Action Potential Research Articles

Magnetic assembly of microwires on a flexible substrate for minimally invasive electrophysiological recording

Understanding the neural system in the brain requires the detection of signals from the tissue. Microscale electrodes enable high spatiotemporal neural recording, whereas traditional microelectrodes cause material and geometry mismatches between the electrode and the tissue, leading to injury and signal loss during recording. In this study, we propose a fabrication technique that uses magnetic force to facilitate assembly of vertical microscale wire-electrodes on a flexible substrate. Two-channel 15-μm-diameter and 400-μm-length nickel-microwire electrodes on a 5-μm-thick flexible parylene film are designed and fabricated. Impedance characteristics of these electrodes are <500 kΩ at 1 kHz, with output/input signal amplitude ratios of over 90%. In vivo acute neural recording in mice demonstrates that both local field potentials and action potentials are detected through each wire electrode, confirming the minimal invasiveness during the electrode penetration and through immunohistochemical tissue analysis.

Distribution and calcium signaling function of somatostatin receptor subtypes in rat pituitary

The somatostatin (SST) receptor family controls pituitary hormone secretion, but the distribution and specific roles of these receptors on the excitability and voltage-gated calcium signaling of hormone producing pituitary cells have not been fully characterized. Here we show that the rat pituitary gland expressed Sstr1, Sstr2, Sstr3, and Sstr5 receptor genes in a cell type-specific manner: Sstr1 and Sstr2 in thyrotrophs, Sstr3 in gonadotrophs and lactotrophs, Sstr2, Sstr3, and Sstr5 in somatotrophs, and none in corticotrophs and melanotrophs. Most gonadotrophs and thyrotrophs spontaneously fired high-amplitude single action potentials, which were silenced by SST without affecting intracellular calcium concentrations. In contrast, lactotrophs and somatotrophs spontaneously fired low-amplitude plateau-bursting action potentials in conjunction with calcium transients, both of which were silenced by SST. Moreover, SST inhibited GPCR-induced voltage-gated calcium signaling and hormone secretion in all cell types expressing SST receptors, but the inhibition was more pronounced in somatotrophs. The pattern of inhibition of electrical activity and calcium signaling was consistent with both direct and indirect inhibition of voltage-gated calcium channels, the latter being driven by cell type-specific hyperpolarization. These results indicate that the action of SST in somatotrophs is enhanced by the expression of several types of SST receptors and their slow desensitization, that SST may play a role in the electrical resynchronization of gonadotrophs, thyrotrophs, and lactotrophs, and that the lack of SST receptors in corticotrophs and melanotrophs keeps them excitable and ready to responses to stress.

Clinical Cues for the Early Diagnosis of Transthyretin-Related Polyneuropathy.

The estimated prevalence of hereditary transthyretin-related familial amyloid polyneuropathy (TTR-FAP) and the small number of known patients in Germany indicate that many patients with TTR-FAP remain undiagnosed, and may instead be classified as "idiopathic." The aim of this study was to identify biomarkers for detecting TTR-FAP among a cohort of patients with idiopathic polyneuropathy (PNP). Clinical evaluations (including the Neuropathy Impairment Score and Neuropathy Disability Score), nerve conduction studies (NCSs), quantitative sensory testing, and autonomic function tests were performed on 23 patients with TTR-FAP and 89 with idiopathic PNP. Discriminant analysis was then performed to identify variables useful for predicting TTR-FAP. Patients with TTR-FAP had paresis of the finger and thumb muscles, and reduced vibration perception and increased pressure pain in the upper and lower extremities. The NCSs showed that action potentials were smaller in the median, ulnar (both motor and sensory), and sural nerves in TTR-FAP. The sensory nerve conduction velocity was also reduced in the ulnar nerve. Autonomic neuropathy was confirmed by reduced sympathetic skin responses in the hands and feet in TTR-FAP. Multivariate discriminant analysis revealed that finger abduction strength, sensory ulnar nerve action potential amplitude, and vibration detection and pressure pain thresholds in the upper extremities were sufficient to correctly identify TTR-FAP in 81.3% of cases. Detailed clinical and neurophysiological investigations of standard parameters in the upper limb may help to identify the otherwise-rare TTR-FAP.

ARRHYTHMOGENIC ATRIAL REMODELING DURING PREGNANCY IN MICE

Pregnancy is associated with greater vulnerability to supraventricular tachyarrhythmias (SVT). As the underlying mechanisms remain to be elucidated, we investigated whether pregnancy induces atrial remodeling that might contribute to this. Atrial electrophysiological and contractile properties were examined in non-pregnant (NP) and pregnant (P) mice. Cell shortening and Ca2+ imaging were measured on atrial myocytes. Atrial action potential and ionic currents were recorded using patch-clamp technique. Atrial mRNA and protein expression were analyzed using qPCR and Western blot. The P-wave area on the ECG increased by 50% during pregnancy, suggesting atrial enlargement, confirmed by echocardiography. The atrial myocytes were longer in P mice, adding further evidence to the physiological hypertrophy associated with pregnancy. Echocardiography showed a 50% increase in atrial fractional area change during pregnancy, indicating much stronger contraction. A similar increase in cell shortening was observed in P mice and was associated with a decrease in sarcomere length and changes in myofilament protein phosphorylation. However, pregnancy did not affect L-type Ca2+ current, Ca2+ transients, and SR Ca2+ load. Myocytes from P mice showed twice as many spontaneous contractions and spontaneous diastolic Ca2+ releases. Moreover, pregnancy was associated with a 50% increase in action potential duration, linked to a reduction in the density of the transient outward K+ current Ito and the underlying KV4.3 channel. During pregnancy, atrial tissues undergo substantial remodeling, potentially contributing to the development of SVT.

Study of the parametric effect of the wave profiles of the time-space fractional soliton neuron model equation arising in the topic of neuroscience

Time-space fractional nonlinear problems (T-SFNLPs) play a crucial role in the study of nonlinear wave propagation. Time-space nonlinearity is prevalent across various fields of applied science, nonlinear dynamics, mathematical physics, and engineering, including biosciences, neurosciences, plasma physics, geochemistry, and fluid mechanics. In this context, we examine the time-space fractional soliton neuron model (TSFSNM), which holds significant importance in neuroscience. This model explains how action potentials are initiated and propagated by axons, based on a thermodynamic theory of nerve pulse transmission. The signals passing through the cell membrane (CM) are proposed to take the form of solitary sound pulses, which can be represented as solitons. To investigate these soliton solutions, nonlinear fractional differential equations (NLFDEs) are transformed into corresponding partial differential equations (PDEs) using a fractional complex transform (FCT). The Kudryashov method is then applied to determine the wave profiles for the TSFSNM equation. We present 3D, 2D, contour, and density plots of the TSFSNM equation, and further analyze how fractional and time-space parameters influence these wave profiles through additional graphical representations. Kink, singular kink and different types of soliton solutions are successfully recovered through the Kudryashov method. The outcomes of various studies show that the applied method is highly efficient and well-suited for tackling problems in applied sciences and mathematical physics. Graphical representations, coupled with numerical data, reinforce the validity and accuracy of the technique. The proposed method is a convenient and powerful tool for handling the solution of nonlinear equations, making it particularly effective in exploring complex wave phenomena in diverse scientific fields.

Investigative Study on Potential Electrical Signals from Palm Trees in Gaza Strip

Abstract: This paper aims to analyze the electrical signals in a group of palm trees in the regions of Gaza City and Deir Albalah. It also investigates the stimuli of electrical potential (EP) in dates palm trees. It also explains factors such as: light, temperature, water level, tree age, sunset, direction, changing the reference, distance and proximity to the reference and electrode placement as stimuli on the EP of date palm trees. It assesses electrical signals (ESs) types in dates palm trees, generation and propagation of several ESs such action potential (AP), virtual potential (VP). The paper also demonstrates energy harvest potential, perspective applications and practical applications. The paper also provides a review summary of the effects of ESs on tipping, pruning, sunset, daybreak, photosynthesis, of lighting conditions, water availability.

Effects of the two-pore potassium channel subunit Task5 on neuronal function and signal processing in the auditory brainstem

Processing of auditory signals critically depends on the neuron’s ability to fire brief, precisely timed action potentials (APs) at high frequencies and high fidelity for prolonged times. This requires the expression of specialized sets of ion channels to quickly repolarize neurons, prevent aberrant AP firing and tightly regulate neuronal excitability. Although critically important, the regulation of neuronal excitability has received little attention in the auditory system. Neuronal excitability is determined to a large extent by the resting membrane potential (RMP), which in turn depends on the kind and number of ion channels open at rest; mostly potassium channels. A large part of this resting potassium conductance is carried by two-pore potassium channels (K2P channels). Among the K2P channels, the subunit Task5 is expressed almost exclusively in the auditory brainstem, suggesting a specialized role in auditory processing. However, since it failed to form functional ion channels in heterologous expression systems, it was classified “non-functional” for a long time and its role in the auditory system remained elusive. Here, we generated Task5 knock-out (KO) mice. The loss of Task5 resulted in changes in neuronal excitability in bushy cells of the ventral cochlear nucleus (VCN) and principal neurons of the medial nucleus of the trapezoid body (MNTB). Moreover, auditory brainstem responses (ABRs) to loud sounds were altered in Tasko5-KO mice. Thus, our study provides evidence that Task5 is indeed a functional K2P subunit and contributes to sound processing in the auditory brainstem.

Inverse stochastic resonance in adaptive small-world neural networks.

Inverse stochastic resonance (ISR) is a counterintuitive phenomenon where noise reduces the oscillation frequency of an oscillator to a minimum occurring at an intermediate noise intensity, and sometimes even to the complete absence of oscillations. In neuroscience, ISR was first experimentally verified with cerebellar Purkinje neurons [Buchin et al., PLOS Comput. Biol. 12, e1005000 (2016)]. These experiments showed that ISR enables a locally optimal information transfer between the input and output spike train of neurons. Subsequent studies have further demonstrated the efficiency of information processing and transfer in neural networks with small-world network topology. We have conducted a numerical investigation into the impact of adaptivity on ISR in a small-world network of noisy FitzHugh-Nagumo (FHN) neurons, operating in a bi-metastable regime consisting of a metastable fixed point and a metastable limit cycle. Our results show that the degree of ISR is highly dependent on the value of the FHN model's timescale separation parameter ε. The network structure undergoes dynamic adaptation via mechanisms of either spike-time-dependent plasticity (STDP) with potentiation-/depression-domination parameter P or homeostatic structural plasticity (HSP) with rewiring frequency F. We demonstrate that both STDP and HSP amplify the effect of ISR when ε lies within the bi-stability region of FHN neurons. Specifically, at larger values of ε within the bi-stability regime, higher rewiring frequencies F are observed to enhance ISR at intermediate (weak) synaptic noise intensities, while values of P consistent with depression-domination (potentiation-domination) consistently enhance (deteriorate) ISR. Moreover, although STDP and HSP control parameters may jointly enhance ISR, P has a greater impact on improving ISR compared to F. Our findings inform future ISR enhancement strategies in noisy artificial neural circuits, aiming to optimize local information transfer between input and output spike trains in neuromorphic systems and prompt venues for experiments in neural networks.

Dopamine-induced Relaxation of Spike Synchrony Diversifies Burst Patterns in Cultured Hippocampal Networks

The intricate interplay of neurotransmitters orchestrates a symphony of neural activity in the hippocampus, with dopamine emerging as a key conductor in this complex ensemble. Despite numerous studies uncovering the cellular mechanisms of dopamine, its influence on hippocampal neural networks remains elusive. Combining in vitro electrophysiological recordings of rat embryonic hippocampal neurons, pharmacological interventions, and computational analyses of spike trains, we found that dopamine induces a relaxation in network synchrony. This relaxation expands the repertoire of burst dynamics within these hippocampal networks, a phenomenon notably absent under the administration of dopamine antagonists. Our study provides a thorough understanding of how dopamine signaling influences the formation of functional networks among hippocampal neurons.

A finite element model for biomechanical characterization of exvivo peripheral nerve dysfunction during stretch.

Peripheral nerve damage can cause debilitating symptoms ranging from numbness and pain to sensory loss and atrophy. To uncover the underlying mechanisms of peripheral nerve injury, our research aims to develop a relationship between biomechanical peripheral nerve damage and function through finite element modeling. A noncontact, exvivo electrophysiology chamber, capable of axially stretching explanted nerves while recording electrical signals, was used to investigate peripheral nerve injury. Successive stretch trials were run on eight sciatic nerves (four females and four males) excised from Sprague-Dawley rats. Nerves were stretched until 50% compound action potential (CAP) amplitude reduction was obtained. A constitutive model developed by Raghavan and Vorp was suitable for rat sciatic nerves, with an average α and β of 0.183 MPa and 1.88 MPa, respectively. We then generated 95% confidence intervals for the stretch at which specific CAP amplitude reductions would occur, which compares well to previous studies. We also developed a finite element model that can predict stretch-induced signaling deficits, applicable for complex nerve geometries and injuries. This relationship between nerve biomechanics and function can be expanded upon to create a clinical model for peripheral nerve dysfunction due to stretch.

Silicone-based AC102-loaded cochlear implant coatings protect residual hearing in an animal model of cochlear implantation

Cochlear implant users with residual hearing benefit synergistically from combined electrical stimulation via the cochlear implant and preserved residual hearing after surgery. However, direct mechanical trauma and subsequent inflammation may deteriorate hearing function. AC102, a novel otoprotective pyridoindole with anti-apoptotic and anti-oxidative properties significantly improved hearing recovery following cochlear implantation when administered intratympanically prior to surgery. Additionally, AC102 exerts neurotrophic effects, possibly aiding in the preservation of auditory nerve fibers and spiral ganglion neurons. Rapid clearance of the drug, however, might be a limiting factor to further attenuate the inflammatory response and maintain neuronal health. The aim of the current study was to design an AC102-loaded electrode array for sustained drug delivery and investigate its effects in hearing preservation cochlear implantation.First, the release-kinetics of AC102 were investigated in vitro and modelled by the Higuchi equation of drug release. An electrode array coated with 10% AC102 was manufactured, its release kinetics evaluated, and subsequently tested in vivo. 20 normal hearing Mongolian gerbils were unilaterally implanted with an AC102-loaded or an unloaded control electrode. Compound action potentials were measured prior to cochlear implantation and serially over 28 days. Hair cells, inner hair cell synapses, and auditory nerve fibers were quantified in cochlear whole-mounts by immunofluorescence staining.AC102 release from silicone coating could be predictably modelled by the Higuchi equation of drug release. The electrode array with an AC102-silicone depot enabled non-linear sustained drug release with initially higher release concentrations. In vivo, the AC102-loaded electrode array significantly recovered auditory threshold shifts near the maximum insertion depth over 28 days. In the apical region, a significant recovery was noticed only until day 14, after which threshold shifts aligned between groups. Histologically, AC102-loaded electrodes significantly preserved outer hair cells apical of the maximum insertion depth and inner hair cells and neuronal structures at the tip of the inserted electrode.In conclusion, the drug-loaded electrode arrays could predictably release AC102 over a period of 28 days. AC102 enabled the restoration of auditory thresholds near the area of maximum insertion, which is the desired region to be preserved in cochlear implant recipients with residual hearing.

Reconstructing ventricular cardiomyocyte dynamics and parameter estimation using data-assimilation

Cardiac ventricular myocyte action potential dynamics are regulated by intricate and nonlinear interactions between the cell transmembrane potential and ionic currents and concentrations. Present technology limits the ability to measure transmembrane potential and multiple ionic currents simultaneously, which narrows the scope of experiments to provide a complete snapshot of the cardiac myocyte state. This limitation presents an obstacle for understanding how perturbations can trigger arrhythmias and more broadly how the myocyte responds to different conditions, such as changes in pacing rate or responses to drug treatment. In this study, we demonstrate that a data-assimilation approach can successfully reconstruct and predict the dynamics of a heterogeneous virtual cardiac ventricular myocyte population in the presence of parameter uncertainty. A population of heterogeneous cardiac ventricular myocytes is generated by varying ionic current conductance parameters, and additional observational uncertainty is mimicked by the addition of Gaussian noise to the transmembrane potential. We demonstrate that the data-assimilation approach accurately reconstructs transmembrane potential, with error less than the magnitude of the noise. Further, the data-assimilation approach successfully estimates the conductances of ionic currents generally with high accuracy and requiring low computational time. As a proof of concept, we apply the data-assimilation approach to reconstruct action potential dynamics from optical mapping experiments in an ex vivo isolated guinea pig heart. Critically, we demonstrate that the ionic conductance parameters estimated from a recording at one pacing frequency can accurately predict action potential dynamics at different rates.

Centerline-guided reinforcement learning model for pancreatic duct identifications.

Pancreatic ductal adenocarcinoma is forecast to become the second most significant cause of cancer mortality as the number of patients with cancer in the main duct of the pancreas grows, and measurement of the pancreatic duct diameter from medical images has been identified as relevant for its early diagnosis. We propose an automated pancreatic duct centerline tracing method from computed tomography (CT) images that is based on deep reinforcement learning, which employs an artificial agent to interact with the environment and calculates rewards by combining the distances from the target and the centerline. A deep neural network is implemented to forecast step-wise values for each potential action. With the help of this mechanism, the agent can probe along the pancreatic duct centerline using the best possible navigational path. To enhance the tracing accuracy, we employ landmark-based registration, which enables the generation of a probability map of the pancreatic duct. Subsequently, we utilize a gradient-based method on the registered data to extract a probability map specifically indicating the centerline of the pancreatic duct. Three datasets with a total of 115 CT images were used to evaluate the proposed method. Using image hold-out from the first two datasets, the method performance was 2.0, 4.0, and 2.1mm measured in terms of the mean detection error, Hausdorff distance (HD), and root mean squared error (RMSE), respectively. Using the first two datasets for training and the third one for testing, the method accuracy was 2.2, 4.9, and 2.6mm measured in terms of the mean detection error, HD, and RMSE, respectively. We present an algorithm for automated pancreatic duct centerline tracing using deep reinforcement learning. We observe that validation on an external dataset confirms the potential for practical utilization of the presented method.

Axon Initial Segment Structure and Function in Health and Disease.

At the simplest level, neurons are structured to integrate synaptic input and perform computational transforms on that input, converting it into an action potential (AP) code. This process-converting synaptic input into AP output-typically occurs in a specialized region of axon termed the axon initial segment (AIS). The AIS, as its name implies, is often contained to the first section of axon abutted to the soma and is home to a dizzying array of ion channels, attendant scaffolding proteins, intracellular organelles, extracellular proteins, and, in some cases, synapses. The AIS serves multiple roles as the final arbiter for determining if inputs are sufficient to evoke APs, as a gatekeeper that physically separates the somatodendritic domain from the axon proper, and as a regulator of overall neuronal excitability, dynamically tuning its size to best suit the needs of parent neurons. These complex roles have received considerable attention from experimentalists and theoreticians alike. Here, we review recent advances in our understanding of the AIS and its role in neuronal integration and polarity in health and disease.

Random-Coupled Neural Network

Improving the efficiency of current neural networks and modeling them on biological neural systems have become prominent research directions in recent years. The pulse-coupled neural network (PCNN) is widely used to mimic the computational characteristics of the human brain in computer vision and neural network fields. However, PCNN faces limitations such as limited neural connections, high computational costs, and a lack of stochastic properties. This study proposes a random-coupled neural network (RCNN) to address these limitations. RCNN employs a stochastic inactivation process, selectively inactivating neural connections using a random inactivation weight matrix. This method reduces the computational burden and allows for extensive neural connections. RCNN encodes constant stimuli as periodic spike trains and periodic stimuli as chaotic spike trains, reflecting the information encoding characteristics of biological neural systems. Our experiments applied RCNN to image segmentation and fusion tasks, demonstrating its robustness, efficiency, and high noise resistance. Results indicate that RCNN surpasses traditional methods in performance across these applications.

Discovery of MD-265: A Potent MDM2 Degrader That Achieves Complete Tumor Regression and Improves Long-Term Survival of Mice with Leukemia.

MDM2 has been pursued as an attractive therapeutic target for human cancers. Herein, we describe our discovery of MD-265 as a promising PROTAC MDM2 degrader and extensive in vitro and in vivo evaluations of its therapeutic potential and mechanism of action. MD-265 effectively depleted MDM2 protein in cancer cells at concentrations as low as 1 nM, leading to strong activation of p53 in cancer cells carrying wild-type p53. It selectively inhibited the growth of wild-type p53 leukemia cell lines and showed no activity in mutated p53 lines. MD-265 achieved persistent tumor regression in a leukemia xenograft model without causing any signs of toxicity and dramatically improved survival of mice in a disseminated leukemia model even with a weekly administration. MD-265 displayed an excellent intravenous PK profile in mice, rats, and dogs. MD-265 is a promising MDM2 degrader for advanced preclinical development for the treatment of human cancers.

Measuring Compound Muscle Action Potentials in Mouse Forelimb Muscles In Vivo

Measuring Compound Muscle Action Potentials in Mouse Forelimb Muscles In Vivo

Measuring Compound Muscle Action Potentials in Mouse Forelimb Muscles In Vivo

Measuring Compound Muscle Action Potentials in Mouse Forelimb Muscles In Vivo

Neuroplasticity Meets Artificial Intelligence: A Hippocampus-Inspired Approach to the Stability–Plasticity Dilemma

The stability–plasticity dilemma remains a critical challenge in developing artificial intelligence (AI) systems capable of continuous learning. This perspective paper presents a novel approach by drawing inspiration from the mammalian hippocampus–cortex system. We elucidate how this biological system’s ability to balance rapid learning with long-term memory retention can inspire novel AI architectures. Our analysis focuses on key mechanisms, including complementary learning systems and memory consolidation, with emphasis on recent discoveries about sharp-wave ripples and barrages of action potentials. We propose innovative AI designs incorporating dual learning rates, offline consolidation, and dynamic plasticity modulation. This interdisciplinary approach offers a framework for more adaptive AI systems while providing insights into biological learning. We present testable predictions and discuss potential implementations and implications of these biologically inspired principles. By bridging neuroscience and AI, our perspective aims to catalyze advancements in both fields, potentially revolutionizing AI capabilities while deepening our understanding of neural processes.

PUFA stabilizes a conductive state of the selectivity filter in IKs channels.

In cardiomyocytes, the KCNQ1/KCNE1 channel complex mediates the slow delayed-rectifier current (IKs), pivotal during the repolarization phase of the ventricular action potential. Mutations in IKs cause long QT syndrome (LQTS), a syndrome with a prolonged QT interval on the ECG, which increases the risk of ventricular arrhythmia and sudden cardiac death. One potential therapeutical intervention for LQTS is based on targeting IKs channels to restore channel function and/or the physiological QT interval. Polyunsaturated fatty acids (PUFAs) are potent activators of KCNQ1 channels and activate IKs channels by binding to two different sites, one in the voltage sensor domain - which shifts the voltage dependence to more negative voltages - and the other in the pore domain - which increases the maximal conductance of the channels (Gmax). However, the mechanism by which PUFAs increase the Gmax of the IKs channels is still poorly understood. In addition, it is unclear why IKs channels have a very small single-channel conductance and a low open probability or whether PUFAs affect any of these properties of IKs channels. Our results suggest that the selectivity filter in KCNQ1 is normally unstable, contributing to the low open probability, and that the PUFA-induced increase in Gmax is caused by a stabilization of the selectivity filter in an open-conductive state.