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Neuroscience

Find bioRxiv neuroscience preprints. Search neural circuits, brain imaging and behavior in indexed metadata; check the original manuscript and its version. Preprints are not peer reviewed.

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At least 73 records · Page 4Linked to original sources

bioRxiv2023

Feasibility of cognitive neuroscience data collection during a speleological expedition

In human cognitive neuroscience and neuropsychology studies, laboratory-based research tasks have been important to establish principles of brain function and its relationship to behaviour; however, they differ greatly from real-life experiences. Several elements of real-life situations that impact human performance, such as stressors, are difficult or impossible to replicate in the laboratory. Expeditions offer unique possibilities for studying human cognition in complex environments that can transfer to other situations with similar features. For example, as caves share several of the physical and psychological challenges of safety-critical environments such as spaceflight, underground expeditions have been developed as an analogue for astronaut training purposes, suggesting that they might also be suitable for studying aspects of behaviour and cognition that cannot be fully examined under laboratory conditions. While a large range of topics and tools have been proposed for use in such environments, few have been evaluated in the field. We tested the feasibility of collecting human physiological, cognitive, and subjective experience data concerning brain state, sleep, cognitive workload, and fatigue, during a speleological expedition in a remote region. We document our approaches and challenges experienced, and provide recommendations and suggestions to aid future work. The data support the idea that cave expeditions are relevant naturalistic paradigms that offer unique possibilities for cognitive neuroscience to complement laboratory work and help improve human performance and safety in operational environments.

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bioRxiv2024

Structured behavioral data format: An NWB extension standard for task-based behavioral neuroscience experiments

Understanding brain function necessitates linking neural activity with corresponding behavior. Structured behavioral experiments are crucial for probing the neural computations and dynamics underlying behavior; however, adequately representing their complex data is a significant challenge. Currently, a comprehensive data standard that fully encapsulates task-based experiments, integrating neural activity with the richness of behavioral context, is lacking. We designed a data model, as an extension to the NWB neurophysiology data standard, to represent structured behavioral neuroscience experiments, spanning stimulus delivery, timestamped events and responses, and simultaneous neural recordings. This data format is validated through its application to a variety of experimental designs, showcasing its potential to advance integrative analyses of neural circuits and complex behaviors. This work introduces a comprehensive data standard designed to capture and store a spectrum of behavioral data, encapsulating the multifaceted nature of modern neuroscience experiments.

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bioRxiv2024

The Increasing Authorship trend in Neuroscience: A scientometric analysis across 11 countries

Previous studies have demonstrated an increasing trend of the number of authors across various fields over the years. This trend has been attributed to the necessity for larger collaborations and, at times, to ethical issues regarding authorship attribution. Our study focuses on the evolution of authorship trends in the field of Neuroscience. We conducted our analysis based on a dataset containing 576,647 neuroscience publications produced from 2000 to 2022, focusing on the publications within the Group of ten (G10) countries. Using a matrix-based methodology, we extracted and analyzed the average number of authors per country. Our findings reveal a consistent rise in authorship across all G10 countries over the past two decades. Italy emerged with the highest average number of authors, while the Netherlands stood out for experiencing the most significant increase, particularly in the last decade. The countries with the lowest number of authors per publication were the USA, UK, and Canada.

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bioRxiv2024

Data science and its future in large neuroscience collaborations.

The rise of large scientific collaborations in neuroscience requires systematic, scalable, and reliable data management. How this is best done in practice remains an open question. To address this, we conducted a data science survey among currently active U19 grants, funded through the NIHs BRAIN Initiative. The survey was answered by both data science liaisons and Principal Investigators, speaking for [~]500 researchers across 21 nation-wide collaborations. We describe the tools, technologies, and methods currently in use, and identify several shortcomings of current data science practice. Building on this survey, we develop plans and propose policies to improve data collection, use, publication, re-use and training in the neuroscience community.

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bioRxiv2024

Automated, Stress-Free, and Precise Measurement of Songbird Weight in Neuroscience Experiments

Monitoring the health and well-being of research animals is essential for both ethical and scientific purposes. In songbirds, body weight is one of the main indicators for their overall condition, yet traditional weighing methods can be intrusive and stress-inducing, which could decrease their song rate. We developed a novel, automated system designed to continuously monitor the weight of untethered and tethered birds without disrupting their natural behavior in neuroscience experiments. We used the system to track weight fluctuations in six canaries over several weeks, revealing physiological patterns such as overnight weight loss, with one bird losing approximately 5.17% of its body weight during a 9.5-hour period of inactivity. Our systems high sensitivity detected weight changes below 1% of body mass, validating its reliability for long-term studies. Control experiments confirmed that weight fluctuations observed were physiological rather than due to equipment deviations. By eliminating the need for manual handling, this system offers a non-invasive, hands-free approach that reduces stress and improves the accuracy of health assessments. This study demonstrates the systems potential for expanding research on how environmental factors, diet, and other variables influence bird physiology and behavior. Future applications could integrate additional health metrics, providing a more comprehensive understanding of animal welfare in neuroscience and behavioral studies.

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bioRxiv2024

NeuroTorch: A Python library for neuroscience-oriented machine learning

Machine learning (ML) has become a powerful tool for data analysis, leading to significant advances in neuroscience research. While ML algorithms are proficient in general-purpose tasks, their highly technical nature often hinders their compatibility with the observed biological principles and constraints in the brain, thereby limiting their suitability for neuroscience applications. In this work, we introduce NeuroTorch, a comprehensive ML pipeline specifically designed to assist neuroscientists in leveraging ML techniques using biologically inspired neural network models. NeuroTorch enables the training of recurrent neural networks equipped with either spiking or firing-rate dynamics, incorporating additional biological constraints such as Dales law and synaptic excitatory-inhibitory balance. The pipeline offers various learning methods, including backpropagation through time and eligibility trace forward propagation, aiming to allow neuroscientists to effectively employ ML approaches. To evaluate the performance of NeuroTorch, we conducted experiments on well-established public datasets for classification tasks, namely MNIST, Fashion-MNIST, and Heidelberg. Notably, NeuroTorch achieved accuracies that replicated the results obtained using the Norse and SpyTorch packages. Additionally, we tested NeuroTorch on real neuronal activity data obtained through volumetric calcium imaging in larval zebrafish. On training sets representing 9.3 minutes of activity under darkflash stimuli from 522 neurons, the mean proportion of variance explained for the spiking and firing-rate neural network models, subject to Dales law, exceeded 0.97 and 0.96, respectively. Our analysis of networks trained on these datasets indicates that both Dales law and spiking dynamics have a beneficial impact on the resilience of network models when subjected to connection ablations. NeuroTorch provides an accessible and well-performing tool for neuroscientists, granting them access to state-of-the-art ML models used in the field without requiring in-depth expertise in computer science.

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bioRxiv2025

The Evolving Landscape of Neuroscience

Understanding the evolution and structure of large scientific fields is crucial for optimizing knowledge production. Neuroscience is a rapidly expanding and diversifying field. To retain an overview of its cross-domain insights and research questions, this study leverages text-embedding and clustering techniques together with large language models for analyzing 461,316 articles published between 1999 and 2023 and reveals the fields structural organization and dominant research domains. Inter-cluster citation analysis uncovers a surprisingly integrated picture and key intellectual hubs that shape the broader landscape. The field further exhibits a strong experimental focus, widespread reliance on specific mechanistic explanations rather than unifying theoretical frameworks, and a growing emphasis on applied research. Fundamental research is at the risk of decline and cross-scale integration remains limited. This study provides a framework for understanding neurosciences trajectory and identifies potential avenues for strengthening the field, offering a model for understanding the trajectory of complex research communities.

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bioRxiv2025

Gender imbalance in citations most pronounced in high-impact neuroscience journals

In the past several years, neuroscience, like many other fields, has worked to address pervasive gender imbalances. Although tangible improvements have been made in academic publishing and conference participation, gender imbalances in citations among leading neuroscience journals persist and are increasing with time. Here, we expand upon prior work by providing a more comprehensive analysis of citation practices across fifty journals to improve our understanding of the dynamics of the field as a whole, particularly across different kinds of journals. We first confirm that reference lists tend to include more papers with men as first and last author than expected if gender were unrelated to citation practices. Further, we demonstrate that this effect is strongest in the most influential journals. We also find that the apparent closure of the gender gap is due to the interaction of three unique, nuanced patterns of citation: one that is characterized by relative overcitation of papers with men as both first and last author; one that is characterized by relative overcitation of women as the last author, and one that is characterized by relative undercitation of women as the first author. In addition, we find that these patterns of citation are not distributed evenly across the field: more prestigious journals tend to have articles with citation gaps favoring men, while less prestigious journals tend to overcite papers with women as last authors. We discuss potential drivers for these patterns and suggest some implications for the field moving forward.

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bioRxiv2025

BRAPH 2: a flexible, open-source, reproducible, community-oriented, easy-to-use framework for network analyses in neurosciences

As network analyses in neuroscience continue to grow in both complexity and size, flexible methods are urgently needed to provide unbiased, reproducible insights into brain function. BRAPH 2 is a versatile, open-source framework that meets this challenge by offering streamlined workflows for advanced statistical models and deep learning in a community-oriented environment. Through its Genesis compiler, users can build specialized distributions with custom pipelines, ensuring flexibility and scalability across diverse research domains. These powerful capabilities will ensure reproducibility and accelerate discoveries in neuroscience.

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bioRxiv2025

OpenIrisDPI: An Open-Source Digital Dual Purkinje Image Eye Tracker for Visual Neuroscience

BackgroundVideo-based eye trackers are widely used in vision science, psychology, clinical assessment, and neurophysiology. Many such systems track the pupil center and corneal reflection (P-CR) and compare their positions to estimate the direction of gaze. However, P-CR eye trackers are often too imprecise for applications with stringent eye tracking quality requirements. New methodWe present OpenIrisDPI, an open-source plugin for the OpenIris frame-work that implements dual Purkinje image (DPI) tracking. OpenIrisDPI supports simultaneous pupillography, a technique widely used in perceptual psychology and neuroscience, and it enables direct comparison between P-CR and DPI signals. ResultsData collected from macaque monkeys using OpenIrisDPI show that the P-CR method overestimates the amount of fixational drift between saccades compared to DPI. The accuracy of the DPI signal was further validated using high-density extracellular recording of neurons in the lateral geniculate nucleus. Compensating for the effects of fixational eye movements using DPI signals produced sharper estimates of neuronal receptive fields than using simultaneously collected P-CR signals. Comparison with existing methodsOpenIrisDPI is provided as open-source software and operates on consumer-grade hardware, making it more accessible than previously described DPI eye trackers and less costly than many P-CR systems. To our knowledge, OpenIrisDPI is the first eye tracker to perform both pupillography and DPI eye tracking. ConclusionOpenIrisDPI makes high-precision eye tracking readily available to the research community. It is well suited for visual neuroscience applications, where accurate knowledge of the retinal image during experiments is critical. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=105 SRC="FIGDIR/small/649589v2_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@596dd2org.highwire.dtl.DTLVardef@139027borg.highwire.dtl.DTLVardef@1d1e526org.highwire.dtl.DTLVardef@11b321c_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIOpenIrisDPI is a new open-source eye tracking system. C_LIO_LIOpenIrisDPI tracks the pupil, corneal reflection, & fourth Purkinje image at 500 Hz. C_LIO_LIDual Purkinje image-based eye tracking is more precise than pupilbased tracking. C_LIO_LIDPI improves receptive field characterization of LGN neurons in fixating macaques. C_LI

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bioRxiv2025

NeuroConText: Contrastive Learning for Neuroscience Meta-Analysis with Rich Text Representation

Brain meta-analysis is the common way to gather information about human brain function across the existing literature in order to formulate hypotheses and contextualize new findings. However, automated meta-analysis tools face challenges such as inconsistent terminology and difficulties in analyzing long texts and capturing semantic meaning because they still rely on bag-of-words approaches; furthermore, sparse coordinate reporting in articles distorts the activation distribution due to incomplete data. This paper introduces NeuroConText, a predictive text-to-brain modeling framework designed to support brain meta-analysis by bridging neuroscience text, brain location coordinates, and brain images within a shared latent space. This framework follows the predictive brain meta-analysis paradigm: it learns a regression from text descriptions to whole-brain activation maps and also enables the retrieval of relevant studies through contrastive learning, optimizing a multi-objective loss that combines retrieval and reconstruction objectives. Furthermore, NeuroConText supports second-level statistical synthesis by providing activation associated with top-K retrieved studies that can serve as input to coordinate-based meta-analysis (CBMA) methods. NeuroConText also leverages large language models (LLMs) to capture neuroscientific information from full-text articles, plus an LLM-based text augmentation strategy to handle short-text inputs. Quantitative and qualitative analyses demonstrate NeuroConTexts ability to enhance text-to-brain retrieval performance and reconstruct brain maps from neuroscience texts. We also show that predictive brain meta-analysis tools can infer brain activations in regions discussed in articles but absent in reported coordinates, potentially addressing the challenge of sparse coordinate reporting.

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bioRxiv2025

NERV: A Comprehensive Framework for Rapid, Reproducible, and Hardware-Synchronized Neuroscience Experiment Design and Execution

BackgroundBehavioral neuroscience experiments require precise stimulus control, millisecond timing, hardware integration, and robust data provenance. Increasing use of 3D environments and multimodal recordings adds challenges for development, accessibility, and reproducibility. Fragmented tools often separate presentation, synchronization, and logging, leading to inefficiencies. New MethodThe Neuroscience Experimental Runtime by Vanderbilt (NERV) is a Unity-based framework that unifies experiment design, execution, and data logging. It enables rapid, no-code prototyping by automating scene and script generation, event timing, state management, hardware-synchronized data acquisition, and archival of code and experimental configurations. The modular, open-source framework implements a "low floor, high ceiling" design that lowers barriers for non-programmers while remaining extensible for advanced customization. ResultsAcross 500 trials, Unity-to-TTL delay was 2.10 {+/-} 1.21 ms, TTL-to-photodiode delay was 28.93 {+/-} 0.76 ms, and Unity-to-screen delay was 31.04 {+/-} 1.41 ms. These results confirm stable millisecond precision and frame-locked timing, enabling reliable alignment of neural, behavioral, and visual events. Comparison with existing methodsExisting frameworks involve trade-offs. Some achieve precise timing but require advanced coding, while others improve accessibility but struggle with hardware or 3D graphics. Commercial platforms offer polish yet remain costly, closed-source, and inflexible. NERV combines millisecond precision, modular open-source design, and provenance in a single platform, reducing workflow fragmentation and enabling reproducible, scalable experiments. ConclusionNERV is an accessible yet extensible framework that unites rapid development, robust data provenance, and millisecond precision. It accelerates development, ensures reproducibility, and establishes a scalable foundation for next-generation neuro-science research.

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bioRxiv2025

RetINaBox: A hands-on learning tool for experimental neuroscience

An exciting aspect of neuroscience is developing and testing hypotheses via experimentation. However, due to logistical and financial hurdles, the experiment and discovery component of neuroscience is generally lacking in classroom and outreach settings. To address this issue, here we introduce RetINaBox: a low-cost open-source electronic visual system simulator that provides users with a hands-on tool to discover how the visual system builds feature detectors. RetINaBox features an LED array for generating visual stimuli and a photodiode array that acts as a mosaic of model photoreceptors. Custom software on a Raspberry Pi computer reads out responses from model photoreceptors and allows users to control the polarity and delay of the signal transfer from model photoreceptors to model retinal ganglion cells. Interactive lesson plans are provided, guiding users to discover different types of visual feature detectors--including ON/OFF, center-surround, orientation selective, and direction selective receptive fields--as well as their underlying circuit computations.

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bioRxiv2018

RTHybrid: a standardized and open-source real-time software model library for experimental neuroscience

Closed-loop technologies provide novel ways of online observation, control and bidirectional interaction with the nervous system, which help to study complex non-linear and partially observable neural dynamics. These protocols are often difficult to implement due to the temporal precision required when interacting with biological components, which in many cases can only be achieved using real-time technology. In this paper we introduce RTHybrid (www.github.com/GNB-UAM/RTHybrid), a free and open-source software that includes a neuron and synapse model library to build hybrid circuits with living neurons in a wide variety of experimental contexts. In an effort to encourage the standardization of real-time software technology in neuroscience research, we compared different open-source real-time operating system patches, RTAI, Xenomai 3 and Preempt-RT, according to their performance and usability. RTHybrid has been developed to run over Linux operating systems supporting both Xenomai 3 and Preempt-RT real-time patches, and thus allowing an easy implementation in any laboratory. We report a set of validation tests and latency benchmarks for the construction of hybrid circuits using this library. With this work we want to promote the dissemination of standardized, user-friendly and open-source software tools developed for open- and closed-loop experimental neuroscience.

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bioRxiv2019

A platform for semi-automated voluntary training of common marmosets for behavioral neuroscience

In most cases, behavioral neuroscience studies of the common marmoset employ adaptations of well-established methods used with macaque monkeys. However, in most cases these approaches do not readily generalize to marmosets indicating a need for alternatives. Here we present the development of one such alternate: a platform for semi-automated, voluntary in-home cage behavioral training that allows for the study of naturalistic behaviors. We describe the design and production of a modular behavioral training apparatus using CAD software and digital fabrication. We demonstrate that this apparatus permits voluntary behavioral training and data collection throughout the marmosets waking hours with little experimenter intervention. Further we demonstrate the use of this apparatus to reconstruct the kinematics of the marmosets upper limb movement during natural foraging behavior.\n\nNEW AND NOTEWORTHYThe study of marmosets in neuroscience has grown rapidly and this model organism presents challenges that are unique to this primate species. Here we address those challenges with an innovative platform for semi-automated and voluntary training of common marmosets. The platform allows marmosets to train throughout their waking hours with little to no experimenter intervention. We describe the use of this platform to capture the kinematics of the upper limb during natural foraging behavior and to expand the opportunities for behavioral training beyond the limits of traditional behavioral training sessions. The platform is flexible and can be easily extended to incorporate other motor tasks (e.g. visually cued reaching or manipulandum based tasks) using CAD models and digital fabrication.

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bioRxiv2019

Application of the hierarchical bootstrap to multi-level data in neuroscience

AO_SCPLOWBSTRACTC_SCPLOWA common feature in many neuroscience datasets is the presence of hierarchical data structures, most commonly recording the activity of multiple neurons in multiple animals across multiple trials. Accordingly, the measurements constituting the dataset are not independent, even though the traditional statistical analyses often applied in such cases (e.g. Students t-test) treat them as such. The hierarchical bootstrap has been shown to be an effective tool to accurately analyze such data and while it has been used extensively in the statistical literature, its use is not widespread in neuroscience - despite the ubiquity of hierarchical datasets. In this paper, we illustrate the intuitiveness and utility of this approach to analyze hierarchically nested datasets. We use simulated neural data to show that traditional statistical tests can result in a false positive rate of over 45%, even if the Type-I error rate is set at 5%. While summarizing data across non-independent points (or lower levels) can potentially fix this problem, this approach greatly reduces the statistical power of the analysis. The hierarchical bootstrap, when applied sequentially over the levels of the hierarchical structure, keeps the Type-I error rate within the intended bound and retains more statistical power than summarizing methods. We conclude by demonstrating the effectiveness of the method in two real-world examples, first analyzing singing data in male Bengalese finches (Lonchura striata var. domestica) and second quantifying changes in behavior under optogenetic control in flies (Drosophila melanogaster).

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bioRxiv2019

Crowdsourcing neuroscience: inter-brain coupling during face-to-face interactions outside the laboratory

When we feel connected or engaged during social behavior, are our brains in fact "in sync" in a formal, quantifiable sense? Most studies addressing this question use highly controlled tasks with homogenous subject pools. In an effort to take a more naturalistic approach, we collaborated with art institutions to crowd-source neuroscience data: Over the course of 5 years, we collected electroencephalogram (EEG) data from thousands of museum and festival visitors who volunteered to engage in a 10-minute face-to-face interaction. Pairs of participants with various levels of familiarity sat inside the Mutual Wave Machine--an art/science neurofeedback installation that uses Brain-Computer Interface technology (BCI) to translate real-time correlations of each pairs EEG activity into light patterns. Because such inter-participant EEG correlations are prone to noise contamination, in subsequent offline analyses we computed inter-brain synchrony using Imaginary Coherence and Projected Power Correlations, two synchrony metrics that are largely immune to instantaneous, noise-driven correlations. When applying these methods to two subsets of recorded data with the most consistent protocols, we found that pairs trait empathy, social closeness, engagement, and social behavior (joint action and eye contact) consistently predicted the extent to which their brain activity became synchronized, most prominently in low alpha ([~]7-10 Hz) and beta ([~]20-22 Hz) oscillations. These findings support an account where shared engagement and joint action drive coupled neural activity and behavior during dynamic, naturalistic social interactions. To our knowledge, this work constitutes a first demonstration that an interdisciplinary, real-world, crowdsourcing neuroscience approach may provide a promising method to collect large, rich datasets pertaining to real-life face-to-face interactions. Additionally, it is a demonstration of how the general public can participate and engage in the scientific process outside of the laboratory. Institutions such as museums, galleries, or any other organization where the public actively engages out of self-motivation, can help facilitate this type of citizen science research, and support the collection of large datasets under scientifically controlled experimental conditions. To further enhance the public interest for the out-of-the-lab experimental approach, the data and results of this study are disseminated through a website tailored to the general public (wp.nyu.edu/mutualwavemachine).

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bioRxiv2019

An illustration of reproducibility in neuroscience research in the absence of selective reporting

The problem of poor reproducibility of scientific findings has received much attention over recent years, in a variety of fields including psychology and neuroscience. The problem has been partly attributed to publication bias and unwanted practices such as p-hacking. Low statistical power in individual studies is also understood to be an important factor. In a recent multi-site collaborative study, we mapped brain anatomical left-right asymmetries for regional measures of surface area and cortical thickness, in 99 MRI datasets from around the world, for a total of over 17,000 participants. In the present study, we re-visited these hemispheric effects from the perspective of reproducibility. Within each dataset, we considered that an effect had been reproduced when it matched the meta-analytic effect from the 98 other datasets, in terms of effect direction and uncorrected significance at p<0.05. In this sense, the results within each dataset were viewed as coming from separate studies in an ideal publishing environment, i.e. free from selective reporting and p hacking. We found an average reproducibility rate per dataset, over all effects, of 63.2% (SD = 22.9%, min = 22.2%, max = 97.0%). As expected, reproducibility was higher for larger effects and in larger datasets. There is clearly substantial room to improve reproducibility in brain MRI research through increasing statistical power. These findings constitute an empirical illustration of reproducibility in the absence of publication bias or p hacking, when assessing realistic biological effects in heterogeneous neuroscience data, and given typically-used sample sizes.

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