Assessment of the excitation-inhibition ratio in the Fmr1 KO2 mouse using neuronal oscillation dynamics.

In vitro and ex vivo studies have shown consistent indications of hyperexcitability in the Fragile X Messenger Ribonucleoprotein 1 (Fmr1) knockout mouse model of autism spectrum disorder. We recently introduced a method to quantify network-level functional excitation-inhibition ratio from the neuronal oscillations. Here, we used this measure to study whether the implicated synaptic excitation-inhibition disturbances translate to disturbances in network physiology in the Fragile X Messenger Ribonucleoprotein 1 (Fmr1) gene knockout model. Vigilance-state scoring was used to extract segments of inactive wakefulness as an equivalent behavioral condition to the human resting-state and, subsequently, we performed high-frequency resolution analysis of the functional excitation-inhibition biomarker, long-range temporal correlations, and spectral power. We corroborated earlier studies showing increased high-frequency power in Fragile X Messenger Ribonucleoprotein 1 (Fmr1) knockout mice. Long-range temporal correlations were higher in the gamma frequency ranges. Contrary to expectations, functional excitation-inhibition was lower in the knockout mice in high frequency ranges, suggesting more inhibition-dominated networks. Exposure to the Gamma-aminobutyric acid (GABA)-agonist clonazepam decreased the functional excitation-inhibition in both genotypes, confirming that increasing inhibitory tone results in a reduction of functional excitation-inhibition. In addition, clonazepam decreased electroencephalogram power and increased long-range temporal correlations in both genotypes. These findings show applicability of these new resting-state electroencephalogram biomarkers to animal for translational studies and allow investigation of the effects of lower-level disturbances in excitation-inhibition balance.

Neuron-level explainable AI for Alzheimer's Disease assessment from fundus images.

Alzheimer's Disease (AD) is a progressive neurodegenerative disease and the leading cause of dementia. Early diagnosis is critical for patients to benefit from potential intervention and treatment. The retina has emerged as a plausible diagnostic site for AD detection owing to its anatomical connection with the brain. However, existing AI models for this purpose have yet to provide a rational explanation behind their decisions and have not been able to infer the stage of the disease's progression. Along this direction, we propose a novel model-agnostic explainable-AI framework, called Granu la ̲ r Neuron-le v ̲ el Expl a ̲ iner (LAVA), an interpretation prototype that probes into intermediate layers of the Convolutional Neural Network (CNN) models to directly assess the continuum of AD from the retinal imaging without the need for longitudinal or clinical evaluations. This innovative approach aims to validate retinal vasculature as a biomarker and diagnostic modality for evaluating Alzheimer's Disease. Leveraged UK Biobank cognitive tests and vascular morphological features demonstrate significant promise and effectiveness of LAVA in identifying AD stages across the progression continuum.

The Determining Role of Covariances in Large Networks of Stochastic Neurons.

Biological neural networks are notoriously hard to model due to their stochastic behavior and high dimensionality. We tackle this problem by constructing a dynamical model of both the expectations and covariances of the fractions of active and refractory neurons in the network's populations. We do so by describing the evolution of the states of individual neurons with a continuous-time Markov chain, from which we formally derive a low-dimensional dynamical system. This is done by solving a moment closure problem in a way that is compatible with the nonlinearity and boundedness of the activation function. Our dynamical system captures the behavior of the high-dimensional stochastic model even in cases where the mean-field approximation fails to do so. Taking into account the second-order moments modifies the solutions that would be obtained with the mean-field approximation and can lead to the appearance or disappearance of fixed points and limit cycles. We moreover perform numerical experiments where the mean-field approximation leads to periodically oscillating solutions, while the solutions of the second-order model can be interpreted as an average taken over many realizations of the stochastic model. Altogether, our results highlight the importance of including higher moments when studying stochastic networks and deepen our understanding of correlated neuronal activity.

Bayesian inference of structured latent spaces from neural population activity with the orthogonal stochastic linear mixing model.

The brain produces diverse functions, from perceiving sounds to producing arm reaches, through the collective activity of populations of many neurons. Determining if and how the features of these exogenous variables (e.g., sound frequency, reach angle) are reflected in population neural activity is important for understanding how the brain operates. Often, high-dimensional neural population activity is confined to low-dimensional latent spaces. However, many current methods fail to extract latent spaces that are clearly structured by exogenous variables. This has contributed to a debate about whether or not brains should be thought of as dynamical systems or representational systems. Here, we developed a new latent process Bayesian regression framework, the orthogonal stochastic linear mixing model (OSLMM) which introduces an orthogonality constraint amongst time-varying mixture coefficients, and provide Markov chain Monte Carlo inference procedures. We demonstrate superior performance of OSLMM on latent trajectory recovery in synthetic experiments and show superior computational efficiency and prediction performance on several real-world benchmark data sets. We primarily focus on demonstrating the utility of OSLMM in two neural data sets: µECoG recordings from rat auditory cortex during presentation of pure tones and multi-single unit recordings form monkey motor cortex during complex arm reaching. We show that OSLMM achieves superior or comparable predictive accuracy of neural data and decoding of external variables (e.g., reach velocity). Most importantly, in both experimental contexts, we demonstrate that OSLMM latent trajectories directly reflect features of the sounds and reaches, demonstrating that neural dynamics are structured by neural representations. Together, these results demonstrate that OSLMM will be useful for the analysis of diverse, large-scale biological time-series datasets.

Nucleus accumbens D1- and D2-expressing neurons control the balance between feeding and activity-mediated energy expenditure.

Accumulating evidence points to dysregulations of the Nucleus Accumbens (NAc) in eating disorders (ED), however its precise contribution to ED symptomatic dimensions remains unclear. Using chemogenetic manipulations in male mice, we found that activity of dopamine D1 receptor-expressing neurons of the NAc core subregion facilitated effort for a food reward as well as voluntary exercise, but decreased food intake, while D2-expressing neurons have opposite effects. These effects are congruent with D2-neurons being more active than D1-neurons during feeding while it is the opposite during running. Chronic manipulations of each subpopulations had limited effects on energy balance. However, repeated activation of D1-neurons combined with inhibition of D2-neurons biased behavior toward activity-related energy expenditure, whilst the opposite manipulations favored energy intake. Strikingly, concomitant activation of D1-neurons and inhibition of D2-neurons precipitated weight loss in anorexia models. These results suggest that dysregulations of NAc dopaminoceptive neurons might be at the core of EDs.

Sildenafil as a Candidate Drug for Alzheimer's Disease: Real-World Patient Data Observation and Mechanistic Observations from Patient-Induced Pluripotent Stem Cell-Derived Neurons.

Background: Alzheimer's disease (AD) is a chronic neurodegenerative disease needing effective therapeutics urgently. Sildenafil, one of the approved phosphodiesterase-5 inhibitors, has been implicated as having potential effect in AD. Objective: To investigate the potential therapeutic benefit of sildenafil on AD. Methods: We performed real-world patient data analysis using the MarketScan® Medicare Supplemental and the Clinformatics® databases. We conducted propensity score-stratified analyses after adjusting confounding factors (i.e., sex, age, race, and comorbidities). We used both familial and sporadic AD patient induced pluripotent stem cells (iPSC) derived neurons to evaluate the sildenafil's mechanism-of-action. Results: We showed that sildenafil usage is associated with reduced likelihood of AD across four new drug compactor cohorts, including bumetanide, furosemide, spironolactone, and nifedipine. For instance, sildenafil usage is associated with a 54% reduced incidence of AD in MarketScan® (hazard ratio [HR] = 0.46, 95% CI 0.32- 0.66) and a 30% reduced prevalence of AD in Clinformatics® (HR = 0.70, 95% CI 0.49- 1.00) compared to spironolactone. We found that sildenafil treatment reduced tau hyperphosphorylation (pTau181 and pTau205) in a dose-dependent manner in both familial and sporadic AD patient iPSC-derived neurons. RNA-sequencing data analysis of sildenafil-treated AD patient iPSC-derived neurons reveals that sildenafil specifically target AD related genes and pathobiological pathways, mechanistically supporting the beneficial effect of sildenafil in AD. Conclusions: These real-world patient data validation and mechanistic observations from patient iPSC-derived neurons further suggested that sildenafil is a potential repurposable drug for AD. Yet, randomized clinical trials are warranted to validate the causal treatment effects of sildenafil in AD.

Performance of assessment tools in predicting neural autoantibody positivity in patients with seizures.

BACKGROUND: The identification of patients with seizures of unknown etiology who would benefit from neural antibody testing necessitates effective assessment tools. The study aimed to compare the performance of the Antibody Prevalence in Epilepsy and Encephalopathy (APE2) score and the "Obvious" Indications for Neural Antibody Testing in Epilepsy or Seizures (ONES) checklist. We also intended to evaluate whether the performance of the tools varied by types of antibody. METHODS: Patients diagnosed with epilepsy, seizures, or status epilepticus of unknown etiology at West China Hospital from January 2019 to December 2021 were included. Paired serum/cerebrospinal fluid samples were analyzed for antineuronal and antiglial antibodies. The APE2 score and ONES checklist were applied, and their outcomes were compared to laboratory antibody test results. Possible false positive neuronal antibody results were excluded in sensitivity/specificity analysis reasonably. RESULTS: A total of 113 antibody-positive and 159 antibody-negative patients were enrolled in sensitivity/specificity analysis. The ONES checklist showed superior sensitivity than APE2 score (95.6 % vs.79.6 %, P < 0.001). Specificity was not statistically different (60.4 % vs. 57.9 %, P = 0.557). The negative predictive value (NPV) of ONES checklist was higher than that of APE2 score (94.8 % vs 80.7 %, P < 0.001). The positive predictive value of them was not statistically different (61.7 % vs 58.8 %, P = 0.557). APE2 score exhibited lower sensitivity for predicting LGI-Abs (52.9 % vs. 80.3 %, P = 0.022) compared to NMDAR-Abs. Similarly, ONES checklist showed lower sensitivity for LGI1-Abs than NMDAR-Abs (82.4 % vs. 100.0 %, P = 0.009). CONCLUSIONS: The ONES checklist demonstrates superior sensitivity for neural antibody positivity than APE2 score. Specificity of the two assessment tools was similar. ONES checklist performed better NPV than the APE2 score. Both assessment tools performed less well in predicting the presence of LGI1- Abs when compared to NMDAR-Abs.

The assessment of neuronal plasticity following sciatic nerve injuries in rats using electron microscopy and stereological methods.

The transmission of signals to the cell body from injured axons induces significant alterations in primary sensory neurons located in the ganglion tissue, the site of the perikaryon of the affected nerve fibers. Disruption of the continuity between the proximal and distal ends leads to substantial adaptability in ganglion cells and induces macrophage-like activity in the satellite cells. Research findings have demonstrated the plasticity of satellite cells following injury. Satellite cells work together with sensory neurons to extend the interconnected surface area in order to permit effective communication. The dynamic cellular environment within the ganglion undergoes several alterations that ultimately lead to differentiation, transformation, or cell death. In addition to necrotic and apoptotic cell morphology, phenomena such as histomorphometric alterations, including the development of autophagic vacuoles, chromatolysis, cytosolic degeneration, and other changes, are frequently observed in cells following injury. The use of electron microscopic and stereological techniques for assessing ganglia and nerve fibers is considered a gold standard in terms of investigating neuropathic pain models, regenerative therapies, some treatment methods, and quantifying the outcomes of pharmacological and bioengineering interventions. Stereological techniques provide observer-independent and reliable results, which are particularly useful in the quantitative assessment of three-dimensional structures from two-dimensional images. Employing the fractionator and disector techniques within stereological methodologies yields unbiased data when assessing parameters such as number. The fundamental concept underlying these methodologies involves ensuring that each part of the structure under evaluation has an equal opportunity of being sampled. This review describes the stereological and histomorphometric evaluation of dorsal root ganglion neurons and satellite cells following nerve injury models.

Floating-Point Approximation Enabling Cost-Effective and High-Precision Digital Implementation of FitzHugh-Nagumo Neural Networks.

The study of neuron interactions and hardware implementations are crucial research directions in neuroscience, particularly in developing large-scale biological neural networks. The FitzHugh-Nagumo (FHN) model is a popular neuron model with highly biological plausibility, but its complexity makes it difficult to apply at scale. This paper presents a cost-saving and improved precision approximation algorithm for the digital implementation of the FHN model. By converting the computational data into floating-point numbers, the original multiplication calculations are replaced by adding the floating-point exponent part and fitting the mantissa part with piecewise linear. In the hardware implementation, shifters and adders are used, greatly reducing resource overhead. Implementing FHN neurons by this approximation calculations on FPGA reduces the normalized root mean square error (RMSE) to 3.5% of the state-of-the-art (SOTA) while maintaining a performance overhead ratio improvement of 1.09 times. Compared to implementations based on approximate multipliers, the proposed method achieves a 20% reduction in error at the cost of a 2.8% increase in overhead.This model gained additional biological properties compared to LIF while reducing the deployment scale by only 9%. Furthermore, the hardware implementation of nine coupled circular networks with eight nodes and directional diffusion was carried out to demonstrate the algorithm's effectiveness on neural networks. The error decreased to 60% compared to the single neuron of the SOTA. This hardware-friendly algorithm allows for the low-cost implementation of high-precision hardware simulation, providing a novel perspective for studying large-scale, biologically plausible neural networks.

High-Matching and Low-Cost Realization of the FHN Neuron Model on Reconfigurable FPGA Board.

The main objectives of neuromorphic engineering are the research, modeling, and implementation of neural functioning in the human brain. We provide a hardware solution that can replicate such a nature-inspired system by merging multiple scientific domains and is based on neural cell processes. This work provides a modified version of the original Fitz-Hugh Nagumo (FHN) neuron using a simple 2V term called Hybrid Piece-Wised Base-2 Model (HPWBM), which accurately reproduces numerous patterns of the original neuron model. With reduced terms, we suggest modifying the original nonlinear term to achieve high matching accuracy and little computing error. Time domain and phase portraits are used to validate the proposed model, which shows that it can reproduce all of the FHN model's properties with high accuracy and little mistake. We provide an effective digital hardware approach for large-scale neuron implementations based on resource-sharing and pipelining strategies. The Hardware Description Language (HDL) is used to construct the hardware on an FPGA as a proof of concept. The recommended model hardly uses 0.48 percent of the resources on a Virtex 4 FPGA board, according to the results of the hardware implementation. The circuit can run at a maximum frequency of 448.236 MHz, according to the static timing study.

Rapid threat assessment in the Drosophila thermosensory system.

Neurons that participate in sensory processing often display "ON" responses, i.e., fire transiently at the onset of a stimulus. ON transients are widespread, perhaps universal to sensory coding, yet their function is not always well-understood. Here, we show that ON responses in the Drosophila thermosensory system extrapolate the trajectory of temperature change, priming escape behavior if unsafe thermal conditions are imminent. First, we show that second-order thermosensory projection neurons (TPN-IIIs) and their Lateral Horn targets (TLHONs), display ON responses to thermal stimuli, independent of direction of change (heating or cooling) and of absolute temperature. Instead, they track the rate of temperature change, with TLHONs firing exclusively to rapid changes (>0.2 °C/s). Next, we use connectomics to track TLHONs' output to descending neurons that control walking and escape, and modeling and genetic silencing to demonstrate how ON transients can flexibly amplify aversive responses to small thermal change. Our results suggest that, across sensory systems, ON transients may represent a general mechanism to systematically anticipate and respond to salient or dangerous conditions.

The Hodgkin-Huxley-Katz Prize Lecture: A Markov model with permeation-dependent gating that accounts for resurgent current of voltage-gated Na channels.

Many neurons that fire high-frequency action potentials express specialized voltage-gated Na channel complexes that not only conduct transient current upon depolarization, but also pass resurgent current upon repolarization. The resurgent current is associated with recovery of transient current, even at moderately negative potentials where fast inactivation is usually absorbing. The combined results of many experimental studies have led to the hypothesis that resurgent current flows upon repolarization when an endogenous blocking protein that occludes open channels at depolarized potentials is expelled by inwardly permeating Na ions. Additional observations have suggested that the position of the voltage sensor of domain IV regulates the affinity of the channel for the putative blocker. To test the effectiveness of a kinetic scheme incorporating these features, here we develop and justify a Markov model with states grounded in known Na channel conformations. Simulations were designed to investigate whether including a permeation-dependent unblocking rate constant and two open-blocked states, superimposed on conformations and voltage-sensitive movements present in all voltage-gated Na channels, is sufficient to account for the unusual gating of channels with a resurgent component. Optimizing rate constant parameters against a wide range of experimental data from cerebellar Purkinje cells demonstrates that a kinetic scheme for Na channels incorporating the novel aspects of a permeation-dependent unblock, as well as distinct high- and low-affinity blocked states, reproduces all the attributes of experimentally recorded Na currents in a physiologically plausible manner.

The rat frontal orienting field dynamically encodes value for economic decisions under risk.

Frontal and parietal cortex are implicated in economic decision-making, but their causal roles are untested. Here we silenced the frontal orienting field (FOF) and posterior parietal cortex (PPC) while rats chose between a cued lottery and a small stable surebet. PPC inactivations produced minimal short-lived effects. FOF inactivations reliably reduced lottery choices. A mixed-agent model of choice indicated that silencing the FOF caused a change in the curvature of the rats' utility function (U = Vρ). Consistent with this finding, single-neuron and population analyses of neural activity confirmed that the FOF encodes the lottery value on each trial. A dynamical model, which accounts for electrophysiological and silencing results, suggests that the FOF represents the current lottery value to compare against the remembered surebet value. These results demonstrate that the FOF is a critical node in the neural circuit for the dynamic representation of action values for choice under risk.

Reliable population code for subjective economic value from heterogeneous neuronal signals in primate orbitofrontal cortex.

Behavior-related neuronal signals often vary between neurons, which might reflect the unreliability of individual neurons or a truly heterogeneous code. This notion may also apply to economic ("value-based") choices and the underlying reward signals. Reward value is subjective and can be described by a nonlinearly weighted magnitude (utility) and probability. Defining subjective values relies on the continuity axiom, whose testing involves structured variations of a wide range of reward magnitudes and probabilities. Axiom compliance demonstrates understanding of the stimuli and the meaningful character of choices. Using these tests, we investigated the encoding of subjective economic value by neurons in a key economic-decision structure of the monkey brain, the orbitofrontal cortex (OFC). We found that individual neurons carry heterogeneous neuronal value signals that largely fail to match the animal's choices. However, neuronal population signals matched the animal's choices well, suggesting accurate subjective economic value encoding by a heterogeneous population of unreliable neurons.

Isoflurane Alters Presynaptic Endoplasmic Reticulum Calcium Dynamics in Wild-Type and Malignant Hyperthermia-Susceptible Rodent Hippocampal Neurons.

Volatile anesthetics reduce excitatory synaptic transmission by both presynaptic and postsynaptic mechanisms which include inhibition of depolarization-evoked increases in presynaptic Ca2+ concentration and blockade of postsynaptic excitatory glutamate receptors. The presynaptic sites of action leading to reduced electrically evoked increases in presynaptic Ca2+ concentration and Ca2+-dependent exocytosis are unknown. Endoplasmic reticulum (ER) of Ca2+ release via ryanodine receptor 1 (RyR1) and uptake by SERCA are essential for regulation intracellular Ca2+ and are potential targets for anesthetic action. Mutations in sarcoplasmic reticulum (SR) release channels mediate volatile anesthetic-induced malignant hyperthermia (MH), a potentially fatal pharmacogenetic condition characterized by unregulated Ca2+ release and muscle hypermetabolism. However, the impact of MH mutations on neuronal function are unknown. We used primary cultures of postnatal hippocampal neurons to analyze volatile anesthetic-induced changes in ER Ca2+ dynamics using a genetically encoded ER-targeted fluorescent Ca2+ sensor in both rat and mouse wild-type (WT) neurons and in mouse mutant neurons harboring the RYR1 T4826I MH-susceptibility mutation. The volatile anesthetic isoflurane reduced both baseline and electrical stimulation-evoked increases in ER Ca2+ concentration in neurons independent of its depression of presynaptic cytoplasmic Ca2+ concentrations. Isoflurane and sevoflurane, but not propofol, depressed depolarization-evoked increases in ER Ca2+ concentration significantly more in mouse RYR1 T4826I mutant neurons than in wild-type neurons. The RYR1 T4826I mutant neurons also showed markedly greater isoflurane-induced reductions in presynaptic cytosolic Ca2+ concentration and synaptic vesicle (SV) exocytosis. These findings implicate RyR1 as a molecular target for the effects of isoflurane on presynaptic Ca2+ handling.

Electrophysiological signatures of inequity-dependent reward encoding in the human OFC.

Social decision making requires the integration of reward valuation and social cognition systems, both dependent on the orbitofrontal cortex (OFC). How these two OFC functions interact is largely unknown. We recorded intracranial activity from the OFC of ten patients making choices in a social context where reward inequity with a social counterpart varied and could be either advantageous or disadvantageous. We find that OFC high-frequency activity (HFA; 70-150 Hz) encodes self-reward, consistent with previous reports. We also observe encoding of the social counterpart's reward, as well as the type of inequity being experienced. Additionally, we find evidence of inequity-dependent reward encoding: depending on the type of inequity, electrodes rapidly and reversibly switch between different reward-encoding profiles. These results provide direct evidence for encoding of self- and other rewards in the human OFC and highlight the dynamic nature of encoding in the OFC as a function of social context.

Comparative assessment of Ca2+ oscillations in 2- and 3-dimensional hiPSC derived and isolated cortical neuronal networks.

Human induced Pluripotent Stem Cell (hiPSC) derived neural cells offer great potential for modelling neurological diseases and toxicities and have found application in drug discovery and toxicology. As part of the European Innovative Medicines Initiative (IMI2) NeuroDeRisk (Neurotoxicity De-Risking in Preclinical Drug Discovery), we here explore the Ca2+ oscillation responses of 2D and 3D hiPSC derived neuronal networks of mixed Glutamatergic/GABAergic activity with a compound set encompassing both clinically as well as experimentally determined seizurogenic compounds. Both types of networks are scored against Ca2+ responses of a primary mouse cortical neuronal 2D network model serving as an established comparator assay. Parameters of frequency and amplitude of spontaneous global network Ca2+ oscillations and the drug-dependent directional changes to these were assessed, and predictivity of seizurogenicity scored using contingency table analysis. In addition, responses between models were compared between both 2D models as well as between 2D and 3D models. Concordance of parameter responses was best between the hiPSC neurospheroid and the mouse primary cortical neuron model (77% for frequency and 65% for amplitude). Decreases in spontaneous Ca2+ oscillation frequency and amplitude were found to be the most basic shared determinants of risk of seizurogenicity between the mouse and the neurospheroid model based on testing of clinical compounds with documented seizurogenic activity. Increases in spontaneous Ca2+ oscillation frequency were primarily observed with the 2D hIPSC model, though the specificity of this effect to seizurogenic clinical compounds was low (33%), while decreases to spike amplitude in this model were more predictive of seizurogenicity. Overall predictivities of the models were similar, with sensitivity of the assays typically exceeding specificity due to high false positive rates. Higher concordance of the hiPSC 3D model over the 2D model when compared to mouse cortical 2D responses may be the result of both a longer maturation time of the neurospheroid (84-87 days for 3D vs. 22-24 days for 2D maturation) as well as the 3-dimensional nature of network connections established. The simplicity and reproducibility of spontaneous Ca2+ oscillation readouts support further investigation of hiPSC derived neuronal sources and their 2- and 3-dimensional networks for neuropharmacological safety screening.

Engineering a multicompartment in vitro model for dorsal root ganglia phenotypic assessment.

Despite the significant global prevalence of chronic pain, current methods to identify pain therapeutics often fail translation to the clinic. Phenotypic screening platforms rely on modeling and assessing key pathologies relevant to chronic pain, improving predictive capability. Patients with chronic pain often present with sensitization of primary sensory neurons (that extend from dorsal root ganglia [DRG]). During neuronal sensitization, painful nociceptors display lowered stimulation thresholds. To model neuronal excitability, it is necessary to maintain three key anatomical features of DRGs to have a physiologically relevant platform: (1) isolation between DRG cell bodies and neurons, (2) 3D platform to preserve cell-cell and cell-matrix interactions, and (3) presence of native non-neuronal support cells, including Schwann cells and satellite glial cells. Currently, no culture platforms maintain the three anatomical features of DRGs. Herein, we demonstrate an engineered 3D multicompartment device that isolates DRG cell bodies and neurites and maintains native support cells. We observed neurite growth into isolated compartments from the DRG using two formulations of collagen, hyaluronic acid, and laminin-based hydrogels. Further, we characterized the rheological, gelation and diffusivity properties of the two hydrogel formulations and found the mechanical properties mimic native neuronal tissue. Importantly, we successfully limited fluidic diffusion between the DRG and neurite compartment for up to 72 h, suggesting physiological relevance. Lastly, we developed a platform with the capability of phenotypic assessment of neuronal excitability using calcium imaging. Ultimately, our culture platform can screen neuronal excitability, providing a more translational and predictive system to identify novel pain therapeutics to treat chronic pain.

The long-loop recycling (LLR) of synaptic components as a question of economics.

The pre- and post-synaptic compartments contain a variety of molecules that are known to recycle between the plasma membrane and intracellular organelles. The recycling steps have been amply described in functional terms, with, for example, synaptic vesicle recycling being essential for neurotransmitter release, and postsynaptic receptor recycling being a fundamental feature of synaptic plasticity. However, synaptic protein recycling may also serve a more prosaic role, simply ensuring the repeated use of specific components, thereby minimizing the energy expenditure on the synthesis of synaptic proteins. This type of process has been recently described for components of the extracellular matrix, which undergo long-loop recycling (LLR), to and from the cell body. Here we suggest that the energy-saving recycling of synaptic components may be more widespread than is generally acknowledged, potentially playing a role in both synaptic vesicle protein usage and postsynaptic receptor metabolism.

Assessment of a single trial impact on the amplitude of the averaged event related potentials.

Widely used in neuroscience the averaging of event related potentials is based on the assumption that small responses to the investigated events are present in every trial but can be hidden under the random noise. This situation often takes place, especially in experiments performed at hierarchically lower levels of sensory systems. However, in the studies of higher order complex neuronal networks evoked responses might appear only under particular conditions and be absent otherwise. We encountered this problem studying a propagation of interoceptive information to the cortical areas in the sleep-wake cycle. Cortical responses to various visceral events were present during some periods of sleep, then disappeared for a while and restored again after a period of absence. Further investigation of the viscero-cortical communication required a method that would allow labeling the trials contributing to the averaged event related responses-"efficient trials," and separating them from the trials without any response. Here we describe a heuristic approach to solving this problem in the context of viscero-cortical interactions occurring during sleep. However, we think that the proposed technique can be applicable to any situation where neuronal processing of the same events is expected to be variable due to internal or external factors modulating neuronal activity. The method was first implemented as a script for Spike 2 program version 6.16 (CED). However, at present a functionally equivalent version of this algorithm is also available as Matlab code at https://github.com/george-fedorov/erp-correlations.