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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2019

Hypothesis-testing Improves the Predicted Reliability of Neuroscience Research

Critics often cite statistical problems as prime contributors to the "reproducibility crisis" of science, expressing great concern about research that bases major conclusions on single p-valued statistical tests. The critics also argue that the predicted reliability of neuroscience research in particular is low because much of the work depends heavily on small experimental sample sizes and, hence, its statistical tests lack adequate "power." It isnt known how common the practice of basing major conclusions on single tests is in neuroscience or how the statistical criticisms affect the validity of conclusions drawn by laboratory research that evaluates hypotheses via multiple tests. I review a sample of neuroscience publications to estimate the prevalence and extensiveness of hypothesis-testing research. I then apply R.A. Fishers method for combining test results to show that the practice of testing multiple predictions of hypotheses increases the predicted reliability of neuroscience research.

neuroscience↗
bioRxiv2022

CoBeL-RL: A neuroscience-oriented simulation framework for complex behavior and learning

Reinforcement learning (RL) has become a popular paradigm for modeling animal behavior, analyzing neuronal representations, and studying their emergence during learning. This development has been fueled by advances in understanding the role of RL in both the brain and artificial intelligence. However, while in machine learning a set of tools and standardized benchmarks facilitate the development of new methods and their comparison to existing ones, in neuroscience, the software infrastructure is much more fragmented. Even if sharing theoretical principles, computational studies rarely share software frameworks, thereby impeding the integration or comparison of different results. Machine learning tools are also difficult to port to computational neuroscience since the experimental requirements are usually not well aligned. To address these challenges we introduce CoBeL-RL, a closed-loop simulator of complex behavior and learning based on RL and deep neural networks. It provides a neuroscience-oriented framework for efficiently setting up and running simulations. CoBeL-RL offers a set of virtual environments, e.g. T-maze and Morris water maze, which can be simulated at different levels of abstraction, e.g. a simple gridworld or a 3D environment with complex visual stimuli, and set up using intuitive GUI tools. A range of RL algorithms, e.g. Dyna-Q and deep Q-network algorithms, is provided and can be easily extended. CoBeL-RL provides tools for monitoring and analyzing behavior and unit activity, and allows for fine-grained control of the simulation via interfaces to relevant points in its closed-loop. In summary, CoBeL-RL fills an important gap in the software toolbox of computational neuroscience.

bioinformatics↗
bioRxiv2024

Building Brains for Robots: A Hands-On Approach to Learning Neuroscience in the Classroom

As the relevance of neuroscience in education grows, effective methods for teaching this complex subject in high school classrooms remain elusive. Integrating classroom experiments with brain-based robots offers a promising solution. This paper presents a structured curriculum designed around the use of camera-equipped mobile robots which enables students to construct and explore artificial neural networks. Through this hands-on approach, students engage directly with core concepts in neuroscience, learning to model spiking neural networks, decision-making processes in the basal ganglia, and principles of learning and memory. The curriculum not only makes challenging neuroscience concepts accessible and engaging but also demonstrates significant improvements in students understanding and self-efficacy. By detailing the curriculums development, implementation, and educational outcomes, this study outlines a scalable model for incorporating advanced scientific topics into secondary education, paving the way for a deeper student understanding of both theoretical neuroscience and its practical applications.

scientific communication and education↗
bioRxiv2019

The state of discovery-driven neuroscience research and experimental organism usage in the United States

Purely discovery-driven biological research \"...performed without thought of practical ends...\" establishes fundamental conceptual frameworks for technological and medical breakthroughs that often occur many years later. Despite the critical importance of discovery-driven research for scientific progress, there is increasing concern that it is increasingly less favored by funding agencies than research with explicit goals of application and innovation, resulting in a decline in discovery-driven research output. This in turn appears to promote the use of genetically modified organisms (those with advanced molecular toolkits for gene manipulation and visualization) for which genetic models of human disease can be studied at molecular and cellular resolution using state of the art methodology, and to discourage use of other experimental organisms that provide necessary evolutionary context. This field of neuroscience encompasses both applied and discovery-driven research, providing an opportunity to empirically determine whether funding and publication rates for the latter have indeed declined. Additionally, the diversity of experimental organisms traditionally employed in neuroscience research provides a means to quantify changes in use of study organisms that lack genetic tools over time. In particular, the basic research field of neuroethology is characterized by its distinct approach to selection of study organisms based on their adaptive behaviors, evolutionary history, and suitability for answering the question of interest, providing a stronger basis for the assumption that findings reflect fundamental concepts of nervous system function and behavior. A 30-year analysis of National Science Foundation (NSF) funding of neuroethology research finds that the agency has funded progressively fewer researchers with smaller average award amounts, with a decline in awards for research on non-genetically modified organisms. Neuroscience funding by the National Institutes of Health (NIH) shows the same trend but also increasing support for genetically modified organisms. The same pattern is observed in the neuroscience literature but occurs prior to changes in funding, suggesting that the shift to genetically modified organisms was likely initiated by researchers but may potentially have been later reinforced by funding agency and journal publisher preferences.

scientific communication and education↗
bioRxiv2015

BRAINformat: A Data Standardization Framework for Neuroscience Data

Neuroscience is entering the era of extreme data with little experience and few plans for the associated volume, velocity, variety, and veracity challenges. This is a serious impediment for both the sharing of data across labs, as well as the utilization of modern and high-performance computing capabilities to enable data driven discovery. Here, we introduce BRAINformat, a novel file format and model for management and storage of neuroscience data. The BRAINformat library defines application-independent design concepts and modules that together create a general framework for standardization of scientific data.\n\nWe describe the formal specification of scientific data standards, which facilitates sharing and verification of data and formats. We introduce the concept of Managed Objects, enabling semantic components of data formats to be specified as self-contained units, supporting modular and reusable design of data format components and file storage. The BRAINformat is built off of HDF5, enabling portable, scalable, and self-describing data storage. We introduce the novel concept of Relationship Attributes for modeling and use of semantic relationships between data objects, and discuss the annotation of data using dedicated data annotation modules provided by the BRAINformat library. Based on these concepts we implement dedicated, application-oriented modules and design a data standard for neuroscience data. The BRAINformat software library is open source, easy-to-use, and provides detailed user and developer documentation and is freely available at: https://bitbucket.org/oruebel/brainformat.

Neuroscience↗
bioRxiv2016

Empirical assessment of published effect sizes and power in the recent cognitive neuroscience and psychology literature

We have empirically assessed the distribution of published effect sizes and estimated power by extracting more than 100,000 statistical records from about 10,000 cognitive neuroscience and psychology papers published during the past 5 years. The reported median effect size was d=0.93 (inter-quartile range: 0.64-1.46) for nominally statistically significant results and d=0.24 (0.11-0.42) for non-significant results. Median power to detect small, medium and large effects was 0.12, 0.44 and 0.73, reflecting no improvement through the past half-century. Power was lowest for cognitive neuroscience journals. 14% of papers reported some statistically significant results, although the respective F statistic and degrees of freedom proved that these were non-significant; p value errors positively correlated with journal impact factors. False report probability is likely to exceed 50% for the whole literature. In light of our findings the recently reported low replication success in psychology is realistic and worse performance may be expected for cognitive neuroscience.

Neuroscience↗
bioRxiv2017

The Role Of Diffusion MRI In Neuroscience

Diffusion weighted imaging has further pushed the boundaries of neuroscience by allowing us to peer farther into the white matter microstructure of the living human brain. By doing so, it has provided answers to fundamental neuroscientific questions, launching a new field of research that had been largely inaccessible. We will briefly summarise key questions, that have historically been raised in neuroscience, concerning the brains white matter. We will then expand on the benefits of diffusion weighted imaging and its contribution to the fields of brain anatomy, functional models and plasticity. In doing so, this review will highlight the invaluable contribution of diffusion weighted imaging in neuroscience, present its limitations and put forth new challenges for the future generations who may wish to exploit this powerful technology to gain novel insights.

neuroscience↗
bioRxiv2017

Neural data science: accelerating the experiment-analysis-theory cycle in large-scale neuroscience

Modern large - scale multineuronal recording methodologies, including multielectrode arrays, calcium imaging, and optogenetic techniques, produce single - neuron resolution data of a magnitude and precision that were the realm of science fiction twenty years ago. The major bottlenecks in systems and circuit neuroscience no longer lie in simply collecting data from large neural populations, but also in understanding this data: developing novel scientific questions, with corresponding analysis techniques and experimental designs to fully harness these new capabilities and meaningfully interrogate these questions. Advances in methods for signal processing, network analysis, dimensionality reduction, and optimal control - developed in lockstep with advances in experimental neurotechnology - - promise major breakthroughs in multiple fundamental neuroscience problems. These trends are clear in a broad array of subfields of modern neuroscience; this review focuses on recent advances in methods for analyzing neural time - series data with single - neuronal precision.\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=46 SRC=\"FIGDIR/small/196949_fig1.gif\" ALT=\"Figure 1\">\nView larger version (13K):\norg.highwire.dtl.DTLVardef@16fff01org.highwire.dtl.DTLVardef@1ceacd4org.highwire.dtl.DTLVardef@1b49960org.highwire.dtl.DTLVardef@1c1ad13_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1.C_FLOATNO The central role of data science in modern large - scale neuroscience.Topics reviewed herein are indicated in black.\n\nC_FIG

neuroscience↗
bioRxiv2020

Neuroscience Cloud Analysis As a Service

A major goal of computational neuroscience is the development of powerful data analyses that operate on large datasets. These analyses form an essential toolset to derive scientific insights from new experiments. Unfortunately, a major obstacle currently impedes progress: novel data analyses have a hidden dependence upon complex computing infrastructure (e.g. software dependencies, hardware), acting as an unaddressed deterrent to potential analysis users. While existing analyses are increasingly shared as open source software, the infrastructure needed to deploy these analyses - at scale, reproducibly, cheaply, and quickly - remains totally inaccessible to all but a minority of expert users. In this work we develop Neuroscience Cloud Analysis As a Service (NO_SCPLOWEUROC_SCPLOWCAAS): a fully automated analysis platform that makes state-of-the-art data analysis tools accessible to the neuroscience community. Based on modern large-scale computing advances, NO_SCPLOWEUROC_SCPLOWCAAS is an open source platform with a drag-and-drop interface, entirely removing the burden of infrastructure purchase, configuration, deployment, and maintenance from analysis users and developers alike. NO_SCPLOWEUROC_SCPLOWCAAS offers two major scientific benefits to any data analysis. First, NO_SCPLOWEUROC_SCPLOWCAAS provides automatic reproducibility of analyses at no extra effort to the analysis developer or user. Second, NO_SCPLOWEUROC_SCPLOWCAAS cleanly separates tool implementation from usage, allowing for immediate use of arbitrarily complex analyses, at scale. We show how these benefits drive the design of simpler, more powerful data analyses. Furthermore, we show that many popular data analysis tools offered through NO_SCPLOWEUROC_SCPLOWCAAS outperform typical analysis solutions (in terms of speed and cost) while improving ease of use, dispelling the myth that cloud compute is prohibitively expensive and technically inaccessible. By removing barriers to fast, efficient cloud computation, NO_SCPLOWEUROC_SCPLOWCAAS can dramatically accelerate both the dissemination and the effective use of cutting-edge analysis tools for neuroscientific discovery.

neuroscience↗
bioRxiv2021

Removing Inter-Experimental Variability from Functional Data in Systems Neuroscience

Integrating data from multiple experiments is common practice in systems neuroscience but it requires inter-experimental variability to be negligible compared to the biological signal of interest. This requirement is rarely fulfilled; systematic changes between experiments can drastically affect the outcome of complex analysis pipelines. Modern machine learning approaches designed to adapt models across multiple data domains offer flexible ways of removing inter-experimental variability where classical statistical methods often fail. While applications of these methods have been mostly limited to single-cell genomics, in this work, we develop a theoretical framework for domain adaptation in systems neuroscience. We implement this in an adversarial optimization scheme that removes inter-experimental variability while preserving the biological signal. We compare our method to previous approaches on a large-scale dataset of two-photon imaging recordings of retinal bipolar cell responses to visual stimuli. This dataset provides a unique benchmark as it contains biological signal from well-defined cell types that is obscured by large inter-experimental variability. In a supervised setting, we compare the generalization performance of cell type classifiers across experiments, which we validate with anatomical cell type distributions from electron microscopy data. In an unsupervised setting, we remove inter-experimental variability from data which can then be fed into arbitrary downstream analyses. In both settings, we find that our method achieves the best trade-off between removing inter-experimental variability and preserving biological signal. Thus, we offer a flexible approach to remove inter-experimental variability and integrate datasets across experiments in systems neuroscience. Code available at https://github.com/eulerlab/rave.

neuroscience↗
bioRxiv2022

Network controllability measures of subnetworks: implications for neurosciences

Recent progress in network sciences has made it possible to apply key findings from control theory to the study of networks. Referred to as network control theory, this framework describes how the interactions between interconnected system elements and external energy sources, potentially constrained by different optimality criteria, result in complex network behavior. A typical example is the quantification of the functional role certain brain regions or symptoms play in shaping the temporal dynamics of brain activity or the clinical course of a disease, a property that is quantified in terms of the so-called controllability metrics. Critically though, contrary to the engineering context in which control theory was originally developed, a mathematical understanding of the network nodes and connections in neurosciences cannot be assumed. For instance, in the case of psychological systems such as those studied to understand the psychiatric disorders, a potentially large set variables are unknown. As such, while the measures offered by network control theory would be mathematically correct, in that they can be calculated with high precision, they could have little translational values with respect to their putative role suggested by controllability metrics. It is therefore critical to understand if and how the controllability metrics computer over subnetworks would deviate, if access to the complete set of variables, as in neurosciences, cannot be taken for granted. In this paper, we use a host of simulations based on synthetic as well as structural MRI data to study the potential deviation of controllability metrics in sub-compared to the full networks. Specifically, we estimate average- and modal-controllability, two of the most widely used controllability measures in neurosciences, in a large number of settings where we systematically vary network type, network size, and edge density. We find out, across all network types we test, that average and modal controllability are systematically, either over- or underestimated depending on the number of nodes in the sub- and full network and the edge density. Finally, we provide a formal theoretical proof that our observations generalize to any network type and discuss the ramifications of this systematic bias and potential solutions to alleviate the problem.

neuroscience↗
bioRxiv2022

Pynapple: a toolbox for data analysis in neuroscience.

Datasets collected in neuroscientific studies are of ever-growing complexity, often combining high dimensional time series data from multiple data acquisition modalities. Handling and manipulating these various data streams in an adequate programming environment is crucial to ensure reliable analysis, and to facilitate sharing of reproducible analysis pipelines. Here, we present Pynapple, the PYthon Neural Analysis Package, a lightweight python package designed to process a broad range of time-resolved data in systems neuroscience. The core feature of this package is a small number of versatile objects that support the manipulation of any data streams and task parameters. The package includes a set of methods to read common data formats and allows users to easily write their own. The resulting code is easy to read and write, avoids low-level data processing and other error-prone steps, and is open source. Libraries for higher-level analyses are developed within the Pynapple framework but are contained within in a collaborative repository of specialized and continuously updated analysis routines. This provides flexibility while ensuring long-term stability of the core package. In conclusion, Pynapple provides a common framework for data analysis in neuroscience. HighlightsO_LIAn open-source framework for data analysis in systems neuroscience. C_LIO_LIEasy-to-use object-oriented programming for data manipulation. C_LIO_LIA lightweight and standalone package ensuring long-term backward compatibility. C_LI

neuroscience↗
bioRxiv2023

A longitudinal resource for population neuroscience of school-age children and adolescents in China

During the past decade, cognitive neuroscience has been calling for population diversity to address the challenge of validity and generalizability, ushering in a new era of population neuroscience. The developing Chinese Color Nest Project (devCCNP, 2013-2022), a ten-year pilot stage of the lifespan CCNP (2013-2032), is an ongoing project focusing on brain-mind development. The project aims to create and share a large-scale, longitudinal and multimodal dataset of typically developing children and adolescents (ages 6.0-17.9 at enrolment) in the Chinese population. The devCCNP houses not only phenotypes measured by demographic, biophysical, psychological and behavioural, cognitive, affective, and ocular-tracking assessments but also neurotypes measured with magnetic resonance imaging (MRI) of brain morphometry, resting-state function, naturalistic viewing function and diffusion structure. This Data Descriptor introduces the first data release of devCCNP including a total of 864 visits from 479 participants. Herein, we provided details of the experimental design, sampling strategies, and technical validation of the devCCNP resource. We demonstrate and discuss the potential of a multicohort longitudinal design to depict normative brain growth curves from the perspective of developmental population neuroscience. The devCCNP resource is shared as part of the "Chinese Data-sharing Warehouse for In-vivo Imaging Brain" in the Chinese Color Nest Project (CCNP) - Lifespan Brain-Mind Development Data Community (https://www.scidb.cn/en/c/ccnp) at the Science Data Bank. Design TypesO_LIAccelerated longitudinal design C_LIO_LIBrain-mind development C_LIO_LIPopulation imaging C_LIO_LIBrain chart C_LIO_LIRepeated measure C_LI MeasurementsO_LIPsychological behaviours C_LIO_LIBiophysical and physical measures C_LIO_LIIntelligence quotient measure C_LIO_LINeuroimaging C_LI Sample Characteristic - OrganismO_LIHomo sapiens C_LI Sample Characteristic - EnvironmentO_LISchool- and community-based sample C_LI Sample Characteristic - LocationO_LIChongqing and Beijing, China C_LI DurationO_LI10 years (2013-2022) C_LI

neuroscience↗
bioRxiv2023

NeuroMabSeq: high volume acquisition, processing, and curation of hybridoma sequences and their use in generating recombinant monoclonal antibodies and scFvs for neuroscience research

The Neuroscience Monoclonal Antibody Sequencing Initiative (NeuroMabSeq) is a concerted effort to determine and make publicly available hybridoma-derived sequences of monoclonal antibodies (mAbs) valuable to neuroscience research. Over 30 years of research and development efforts including those at the UC Davis/NIH NeuroMab Facility have resulted in the generation of a large collection of mouse mAbs validated for neuroscience research. To enhance dissemination and increase the utility of this valuable resource, we applied a high-throughput DNA sequencing approach to determine immunoglobulin heavy and light chain variable domain sequences from source hybridoma cells. The resultant set of sequences was made publicly available as searchable DNA sequence database (neuromabseq.ucdavis.edu) for sharing, analysis and use in downstream applications. We enhanced the utility, transparency, and reproducibility of the existing mAb collection by using these sequences to develop recombinant mAbs. This enabled their subsequent engineering into alternate forms with distinct utility, including alternate modes of detection in multiplexed labeling, and as miniaturized single chain variable fragments or scFvs. The NeuroMabSeq website and database and the corresponding recombinant antibody collection together serve as a public DNA sequence repository of mouse mAb heavy and light chain variable domain sequences and as an open resource for enhancing dissemination and utility of this valuable collection of validated mAbs.

neuroscience↗
bioRxiv2023

Toolbox for Gromov-Wasserstein optimal transport: Application to unsupervised alignment in neuroscience

BackgroundUnderstanding how sensory stimuli are represented across different brains, species, and artificial neural networks is a critical topic in neuroscience. Traditional methods for comparing these representations typically rely on supervised alignment, which assumes direct correspondence between stimuli representations across brains or models. However, it has limitations when this assumption is not valid, or when validating the assumption itself is the goal of the research. New methodTo address the limitations of supervised alignment, we propose an unsupervised alignment method based on Gromov-Wasserstein optimal transport (GWOT). GWOT optimally identifies correspondences between representations by leveraging internal relationships without external labels, revealing intricate structural correspondences such as one-to-one, group-to-group, and shifted mappings. ResultsWe provide a comprehensive methodological guide and introduce a toolbox called GWTune for using GWOT in neuroscience. Our results show that GWOT can reveal detailed structural distinctions that supervised methods may overlook. We also demonstrate successful unsupervised alignment in key data domains, including behavioral data, neural activity recordings, and artificial neural network models, demonstrating its flexibility and broad applicability. Comparison with existing methodsUnlike traditional supervised alignment methods such as Representational Similarity Analysis, which assume direct correspondence between stimuli, GWOT provides a nuanced approach that can handle different types of structural correspondence, including fine-grained and coarse correspondences. Our method would provide richer insights into the similarity or difference of representations by revealing finer structural differences. ConclusionWe anticipate that our work will significantly broaden the accessibility and application of unsupervised alignment in neuroscience, offering novel perspectives on complex representational structures. By providing a user-friendly toolbox and a detailed tutorial, we aim to facilitate the adoption of unsupervised alignment techniques, enabling researchers to achieve a deeper understanding of cross-brain and cross-species representation analysis.

neuroscience↗
bioRxiv2024

Exploring Neuroscience Researchers' Trust in Preprints through Citation Analysis

Preprints have emerged as efficient tools for fast and free dissemination of scientific findings. The present study explores the evolving landscape of preprints among the field of neuroscience and examines patterns and evolution of citations to preprints over time in this field. Leveraging bibliometric methods, we identified over 33,000 citations (1993-2022) to preprints within neuroscience publications indexed in Scopus. The findings elucidate a significant temporal increase in the number of documents citing preprints, reaching a peak of around 60 per 1,000 Scopus documents in 2021. Diverse document types, particularly reviews, exhibit a growing reliance on preprints as references. The most frequently cited preprint servers include bioRxiv, ArXiv, medRxiv, and PsyArXiv. Leading journals such as eLife and PLOS Computational Biology have cited preprints more than others. The United States takes the lead in citing preprints, followed by the United Kingdom and Germany. Using Scite.ai, motivations underlying preprint citations and the context in which they were cited were explored and the results are indicative of the "mentioning" nature of 93% of citations to preprints. Furthermore, the introduction and discussion sections have shown to include the highest number of citations to preprints. The findings highlight the dynamic transformation of preprints in neuroscience field.

neuroscience↗
bioRxiv2024

From solo to collaborative: the global increase in neuroscience authors over two decades

The increase in the number of authors per article is a well-documented phenomenon across various academic disciplines. While prior studies have examined this trend in specific fields and countries, they in most cases did not compare the increase in the number of authors between countries. While it has previously shown that the number of authors in neuroscience publications has risen in the G10 countries, no study has yet addressed whether it reflects a global trend in the field. To address this gap, we quantified the global trend in the number of authors in neuroscience publications from 2001 to 2022. Our findings reveal a consistent increase in authorship across nearly all the countries examined. Italy ranks highest in terms of average authorship per article, while Ukraine ranks the lowest. On the other hand, China shows the largest increase in authorship over the years, followed by Norway and Egypt. South Korea is the only country showing a slight decreasing trend rather than growth. These results contribute to a better understanding of authorship patterns in neuroscience, and can stimulate further investigations on the reasons behind such an increase in terms of socio-economic factors, the need for collaborative efforts in some fields, or, on the negative side, the effect of utilitarian reasons to meet career evaluation criteria.

neuroscience↗