SEARCH · Search bioRxiv

Results for “Neuroscience”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 235 records · Page 13Linked to original sources

bioRxiv2025

Larvaworld : A behavioral simulation and analysis platform for Drosophila larva

Behavioral modeling supports theory building and evaluation across disciplines. Leveraging advances in motion-tracking and computational tools, we present a virtual laboratory for Drosophila larvae that integrates agent-based modeling with multiscale neural control and supports analysis of both simulated and experimental data. Virtual larvae are implemented as 2D agents capable of realistic locomotion, guided by multimodal sensory input and constrained by a dynamic energy-budget model that balances exploration and exploitation. Each agent is organized as a hierarchical, behavior-based control system comprising three layers: low-level locomotion, optionally incorporating neuromechanical models; mid-level sensory processing; and high-level behavioral adaptation. Neural control models can range from simple linear transfer models to rate-based or spiking neural network models, e.g. to accomodate associative learning. Simulations operate across sub-millisecond neuronal dynamics, sub-second closed-loop behavior, and circadian-scale metabolic regulation. Users can configure both larval models and virtual environments, including sensory landscapes, nutrient sources, and physical arenas. Real-time visualization is integrated into the simulation and analysis pipeline, which also allows for standardized processing of motion-tracking data from real experiments. Distributed as an open-source Python package, the platform includes tutorial experiments to support accessibility, customization, and use in both research and education. Author summaryLarvaworld was developed to address two key challenges in behavioral neuroscience and computational modeling. First, it responds to the growing call for closer collaboration between experimentalists and modelers by providing a shared platform -a virtual laboratory-where experimental data analysis and behavioral modeling can be seamlessly integrated. By standardizing dataset formats and ensuring identical, unbiased analysis pipelines for experimental and simulated data, Larvaworld facilitates methodological consistency and enables rigorous model evaluation. Second, it aims to bridge a long-standing gap in theory building and computational modeling at the level of the individual behaving organism. Historically, neuroscience has focused on sub-individual processes, while ecology has concentrated on supra-individual dynamics, resulting in discontinuities among the respective modeling approaches. Recent advances, however, have begun to align these fields, with neuroscience incorporating slower homeostatic processes and ecology integrating faster neurally-mediated mechanisms. Larvaworld boosts this convergence by adopting a nested, multi-timescale modeling approach, thus achieving behavioral regulation within the normative homeostatic constraints as these dynamically unfold during larval development. By combining established modeling paradigms from neuroscience and ecology, it provides a novel and flexible platform for studying behavior at the level of the individual organism, promoting cross-disciplinary insights and advancing computational neuroethology [1].

neuroscience↗

bioRxiv2026

Zero-shot evaluations expose structured generalization limits in predictive models of the brain

Artificial neural networks (ANNs) can predict neural responses to natural images with remarkable accuracy, making them promising models of human vision. However, prediction scores alone reveal little about what computations these models have captured or how well they generalize beyond the images on which they are trained. Here, we introduce a principled zero-shot model evaluation framework in which ANN-based encoding models are frozen before being tested on not just held out images but entirely new datasets. Our strongest tests repurpose decades of cognitive neuroscience experiments as diagnostic benchmarks for identifying which computations current models capture and where they fail. To implement this framework we built encoding models of human category-selective regions (FFA, PPA, and EBA) using several pretrained ANNs and evaluated these fixed models across 7 independent fMRI datasets and 31 cognitive experiments from 10 published studies. This framework revealed three broad patterns. First, zero-shot evaluations exposed systematic limits to model predictivity that were largely hidden by standard within-dataset cross-validation. These limits were structured varying across brain regions and model classes and depending strongly on the similarity between the images used to build and test the models. Second, current models reproduced some, but not all, classical findings from cognitive neuroscience, providing a diagnosis of the computations that current models have yet to capture. Finally, models that performed well on prediction tests also tended to succeed at reproducing cognitive neuroscience findings, suggesting that prediction and explanation are closely linked. Unlike prediction scores however, cognitive tests help diagnose where and why models fail. Together, the zero-shot evaluation framework provides a scalable and unified approach for evaluating brain models across prediction and cognitive neuroscience tests, which can help us better understand what current models capture and what still remains to be explained.

neuroscience↗

bioRxiv2020

Deep Recurrent Neural Network and Point Process Filter Approaches in Multidimensional Neural Decoding Problems

Recent technological and experimental advances in recording from neural systems have led to a significant increase in the type and volume of data being collected in neuroscience experiments. This brings an increasing demand for development of appropriate analytical tools to analyze large scale neuroscience data. Simultaneously, advancement in deep neural networks (DNNs) and statistical modeling frameworks have provided new techniques for analysis of diverse forms of neuroscience data. DNNs like Long short-term memory (LSTM) or statistical modeling approaches like state-space point-process (SSPP) are widely used in the analysis of neural data including neural coding and inference analysis. Despite wide utilization of these techniques, there is a lack of comprehensive studies which systematically assess attributes of LSTM and SSPP approaches on a common neuroscience data analysis problem. As a result, this occasionally leads to inconsistent and divergent conclusions on the strength or weakness of either of the methodologies and also statistical significance of the analytical outcomes. In this research, we focus on providing a more systematic and multifaceted assessment of LSTM and SSPP techniques in a neural decoding problem. We examine different settings and modeling specifications to attain the optimal modeling solutions. We propose new LSTM network topologies and approximate filter solution to estimate a rat movement trajectory in a 2-D spaces using an ensemble of place cells spiking activity. For each technique; we then study performance, computational efficiency, and generalizability of each technique in this decoding problem. By utilizing these results, we provided a succinct picture of the strength and weakness of each modeling approach and suggest who each of these techniques can be properly utilized in neural decoding problems.

bioinformatics↗

bioRxiv2021

Social Housing Status Impacts Rhesus Monkeys' Affective Responding in Classic Threat Processing Tasks

The established literature clearly demonstrates that whether or not monkeys are socially reared has long term consequences for their affective behavior. Yet, in the context of behavioral neuroscience and pharmacological studies, social context of adult animals is often ignored. When social context has been studied in adult monkeys, such studies have typically focused on welfare-related issues, as social isolation often leads to the development of abnormal behavior, rather than the impact on outcomes in behavioral neuroscience studies. Variation in social housing conditions for adult animals could have an impact on affective responding and may have significant implications for the interpretation of data from biopsychiatry and behavioral neuroscience studies. We evaluated the affective reactivity of rhesus monkeys (Macaca mulatta) maintained in one of four housing conditions (individually-housed, grate-paired, intermittently-paired, and continuously-paired) using two classic threat processing tasks--a test of responsivity to objects and the Human Intruder Test. Individually-housed monkeys exhibited consistently blunted sensitivity to ostensibly threatening stimuli as compared to socially-housed monkeys. Within the three socially-housed conditions, intermittently- and continuously-paired monkeys behaved similarly to each other and grate-paired monkeys exhibited relatively enhanced sensitivity to threatening stimuli. These findings suggest that the adult housing conditions of monkeys can robustly modulate affective responding in a way that may be consistent with behavioral phenotypes observed in human psychiatric conditions. Results are considered in the context of the broad behavioral and psychiatric neuroscience literatures, which have historically used individually-housed animals, pointing to the potential need to reconsider inferences drawn from those studies.

animal behavior and cognition↗

bioRxiv2025

PyReconstruct: A fully opensource, collaborative successor to Reconstruct

As the serial section community transitions to volume electron microscopy, tools are needed to balance rapid segmentation efforts with documenting the fine detail of structures that support cell function. New annotation applications should be accessible to users and meet the needs of the neuroscience and connectomics communities while also being useful across other disciplines. Issues not currently addressed by a single, modern annotation application include: 1) built-in curation systems with utilities for expert intervention to provide quality assurance, 2) integrated alignment features that allow for image registration on-the-fly as image flaws are discovered during annotation, 3) simplicity for non-specialists within and beyond the neuroscience community, 5) a system to store experimental meta-data with annotation data in a way that researchers remain masked regarding condition to avoid potential biases, 6) local management of large datasets, 7) fully open-source codebase allowing development of new tools, and more. Here, we present PyReconstruct, a modern successor to the Reconstruct annotation tool. PyReconstruct operates in a field-agnostic manner, runs on all major operating systems, breaks through legacy RAM limitations, features an intuitive and collaborative curation system, and employs a flexible and dynamic approach to image registration. It can be used to analyze, display, and publish experimental or connectomics data. PyReconstruct is suited for generating ground truth to implement in automated segmentation, outcomes of which can be returned to PyReconstruct for proofreading and quality control. Significance statementIn neuroscience, the emerging field of connectomics has produced annotation tools for reconstruction that prioritize circuit connectivity across microns to centimeters and farther. Determining the strength of synapses forming the connections is crucial to understand function and requires quantification of their nanoscale dimensions and subcellular composition. PyReconstruct, successor to the early annotation tool Reconstruct, meets these requirements for synapses and other structures well beyond neuroscience. PyReconstruct lifts many restrictions of legacy Reconstruct and offers a user-friendly interface, integrated curation, dynamic alignment, nanoscale quantification, 3D visualization, and more. Extensive compatibility with third-party software provides access to the expanding tools from the connectomics and imaging communities.

cell biology↗

bioRxiv2020

Accurate inference in parametric models reshapes neuroscientific interpretation and improves data-driven discovery

A central goal of systems neuroscience is to understand the relationships amongst constituent units in neural populations and their modulation by external factors using high-dimensional and stochastic neural recordings. Statistical models, particularly parametric models, play an instrumental role in accomplishing this goal, because their fitted parameters can provide insight into the underlying biological processes that generated the data. However, extracting conclusions from a parametric model requires that it is fit using an inference procedure capable of selecting the correct parameters and properly estimating their values. Traditional approaches to parameter inference have been shown to suffer from failures in both selection and estimation. Recent development of algorithms that ameliorate these deficiencies raises the question of whether past work relying on such inference procedures have produced inaccurate systems neuroscience models, thereby impairing their interpretation. Here, we used the Union of Intersections, a statistical inference framework capable of state-of-the-art selection and estimation performance, to fit functional coupling, encoding, and decoding models across a battery of neural datasets. We found that, compared to baseline procedures, UoI inferred models with increased sparsity, improved stability, and qualitatively different parameter distributions, while maintaining predictive performance across recording modality, brain region, and task. Specifically, we obtained highly sparse functional coupling networks with substantially different community structure, more parsimonious encoding models, and decoding models that rely on fewer single-units. Together, these results demonstrate that accurate parameter inference reshapes interpretation in diverse neuroscience contexts. The ubiquity of model-based data-driven discovery in biology suggests that analogous results would be seen in other fields.

neuroscience↗

bioRxiv2020

Computational Psychiatry Research Map (CPSYMAP): a New Database for Visualizing Research Papers

The field of computational psychiatry is growing in prominence along with the recent advances in computational neuroscience, machine learning, and the cumulative scientific evidence of psychiatric disorders. Computational approaches provide an understanding of disorders using psychological and neuroscience terms and help to determine treatment protocols based on high-dimensional data. However, the multidisciplinary nature of this field seems to limit the development of computational psychiatry studies. Computational psychiatry combines knowledge from neuroscience, psychiatry, and computation; thus, there is an emerging need for a platform to integrate and coordinate these perspectives. In this study, we developed a new database for visualizing research papers as a two-dimensional "map" called the Computational Psychiatry Research Map (CPSYMAP). This map shows the distribution of papers along neuroscientific, psychiatric, and computational dimensions to enable anyone to find niche research and deepen their understanding of the field.

neuroscience↗

bioRxiv2020

NBR: Network-based R-statistics for (unbalanced) longitudinal samples

Network neuroscience models the brain as interacting elements. However, a large number of elements imply a vast number of interactions, making it difficult to assess which connections are relevant and which are spurious. Zalesky et al. (2010) proposed the Network-Based Statistics (NBS), which identifies clusters of connections and tests their likelihood via permutation tests. This framework shows a better trade-off of Type I and II errors compared to conventional multiple comparison corrections. NBS uses General Linear Hypothesis Testing (GLHT), which may underestimate the within-subject variance structure when dealing with longitudinal samples with a varying number of observations (unbalanced samples). We implemented NBR, an R-package that extends the NBS framework adding (non)linear mixed-effects (LME) models. LME models the within-subject variance in more detail, and deals with missing values more flexibly. To illustrate its advantages, we used a public dataset of 333 human participants (188/145 females/males; age range: 17.0-28.4 y.o.) with two (n=212) or three (n=121) sessions each. Sessions include a resting-state fMRI scan and psychometric data. State anxiety scores and connectivity matrices between brain lobes were extracted. We tested their relationship using GLHT and LME models for balanced and unbalanced datasets, respectively. Only the LME approach found a significant association between state anxiety and a subnetwork that includes the cingulum, frontal, parietal, occipital, and cerebellum. Given that missing data is very common in longitudinal studies, we expect that NBR will be very useful to explore unbalanced samples. Significant StatementLongitudinal studies are increasing in neuroscience, providing new insights into the brain under treatment, development, or aging. Nevertheless, missing data is highly frequent in those studies, and conventional designs may discard incomplete observations or underestimate the within-subject variance. We developed a publicly available software (R package: NBR) that implements mixed-effect models into every possible connection in a sample of networks, and it can find significant subsets of connections using non-parametric permutation tests. We demonstrate that using NBR on larger unbalanced samples has higher statistical power than when exploring the balanced subsamples. Although this method is applicable in general network analysis, we anticipate this method being potentially useful in systems neuroscience considering the increase of longitudinal samples in the field.

neuroscience↗

bioRxiv2021

The unbiased estimation of r2 between two sets of noisy neural responses

The Pearson correlation coefficient squared, r2, is often used in the analysis of neural data to estimate the relationship between neural tuning curves. Yet this metric is biased by trial-to-trial variability: as trial-to-trial variability increases, measured correlation decreases. Major lines of research are confounded by this bias, including the study of invariance of neural tuning across conditions and the similarity of tuning across neurons. To address this, we extend the estimator, [Formula], developed for estimating model-to-neuron correlation to the neuron-to-neuron case. We compare the estimator to a prior method developed by Spearman, commonly used in other fields but widely overlooked in neuroscience, and find that our method has less bias. We then apply our estimator to the study of two forms of invariance and demonstrate how it avoids drastic confounds introduced by trial-to-trial variability. Significant StatementQuantifying the similarity between two sets of averaged neural responses is fundamental to the analysis of neural data. A ubiquitous metric of similarity, the correlation coefficient, is attenuated by trial-to-trial variability that depends on a variety of irrelevant factors. Spearman recognized this problem and proposed corrected methods that have been extended over a century. We show this method has large asymptotic biases and derive a novel estimator to overcome this. Despite the frequent use of the correlation coefficient in neuroscience, consensus on how to address this fundamental statistical issue has not been reached. We both explicate this issue in a neuroscience setting while at the same time making major strides in addressing it.

neuroscience↗

bioRxiv2021

Off-target expression of Cre-dependent adeno-associated viruses in wild type C57BL/6J mice

Adeno-associated viruses (AAVs) are a commonly used tool in neuroscience to efficiently label, trace, and/or manipulate neuronal populations. Highly specific targeting can be achieved through recombinase-dependent AAVs in combination with transgenic rodent lines that express Cre-recombinase in specific cell types. Visualization of viral expression is typically achieved through fluorescent reporter proteins (e.g., GFP or mCherry) packaged within the AAV genome. Although non-amplified fluorescence is usually sufficient to observe viral expression, immunohistochemical amplification of the fluorescent reporter is routinely used to improve viral visualization. In the present study, Cre-dependent AAVs were injected into the hippocampus and cortex of wild-type C57BL/6J mice. While we observed weak but consistent non-amplified off-target DIO expression in C57BL/6J mice, antibody amplification of the GFP or mCherry reporter revealed extensive Cre-independent viral expression. Off-target expression of DIO constructs in wild-type C57BL/6J mice occurred independent of vendor, AAV serotype or promoter. We also evaluated whether Cre-independent expression had functional effects via Designer Receptors Exclusively Activated by Designer Drugs (DREADDs). The DREADD agonist C21 had no effect on contextual fear conditioning or cFos expression in DIO-hM3Dq-mCherry+ cells of C57BL/6J mice. Taken together, our results indicate that DIO constructs have considerable off-target expression in wild type subjects. Our findings are particularly important for the design of experiments featuring sensitive systems and/or quantitative measurements that could be negatively impacted by off-target expression. Significance StatementAdeno-associated viruses (AAV) are widely used in neuroscience because of their safety and ease of use. Combined with specific promoters, Cre/loxP, and stereotaxic injections, highly specific targeting of cells and circuits within the brain can be achieved. In the present study we injected Cre-dependent AAVs into wild-type C57BL/6J mice and found considerable Cre-independent viral expression of AAVs encoding mCherry, GFP, or hM3Dq following immunohistochemical amplification of the fluorescent reporter protein. Importantly, we observed no functional effects of the Cre-independent expression in the hippocampus, as C21 had no detectable effect on DIO-hM3Dq-mCherry infected neurons in C57BL/6J mice. Given the widespread use of DIO rAAVs by the neuroscience community, our data supports careful consideration when using DIO constructs in control animals.

neuroscience↗

bioRxiv2021

Topological insights into the neural basis of flexible behavior

It is widely accepted that there is an inextricable link between neural computations, biological mechanisms, and behavior, but it is challenging to simultaneously relate all three. Here, we show that topological data analysis (TDA) provides an important bridge between these approaches to studying how brains mediate behavior. We demonstrate that cognitive processes change the topological description of the shared activity of populations of visual neurons. These topological changes constrain and distinguish between competing mechanistic models, are connected to subjects performance on a visual change detection task, and, via a link with network control theory, reveal a tradeoff between improving sensitivity to subtle visual stimulus changes and increasing the chance that the subject will stray off task. These connections provide a blueprint for using TDA to uncover the biological and computational mechanisms by which cognition affects behavior in health and disease. Significance StatementAs the fields of systems, computational, and cognitive neuroscience strive to establish links between computations, biology, and behavior, there is an increasing need for an analysis framework to bridge levels of analysis. We demonstrate that topological data analysis (TDA) of the shared activity of populations of neurons provides that link. TDA allows us to distinguish between competing mechanistic models and to answer longstanding questions in cognitive neuroscience, such as why there is a tradeoff between visual sensitivity and staying on task. These results and analysis framework have applications to many systems within neuroscience and beyond.

neuroscience↗

bioRxiv2022

Assessing Quantitative MRI Techniques using Multimodal Comparisons

The study of brain structure and change in neuroscience is commonly conducted using macroscopic morphological measures of the brain such as regional volume or cortical thickness, providing little insight into the microstructure and physiology of the brain. In contrast, quantitative MRI allows the monitoring of microscopic brain change non-invasively in-vivo, and provides normative values for comparisons between tissues, regions, and individuals. To support the development and common use of qMRI for cognitive neuroscience, we analysed a set of qMRI metrics (R1, R2*, Magnetization Transfer saturation, Proton Density saturation, Fractional Anisotropy, Mean Diffusivity) in 101 healthy young adults. Here we provide a comprehensive descriptive analysis of these metrics and their linear relationships to each other in grey and white matter to develop a more complete understanding of the relationship to tissue microstructure. Furthermore, we provide evidence that combinations of metrics may uncover informative gradients across the brain by showing that lower variance components of PCA may be used to identify cortical gradients otherwise hidden within individual metrics. We discuss these results within the context of microstructural and physiological neuroscience research.

neuroscience↗

bioRxiv2022

Acquisition and processing methods of whole-brain layer-fMRI VASO and BOLD: The Kenshu dataset.

Cortical depth-dependent functional magnetic resonance image (fMRI), also known as layer-fMRI, has the potential to capture directional neural information flow of brain computations within and across large-scale cortical brain networks. E.g., layer-fMRI can differentiate feedforward and feedback cortical input in hierarchically organized brain networks. Recent advancements in 3D-EPI sampling approaches and MR contrast generation strategies have allowed proof-of-principle studies showing that layer-fMRI can provide sufficient data quality for capturing laminar changes in functional connectivity. These studies have however not shown how reliable the signal is and how repeatable the respective results are. It is especially unclear whether whole-brain layer-fMRI functional connectivity protocols are widely applicable across common neuroscience-driven analysis approaches. Moreover, there are no established preprocessing fMRI methods that are optimized to work for whole-brain layer-fMRI datasets. In this work, we aimed to serve the field of layer-fMRI and build tools for future routine whole-brain layer-fMRI in application-based neuroscience research. We have developed publicly available sequences, acquisition protocols, and processing pipelines for whole-brain layer-fMRI. These protocols are validated across 60 hours of scanning in nine participants. Specifically, we identified and exploited methodological advancements for maximizing tSNR efficiency and test-retest reliability. We are sharing an extensive multi-modal whole-brain layer-fMRI dataset (20 scan hours of movie-watching in a single participant) for the purpose of benchmarking future method developments: The Kenshu dataset. With this dataset, we are also exemplifying the usefulness of whole brain layer-fMRI for commonly applied analysis approaches in modern cognitive neuroscience fMRI studies. This includes connectivity analyses, representational similarity matrix estimations, general linear model analyses, principal component analysis clustering, etc. We believe that this work paves the road for future routine measurements of directional functional connectivity across the entire brain. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=132 SRC="FIGDIR/small/504502v1_ufig1.gif" ALT="Figure 1"> View larger version (89K): org.highwire.dtl.DTLVardef@1c6579forg.highwire.dtl.DTLVardef@a2bbecorg.highwire.dtl.DTLVardef@1dfa41aorg.highwire.dtl.DTLVardef@18ee67f_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2022

Meggie - easy-to-use graphical user interface for M/EEG analysis based on MNE-python

BackgroundIn the last decades, electrophysiological imaging methodology has seen many advances and the computational power in the neuroscience laboratories has steadily increased. Still, the new methodologies are unavailable for many. There is a need for more versatile analysis approaches for neuroscience specialists without a programming background. New methodUsing a software which provides standard pipelines, provides good default values for parameters, has a good multi-subject support, and stores the used analysis steps with the parameters in one place for reporting, is efficient and fast. In addition to enabling analysis for people without background in programming, it enables analysis for people with background in programming but a limited background in neuroscience. When constructed with care, the GUI may guide the researcher to apply analysis steps in correct order with reasonable default parameters. Comparison to existing methodsTwo existing software, EEGLAB and Brainstorm, both provide an easy-to-use graphical user interface for end-to-end analysis for multiple subjects. The key difference to work presented here is the choice of language. The scientific community is moving en masse towards the python programming language, thus making it an ideal platform for extendable software. Another problem with Matlab is that it is not free - both from the perspective of open source and concrete monetary resources. Within the current trend towards increasing open science, covering data, analysis and reporting, the need for open source software is imperative. ResultsMeggie is an open source software for running MEG and EEG analysis with easy-to-use graphical user interface. It is written in Python 3, runs on Linux, macOS and Windows, and uses the MNE-python library under the hood to do heavy lifting. It is designed to allow end-to-end analysis of MEG and EEG datasets from multiple subjects with common sensor-level analysis steps such as preprocessing, epoching and averaging, spectral analysis and time-frequency analysis. Most of the analysis steps can be run for all the subjects in one go, and combining the results across subjects is made possible with grand averages. We have emphasized the extendibility of Meggie by implementing most of the Meggie itself as plugins, thus ensuring that new plugins have access to all necessary core features. ConclusionMeggie answers the demand for easy-to-use and extendable python-based graphical user interface that provides an end-to-end analysis environment for M/EEG data analysis. It is freely available at https://github.com/cibr-jyu/meggie under the BSD license. Installation instructions, documentation and tutorials are found on that website. Highlights- MNE-python-based graphical user interface for M/EEG analysis - Easy to use even without programming background - Has good support for multiple subjects - Written in python, and can be easily extended with plugins - Free and open source with BSD license

neuroscience↗

bioRxiv2022

Observing the self and other in motion modulates the excitability of vestibulocollic reflexes

Vestibular inputs from the inner ear are at the basis of the vestibulo-spinal and vestibulocollic reflexes involved in balance control. Studies have focused on how attentional load and emotions influence balance, but low-level social cues, such as observing human bodies in motion, have been neglected. Yet, individuals observing another person in a challenging posture or in motion can experience imbalance, indicating that sensorimotor resonance between self and others is involved. The present study examines how the observation of videos depicting human bodies in motion modulates well-established neurophysiological signatures of vestibular information processing. The excitability of vestibulocollic reflexes was assessed by analyzing the waveform of vestibular-evoked myogenic potentials (VEMPs) over the sternocleidomastoid and trapezius muscles of 25 healthy participants (13 females, 12 males). Here we show that observing human bodies undergoing passive whole-body rotations reduced the VEMPs amplitude when compared to observing an object. Importantly, the modulation depended on the person depicted in the video as VEMPs were reduced when observing oneself, compared to someone else being moved. Direction-specific effects and electromyography recordings ruled out non-specific emotional and attentional effects. These results show that the vestibular system is sensitive to observing human bodies in motion, establishing new connections between social neuroscience and vestibular neurophysiology. Significance StatementVestibulocollic reflexes are thought to be consistent and of short latency. Yet, previous results show that observing conspecifics influences balance. We combined approaches from social neuroscience and vestibular electrophysiology to describe how the observation of self and other bodies in motion influences vestibular information processing. The results show that observing human bodies in motion reduces the amplitude of vestibulocollic reflexes involved in the stabilization of the head and balance. These results establish new relations between the sense of balance and social cognition and challenge classical views in vestibular neuroscience.

neuroscience↗

bioRxiv2022

High sensitivity intrinsic optical signal imaging through flexible, low-cost adaptations of an upright microscope

Intrinsic optical signal imaging (IOSI) is a staple technique in modern neuroscience. Pioneered over thirty years ago, IOSI allows macroscopic mapping of neuronal activity throughout the cortex. The technique has been used to study sensory processing and experience-dependent plasticity, and is often used as an adjunctive procedure to localize cortical areas for subsequent targeting by other imaging or physiology techniques. Despite the ubiquity of IOSI in neuroscience there are few commercially available IOSI systems. As a result, investigators have typically resorted to building their own imaging systems. Over the years, simplified systems built on existing microscope platforms have been proposed. Still, these often require additional, sometimes costly, custom-built hardware, which presents challenges for investigators without significant experience in optics or microscopy design. Here we present a straightforward set of adaptations that can be applied to any standard upright microscope, using readily available, inexpensive, commercial parts for illumination, optics, and signal detection, that enables high sensitivity IOSI. Using these adaptations, we are able to readily map sensory-evoked signals across the somatosensory and visual cortex, including single-whisker barrel cortical activity maps in mice. We show that these IOSI maps are highly reproducible across animals and can be used to study plasticity mechanisms in the somatosensory cortex. We also provide open-source applications to control illumination and analyze raw data to generate activity maps. We anticipate these resources will be particularly useful for neuroscience investigators from broad technical backgrounds looking to add IOSI capabilities to an existing microscope on a budget.

neuroscience↗

bioRxiv2023

Relationship between neural activity and neuronal cell fate in regenerating Hydra revealed by cell-type specific imaging

Understanding how neural circuits are regenerated following injury is a fundamental question in neuroscience. Hydra is a powerful model for studying this process because it has significant and reproducible regenerative abilities, a simple and transparent body that allows for whole nervous system imaging, and established methods for creating transgenics with cell-type-specific expression. In addition, cnidarians such as Hydra split from bilaterians (the group that encompasses most model organisms used in neuroscience) over 500 million years ago, so similarities with other models likely indicates deeply conserved biological processes. Hydra is a long-standing regeneration model and is an emerging model for neuroscience; however, relatively little is known regarding the restoration of neural activity and behavior following significant injury. In this study, we ask if regenerating neurons reach a terminal cell fate and then reform functional neural circuits, or if neural circuits regenerate first and then guide the constituent cells toward their terminal fate. To address this question, we developed a dual-expression transgenic Hydra line that expresses a cell-type-specific red fluorescent protein (tdTomato) in ec5 peduncle neurons, and a calcium indicator (GCaMP7s) in all neurons. This transgenic line allowed us to monitor neural activity while we simultaneously track the reappearance of terminally differentiated ec5 neurons as determined by the expression of tdTomato. Using SCAPE (Swept Confocally Aligned Planar Excitation) microscopy, we tracked both calcium activity and expression of tdTomato-positive neurons in 3D with single-cell resolution during regeneration of Hydras aboral end. We observed tdTomato expression in ec5 neurons approximately four hours before the neural activity begins to display synchronized patterns associated with a regenerated neural circuit. These data suggest that regenerating neurons undergo terminal differentiation prior to re-establishing their functional role in the nervous system. The combination of dynamic imaging of neural activity and gene expression during regeneration make Hydra a powerful model system for understanding the key molecular and functional processes involved in neuro-regeneration following injury.

neuroscience↗

bioRxiv2023

Single-cell analysis of the nervous system at small and large scales with instant partitions

Single-cell RNA sequencing is a new frontier across all biology, particularly in neuroscience. While powerful for answering numerous neuroscience questions, limitations in sample input size, and initial capital outlay can exclude some researchers from its application. Here, we tested a recently introduced method for scRNAseq across diverse scales and neuroscience experiments. We benchmarked against a major current scRNAseq technology and found that PIPseq performed similarly, in line with earlier benchmarking data. Across dozens of samples, PIPseq recovered many brain cell types at small and large scales (1,000-100,000 cells/sample) and was able to detect differentially expressed genes in an inflammation paradigm. Similarly, PIPseq could detect expected and new differentially expressed genes in a brain single cell suspension from a knockout mouse model; it could also detect rare, virally-la-belled cells following lentiviral targeting and gene knockdown. Finally, we used PIPseq to investigate gene expression in a nontraditional model species, the little skate (Leucoraja erinacea). In total, PIPSeq was able to detect single-cell gene expression changes across models and species, with an added benefit of large scale capture and sequencing of each sample.

neuroscience↗