Effects of long-term transcutaneous auricular vagus nerve stimulation on circadian vagal activity in people with Prader-Willi Syndrome: A case-series.

BACKGROUND: Prader-Willi Syndrome (PWS) is a genetic neurodevelopmental disorder marked by disruptions in circadian rhythms and autonomic nervous system (ANS) activity, hyperphagia, and episodes of emotional outbursts. Previous trials suggest that both invasive and non-invasive vagus nerve stimulation (VNS) can reduce emotional outbursts in PWS, potentially through its effects on vagal activity. AIM: This case series investigated the effects of transcutaneous auricular VNS (taVNS) on cardiac markers of circadian vagal activity, specifically heart rate variability (HRV) and heart rate (HR), and their potential links to improvements in emotional outbursts. METHODS: Five individuals with PWS (mean age: 26.9 years; 3 males, 2 females) received four hours of daily taVNS for 12 months, followed by one month of two-hour daily sessions. Outcome measures included daily recording of emotional outbursts and every three months 24-h HRV and HR recordings. Mixed cosinor models were applied to analyze changes in circadian rhythms of HRV and HR. A linear mixed model was used to assess the predictive value of cardiac vagal activity on emotional outbursts. RESULTS: Circadian amplitudes of HRV and HR were significantly higher at the end of the treatment compared to baseline (all p's < .01). There was a significant increase in the rhythm-adjusted mean of HRV (p < .01), while the rhythm-adjusted HR mean significantly decreased, both indicating increased cardiac vagal activity. Higher rhythm-adjusted mean HRV predicted a lower number of emotional outbursts. CONCLUSION: The results suggest that taVNS may be effective by targeting ANS activity in individuals with PWS, contributing to improvements in behavioral regulation.

MEA-NAP compares microscale functional connectivity, topology, and network dynamics in organoid or monolayer neuronal cultures.

Microelectrode array (MEA) recordings are commonly used to compare firing and burst rates in neuronal cultures. MEA recordings can also reveal microscale functional connectivity, topology, and network dynamics-patterns seen in brain networks across spatial scales. Network topology is frequently characterized in neuroimaging with graph theoretical metrics. However, few computational tools exist for analyzing microscale functional brain networks from MEA recordings. Here, we present a MATLAB MEA network analysis pipeline (MEA-NAP) for raw voltage time-series acquired from single- or multi-well MEAs. Applications to 3D human cerebral organoids or 2D human-derived or murine cultures reveal differences in network development, including topology, node cartography, and dimensionality. MEA-NAP incorporates multi-unit template-based spike detection, probabilistic thresholding for determining significant functional connections, and normalization techniques for comparing networks. MEA-NAP can identify network-level effects of pharmacologic perturbation and/or disease-causing mutations and, thus, can provide a translational platform for revealing mechanistic insights and screening new therapeutic approaches.

Sepsis-3 criteria in AmsterdamUMCdb: open-source code implementation.

Sepsis is a major healthcare problem with substantial mortality and a common reason for admission to the intensive care unit (ICU). For this reason, the management of sepsis is an important area of ICU research. A number of large-scale, freely-accessible ICU databases are available for observational research and the robust identification of septic patients in such data sets is crucial for research purposes, particularly for comparative studies between critical care sub-populations which may vary around the world. However, data structures are poorly standardised due to inevitable variances in clinical electronic health record system vendor and implementation as well as research database design choices. Robust and well-documented cohort selection (such as patients with sepsis) is crucial for reproducible research. In this work, we operationalise the Sepsis-3 definition on the AmsterdamUMCdb, a recently published large European ICU database, publishing open-access code for wider use by critical care researchers.

Causality indices for bivariate time series data: A comparative review of performance.

Inferring nonlinear and asymmetric causal relationships between multivariate longitudinal data is a challenging task with wide-ranging application areas including clinical medicine, mathematical biology, economics, and environmental research. A number of methods for inferring causal relationships within complex dynamic and stochastic systems have been proposed, but there is not a unified consistent definition of causality in the context of time series data. We evaluate the performance of ten prominent causality indices for bivariate time series across four simulated model systems that have different coupling schemes and characteristics. Pairwise correlations between different methods, averaged across all simulations, show that there is generally strong agreement between methods, with minimum, median, and maximum Pearson correlations between any pair (excluding two similarity indices) of 0.298, 0.719, and 0.955, respectively. In further experiments, we show that these methods are not always invariant to real-world relevant transformations (data availability, standardization and scaling, rounding errors, missing data, and noisy data). We recommend transfer entropy and nonlinear Granger causality as particularly strong approaches for estimating bivariate causal relationships in real-world applications. Both successfully identify causal relationships and a lack thereof across multiple simulations, while remaining robust to rounding errors, at least 20% missing data and small variance Gaussian noise. Finally, we provide flexible open-access Python code for computation of these methods and for the model simulations.

CODECHECK: an Open Science initiative for the independent execution of computations underlying research articles during peer review to improve reproducibility.

The traditional scientific paper falls short of effectively communicating computational research.  To help improve this situation, we propose a system by which the computational workflows underlying research articles are checked. The CODECHECK system uses open infrastructure and tools and can be integrated into review and publication processes in multiple ways. We describe these integrations along multiple dimensions (importance, who, openness, when). In collaboration with academic publishers and conferences, we demonstrate CODECHECK with 25 reproductions of diverse scientific publications. These CODECHECKs show that asking for reproducible workflows during a collaborative review can effectively improve executability. While CODECHECK has clear limitations, it may represent a building block in Open Science and publishing ecosystems for improving the reproducibility, appreciation, and, potentially, the quality of non-textual research artefacts. The CODECHECK website can be accessed here: https://codecheck.org.uk/.

DeepClean: Self-Supervised Artefact Rejection for Intensive Care Waveform Data Using Deep Generative Learning.

Waveform physiological data are important in the treatment of critically ill patients in the intensive care unit. Such recordings are susceptible to artefacts, which must be removed before the data can be reused for alerting or reprocessed for other clinical or research purposes. Accurate removal of artefacts reduces bias and uncertainty in clinical assessment, as well as the false positive rate of ICU alarms, and is therefore a key component in providing optimal clinical care. In this work, we present DeepClean, a prototype self-supervised artefact detection system using a convolutional variational autoencoder deep neural network that avoids costly and painstaking manual annotation, requiring only easily obtained 'good' data for training. For a test case with invasive arterial blood pressure, we demonstrate that our algorithm can detect the presence of an artefact within a 10s sample of data with sensitivity and specificity around 90%. Furthermore, DeepClean was able to identify regions of artefacts within such samples with high accuracy, and we show that it significantly outperforms a baseline principal component analysis approach in both signal reconstruction and artefact detection. DeepClean learns a generative model and therefore may also be used for imputation of missing data.

Ten simple rules for writing Dockerfiles for reproducible data science.

Computational science has been greatly improved by the use of containers for packaging software and data dependencies. In a scholarly context, the main drivers for using these containers are transparency and support of reproducibility; in turn, a workflow's reproducibility can be greatly affected by the choices that are made with respect to building containers. In many cases, the build process for the container's image is created from instructions provided in a Dockerfile format. In support of this approach, we present a set of rules to help researchers write understandable Dockerfiles for typical data science workflows. By following the rules in this article, researchers can create containers suitable for sharing with fellow scientists, for including in scholarly communication such as education or scientific papers, and for effective and sustainable personal workflows.

From random to regular: Variation in the patterning of retinal mosaics.

The various types of retinal neurons are each positioned at their respective depths within the retina where they are believed to be assembled as orderly mosaics, in which like-type neurons minimize proximity to one another. Two common statistical analyses for assessing the spatial properties of retinal mosaics include the nearest neighbor analysis, from which an index of their "regularity" is commonly calculated, and the density recovery profile derived from autocorrelation analysis, revealing the presence of an exclusion zone indicative of anti-clustering. While each of the spatial statistics derived from these analyses, the regularity index and the effective radius, can be useful in characterizing such properties of orderly retinal mosaics, they are rarely sufficient for conveying the natural variation in the self-spacing behavior of different types of retinal neurons and the extent to which that behavior generates uniform intercellular spacing across the mosaic. We consider the strengths and limitations of these and other spatial statistical analyses for assessing the patterning in retinal mosaics, highlighting a number of misconceptions and their frequent misuse. Rather than being diagnostic criteria for determining simply whether a population is "regular," they should be treated as descriptive statistics that convey variation in the factors that influence neuronal positioning. We subsequently apply multiple spatial statistics to the analysis of eight different mosaics in the mouse retina, demonstrating conspicuous variability in the degree of patterning present, from essentially random to notably regular. This variability in patterning has both a developmental as well as a functional significance, reflecting the rules governing the positioning of different types of neurons as the architecture of the retina is assembled, and the distinct mechanisms by which they regulate dendritic growth to generate their characteristic coverage and connectivity.

Functional characterization of human pluripotent stem cell-derived cortical networks differentiated on laminin-521 substrate: comparison to rat cortical cultures.

Human pluripotent stem cell (hPSC)-derived neurons provide exciting opportunities for in vitro modeling of neurological diseases and for advancing drug development and neurotoxicological studies. However, generating electrophysiologically mature neuronal networks from hPSCs has been challenging. Here, we report the differentiation of functionally active hPSC-derived cortical networks on defined laminin-521 substrate. We apply microelectrode array (MEA) measurements to assess network events and compare the activity development of hPSC-derived networks to that of widely used rat embryonic cortical cultures. In both of these networks, activity developed through a similar sequence of stages and time frames; however, the hPSC-derived networks showed unique patterns of bursting activity. The hPSC-derived networks developed synchronous activity, which involved glutamatergic and GABAergic inputs, recapitulating the classical cortical activity also observed in rodent counterparts. Principal component analysis (PCA) based on spike rates, network synchronization and burst features revealed the segregation of hPSC-derived and rat network recordings into different clusters, reflecting the species-specific and maturation state differences between the two networks. Overall, hPSC-derived neural cultures produced with a defined protocol generate cortical type network activity, which validates their applicability as a human-specific model for pharmacological studies and modeling network dysfunctions.

Burst Detection Methods.

'Bursting', defined as periods of high-frequency firing of a neuron separated by periods of quiescence, has been observed in various neuronal systems, both in vitro and in vivo. It has been associated with a range of neuronal processes, including efficient information transfer and the formation of functional networks during development, and has been shown to be sensitive to genetic and pharmacological manipulations. Accurate detection of periods of bursting activity is thus an important aspect of characterising both spontaneous and evoked neuronal network activity. A wide variety of computational methods have been developed to detect periods of bursting in spike trains recorded from neuronal networks. In this chapter, we review several of the most popular and successful of these methods.

meaRtools: An R package for the analysis of neuronal networks recorded on microelectrode arrays.

Here we present an open-source R package 'meaRtools' that provides a platform for analyzing neuronal networks recorded on Microelectrode Arrays (MEAs). Cultured neuronal networks monitored with MEAs are now being widely used to characterize in vitro models of neurological disorders and to evaluate pharmaceutical compounds. meaRtools provides core algorithms for MEA spike train analysis, feature extraction, statistical analysis and plotting of multiple MEA recordings with multiple genotypes and treatments. meaRtools functionality covers novel solutions for spike train analysis, including algorithms to assess electrode cross-correlation using the spike train tiling coefficient (STTC), mutual information, synchronized bursts and entropy within cultured wells. Also integrated is a solution to account for bursts variability originating from mixed-cell neuronal cultures. The package provides a statistical platform built specifically for MEA data that can combine multiple MEA recordings and compare extracted features between different genetic models or treatments. We demonstrate the utilization of meaRtools to successfully identify epilepsy-like phenotypes in neuronal networks from Celf4 knockout mice. The package is freely available under the GPL license (GPL> = 3) and is updated frequently on the CRAN web-server repository. The package, along with full documentation can be downloaded from: https://cran.r-project.org/web/packages/meaRtools/.

Recent developments in scholarly publishing to improve research practices in the life sciences.

We outline recent developments in scholarly publishing that we think will improve the working environment and career prospects for life scientists. Most prominently, we discuss two key developments. (1) Life scientists are now embracing a preprint culture leading to rapid dissemination of research findings. (2) We outline steps to overcome the reproducibility crisis. We also briefly describe other innovations in scholarly publishing, along with changes to open access mandates from funding agencies.

A molecular mechanism for the topographic alignment of convergent neural maps.

Sensory processing requires proper alignment of neural maps throughout the brain. In the superficial layers of the superior colliculus of the midbrain, converging projections from retinal ganglion cells and neurons in visual cortex must be aligned to form a visuotopic map, but the basic mechanisms mediating this alignment remain elusive. In a new mouse model, ectopic expression of ephrin-A3 (Efna3) in a subset of retinal ganglion cells, quantitatively altering the retinal EFNAs gradient, disrupts cortico-collicular map alignment onto the retino-collicular map, creating a visuotopic mismatch. Genetic inactivation of ectopic EFNA3 restores a wild-type cortico-collicular map. Theoretical analyses using a new mapping algorithm model both map formation and alignment, and recapitulate our experimental observations. The algorithm is based on an initial sensory map, the retino-collicular map, which carries intrinsic topographic information, the retinal EFNAs, to the superior colliculus. These EFNAs subsequently topographically align ingrowing visual cortical axons to the retino-collicular map.

A comparison of computational methods for detecting bursts in neuronal spike trains and their application to human stem cell-derived neuronal networks.

Accurate identification of bursting activity is an essential element in the characterization of neuronal network activity. Despite this, no one technique for identifying bursts in spike trains has been widely adopted. Instead, many methods have been developed for the analysis of bursting activity, often on an ad hoc basis. Here we provide an unbiased assessment of the effectiveness of eight of these methods at detecting bursts in a range of spike trains. We suggest a list of features that an ideal burst detection technique should possess and use synthetic data to assess each method in regard to these properties. We further employ each of the methods to reanalyze microelectrode array (MEA) recordings from mouse retinal ganglion cells and examine their coherence with bursts detected by a human observer. We show that several common burst detection techniques perform poorly at analyzing spike trains with a variety of properties. We identify four promising burst detection techniques, which are then applied to MEA recordings of networks of human induced pluripotent stem cell-derived neurons and used to describe the ontogeny of bursting activity in these networks over several months of development. We conclude that no current method can provide "perfect" burst detection results across a range of spike trains; however, two burst detection techniques, the MaxInterval and logISI methods, outperform compared with others. We provide recommendations for the robust analysis of bursting activity in experimental recordings using current techniques.

Homeostatic Activity-Dependent Tuning of Recurrent Networks for Robust Propagation of Activity.

Developing neuronal networks display spontaneous bursts of action potentials that are necessary for circuit organization and tuning. While spontaneous activity has been shown to instruct map formation in sensory circuits, it is unknown whether it plays a role in the organization of motor networks that produce rhythmic output. Using computational modeling, we investigate how recurrent networks of excitatory and inhibitory neuronal populations assemble to produce robust patterns of unidirectional and precisely timed propagating activity during organism locomotion. One example is provided by the motor network inDrosophilalarvae, which generates propagating peristaltic waves of muscle contractions during crawling. We examine two activity-dependent models, which tune weak network connectivity based on spontaneous activity patterns: a Hebbian model, where coincident activity in neighboring populations strengthens connections between them; and a homeostatic model, where connections are homeostatically regulated to maintain a constant level of excitatory activity based on spontaneous input. The homeostatic model successfully tunes network connectivity to generate robust activity patterns with appropriate timing relationships between neighboring populations. These timing relationships can be modulated by the properties of spontaneous activity, suggesting its instructive role for generating functional variability in network output. In contrast, the Hebbian model fails to produce the tight timing relationships between neighboring populations required for unidirectional activity propagation, even when additional assumptions are imposed to constrain synaptic growth. These results argue that homeostatic mechanisms are more likely than Hebbian mechanisms to tune weak connectivity based on spontaneous input in a recurrent network for rhythm generation and robust activity propagation. SIGNIFICANCE STATEMENT: How are neural circuits organized and tuned to maintain stable function and produce robust output? This task is especially difficult during development, when circuit properties change in response to variable environments and internal states. Many developing circuits exhibit spontaneous activity, but its role in the synaptic organization of motor networks that produce rhythmic output is unknown. We studied a model motor network, that when appropriately tuned, generates propagating activity as during crawling inDrosophilalarvae. Based on experimental evidence of activity-dependent tuning of connectivity, we examined plausible mechanisms by which appropriate connectivity emerges. Our results suggest that activity-dependent homeostatic mechanisms are better suited than Hebbian mechanisms for organizing motor network connectivity, and highlight an important difference from sensory areas.

Characterization of Early Cortical Neural Network Development in Multiwell Microelectrode Array Plates.

We examined neural network ontogeny using microelectrode array (MEA) recordings made in multiwell MEA (mwMEA) plates over the first 12 days in vitro (DIV). In primary cortical cultures, action potential spiking activity developed rapidly between DIV 5 and 12. Spiking was sporadic and unorganized at early DIV, and became progressively more organized with time, with bursting parameters, synchrony, and network bursting increasing between DIV 5 and 12. We selected 12 features to describe network activity; principal components analysis using these features demonstrated segregation of data by age at both the well and plate levels. Using random forest classifiers and support vector machines, we demonstrated that four features (coefficient of variation [CV] of within-burst interspike interval, CV of interburst interval, network spike rate, and burst rate) could predict the age of each well recording with >65% accuracy. When restricting the classification to a binary decision, accuracy improved to as high as 95%. Further, we present a novel resampling approach to determine the number of wells needed for comparing different treatments. Overall, these results demonstrate that network development on mwMEA plates is similar to development in single-well MEAs. The increased throughput of mwMEAs will facilitate screening drugs, chemicals, or disease states for effects on neurodevelopment.

Geniculo-Cortical Projection Diversity Revealed within the Mouse Visual Thalamus.

The mouse dorsal lateral geniculate nucleus (dLGN) is an intermediary between retina and primary visual cortex (V1). Recent investigations are beginning to reveal regional complexity in mouse dLGN. Using local injections of retrograde tracers into V1 of adult and neonatal mice, we examined the developing organisation of geniculate projection columns: the population of dLGN-V1 projection neurons that converge in cortex. Serial sectioning of the dLGN enabled the distribution of labelled projection neurons to be reconstructed and collated within a common standardised space. This enabled us to determine: the organisation of cells within the dLGN-V1 projection columns; their internal organisation (topology); and their order relative to V1 (topography). Here, we report parameters of projection columns that are highly variable in young animals and refined in the adult, exhibiting profiles consistent with shell and core zones of the dLGN. Additionally, such profiles are disrupted in adult animals with reduced correlated spontaneous activity during development. Assessing the variability between groups with partial least squares regression suggests that 4-6 cryptic lamina may exist along the length of the projection column. Our findings further spotlight the diversity of the mouse dLGN--an increasingly important model system for understanding the pre-cortical organisation and processing of visual information. Furthermore, our approach of using standardised spaces and pooling information across many animals will enhance future functional studies of the dLGN.

Canalization of genetic and pharmacological perturbations in developing primary neuronal activity patterns.

The function of the nervous system depends on the integrity of synapses and the patterning of electrical activity in brain circuits. The rapid advances in genome sequencing reveal a large number of mutations disrupting synaptic proteins, which potentially result in diseases known as synaptopathies. However, it is also evident that every normal individual carries hundreds of potentially damaging mutations. Although genetic studies in several organisms show that mutations can be masked during development by a process known as canalization, it is unknown if this occurs in the development of the electrical activity in the brain. Using longitudinal recordings of primary cultured neurons on multi-electrode arrays from mice carrying knockout mutations we report evidence of canalization in development of spontaneous activity patterns. Phenotypes in the activity patterns in young cultures from mice lacking the Gria1 subunit of the AMPA receptor were ameliorated as cultures matured. Similarly, the effects of chronic pharmacological NMDA receptor blockade diminished as cultures matured. Moreover, disturbances in activity patterns by simultaneous disruption of Gria1 and NMDA receptors were also canalized by three weeks in culture. Additional mutations and genetic variations also appeared to be canalized to varying degrees. These findings indicate that neuronal network canalization is a form of nervous system plasticity that provides resilience to developmental disruption. This article is part of the Special Issue entitled 'Synaptopathy--from Biology to Therapy'.