In a recent article, Jennifer Tegtmeier and colleagues have shared CAVE: an open-source tool in MATLAB for combined analysis of head-mounted calcium imaging and behavior.
Calcium imaging is spreading through the neuroscience field like melted butter on hot toast. Like other imaging techniques, the data collected with calcium imaging is large and complex. CAVE (Calcium ActiVity Explorer) aims to analyze imaging data from head-mounted microscopes simultaneously with behavioral data. Tegtmeier et al. developed this software in MATLAB with a bundle of unique algorithms to specifically analyze single-photon imaging data, which can then be correlated to behavioral data. A streamlined workflow is available for novice users, with more advanced options available for advanced users. The code is available for download from GitHub.
Read more from Frontiers in Neuroscience, or check it out directly from GitHub.
Tegtmeier, J., Brosch, M., Janitzky, K., Heinze, H., Ohl, F. W., & Lippert, M. T. (2018). CAVE: An Open-Source Tool for Combined Analysis of Head-Mounted Calcium Imaging and Behavior in MATLAB. Frontiers in Neuroscience, 12. doi:10.3389/fnins.2018.00958
February 20, 2019
Francisco Romero Ferrero and colleagues have developed idtracker.ai, an algorithm and software for tracking individuals in large collectives of unmarked animals, recently described in Nature Methods.
Tracking individual animals in large collective groups can give interesting insights to behavior, but has proven to be a challenge for analysis. With advances in artificial intelligence and tracking software, it has become increasingly easier to collect such information from video data. Ferrero et al. have developed an algorithm and tracking software that features two deep networks. The first tracks animal identification and the second tracks when animals touch or cross paths in front of one another. The software has been validated to track individuals with high accuracy in cohorts of up to 100 animals with diverse species from rodents to zebrafish to ants. This software is free, fully-documented and available online with additional jupyter notebooks for data analysis.
Check out their website with full documentation, the recent Nature Methods article, BioRXiv preprint, and a great video of idtracker.ai tracking 100 zebrafish!
Romero-Ferrero, F., Bergomi, M. G., Hinz, R. C., Heras, F. J., & Polavieja, G. G. (2019). Idtracker.ai: Tracking all individuals in small or large collectives of unmarked animals. Nature Methods, 16(2), 179-182. doi:10.1038/s41592-018-0295-5
February 13, 2019
In Nature Methods, Avelino Javer and colleagues developed and shared an open-source platform for analyzing and sharing worm behavioral data.
Collecting behavioral data is important and analyzing this data is just as crucial. Sharing this data is also important because it can further our understanding of behavior and increase replicability of worm behavioral studies. This is achieved by allowing many scientists to re-analyze available data, as well as develop new methods for analysis. Javer and colleagues developed an open resource in an effort to streamline the steps involved in this process — from storing and accessing video files to creating software to read and analyze the data. This platform features: an open-access repository for storing, accessing, and filtering data; an interchange format for notating single or multi-worm behavior; and file formats written in Python for feature extraction, review, and analysis. Together, these tools serve as an accessible suite for quantitative behavior analysis that can be used by experimentalists and computational scientists alike.
Read more about this platform from Nature Methods! (the preprint is also available from bioRxiv!)
Javer, A., Currie, M., Lee, C. W., Hokanson, J., Li, K., Martineau, C. N., . . . Brown, A. E. (2018). An open source platform for analyzing and sharing worm behavior data. Nature Methods. doi:10.1101/377960
January 23, 2019
Hot off the press in eLife, Andrea Giovannucci and colleagues have shared their open-source software library, CaImAn, for one and two-photon Calcium Imaging data Analysis.
In vivo calcium imaging has gained popularity in recent years for its ability to record large quantities of neural activity from multiple brain areas over extended time periods. With advanced tools for recording and collecting data comes large quantities of data. With large datasets comes a need for streamlined ways to analyze it. Giovannucci and colleagues have developed and shared a toolbox for analyzing complex calcium imaging datasets. CaImAn, developed in the open-source Python language (with optional implementation in MATLAB), is designed to correct for motion, estimate spikes, detect new neurons, and assess neuronal activity and locations in a given timeframe. The software can be used on pre-recorded data or can also enabled for real-time analysis. CaImAn is available to download with examples from GitHub, and more information can be obtained through reading the aforementioned manuscript.
Check out GitHub, or the article from eLife!
Giovannucci, A., Friedrich, J., Gunn, P., Kalfon, J., Brown, B. L., Koay, S. A., . . . Pnevmatikakis, E. A. (2019). CaImAn an open source tool for scalable calcium imaging data analysis. ELife, 8. doi:10.7554/elife.38173
January 9, 2019
Kevin Coffey has shared the following about DeepSqueak, a deep learning-based system for detection and analysis of ultrasonic vocalizations, which he developed with Russell Marx.
Rodents engage in social communication through a rich repertoire of ultrasonic vocalizations (USVs). Recording and analysis of USVs can be performed noninvasively in almost any rodent behavioral model to provide rich insights into the emotional state and motor function. Despite strong evidence that USVs serve an array of communicative functions, technical and financial limitations have inhibited widespread adoption of vocalization analysis. Manual USV analysis is slow and laborious, while existing automated analysis software are vulnerable to broad spectrum noise routinely encountered in the testing environment.
To promote accessible and accurate USV research, we present “DeepSqueak”, a fully graphical MATLAB package for high-throughput USV detection, classification, and analysis. DeepSqueak applies state-of-the-art regional object detection neural networks (Faster-RCNN) to detect USVs. This dramatically reduces the false positive rate to facilitate reliable analysis in standard experimental conditions. DeepSqueak included pre-trained detection networks for mouse USVs, and 50 kHz and 22 kHz rat USVs. After detection, USVs can be clustered by k-means models or classified by trainable neural networks.
Read more in their recent publication and check out DeepSqueak on Github!
December 5, 2018
In a recent preprint, Fabrice de Chaumont and colleagues share Live Mouse Tracker, a real-time behavioral analysis system for groups of mice.
Monitoring social interactions of mice is an important aspect to understand pre-clinical models of various psychiatric disorders, however, gathering data on social behaviors can be time-consuming and often limited to a few subjects at a time. With advances in computer vision, machine learning, and individual identification methods, gathering social behavior data from many mice is now easier. de Chaumont and colleagues have developed Live Mouse Tracker which allows for behavior tracking for up to 4 mice at a time with RFID sensors. The use of infrared/depth RGBD cameras allow for tracking of animal shape and posture. This tracking system automatically labels behaviors on an individual, dyadic, and group level. Live Mouse Tracker can be used to assess complex social behavioral differences between mice.
Learn more on BioRXiv, or check out the Live Mouse Tracker website!
We are looking for your feedback to understand how we can better serve the community! We’re also interested to know if/how you’ve implemented some of the open-source tools from our site in your own research.
We would greatly appreciate it if you could fill out a short survey (~5 minutes to complete) about your experiences with OpenBehavior.
October 3, 2018
Thomas Akam and researchers from the Champalimaud Foundation and Oxford University have developed pyControl, a system that combines open-source hardware and software for control of behavioral experiments.
The ability to seamlessly control various aspects of a complex task is important for behavioral neuroscience research. pyControl, an open-source framework, combines Python scripts and a Micropython microcontroller for the control of behavioral experiments. This framework can be run through a command line interface (CLI), or in a user-friendly graphical user interface (GUI) that allows users to manage a variety of devices such as nose pokes, LED drivers, stepper motor controllers and more. The data collected using this system can then be imported easily into Python for data analysis. In addition to complete documentation on the pyControl website, users are welcome to ask questions and interact with the developers and other users via a pyControl Google group.
Read more on the pyControl website.
Purchase the pyControl breakout board at OpenEphys.
Or check out the pyControl Google group!
September 26, 2018
In Computers in Biology and Medicine, Carlos Fernando Crispin Jr. and colleagues share their software EthoWatcher: a computational tool that supports video-tracking, detailed ethography, and extraction of kinematic variables from video files of laboratory animals.
The freely available EthoWatcher software has two modules: a tracking module and an ethography module. The tracking module permits the controlled separation of the target from its background, the extraction of image attributes used to calculate distances traveled, orientation, length, area and a path graph of the target. The ethography module allows recording of catalog-based behaviors from video files, the environment, or frame-by-frame. The output reports latency, frequency, and duration of each behavior as well as the sequence of events in a time-segmented format fixed by the user. EthoWatcher was validated conducting tests on the detection of the known behavioral effects of drugs and on kinematic measurements.
Read more in their paper or download the software from the EthoWatcher webpage!
Junior, C. F., Pederiva, C. N., Bose, R. C., Garcia, V. A., Lino-De-Oliveira, C., & Marino-Neto, J. (2012). ETHOWATCHER: Validation of a tool for behavioral and video-tracking analysis in laboratory animals. Computers in Biology and Medicine,42(2), 257-264. doi:10.1016/j.compbiomed.2011.12.002
September 19, 2018
In HardwareX, Brendan Drackley and colleagues share VASIC, an open source weight-bearing device for high-throughput and unbiased behavioral pain assessment in rodents.
The assessment of pain in animal models is a key component in understanding and developing treatments for chronic pain. Drackley and colleagues developed VASIC (Voluntary Access Static Incapacitance Chamber), a modified version of a weight-bearing test. A brief water deprivation encourages rats or mice to seek water in a test chamber, set up with a weighing platforms under the water spout, which can assess weight shifting to an unaffected side in animals with damage to nerves or inflammatory pain. The design incorporates a custom printed circuit board (available from the paper), infrared sensor, Arduino microcontroller, 3D printed parts, and open source software for analysis. A full parts list, links to files, and data from a validation study are available in their paper.
Read more here!