SignalBuddy

SEPTEMBER 19, 2019

Richard Warren, a graduate student in the Sawtell lab at Columbia University, recently shared his new open-source project called SignalBuddy:

SignalBuddy is an easy-to-make, easy-to-use signal generator for scientific applications. Making friends is hard, but making SignalBuddy is easy. All you need is an Arduino Uno! SignalBuddy replaces more complicated and (much) more expensive signal generators in laboratory settings where one millisecond resolution is sufficient. SignalBuddy generates digital or true analog signals (sine waves, step functions, and pulse trains), can be controlled with an intuitive serial monitor interface, and looks fabulous in an optional 3D printed enclosure.

To get SignalBuddy working, all you need to do is install the SignalBuddy.ino Arduino code provided on their github, and follow the step-by-step instructions on github to get the Arduino programmed up for your specific experimental needs. SignalBuddy can be used for numerous lab purposes, including creating pulse trains for optogenetic light stimulation, microstimulation, electrophysiology, or for programming up stimuli for behavioral paradigms.

Additionally, their hackaday site provides the instructions for 3D printing an enclosure to house the Arduino inside using just two .stl files.

For more information, check out the SignalBuddy github repository here.

You can also get further details on the SignalBuddy Hackaday.io page here.

 

Fun Fact: This group also developed KineMouse Wheel, a project previously posted on OpenBehavior and is now being used in numerous labs! Cheers to another great open-source project from Richard Warren and the Sawtell lab!

eNeuro’s “Open Source Tools and Methods” paper topic

SEPTEMBER 12, 2019

There’s a new place to publish your open-source tools or methods in neuroscience! Christophe Bernard, Editor-in-Chief at the journal eNeuro (an open-access journal of the Society for Neuroscience), recently wrote an editorial detailing the opening of a new topic tract in eNeuro for Open Source Tools and Methods. In his editorial, Bernard details how there has been a recent push for open-source science, and highlights how there are many new open-source projects being developed in neuroscience that need a proper home for publication. While eNeuro has a “Methods/New Tools” submission type already, Bernard says the “Open Source Tools and Methods” submission is available for projects like “low-cost devices to measure animal behavior, a new biophysical model of a single neuron, a better method to realign images when performing in vivo two-photon imaging, scripts and codes to analyze signals” and more.

There is no current publication venue explicitly intended for open-source tools and methods in neuroscience, and through the addition of this article type, new tools/methods/devices/projects can be published in a straightforward manner. By including this publication type, it will aid the neuroscience field in replication, reproducibility, and transparency of methods and tools used. A major point from Bernard is that this may help the developers of the tool or method, since “it allows for acknowledgment of those who developed such tools and methods fully, often rotating students or engineers recruited on a short-duration contract. On a standard research paper, their name ends up in the middle of the list of authors, but the Open Source Tools and Methods type will allow them to be the first author.”

The details for submission of an open source tool or method on the eNeuro site is as follows: “Open Source Tools and Methods are brief reports (limited to 4500 words) describing the creation and use of open-source tools in neuroscience research. Examples of tools include hardware designs used in behavioral or physiological studies and software used for data acquisition and analysis. They must contain a critique of the importance of the tool, how it compares to existing open- and closed-source solutions, and a demonstration of tool use in a neuroscience experiment.”

 

Cheers to you, eNeuro, for your inclusion of open-source projects to help advance the neuroscience field!

Link to the editorial: https://www.eneuro.org/content/6/5/ENEURO.0342-19.2019

Current articles for Open Source Tools and Methods are listed here.

To submit an article under Open Source Tools and Methods, check out the instructions for authors at eNeuro here.

Curated Itinerary on Open-Source Tools at SfN-19

September 5, 2019

OpenBehavior is now an official part of the SfN team for curated itineraries at SfN-19! This year, we will provide an itinerary on Open-Source Tools. Linda Amarante (@L_Amarante) and Samantha White (@samantha6rose) are working on the itinerary now. If you would like your presentation to be included, please DM us through our Twitter account (@OpenBehavior) or send an email message about your presentation to openbehavior@gmail.com before noon on Saturday, September 8. Thanks!

Ratcave

AUGUST 29, 2019

Nicholas A. Del Grosso and Anton Sirota at the Bernstein Centre for Computational Neuroscience recently published their new project called Ratcave, a Python 3D graphics library that allows researchers to create and 3D stimuli in their experiments:

Neuroscience experiments often require the use of software to present stimuli to a subject and subsequently record their responses. Many current libraries lack 3D graphic support necessary for psychophysics experiments. While python and other programming languages may have 3D graphics libraries, it is hard to integrate these into psychophysics libraries without modification. In order to increase programming of 3D graphics suitable for the existing environment of Python software, the authors developed Ratcave.

Ratcave is an open-source, cross-platform Python library that adds 3D stimulus support to all OpenGL-based 2D Python stimulus libraries. These libraries include VisionEgg, Psychopy, Pyglet, and PyGam. Ratcave comes with resources including basic 3D object primitives and wide range of 3D light effects. Ratcave’s intuitive object-oriented interface allows for all objects, which include meshes, lights, and cameras, can be repositioned, rotated, and scaled. Objects can also be parented to one another to specify complex relationships of objects. By sending the data as a single array using OpenGL’s VAO (Vertex Array Object) functionality, the processing of drawing much more efficient. This approach allows over 30,000 vertices to be rendered at a performance level surpassing the needs of most behavioral research studies.

An advantage of Ratcave is that it allows researchers to continue to use their preferred libraries, since Ratcave supplements existing python stimulus libraries, making it easy to add on 3d stimuli to current libraries. The manuscript also reports that Ratcave has been tested and implemented in other’s research, actively showing reproducibility across labs and experiments.

Details on the hardware and software can be found at https://github.com/ratcave/ratcave.

Information on Ratcave can also be found on the https://ratcave.readthedocs.org.

Del Grosso, N. A., & Sirota, A. (2019). Ratcave: A 3D graphics python package for cognitive psychology experiments. Behavior research methods, 1-9.

SpikeGadgets

AUGUST 22, 2019

We’d like to highlight groups and companies that support an open-source framework to their software and/or hardware in behavioral neuroscience. One of these groups is SpikeGadgets, a company co-founded by Mattias Karlsson and Magnus Karlsson.

SpikeGadgets is a group of electrophysiologists and engineers who are working to develop neuroscience hardware and software tools. Their open-source software, Trodes, is a cross-platform software suite for neuroscience data acquisition and experimental control, which is made up of modules that communicate with a centralized GUI to visualize and save electrophysiological data. Trodes has a camera module and a StateScript module, which is a state-based scripting language that can be used to program behavioral tasks through using lights, levels, beam breaks, lasers, stimulation sources, audio, solenoids, etc. The camera module can be used to acquire video that can synchronize to neural recordings; the camera module can track the animal’s position in real-time or play it back after the experiment. The camera module can work with USB webcams or GigE cameras.

Paired with the Trodes software and StateScript language is the SpikeGadgets hardware that can be purchased on their website. The hardware is used for data acquisition (Main Control Unit, used for electrophysiology) and behavioral control (Environmental Control Unit).  SpikeGadgets also provides both Matlab and Python toolboxes on their site that can be used to analyze both behavioral and electrophysiological data. Trodes can be used on Windows, Linux, or Mac, and there are step-by-step instructions for how to install and use Trodes on the group’s bitbucket page.

Spikegadgets mission is “to develop the most advanced neuroscience tools on the market, while preserving ease of use and science-driven customization.”

 

For more information on SpikeGadgets or to download or purchase their software or hardware, check out their website here.

There is additional documentation on their BitBucket Wiki, with a user manual, instructions for installation, and FAQ.

Check out their entire list of collaborators, contributors, and developers here.

The Future is Open

August 16, 2019

This week’s post is about the current state of OpenBehavior (OB) and ongoing efforts within the open source neuroscience community. Next week, we will resume posting about new tools.

Samantha White, Linda Amarante, Lex Kravitz, and Mark Laubach published a commentary in eNeuro last week about how open-source tools are being used in neuroscience. We reported on our experiences in running OB since the summer of 2016, the many wonderful projects that we have posted about over the past three years, two surveys that we conducted on our site and open source tool use in general, and some observations on the mindset that comes from making and using open source tools. A link to our paper is https://www.eneuro.org/content/6/4/ENEURO.0223-19.2019.

The timing of our commentary and the related social media attention that is generated (e.g. https://twitter.com/samantha6rose/status/1159913815393341440) was especially nice as we have been working to expand OB to better serve the research community, and hope to find external support for the project. We would like to address an outstanding problem: it is not currently possible to systematically track the development and use of open source hardware and software in neuroscience research. To address this issue, we would like to to create a database of existing open source projects, characterize them using a newly developed “taxonomy” based on their functions (video analysis, behavioral control system, hardware for measuring or controlling behavior), and register projects using the SciCrunch RRID registry.

If you haven’t heard of SciCrunch, you should check it out: https://scicrunch.org/. Its an awesome project that tracks usage of research tools such as antibodies. RRIDs are citable and, if developed for open source hardware and software, would allow for developers to track how their tools are used in neuroscience publications. This might help provide incentives for sharing and metrics (RRIDs) on tool use and publication.

We are also planning to work with the Society for Neuroscience (SfN) to increase public awareness of neuroscience research by participating in SfN-sponsored advocacy and outreach events, facilitating discussions of open source tools through a new discussion topic in the Neuronline forums (more news on that soon), and continuing to provide curated itineraries on open source tools for attendees of the annual SfN meeting.

 

Pathfinder

AUGUST 8, 2019

Matthew Cooke and colleagues from Jason Snyder’s lab at University of British Columbia recently developed open source software to detect spatial navigation behavior in animals called Pathfinder:

Spatial navigation is studied across several different paradigms for different purposes in animals; through analyzing spatial behaviors we can gain insight into how an animal learns a task, how they change their approach strategy, and generally observing goal-directed behaviors. Pathfinder is an open source software that can analyze rodent navigation. The software intends to automatically classify patterns of navigation as a rodent performs in a task. Pathfinder can analyze subtle patterns in spatial behavior that simple analysis measures may not always be able to pick up on. Specifically, many water maze analyses use escape latency or path length as an analysis measure, but the authors point out that the time it takes to reach the platform may not differ while the strategy does, so using latency may not be the most optimal measure for analyzing an animal’s strategy and therefore experimenters may miss out on key differences in behavior. Therefore, Pathfinder aims to analyze more subtle aspects of the task to determine differences in spatial navigation and strategy.

Originally intended for water maze navigation, pathfinder can also be used to analyze many other spatial behaviors across different tasks, mazes, and species. The software takes x-y coordinates from behavior tracking software (for example, it can open files from Noldus Ethovision, ActiMetrics’ Watermaze, Stoelting’s Anymaze, and the open-source project ezTrack from Denise Cai’s lab), and then calculates the best-fit search strategy for each rodent’s trial. For the morris water maze task, trials are fit into several categories: Direct Swim, Directed Search, Focal Search, Spatial indirect, Chaining, Scanning, Thigmotaxis, and Random Search.

Pathfinder runs in Python and has an easy-to-use GUI; many aspects and parameters can be adjusted to analyze different tasks or behaviors.

For more details, check out their BioRxiV preprint here.

There’s a nice (humorous!) writeup of the project on the Snyder Lab website.

You can also download the project and view more details on their github:
https://matthewbcooke.github.io/Pathfinder/

https://github.com/MatthewBCooke/Pathfinder/

Cooke, M. B., O’Leary, T. P., Harris, P., Brown, R. E., & Snyder, J. (2019). Pathfinder: open source software for analyzing spatial navigation search strategies. bioRxiv, 715961.

RAD

August 1, 2019

In their recent eNeuro article, Bridget Matikainen-Ankney and colleagues from the Kravitz Lab have developed and shared their device, rodent activity detector (RAD), a low-cost system that can track and record activity in rodent home cages.

Physical activity is an important measure used in many research studies and is an important determinant of human health. Current methods for measuring physical activity in laboratory rodents have limitations including high expense, specialized caging/equipment, and high computational overhead. To address these limitations, Matikainen-Ankney et al. designed an open-source and cost-effective device for measuring rodent behavior.

In their new manuscript, they describe the design and implementation of RAD, rodent activity detector. The system allows for high throughput installation, minimal investigator intervention and circadian monitoring.  The design includes a battery powered passive infrared (PIR) sensor, microcontroller, microSD card logger, and an oLED screen for displaying data. All of the build instructions for RAD manufacture and programming, including the Arduino code, are provided on the project’s website.

The system records the number of PIR active bouts and the total duration the PIR is active each minute. The authors report that RAD is useful for quantifying changes across minutes rather than on a second to second time-scale, so the default data-logging frequency is set to one minute. The CSV files can be viewed and data visualized using provided python scripts. Device validation with video monitoring strongly correlated PIR data with speed and showed it recorded place to place locomotion but not slow or in place movements. To verify the device’s utility, RAD was used to collect physical activity data from 40 animals for 10 weeks. RAD detected high fat diet (HFD)-induced changes in activity and quantified individual animals’ circadian rhythms. Several major advantages of this tool are that the PIR sensor is not triggered by activity in other cages, it can detect and quantify within-mouse activity changes over time, and little investigator intervention other than infrequent battery replacement is necessary. Although the design was optimized for the lab’s specific caging, the open-source nature of the project makes it easily modifiable.

More details on RAD can be found in their eNeuro manuscript here, and all documentation can also be found on the project’s Hackaday.io page.

Matikainen-Ankney, B. A., Garmendia-Cedillos, M., Ali, M., Krynitsky, J., Salem, G., Miyazaki, N. L., … Kravitz, A. V. (2019). Rodent Activity Detector (RAD), an Open Source Device for Measuring Activity in Rodent Home Cages. ENeuro, 6(4). https://doi.org/10.1523/ENEURO.0160-19.2019

MouseMove

July 18, 2019

In a 2015 Scientific Reports article, Andre Samson and colleagues shared their project MouseMove, an open-source software for quantifying movement in the open field test:

The Open Field (OF) test is a commonly used assay for monitoring exploratory behavior and locomotion in rodents. Most research groups use commercial systems for recording and analyzing behavior in the OF test, but these commercial systems can be expensive and lack flexibility. A few open-source OF systems have been developed, but are limited in the movement parameters that can be collected and analyzed. MouseMove is the first open-source software capable of providing qualitative and quantitative information on mouse locomotion in a semi-automated and high-throughput approach. With the aim of providing a freely available program for analyzing OF test data, these researchers developed a software that accurately quantifies numerous parameters of movement.

In their manuscript, Samson et al. describe the design and implementation of MouseMove. Their OF system allows for the measurement of distance, speed, and laterality with >96% accuracy. They use MouseMove as a method to analyze OF behavior of mice after experimental stroke to show reduced locomotor activity and quantify laterality deficits. The system is used in combination with the open source program ImageJ and the MTrack2 plugin to analyze pre-recorded OF test video.

The system has two downloadable components, the ImageJ macro and a separate program with the custom-built MouseMove GUI. ImageJ is used to subtract the background video from the experiment and create an image of the animals total trajectory. The MouseMove GUI then completes a detailed analysis of the movement patterns, measuring the fractional time spent stationary, the distance traveled, speed mean and various details of laterality. The results are depicted in both a visual/graphical form and as a saveable text file. In the manuscript, they provide step-wise instructions of how to use Mousemove. The authors additionally highlight the defined region-of-interest (ROI) ability of the software that makes it suitable for analysis of cognitive tests such as Novel Object Recognition. This tool offers relatively fast video-processing of motor cognitive behaviors and has many applications for the study of rodent models of brain injury/stimulation to measure altered locomotion.

 

More information on MouseMove can be found in their manuscript here.

Samson, A. L., Ju, L., Ah Kim, H., Zhang, S. R., Lee, J. A. A., Sturgeon, S. A., … Schoenwaelder, S. M. (2015). MouseMove: an open source program for semi-automated analysis of movement and cognitive testing in rodents. Scientific Reports, 5, 16171.  doi: 10.1038/srep16171