In the Journal of Neuroscience Methods, Bastijn van den Boom and colleagues have shared their ‘how-to’ instructions for implementing behavioral classification with JAABA, featuring bonsai and motr!
In honor of our 100th post on OpenBehavior, we wanted to feature a project that exemplifies how multiple open-source projects can be implemented to address a common theme in behavioral neuroscience: tracking and classifying complex behaviors! The protocol from Van den Boom et al. implements JAABA, an open-source machine learning based behavior detection system; motr, an open-source mouse trajectory tracking software; and bonsai, an open-source system capable of streaming and recording video. Together they use these tools to process videos of mice performing grooming behaviors in a variety of behavioral setups.
They then compare multiple tools for analyzing grooming behavior sequences in both wild-type and genetic knockout mice with a tendency to over groom. The JAABA trained classifier outperforms the commercially available behavior analysis software and more closely aligns with manual analysis of behavior by expert observers. This offers a novel, cost-effective and easy to use method for assessing grooming behavior in mice comparable to that of an expert observer, with the efficient advantage of being automatic. How to instructions for how to train your own JAABA classifier can be found in their paper!
Read more in their publication here!
Boom, B. J., Pavlidi, P., Wolf, C. J., Mooij, A. H., & Willuhn, I. (2017). Automated classification of self-grooming in mice using open-source software. Journal of Neuroscience Methods, 289, 48-56. doi:10.1016/j.jneumeth.2017.05.026
In a recently published article (Erskine et al., 2019), The Schaefer lab at the Francis Crick Institute introduced their new open-source project called AutonoMouse.
AutonoMouse is a fully automated, high-throughput system for self-initiated conditioning and behavior tracking in mice. Many aspects of behavior can be analyzed through having rodents perform in operant conditioning tasks. However, in operant experiments, many variables can potentially alter or confound results (experimenter presence, picking up and handling animals, altered physiological states through water restriction, and the issue that rodents often need to be individually housed to keep track of their individual performances). This was the main motivation for the authors to investigate a way to completely automate operant conditioning. The authors developed AutonoMouse as a fully automated system that can track large numbers (over 25) of socially-housed mice through implanted RFID chips on mice. With the RFID trackers and other analyses, the behavior of mice can be tracked as they train and are subsequently tested on (or self-initiate testing in) an odor discrimination task over months with thousands of trials performed every day. The novelty in this study is the fully automated nature or the entire system (training, experiments, water delivery, weighing the animals are all automated) and the ability to keep mice socially-housed 24/7, all while still training them and tracking their performance in an olfactory operant conditioning task. The modular set-up makes it possible for AutonoMouse to be used to study many other sensory modalities, such as visual stimuli or in decision-making tasks. The authors provide a components list, layouts, construction drawings, and step-by-step instructions for the construction and use of AutonoMouse in their publication and on their project’s github.
For more details, check out this youtube clip interview with Andreas Schaefer, PI on the project.
The github for the project’s control software is located here: https://github.com/RoboDoig/autonomouse-control and for the project’s design and hardware instructions is here: https://github.com/RoboDoig/autonomouse-design. The schedule generation program is located here: https://github.com/RoboDoig/schedule-generator
Vilim Štih has shared their new project from the Portugues lab called Stytra, which was recently published in PLOS Computational Biology (Štih, Petrucco et al., 2019):
“Stytra is a flexible open-source software package written in Python and designed to cover all the general requirements involved in larval zebrafish behavioral experiments. It provides timed stimulus presentation, interfacing with external devices and simultaneous real-time tracking of behavioral parameters such as position, orientation, tail and eye motion in both freely-swimming and head-restrained preparations. Stytra logs all recorded quantities, metadata, and code version in standardized formats to allow full provenance tracking, from data acquisition through analysis to publication. The package is modular and expandable for different experimental protocols and setups. Current releases can be found at https://github.com/portugueslab/stytra. We also provide complete documentation with examples for extending the package to new stimuli and hardware, as well as a schema and parts list for behavioral setups. We showcase Stytra by reproducing previously published behavioral protocols in both head-restrained and freely-swimming larvae. We also demonstrate the use of the software in the context of a calcium imaging experiment, where it interfaces with other acquisition devices. Our aims are to enable more laboratories to easily implement behavioral experiments, as well as to provide a platform for sharing stimulus protocols that permits easy reproduction of experiments and straightforward validation. Finally, we demonstrate how Stytra can serve as a platform to design behavioral experiments involving tracking or visual stimulation with other animals and provide an example integration with the DeepLabCut neural network-based tracking method.”
Check out the paper, the enhanced version with the documentation, at www.portugueslab.com/stytra or the pdf at PLOS Computational Biology
Štih, V., Petrucco, L., Kist, A. M., & Portugues, R. (2019). Stytra: an open-source, integrated system for stimulation, tracking and closed-loop behavioral experiments. PLoS computational biology, 15(4), e1006699.
April 17, 2019
In a recent Nature Protocol’s article, Edoardo Balzani and colleagues from Valter Tucci’s lab have developed and shared Phenopy, a Python-based open-source analytical platform for behavioral phenotyping.
Behavioral phenotyping of mice using classic methods can be a long process and is susceptible to high variability, leading to inconsistent results. To reduce variance and speed up to process of behavioral analysis, Balzani et al. developed Phenopy, an open-source software for recording and analyzing behavioral data for phenotyping. The software allows for recording components of a behavioral task in combination with electrophysiology data. It is capable of performing online analysis as well as analysis of recorded data on a large scale, all within a user-friendly interface. Information about the software is available in their publication, available from Nature Protocols.*
Check out the full article from Nature Protocols!
(*alternatively available on ResearchGate)
March 29, 2019
In a 2011 Journal of Neuroscience Methods article, Pishan Chang and colleagues shared their design for an open-source, novel telemetry system for recording EEG in small animals.
EEG monitoring in freely-behaving small animals is a useful technique for observing natural fluctuations in neural activity over time. Monitoring frequencies above 80 Hz continuously over a period of weeks can be a challenge. Chang et al. have shared their design for a system that combines an implantable telemetric sensor, radio-frequency transmission, and an open-source data acquisition software to collect EEG data over a span of up to 8 weeks. Various modifications to the system have increased the longevity of the device and reduced transmission noise to provide continuous and reliable data. Schematics of the device, transmission system, and validation results in a population of epileptic rodents are available in their publication.
Learn more from the Journal of Neuroscience Methods!
March 21, 2019
Victor Wumbor-Apin Kumbol and colleagues have developed and shared Actifield, an automated open-source actimeter for rodents, in a recent HardwareX publication.
Measuring locomotor activity can be a useful readout for understanding effects of a number of experimental manipulations related to neuroscience research. Commercially available locomotor activity recording devices can be cost-prohibitive and often lack the ability to be customized to fit a specific lab’s needs. Kumbol et al. offer an open-source alternative that utilizes infrared motion detection and an arduino to record activity in a variety of chamber set ups. A full list of build materials, links to 3D-print and laser-cut files, and assembly instructions are available in their publication.
Read more from HardwareX!
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
February 6, 2019
Arne Meyer and colleagues recently shared their design and implementation of a head-mounted camera system for capturing detailed behavior in freely moving mice.
Video monitoring of animals can give great insight to behaviors. Most video monitoring systems to collect precise behavioral data require fixed position cameras and stationary animals, which can limit observation of natural behaviors. To address this, Meyer et al. developed a system which combines a lightweight head-mounted camera and head-movement sensors to detect behaviors in mice. The system, built using commercially available and 3D printed parts, can be used to monitor a variety of subtle behaviors including eye position, whisking, and ear movements in unrestrained animals. Furthermore, this device can be mounted in combination with neural implants for recording brain activity.
Read more here!
Meyer, A. F., Poort, J., O’Keefe, J., Sahani, M., & Linden, J. F. (2018). A Head-Mounted Camera System Integrates Detailed Behavioral Monitoring with Multichannel Electrophysiology in Freely Moving Mice. Neuron, 100(1). doi:10.1016/j.neuron.2018.09.020