Isoflurane anesthesia suppresses gastric myoelectric power in the ferret.

BACKGROUND: Gastric myoelectric signals have been the focus of extensive research; although it is unclear how general anesthesia affects these signals, and studies have often been conducted under general anesthesia. Here, we explore this issue directly by recording gastric myoelectric signals during awake and anesthetized states in the ferret and explore the contribution of behavioral movement to observed changes in signal power. METHODS: Ferrets were surgically implanted with electrodes to record gastric myoelectric activity from the serosal surface of the stomach, and, following recovery, were tested in awake and isoflurane-anesthetized conditions. Video recordings were also analyzed during awake experiments to compare myoelectric activity during behavioral movement and rest. KEY RESULTS: A significant decrease in gastric myoelectric signal power was detected under isoflurane anesthesia compared to the awake condition. Moreover, a detailed analysis of the awake recordings indicates that behavioral movement is associated with increased signal power compared to rest. CONCLUSIONS & INFERENCES: These results suggest that both general anesthesia and behavioral movement can affect the signal power of gastric myoelectric recordings. In summary, caution should be taken in studying myoelectric data collected under anesthesia. Further, behavioral movement could have an important modulatory role on these signals, affecting their interpretation in clinical settings.

Anesthesia suppresses gastric myoelectric power in the ferret.

BACKGROUND: Gastrointestinal myoelectric signals have been the focus of extensive research; although it is unclear how general anesthesia affects these signals, studies have often been conducted under general anesthesia. Here, we explore this issue directly by recording gastric myoelectric signals during awake and anesthetized states in the ferret and also explore the contribution of behavioral movement to observed changes in signal power. METHODS: Ferrets were surgically implanted with electrodes to record gastric myoelectric activity from the serosal surface of the stomach, and, following recovery, were tested in awake and isoflurane-anesthetized conditions. Video recordings were also analyzed during awake experiments to compare myoelectric activity during behavioral movement and rest. KEY RESULTS: A significant decrease in gastric myoelectric signal power was detected under isoflurane anesthesia compared to the awake condition. Moreover, a detailed analysis of the awake recordings indicates that behavioral movement is associated with increased signal power compared to rest. CONCLUSIONS & INFERENCES: These results suggest that both general anesthesia and behavioral movement can affect the amplitude of gastric myoelectric. In summary, caution should be taken in studying myoelectric data collected under anesthesia. Further, behavioral movement could have an important modulatory role on these signals, affecting their interpretation in clinical settings.

Can auxiliary indicators improve COVID-19 forecasting and hotspot prediction?

Short-term forecasts of traditional streams from public health reporting (such as cases, hospitalizations, and deaths) are a key input to public health decision-making during a pandemic. Since early 2020, our research group has worked with data partners to collect, curate, and make publicly available numerous real-time COVID-19 indicators, providing multiple views of pandemic activity in the United States. This paper studies the utility of five such indicators-derived from deidentified medical insurance claims, self-reported symptoms from online surveys, and COVID-related Google search activity-from a forecasting perspective. For each indicator, we ask whether its inclusion in an autoregressive (AR) model leads to improved predictive accuracy relative to the same model excluding it. Such an AR model, without external features, is already competitive with many top COVID-19 forecasting models in use today. Our analysis reveals that 1) inclusion of each of these five indicators improves on the overall predictive accuracy of the AR model; 2) predictive gains are in general most pronounced during times in which COVID cases are trending in "flat" or "down" directions; and 3) one indicator, based on Google searches, seems to be particularly helpful during "up" trends.

Mechanisms of the Osteogenic Switch of Smooth Muscle Cells in Vascular Calcification: WNT Signaling, BMPs, Mechanotransduction, and EndMT.

Characterized by the hardening of arteries, vascular calcification is the deposition of hydroxyapatite crystals in the arterial tissue. Calcification is now understood to be a cell-regulated process involving the phenotypic transition of vascular smooth muscle cells into osteoblast-like cells. There are various pathways of initiation and mechanisms behind vascular calcification, but this literature review highlights the wingless-related integration site (WNT) pathway, along with bone morphogenic proteins (BMPs) and mechanical strain. The process mirrors that of bone formation and remodeling, as an increase in mechanical stress causes osteogenesis. Observing the similarities between the two may aid in the development of a deeper understanding of calcification. Both are thought to be regulated by the WNT signaling cascade and bone morphogenetic protein signaling and can also be activated in response to stress. In a pro-calcific environment, integrins and cadherins of vascular smooth muscle cells respond to a mechanical stimulus, activating cellular signaling pathways, ultimately resulting in gene regulation that promotes calcification of the vascular extracellular matrix (ECM). The endothelium is also thought to contribute to vascular calcification via endothelial to mesenchymal transition, creating greater cell plasticity. Each of these factors contributes to calcification, leading to increased cardiovascular mortality in patients, especially those suffering from other conditions, such as diabetes and kidney failure. Developing a better understanding of the mechanisms behind calcification may lead to the development of a potential treatment in the future.

Latent Dynamic Factor Analysis of High-Dimensional Neural Recordings.

High-dimensional neural recordings across multiple brain regions can be used to establish functional connectivity with good spatial and temporal resolution. We designed and implemented a novel method, Latent Dynamic Factor Analysis of High-dimensional time series (LDFA-H), which combines (a) a new approach to estimating the covariance structure among high-dimensional time series (for the observed variables) and (b) a new extension of probabilistic CCA to dynamic time series (for the latent variables). Our interest is in the cross-correlations among the latent variables which, in neural recordings, may capture the flow of information from one brain region to another. Simulations show that LDFA-H outperforms existing methods in the sense that it captures target factors even when within-region correlation due to noise dominates cross-region correlation. We applied our method to local field potential (LFP) recordings from 192 electrodes in Prefrontal Cortex (PFC) and visual area V4 during a memory-guided saccade task. The results capture time-varying lead-lag dependencies between PFC and V4, and display the associated spatial distribution of the signals.

Evaluation of flash glucose monitoring after long-term use: A pediatric survey.

AIMS: To understand the opinions of children with type 1 diabetes about their everyday use of flash glucose monitoring. (FGM). METHODS: Children with type 1 diabetes using the FreeStyle Libre® FGM system and/or their parents were surveyed in several French medical centers between December 2016 and June 2017, regardless of their treatment regimen and metabolic control. RESULTS: Of the 347 patients recruited, 79.5% had been using the sensor for more than three months (average usage time: 285 days). The main reported motivations for initiating this type of monitoring were to avoid finger prick pain (for 85.9% of patients) and to allow parents to check nocturnal glucose levels (60.8%). Two-thirds of respondents experienced difficulties, mainly the sensor falling off (47.6%), measurement discrepancies (25.1%) and cutaneous reactions (22.2%); 89.5% changed their habits: 70.6% took more scans, 37.2% corrected their hyperglycemia more promptly, and 37.5% used trends to adjust their insulin dosage. About one-third of the study group (35.1%) experienced lower HbA1c levels, and two thirds (67.1%) were satisfied with the device. CONCLUSIONS: Our results show that FGM is a widely accepted option for self-monitoring diabetes, but that specific training is required to improve its use for insulin dosage adjustment and metabolic results.

Stability of point process spiking neuron models.

Point process regression models, based on generalized linear model (GLM) technology, have been widely used for spike train analysis, but a recent paper by Gerhard et al. described a kind of instability, in which fitted models can generate simulated spike trains with explosive firing rates. We analyze the problem by extending the methods of Gerhard et al. First, we improve their instability diagnostic and extend it to a wider class of models. Next, we point out some common situations in which instability can be traced to model lack of fit. Finally, we investigate distinctions between models that use a single filter to represent the effects of all spikes prior to any particular time t, as in a 2008 paper by Pillow et al., and those that allow different filters for each spike prior to time t, as in a 2001 paper by Kass and Ventura. We re-analyze the data sets used by Gerhard et al., introduce an additional data set that exhibits bursting, and use a well-known model described by Izhikevich to simulate spike trains from various ground truth scenarios. We conclude that models with multiple filters tend to avoid instability, but there are unlikely to be universal rules. Instead, care in data fitting is required and models need to be assessed for each unique set of data.

Adjusted regularization of cortical covariance.

It is now common to record dozens to hundreds or more neurons simultaneously, and to ask how the network activity changes across experimental conditions. A natural framework for addressing questions of functional connectivity is to apply Gaussian graphical modeling to neural data, where each edge in the graph corresponds to a non-zero partial correlation between neurons. Because the number of possible edges is large, one strategy for estimating the graph has been to apply methods that aim to identify large sparse effects using an [Formula: see text] penalty. However, the partial correlations found in neural spike count data are neither large nor sparse, so techniques that perform well in sparse settings will typically perform poorly in the context of neural spike count data. Fortunately, the correlated firing for any pair of cortical neurons depends strongly on both their distance apart and the features for which they are tuned. We introduce a method that takes advantage of these known, strong effects by allowing the penalty to depend on them: thus, for example, the connection between pairs of neurons that are close together will be penalized less than pairs that are far apart. We show through simulations that this physiologically-motivated procedure performs substantially better than off-the-shelf generic tools, and we illustrate by applying the methodology to populations of neurons recorded with multielectrode arrays implanted in macaque visual cortex areas V1 and V4.

ADJUSTED REGULARIZATION IN LATENT GRAPHICAL MODELS: APPLICATION TO MULTIPLE-NEURON SPIKE COUNT DATA.

A major challenge in contemporary neuroscience is to analyze data from large numbers of neurons recorded simultaneously across many experimental replications (trials), where the data are counts of neural firing events, and one of the basic problems is to characterize the dependence structure among such multivariate counts. Methods of estimating high-dimensional covariation based on ℓ 1-regularization are most appropriate when there are a small number of relatively large partial correlations, but in neural data there are often large numbers of relatively small partial correlations. Furthermore, the variation across trials is often confounded by Poisson-like variation within trials. To overcome these problems we introduce a comprehensive methodology that imbeds a Gaussian graphical model into a hierarchical structure: the counts are assumed Poisson, conditionally on latent variables that follow a Gaussian graphical model, and the graphical model parameters, in turn, are assumed to depend on physiologically-motivated covariates, which can greatly improve correct detection of interactions (non-zero partial correlations). We develop a Bayesian approach to fitting this covariate-adjusted generalized graphical model and we demonstrate its success in simulation studies. We then apply it to data from an experiment on visual attention, where we assess functional interactions between neurons recorded from two brain areas.

Neural Decoding: A Predictive Viewpoint.

Decoding in the context of brain-machine interface is a prediction problem, with the aim of retrieving the most accurate kinematic predictions attainable from the available neural signals. While selecting models that reduce the prediction error is done to various degrees, decoding has not received the attention that the fields of statistics and machine learning have lavished on the prediction problem in the past two decades. Here, we take a more systematic approach to the decoding prediction problem and search for risk-optimized reverse regression, optimal linear estimation (OLE), and Kalman filter models within a large model space composed of several nonlinear transformations of neural spike counts at multiple temporal lags. The reverse regression decoding framework is a standard prediction problem, where penalized methods such as ridge regression or Lasso are routinely used to find minimum risk models. We argue that minimum risk reverse regression is always more efficient than OLE and also happens to be 44% more efficient than a standard Kalman filter in a particular application of offline reconstruction of arm reaches of a rhesus macaque monkey. Yet model selection for tuning curves-based decoding models such as OLE and Kalman filtering is not a standard statistical prediction problem, and no efficient method exists to identify minimum risk models. We apply several methods to build low-risk models and show that in our application, a Kalman filter that includes multiple carefully chosen observation equations per neural unit is 67% more efficient than a standard Kalman filter, but with the drawback that finding such a model is computationally very costly.

Automated acoustic detection of mouse scratching.

Itch is an aversive somatic sense that elicits the desire to scratch. In animal models of itch, scratching behavior is frequently used as a proxy for itch, and this behavior is typically assessed through visual quantification. However, manual scoring of videos has numerous limitations, underscoring the need for an automated approach. Here, we propose a novel automated method for acoustic detection of mouse scratching. Using this approach, we show that chloroquine-induced scratching behavior in C57BL/6 mice can be quantified with reasonable accuracy (85% sensitivity, 75% positive predictive value). This report is the first method to apply supervised learning techniques to automate acoustic scratch detection.

Separating Spike Count Correlation from Firing Rate Correlation.

Populations of cortical neurons exhibit shared fluctuations in spiking activity over time. When measured for a pair of neurons over multiple repetitions of an identical stimulus, this phenomenon emerges as correlated trial-to-trial response variability via spike count correlation (SCC). However, spike counts can be viewed as noisy versions of firing rates, which can vary from trial to trial. From this perspective, the SCC for a pair of neurons becomes a noisy version of the corresponding firing rate correlation (FRC). Furthermore, the magnitude of the SCC is generally smaller than that of the FRC and is likely to be less sensitive to experimental manipulation. We provide statistical methods for disambiguating time-averaged drive from within-trial noise, thereby separating FRC from SCC. We study these methods to document their reliability, and we apply them to neurons recorded in vivo from area V4 in an alert animal. We show how the various effects we describe are reflected in the data: within-trial effects are largely negligible, while attenuation due to trial-to-trial variation dominates and frequently produces comparisons in SCC that, because of noise, do not accurately reflect those based on the underlying FRC.

A computationally efficient method for incorporating spike waveform information into decoding algorithms.

Spike-based brain-computer interfaces (BCIs) have the potential to restore motor ability to people with paralysis and amputation, and have shown impressive performance in the lab. To transition BCI devices from the lab to the clinic, decoding must proceed automatically and in real time, which prohibits the use of algorithms that are computationally intensive or require manual tweaking. A common choice is to avoid spike sorting and treat the signal on each electrode as if it came from a single neuron, which is fast, easy, and therefore desirable for clinical use. But this approach ignores the kinematic information provided by individual neurons recorded on the same electrode. The contribution of this letter is a linear decoding model that extracts kinematic information from individual neurons without spike-sorting the electrode signals. The method relies on modeling sample averages of waveform features as functions of kinematics, which is automatic and requires minimal data storage and computation. In offline reconstruction of arm trajectories of a nonhuman primate performing reaching tasks, the proposed method performs as well as decoders based on expertly manually and automatically sorted spikes.

To sort or not to sort: the impact of spike-sorting on neural decoding performance.

OBJECTIVE: Brain-computer interfaces (BCIs) are a promising technology for restoring motor ability to paralyzed patients. Spiking-based BCIs have successfully been used in clinical trials to control multi-degree-of-freedom robotic devices. Current implementations of these devices require a lengthy spike-sorting step, which is an obstacle to moving this technology from the lab to the clinic. A viable alternative is to avoid spike-sorting, treating all threshold crossings of the voltage waveform on an electrode as coming from one putative neuron. It is not known, however, how much decoding information might be lost by ignoring spike identity. APPROACH: We present a full analysis of the effects of spike-sorting schemes on decoding performance. Specifically, we compare how well two common decoders, the optimal linear estimator and the Kalman filter, reconstruct the arm movements of non-human primates performing reaching tasks, when receiving input from various sorting schemes. The schemes we tested included: using threshold crossings without spike-sorting; expert-sorting discarding the noise; expert-sorting, including the noise as if it were another neuron; and automatic spike-sorting using waveform features. We also decoded from a joint statistical model for the waveforms and tuning curves, which does not involve an explicit spike-sorting step. MAIN RESULTS: Discarding the threshold crossings that cannot be assigned to neurons degrades decoding: no spikes should be discarded. Decoding based on spike-sorted units outperforms decoding based on electrodes voltage crossings: spike-sorting is useful. The four waveform based spike-sorting methods tested here yield similar decoding efficiencies: a fast and simple method is competitive. Decoding using the joint waveform and tuning model shows promise but is not consistently superior. SIGNIFICANCE: Our results indicate that simple automated spike-sorting performs as well as the more computationally or manually intensive methods used here. Even basic spike-sorting adds value to the low-threshold waveform-crossing methods often employed in BCI decoding.

Automatic scan test for detection of functional connectivity between cortex and muscles.

Postspike effects (PSEs) in averages of spike-triggered EMG snippets provide physiological evidence of connectivity between CMN cells and spinal motoneurons innervating skeletal muscles. They are typically detected by visual inspection of spike-triggered averages (SpTAs) or by multiple-fragment/single-snippet analyses [MFA (Poliakov AV, Schieber MH. J Neurosci Methods 79: 143-150, 1998) and SSA (Perel S, Schwartz AB, Ventura V. Neural Comput 26: 40-56, 2014)]; the latter are automatic tests that yield P values. However, MFA/SSA are only effective to detect PSEs that occur at about 6-16 ms posttrigger. Our first contribution is the scan test, an automatic test that has the same utility as SpTA, i.e., it can detect a wide range of PSEs at any latency, but it also yields a P value. Our second contribution is a thorough investigation of the statistical properties of PSE detection tests. We show that when the PSE is weak or the sample size is small, visual inspections of SpTAs have low power, because it is difficult to distinguish PSEs from background EMG variations. We also show that the scan test has better power and that its rate of spurious detections matches the chosen significance level α. This is especially important for investigators because, when a PSE is detected, this guarantees that the probability of a spurious PSE is less than α. Finally, we illustrate the operational characteristics of the PSE detection tests on 2,059 datasets from 5 experiments. The scan test is particularly useful to identify candidate PSEs, which can then be subject to further evaluation by SpTA inspection, and when PSEs are small and visual detection is ambiguous.

Chronic recruitment of primary afferent neurons by microstimulation in the feline dorsal root ganglia.

OBJECTIVE: This study describes results of primary afferent neural microstimulation experiments using microelectrode arrays implanted chronically in the lumbar dorsal root ganglia (DRG) of four cats. The goal was to test the stability and selectivity of these microelectrode arrays as a potential interface for restoration of somatosensory feedback after damage to the nervous system such as amputation. APPROACH: A five-contact nerve-cuff electrode implanted on the sciatic nerve was used to record the antidromic compound action potential response to DRG microstimulation (2-15 µA biphasic pulses, 200 µs cathodal pulse width), and the threshold for eliciting a response was tracked over time. Recorded responses were segregated based on conduction velocity to determine thresholds for recruiting Group I and Group II/Aß primary afferent fibers. MAIN RESULTS: Thresholds were initially low (5.1 ± 2.3 µA for Group I and 6.3 ± 2.0 µA for Group II/Aß) and increased over time. Additionally the number of electrodes with thresholds less than or equal to 15 µA decreased over time. Approximately 12% of tested electrodes continued to elicit responses at 15 µA up to 26 weeks after implantation. Higher stimulation intensities (up to 30 µA) were tested in one cat at 23 weeks post-implantation yielding responses on over 20 additional electrodes. Within the first six weeks after implantation, approximately equal numbers of electrodes elicited only Group I or Group II/Aß responses at threshold, but the relative proportion of Group II/Aß responses decreased over time. SIGNIFICANCE: These results suggest that it is possible to activate Group I or Group II/Aß primary afferent fibers in isolation with penetrating microelectrode arrays implanted in the DRG, and that those responses can be elicited up to 26 weeks after implantation, although it may be difficult to achieve a consistent response day-to-day with currently available electrode technology. The DRG are compelling targets for sensory neuroprostheses with potential to achieve recruitment of a range of sensory fiber types over multiple months after implantation.

Single-snippet analysis for detection of postspike effects.

Corticomotoneuronal cells (CMN), located predominantly in the primary motor cortex, project directly to alpha motoneuronal pools in the spinal cord. The effects of CMN spikes on motoneuronal excitability are traditionally characterized by visualizing postspike effects (PSEs) in spike-triggered averages (SpTA; Fetz, Cheney, & German, 1976; Fetz & Cheney, 1980; McKiernan, Marcario, Karrer, & Cheney, 1998) of electromyography (EMG) data. Poliakov and Schieber (1998) suggested a formal test, the multiple-fragment analysis (MFA), to automatically detect PSEs. However, MFA's performance was not statistically validated, and it is unclear under what conditions it is valid. This paper's contributions are a power study that validates the MFA; an alternative test, the single-snippet analysis (SSA), which has the same functionality as MFA but is easier to calculate and has better power in small samples; a simple bootstrap simulation to estimate SpTA baselines with simulation bands that help visualize potential PSEs; and a bootstrap adjustment to the MFA and SSA to correct for nonlinear SpTA baselines.

Accurately estimating neuronal correlation requires a new spike-sorting paradigm.

Neurophysiology is increasingly focused on identifying coincident activity among neurons. Strong inferences about neural computation are made from the results of such studies, so it is important that these results be accurate. However, the preliminary step in the analysis of such data, the assignment of spike waveforms to individual neurons ("spike-sorting"), makes a critical assumption which undermines the analysis: that spikes, and hence neurons, are independent. We show that this assumption guarantees that coincident spiking estimates such as correlation coefficients are biased. We also show how to eliminate this bias. Our solution involves sorting spikes jointly, which contrasts with the current practice of sorting spikes independently of other spikes. This new "ensemble sorting" yields unbiased estimates of coincident spiking, and permits more data to be analyzed with confidence, improving the quality and quantity of neurophysiological inferences. These results should be of interest outside the context of neuronal correlations studies. Indeed, simultaneous recording of many neurons has become the rule rather than the exception in experiments, so it is essential to spike sort correctly if we are to make valid inferences about any properties of, and relationships between, neurons.

Automatic spike sorting using tuning information.

Current spike sorting methods focus on clustering neurons' characteristic spike waveforms. The resulting spike-sorted data are typically used to estimate how covariates of interest modulate the firing rates of neurons. However, when these covariates do modulate the firing rates, they provide information about spikes' identities, which thus far have been ignored for the purpose of spike sorting. This letter describes a novel approach to spike sorting, which incorporates both waveform information and tuning information obtained from the modulation of firing rates. Because it efficiently uses all the available information, this spike sorter yields lower spike misclassification rates than traditional automatic spike sorters. This theoretical result is verified empirically on several examples. The proposed method does not require additional assumptions; only its implementation is different. It essentially consists of performing spike sorting and tuning estimation simultaneously rather than sequentially, as is currently done. We used an expectation-maximization maximum likelihood algorithm to implement the new spike sorter. We present the general form of this algorithm and provide a detailed implementable version under the assumptions that neurons are independent and spike according to Poisson processes. Finally, we uncover a systematic flaw of spike sorting based on waveform information only.