synaptic-mediated membrane voltage fluctuations.

Electrophysiological Muscle Classification Using Multiple Instance Learning and Unsupervised Time and Spectral Domain Analysis

OBJECTIVE

Electrophysiological muscle classification (EMC) is a crucial step in the diagnosis of neuromuscular disorders. Existing quantitative techniques are not sufficiently robust and accurate to be reliably clinically used. Here, EMC is modeled as a multiple instance learning (MIL) problem and a system to infer unsupervised motor unit potential (MUP) labels and create supervised muscle classifications is presented.

METHODS

The system has five main steps: MUP representation using morphological, stability, and near fiber parameters as well as spectral features extracted from wavelet coefficients; MUP feature selection using unsupervised Laplacian scores; MUP clustering using neighborhood distance entropy consistency to find representations of MUP normality and abnormality; muscle representation by embedding its MUP cluster associations in a feature vector; and muscle classification using support vector machines or random forests.

RESULTS

The evaluation data consist of 63, 83, 93, and 84 sets of MUPs recorded in deltoid, vastus medialis, first dorsal interosseous, and tibialis anterior muscles, respectively. The proposed system discovered representations of normal, myopathic, and neurogenic MUPs for each specific muscle type and resulted in an average classification accuracy of 98%, which is higher than in previous works.

CONCLUSION

Modeling EMC as an instance of the MIL solves the traditional problem of characterizing MUPs without full supervision. Furthermore, finding representations of MUP normality and abnormality using morphological, stability, near fiber, and spectral features improve muscle classification.

SIGNIFICANCE

The proposed method is able to characterize MUPs with respect to disease categories, with no a priori information.

The bioelectric code: An ancient computational medium for dynamic control of growth and form

What determines large-scale anatomy? DNA does not directly specify geometrical arrangements of tissues and organs, and a process of encoding and decoding for morphogenesis is required. Moreover, many species can regenerate and remodel their structure despite drastic injury. The ability to obtain the correct target morphology from a diversity of initial conditions reveals that the morphogenetic code implements a rich system of pattern-homeostatic processes. Here, we describe an important mechanism by which cellular networks implement pattern regulation and plasticity: bioelectricity. All cells, not only nerves and muscles, produce and sense electrical signals; in vivo, these processes form bioelectric circuits that harness individual cell behaviors toward specific anatomical endpoints. We review emerging progress in reading and re-writing anatomical information encoded in bioelectrical states, and discuss the approaches to this problem from the perspectives of information theory, dynamical systems, and computational neuroscience. Cracking the bioelectric code will enable much-improved control over biological patterning, advancing basic evolutionary developmental biology as well as enabling numerous applications in regenerative medicine and synthetic bioengineering.

Control of recollection by slow gamma dominating mid-frequency gamma in hippocampus CA1

Behavior is used to assess memory and cognitive deficits in animals like Fmr1-null mice that model Fragile X Syndrome, but behavior is a proxy for unknown neural events that define cognitive variables like recollection. We identified an electrophysiological signature of recollection in mouse dorsal Cornu Ammonis 1 (CA1) hippocampus. During a shocked-place avoidance task, slow gamma (SG) (30–50 Hz) dominates mid-frequency gamma (MG) (70–90 Hz) oscillations 2–3 s before successful avoidance, but not failures. Wild-type (WT) but not Fmr1-null mice rapidly adapt to relocating the shock; concurrently, SG/MG maxima (SG_dom) decrease in WT but not in cognitively inflexible Fmr1-null mice. During SG_dom, putative pyramidal cell ensembles represent distant locations; during place avoidance, these are avoided places. During shock relocation, WT ensembles represent distant locations near the currently correct shock zone, but Fmr1-null ensembles represent the formerly correct zone. These findings indicate that recollection occurs when CA1 SG dominates MG and that accurate recollection of inappropriate memories explains Fmr1-null cognitive inflexibility.

Behavior is often used as proxy to study memory and cognitive deficits in animals like Fmr1-KO mice that model Fragile X Syndrome, the most prevalent single-gene cause of intellectual disability and autism. However, it is unclear what neural events define cognitive variables like recollection of memory and cognitive inflexibility. We identified a signature of recollection in the local field potentials of mouse dorsal CA1 hippocampus. When mice on a rotating platform avoided an invisible, fixed shock zone, slow gamma (30–50 Hz) oscillations dominated mid-frequency gamma (70–90 Hz) oscillations (SG_dom) 2–3 s before mice successfully avoided the shock zone. Wild-type but not Fmr1-KO mice adapt to relocating the shock zone; concurrently, SG_dom decreases in wild-type but not in cognitively inflexible Fmr1-KO mice. During SG_dom, principal cell ensembles represent distant locations; during place avoidance, these are avoided places in the shock zone vicinity. During shock relocation, wild-type ensembles encode distant locations near the currently correct shock zone, but Fmr1-KO ensembles manifest representational inflexibility, encoding the formerly correct zone. These findings suggest evidence for competition amongst CA1 inputs for CA1 information-processing modes and indicate that recollection occurs when CA1 slow gamma dominates mid-frequency gamma and that accurate recollection of inappropriate memories explains Fmr1-KO cognitive inflexibility.

Protective effects of curcumin against rotenone-induced rat model of Parkinson's disease: in vivo electrophysiological and behavioral study

Curcumin is a naturally occurring phenolic yellow chemical isolated from the rhizomes of the plant Curcuma longa (turmeric), and is a major component of the spice turmeric. Curcumin has protective effects against rotenone-induced neural damage in Parkinson's disease (PD). The present study aims at providing new evidence for the validity of the rotenone rat model of PD by examining whether neuronal activity in the hippocampus is altered. Male albino rats were treated with rotenone injections (2.5 mg/ml intraperitoneally) for 21 days. We examined the effects of curcumin (200 mg/kg) on behavior and electrophysiology in a rat model of PD induced by rotenone. Motor activity was assessed by cylinder test. The electrical activity of neurons was measured in hippocampus. Rotenone causes significant reduction of neuronal activity. The results show that curcumin can improve the motor impairments and electrophysiological parameters and may be beneficial in the treatment of PD.

The reliability of commonly used electrophysiology measures

BACKGROUND

Electrophysiological measures can help understand brain function both in healthy individuals and in the context of a disease. Given the amount of information that can be extracted from these measures and their frequent use, it is essential to know more about their inherent reliability.

OBJECTIVE/HYPOTHESIS

To understand the reliability of electrophysiology measures in healthy individuals. We hypothesized that measures of threshold and latency would be the most reliable and least susceptible to methodological differences between study sites.

METHODS

Somatosensory evoked potentials from 112 control participants; long-latency reflexes, transcranial magnetic stimulation with resting and active motor thresholds, motor evoked potential latencies, input/output curves, and short-latency sensory afferent inhibition and facilitation from 84 controls were collected at 3 visits over 24 months at 4 Track-On HD study sites. Reliability was assessed using intra-class correlation coefficients for absolute agreement, and the effects of reliability on statistical power are demonstrated for different sample sizes and study designs.

RESULTS

Measures quantifying latencies, thresholds, and evoked responses at high stimulator intensities had the highest reliability, and required the smallest sample sizes to adequately power a study. Very few between-site differences were detected.

CONCLUSIONS

Reliability and susceptibility to between-site differences should be evaluated for electrophysiological measures before including them in study designs. Levels of reliability vary substantially across electrophysiological measures, though there are few between-site differences. To address this, reliability should be used in conjunction with theoretical calculations to inform sample size and ensure studies are adequately powered to detect true change in measures of interest.

Differential receptive field organizations give rise to nearly identical neural correlations across three parallel sensory maps in weakly electric fish

Understanding how neural populations encode sensory information thereby leading to perception and behavior (i.e., the neural code) remains an important problem in neuroscience. When investigating the neural code, one must take into account the fact that neural activities are not independent but are actually correlated with one another. Such correlations are seen ubiquitously and have a strong impact on neural coding. Here we investigated how differences in the antagonistic center-surround receptive field (RF) organization across three parallel sensory maps influence correlations between the activities of electrosensory pyramidal neurons. Using a model based on known anatomical differences in receptive field center size and overlap, we initially predicted large differences in correlated activity across the maps. However, in vivo electrophysiological recordings showed that, contrary to modeling predictions, electrosensory pyramidal neurons across all three segments displayed nearly identical correlations. To explain this surprising result, we incorporated the effects of RF surround in our model. By systematically varying both the RF surround gain and size relative to that of the RF center, we found that multiple RF structures gave rise to similar levels of correlation. In particular, incorporating known physiological differences in RF structure between the three maps in our model gave rise to similar levels of correlation. Our results show that RF center overlap alone does not determine correlations which has important implications for understanding how RF structure influences correlated neural activity.

Growing evidence across nervous systems and species shows that the activities of neighboring neurons are not independent but are correlated with one another, which has important implications for neural coding. Such correlations are generally thought to be due to shared input. However, how this shared input is integrated by neurons in order to give rise to correlated activity is not well understood in general. Here we investigated how receptive field structure determines correlations between the activities of electrosensory pyramidal neurons in weakly electric fish. To do so, we used a combination of mathematical modeling of the known antagonistic center-surround RF structure as well as in vivo electrophysiological recordings. Our results show that the amount of receptive field center overlap alone is not sufficient to explain experimentally observed neural correlations in general. This is because our experimental data shows that pyramidal neurons with very different amounts of receptive field center overlap display almost identical correlations between their activities. Further, our modeling shows that both receptive field center and surround play important roles in determining correlated activity, such that very different combinations of relative RF surround strength and size can generate nearly identical correlations between neural activities. We discuss the implications of our results for sensory processing.

Hybrid automata models of cardiac ventricular electrophysiology for real-time computational applications

Virtual heart models have been proposed for closed loop validation of safety-critical embedded medical devices, such as pacemakers. These models must react in real-time to off-the-shelf medical devices. Real-time performance can be obtained by implementing models in computer hardware, and methods of compiling classes of Hybrid Automata (HA) onto FPGA have been developed. Models of ventricular cardiac cell electrophysiology have been described using HA which capture the complex nonlinear behavior of biological systems. However, many models that have been used for closed-loop validation of pacemakers are highly abstract and do not capture important characteristics of the dynamic rate response. We developed a new HA model of cardiac cells which captures dynamic behavior and we implemented the model in hardware. This potentially enables modeling the heart with over 1 million dynamic cells, making the approach ideal for closed loop testing of medical devices.

Vibrissa sensory neurons: Linking distinct morphology to specific physiology and function

Rodents use an array of long tactile facial hairs, the vibrissae, to locate and discriminate objects. Each vibrissa is densely innervated by multiple different types of trigeminal (TG) sensory neurons. Based on the sensory ending morphology, there are at least six types of vibrissa innervating neurons; whereas based on electrophysiological recordings, vibrissa neurons are generally classified as rapidly adapting (RA) and slowly adapting (SA), and show different responses to whisking movement and/or touch. There is a clear missing link between the morphologically defined neuronal types and their exact physiological properties and functions. We briefly summarize recent advances and consider single-cell transcriptome profiling, together with optogenetics-assisted in vivo electrophysiology, as a way to fill this major gap in our knowledge of the vibrissa sensory system.

A New Method for Neural Spike Alignment: The Centroid Filter

Recordings made directly from the nervous system are a key tool in experimental electrophysiology and the development of bioelectronic medicines. Analysis of these recordings involves the identification of signals from individual neurons, a process known as spike sorting. A critical and limiting feature of spike sorting is the need to align individual spikes in time. However, electrophysiological recordings are made in extremely noisy environments that seriously limit the performance of the spike-alignment process. We present a new centroid-based method and demonstrate its effectiveness using deterministic models of nerve signals. We show that spike alignment in the presence of noise is possible with a 30 dB reduction in minimum SNR compared with the conventional methods. We present a mathematical analysis of the centroid method, characterizing its fundamental operation and performance. Furthermore, we show that the centroid method lends itself particularly well to hardware realization, and we present results from a low-power implementation that operates on an FPGA, consuming ten times less power than conventional techniques - an important property for implanted devices. Our centroid method enables the accurate alignment of spikes in sub-0 dB SNR recordings and has the potential to enable the analysis of spikes in a wider range of environments than has been previously possible. Our method thus has the potential to influence significantly the design of electrophysiological recording systems in the future.

Development of Tetramethylenedisulfotetramine (TETS) Hapten Library: Synthesis, Electrophysiological Studies, and Immune Response in Rabbits

There is a need for fast detection methods for the banned rodenticide tetramethylenedisulfotetramine (TETS), a highly potent blocker of the γ-aminobutyric acid (GABAA ) receptors. General synthetic approach toward two groups of analogues was developed. Screening of the resulting library of compounds by FLIPR or whole-cell voltage-clamp revealed that, despite the structural differences, some of the TETS analogues retained GABAA receptor inhibition; however, their potency was an order of magnitude lower. Antibodies raised in rabbits against some of the TETS analogues conjugated to protein recognized free TETS and will be used for the development of an immunoassay for TETS.

Characterization of the CA1 pyramidal neurons in rat model of hepatic cirrhosis: insights into their electrophysiological properties

Although the key contributors of altering neurological function in hepatic encephalopathy are relatively well known, the electrophysiological mechanism of CA1 damage, a key vulnerable area during hyperammonemia, have not yet been defined. Therefore, here we focus on the electrophysiological mechanisms of cognitive impairments following bile duct ligation (BDL). We performed patch-clamp recordings from the CA1 pyramidal neurons in hippocampus of male Wistar rats, which underwent sham or BDL surgery. A striking electrophysiological change of hippocampal neurons in experimental model of BDL was observed in the present study. Spontaneous firing frequency and rate of action potential (AP) rebound was decreased and afterhyperpolarization amplitude (AHP) was increased significantly in hippocampal cells of BDL animals compared to sham group. Together, the results suggest that altered intrinsic properties of the hippocampal neurons may contribute to the cognitive abnormalities during hepatic encephalopathy (HE), highlighting the electrophysiological mechanisms for providing new treatments against HE.

Single-compartment models of retinal ganglion cells with different electrophysiologies

There are more than 15 different types of retinal ganglion cells (RGCs) in the mammalian retina. To model responses of RGCs to electrical stimulation, we use single-compartment Hodgkin-Huxley-type models and run simulations in the Neuron environment. We use our recently published in vitro data of different morphological cell types to constrain the model, and study the effects of electrophysiology on the cell responses separately from the effects of morphology. We find simple models that can match the spike patterns of different types of RGCs. These models, with different input-output properties, may be used in a network to study retinal network dynamics and interactions.

Soups and Sparks Revisited John Eccles’ Path from the War on Electrical Transmission to Mental Sparks

In the famous debate whether neurons communicate via chemical mediators or electrical signals, Henry Dale and Otto Loewi mounted powerful evidence on the mediation of nervous activity by chemical transmitters, while John Eccles led the campaign for the electrophysiologists. Eventually, Eccles converted to chemical transmission, when he identified excitatory and inhibitory postsynaptic potentials initiated by the release of chemical neurotransmitters in the synaptic cleft. This well-known episode from the history of neurophysiology counts as a rare instance of philosophy of science advancing scientific research, because the philosopher Karl Popper had encouraged Eccles to theorize an experiment proving the falsity of his own interpretation – according to Popper’s philosophy of science progressing by falsification. The paper shows how Eccles’ intellectual mobilization was grounded in a series of geographical moves, technological adaptations and re-arrangements of his group. This massive travel of people, ideas, instruments, and techniques mediated between the contradictory views, long before Popper kindled Eccles to reflect about the conflicting paradigms and the new theorizing did hardly change his experimental practice. Popper’s immediate effect was a critical and reflexive distance that enabled Eccles to present his evidence more persuasively, as can be shown from archival sources. The exchanges between Eccles and Popper thus shaped the philosophy of falsification to a powerful strategy for writing science.

Electrophysiological property and chemical sensitivity of primary afferent neurons that innervate rat whisker hair follicles

Whisker hair follicles are sensory organs that sense touch and perform tactile discrimination in animals, and they are sites where sensory impulses are initiated when whisker hairs touch an object. The sensory signals are then conveyed by whisker afferent fibers to the brain for sensory perception. Electrophysiological property and chemical sensitivity of whisker afferent fibers, important factors affecting whisker sensory processing, are largely not known. In the present study, we performed patch-clamp recordings from pre-identified whisker afferent neurons in whole-mount trigeminal ganglion preparations and characterized their electrophysiological property and sensitivity to ATP, serotonin and glutamate. Of 97 whisker afferent neurons examined, 67% of them are found to be large-sized (diameter ≥45 µm) cells and 33% of them are medium- to small-sized (diameter <45 µm) cells. Almost every large-sized whisker afferent neuron fires a single action potential but many (40%) small/medium-sized whisker afferent neurons fire multiple action potentials in response to prolonged stepwise depolarization. Other electrophysiological properties including resting membrane potential, action potential threshold, and membrane input resistance are also significantly different between large-sized and small/medium-sized whisker afferent neurons. Most large-sized and many small/medium-sized whisker afferent neurons are sensitive to ATP and/or serotonin, and ATP and/or serotonin could evoke strong inward currents in these cells. In contrast, few whisker afferent neurons are sensitive to glutamate. Our results raise a possibility that ATP and/or serotonin may be chemical messengers involving sensory signaling for different types of rat whisker afferent fibers.

Determining conduction patterns on a sparse electrode grid: Implications for the analysis of clinical arrhythmias

We present a general method of utilizing bioelectric recordings from a spatially sparse electrode grid to compute a dynamic vector field describing the underlying propagation of electrical activity. This vector field, termed the wave-front flow field, permits quantitative analysis of the magnitude of rotational activity (vorticity) and focal activity (divergence) at each spatial point. We apply this method to signals recorded during arrhythmias in human atria and ventricles using a multipolar contact catheter and show that the flow fields correlate with corresponding activation maps. Further, regions of elevated vorticity and divergence correspond to sites identified as clinically significant rotors and focal sources where therapeutic intervention can be effective. These flow fields can provide quantitative insights into the dynamics of normal and abnormal conduction in humans and could potentially be used to enhance therapies for cardiac arrhythmias.

Improving data quality in neuronal population recordings

Understanding how the brain operates requires understanding how large sets of neurons function together. Modern recording technology makes it possible to simultaneously record the activity of hundreds of neurons, and technological developments will soon allow recording of thousands or tens of thousands. As with all experimental techniques, these methods are subject to confounds that complicate the interpretation of such recordings, and could lead to erroneous scientific conclusions. Here we discuss methods for assessing and improving the quality of data from these techniques and outline likely future directions in this field.

Modeling Electrophysiological Coupling and Fusion between Human Mesenchymal Stem Cells and Cardiomyocytes

Human mesenchymal stem cell (hMSC) delivery has demonstrated promise in preclinical and clinical trials for myocardial infarction therapy; however, broad acceptance is hindered by limited understanding of hMSC-human cardiomyocyte (hCM) interactions. To better understand the electrophysiological consequences of direct heterocellular connections between hMSCs and hCMs, three original mathematical models were developed, representing an experimentally verified triad of hMSC families with distinct functional ion channel currents. The arrhythmogenic risk of such direct electrical interactions in the setting of healthy adult myocardium was predicted by coupling and fusing these hMSC models to the published ten Tusscher midcardial hCM model. Substantial variations in action potential waveform—such as decreased action potential duration (APD) and plateau height—were found when hCMs were coupled to the two hMSC models expressing functional delayed rectifier-like human ether à-go-go K⁺ channel 1 (hEAG1); the effects were exacerbated for fused hMSC-hCM hybrid cells. The third family of hMSCs (Type C), absent of hEAG1 activity, led to smaller single-cell action potential alterations during coupling and fusion, translating to longer tissue-level mean action potential wavelength. In a simulated 2-D monolayer of cardiac tissue, re-entry vulnerability with low (5%) hMSC insertion was approximately eight-fold lower with Type C hMSCs compared to hEAG1-functional hMSCs. A 20% decrease in APD dispersion by Type C hMSCs compared to hEAG1-active hMSCs supports the claim of reduced arrhythmogenic potential of this cell type with low hMSC insertion. However, at moderate (15%) and high (25%) hMSC insertion, the vulnerable window increased independent of hMSC type. In summary, this study provides novel electrophysiological models of hMSCs, predicts possible arrhythmogenic effects of hMSCs when directly coupled to healthy hCMs, and proposes that isolating a subset of hMSCs absent of hEAG1 activity may offer increased safety as a cell delivery cardiotherapy at low levels of hMSC-hCM coupling.

Myocardial infarction—better known as a heart attack—strikes on average every 43 seconds in America. An emerging approach to treat myocardial infarction patients involves the delivery of human mesenchymal stem cells (hMSCs) to the damaged heart. While clinical trials of this therapeutic approach have yet to report adverse effects on heart electrical rhythm, such consequences have been implicated in simpler experimental systems and thus remain a concern. In this study, we utilized mathematical modeling to simulate electrical interactions arising from direct coupling between hMSCs and human heart cells to develop insight into the possible adverse effects of this therapeutic approach on human heart electrical activity, and to assess a novel strategy for reducing some potential risks of this therapy. We developed the first mathematical models of electrical activity of three families of hMSCs based on published experimental data, and integrated these with previously established mathematical models of human heart cell electrical activity. Our computer simulations demonstrated that one particular family of hMSCs minimized the disturbances in cardiac electrical activity both at the single-cell and tissue levels, suggesting that isolating this specific sub-population of hMSCs for myocardial delivery could potentially increase the safety of future hMSC-based heart therapies.

Phasic Burst Stimulation: A Closed-Loop Approach to Tuning Deep Brain Stimulation Parameters for Parkinson's Disease

We propose a novel, closed-loop approach to tuning deep brain stimulation (DBS) for Parkinson's disease (PD). The approach, termed Phasic Burst Stimulation (PhaBS), applies a burst of stimulus pulses over a range of phases predicted to disrupt pathological oscillations seen in PD. Stimulation parameters are optimized based on phase response curves (PRCs), which would be measured from each patient. This approach is tested in a computational model of PD with an emergent population oscillation. We show that the stimulus phase can be optimized using the PRC, and that PhaBS is more effective at suppressing the pathological oscillation than a single phasic stimulus pulse. PhaBS provides a closed-loop approach to DBS that can be optimized for each patient.

Chronic cyclic vagus nerve stimulation has beneficial electrophysiological effects on healthy hearts in the absence of autonomic imbalance

Cardiovascular disease degrades the regulatory function of the autonomic nervous system. Cyclic vagus nerve stimulation (VNS) is an already FDA-approved therapy for drug-resistant epilepsy and depression, and has been shown to normalize autonomic function and improve objective measures of heart function and subjective measures of heart failure symptoms. However, it remains unclear whether VNS may induce negative effects in patients with potentially healthy hearts where VNS can be used for epileptic patients. Hence, this study aims to investigate the effects of VNS on the hearts of healthy rats with normal autonomic balance. Sprague-Dawley rats were implanted with stimulators and randomized to either Sham or VNS groups. Rats in VNS group received 10 weeks of chronic intermittent VNS via stimulation of the right cervical vagus nerve. Echocardiography was performed at Baseline (prior to VNS), Week 2, and Week 9. After 10 weeks, high-resolution optical mapping was performed in ex vivo perfused hearts to evaluate the electrophysiological remodeling that occurs in the heart as a result of the VNS therapy. Chronic VNS modified the electrophysiological properties of healthy rat hearts by reducing the action potential duration at 50% (APD50) and 80% (APD80) repolarization. Chronic VNS also affected the restitution properties of the heart at the APD50 level and increased myocardial conduction velocity (CV). VNS did not induce any significant changes to ventricular ejection fraction (EF) and spatial dispersion of APD, thus indicating that VNS did not negatively affect cardiac function. VNS also reduced the susceptibility to ventricular arrhythmias (ventricular fibrillation [VF] and ventricular tachycardia [VT]) during ex vivo programmed electrical stimulation. In summary, chronic application of cyclic VNS induces changes to the electrophysiological properties of healthy rat hearts. The observed decrease in APD and increase in CV suggest that the beneficial effects of VNS do not require the presence of existing autonomic imbalance.