The Neuroscience Experiments System (NES)–A Software Tool to Manage Experimental Data and Its Provenance

Computational tools can transform the manner by which neuroscientists perform their experiments. More than helping researchers to manage the complexity of experimental data, these tools can increase the value of experiments by enabling reproducibility and supporting the sharing and reuse of data. Despite the remarkable advances made in the Neuroinformatics field in recent years, there is still a lack of open-source computational tools to cope with the heterogeneity and volume of neuroscientific data and the related metadata that needs to be collected during an experiment and stored for posterior analysis. In this work, we present the Neuroscience Experiments System (NES), a free software to assist researchers in data collecting routines of clinical, electrophysiological, and behavioral experiments. NES enables researchers to efficiently perform the management of their experimental data in a secure and user-friendly environment, providing a unified repository for the experimental data of an entire research group. Furthermore, its modular software architecture is aligned with several initiatives of the neuroscience community and promotes standardized data formats for experiments and analysis reporting.

New Horizons for Phenotyping Behavior in Rodents: The Example of Depressive-Like Behavior

The evolution of the field of behavioral neuroscience is significantly dependent on innovative disruption triggered by our ability to model and phenotype animal models of neuropsychiatric disorders. The ability to adequately elicit and measure behavioral parameters are the fundaments on which the behavioral neuroscience community establishes the pathophysiological mechanisms of neuropsychiatric disorders as well as contributes to the development of treatment strategies for those conditions. Herein, we review how mood disorders, in particular depression, are currently modeled in rodents, focusing on the limitations of these models and particularly on the analyses of the data obtained with different behavioral tests. Finally, we propose the use of new paradigms to study behavior using multidimensional strategies that better encompasses the complexity of psychiatric conditions, namely depression; these paradigms provide holistic phenotyping that is applicable to other conditions, thus promoting the emergence of novel findings that will leverage this field.

The thermodynamic theory of action potential propagation: a sound basis for unification of the physics of nerve impulses

The thermodynamic theory of action potential propagation challenges the conventional understanding of the nerve signal as an exclusively electrical phenomenon. Often misunderstood as to its basic tenets and predictions, the thermodynamic theory is virtually ignored in mainstream neuroscience. Addressing a broad audience of neuroscientists, we here attempt to stimulate interest in the theory. We do this by providing a concise overview of its background, discussion of its intimate connection to Albert Einstein’s treatment of the thermodynamics of interfaces and outlining its potential contribution to the building of a physical brain theory firmly grounded in first principles and the biophysical reality of individual nerve cells. As such, the paper does not attempt to advocate the superiority of the thermodynamic theory over any other approach to model the nerve impulse, but is meant as an open invitation to the neuroscience community to experimentally test the assumptions and predictions of the theory on their validity.

An open code pledge for the neuroscience community

Sharing of research code would greatly benefit neuroscience, but this practice is hampered by a collective action problem. Since the development of the internet, conditional pledge platforms (e.g., Kickstarter) have increasingly been used to solve globally-dispersed collective action problems (Hallam, 2016). However, this strategy has yet to be implemented within academia. In this brief paper, we introduce a general purpose conditional pledge platform for the research community: Project Free Our Knowledge. We highlight a new conditional pledge campaign that was initiated at Brainhack 2021 and aims to motivate a critical mass of neuroscientists to share their research code. Crucially, this commitment activates only when a user-defined threshold of support is reached. We conclude by sharing our vision for how the research community could use collective action campaigns to create a sustained, evidence-based movement for social change in academia.

Music in All Its Beauty

This chapter provides a behind-the-scenes account of the birth of a naturalistic approach to the neuroscience of the musical aesthetic experience. The story starts from a lab talk giving the inspiration to translate the naturalistic paradigm initially applied to neuroimaging studies of the visual domain into music research. The circumstantial co-presence of neuroscientists and computational musicologists at the same center did the trick, permitting the identification of controlled variables for brain signal processing from the automatic extraction of the acoustic features of real music. This approach is now well accepted by the music neuroscience community while still waiting for full exploitation by aesthetic research.

The Serendipity of Viral Trans-Neuronal Specificity: More Than Meets the Eye

Trans-neuronal viruses are frequently used as neuroanatomical tools for mapping neuronal circuits. Specifically, recombinant one-step rabies viruses (RABV) have been instrumental in the widespread application of viral circuit mapping, as these viruses have enabled labs to map the direct inputs onto defined cell populations. Within the neuroscience community, it is widely believed that RABV spreads directly between neurons via synaptic connections, a hypothesis based principally on two observations. First, the virus labels neurons in a pattern consistent with known anatomical connectivity. Second, few glial cells appear to be infected following RABV injections, despite the fact that glial cells are abundant in the brain. However, there is no direct evidence that RABV can actually be transmitted through synaptic connections. Here we review the immunosubversive mechanisms that are critical to RABV’s success for infiltration of the central nervous system (CNS). These include interfering with and ultimately killing migratory T cells while maintaining levels of interferon (IFN) signaling in the brain parenchyma. Finally, we critically evaluate studies that support or are against synaptically-restricted RABV transmission and the implications of viral-host immune responses for RABV transmission in the brain.

A Comprehensive, FAIR File Format for Neuroanatomical Structure Modeling

With advances in microscopy and computer science, the technique of digitally reconstructing, modeling, and quantifying microscopic anatomies has become central to many fields of biological research. MBF Bioscience has chosen to openly document their digital reconstruction file format, the Neuromorphological File Specification, available at www.mbfbioscience.com/filespecification (Angstman et al., 2020). The format, created and maintained by MBF Bioscience, is broadly utilized by the neuroscience community. The data format’s structure and capabilities have evolved since its inception, with modifications made to keep pace with advancements in microscopy and the scientific questions raised by worldwide experts in the field. More recent modifications to the neuromorphological file format ensure it abides by the Findable, Accessible, Interoperable, and Reusable (FAIR) data principles promoted by the International Neuroinformatics Coordinating Facility (INCF; Wilkinson et al., Scientific Data, 3, 160018,, 2016). The incorporated metadata make it easy to identify and repurpose these data types for downstream applications and investigation. This publication describes key elements of the file format and details their relevant structural advantages in an effort to encourage the reuse of these rich data files for alternative analysis or reproduction of derived conclusions.

Neural Collision Detection: an open source library to study the three-dimensional interactions of neurons and other tree-like structures

The analysis of neuronal structure and its relation to function has become a fundamental pillar in neuroscience since its earliest days, with the underlying premise that morphological properties can modulate neuronal computations. It is often the case that the rich three-dimensional structure of neurons is quantified by tools developed in other fields, such as graph theory and computational geometry; nevertheless, some of the more advanced tools developed in these fields have not yet been made accessible to the neuroscience community. Here we present Neural Collision Detection, a library providing high-level interfaces to collision-detection routines and alpha shape calculations, as well as statistical analysis and visualizations for 3D objects, with the aim to lower the entry gap for neuroscientists into these worlds. Our work here also demonstrates a variety of use cases for the library and exemplary analysis and visualizations that were carried out with it on real neuronal and vascular data.

DotMotif: an open-source tool for connectome subgraph isomorphism search and graph queries

Recent advances in neuroscience have enabled the exploration of brain structure at the level of individual synaptic connections. These connectomics datasets continue to grow in size and complexity; methods to search for and identify interesting graph patterns offer a promising approach to quickly reduce data dimensionality and enable discovery. These graphs are often too large to be analyzed manually, presenting significant barriers to searching for structure and testing hypotheses. We combine graph database and analysis libraries with an easy-to-use neuroscience grammar suitable for rapidly constructing queries and searching for subgraphs and patterns of interest. Our approach abstracts many of the computer science and graph theory challenges associated with nanoscale brain network analysis and allows scientists to quickly conduct research at scale. We demonstrate the utility of these tools by searching for motifs on simulated data and real public connectomics datasets, and we share simple and complex structures relevant to the neuroscience community. We contextualize our findings and provide case studies and software to motivate future neuroscience exploration.