Tag: mouse

Actifield

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!


idtracker.ai

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!


Dual-port Lick Detector

January 16, 2019

In the Journal of Neurophysiology, Brice Williams and colleagues have  shared their design for a novel dual-port lick detector. This device can be used for both real-time measurement and manipulation of licking behavior in head-fixed mice.


Measuring licking behavior in mice provides a valuable metric of sensory-motor processing and can be nicely paired with simultaneous neural recordings. Williams and colleagues have developed their own device for precise measuring of licking behavior as well as for manipulating this behavior in real time. To address limitations of many available lick sensors, the authors designed their device to be smaller (appropriate for mice), contactless (to diminish electric artifacts for neural recording), and precise to a submillisecond timescale. This dual-port detector can be implemented to detect directional licking behavior during sensory tasks and can be used in combination with neural recording. Further, given the submillisecond precision of this device, it can be used in a closed-loop system to perturb licking behaviors via neural inhibition. Overall, this dual-port lick detector is a cost-effective, replicable solution that can be used in a variety of applications.

Learn how to build your own here!

And be sure to check out their Github.


DeepSqueak

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!


TRIO Platform

December 12, 2018

Vladislav Voziyanov and colleagues have developed and shared the TRIO Platform, a low-profile in vivo imaging support and restraint system for mice.


In vivo optical imaging methods are common tools for understanding neural function in mice. This technique is often performed in head-fixed,  anesthetized animals, which requires monitoring of anesthesia level and body temperature while stabilizing the head. Fitting each of the components necessary for these experiments on a standard microscope stage can be rather difficult. Voziyanov and colleagues have shared their design for the TRIO (Three-In-One) Platform. This system is compact and  provides sturdy head fixation, a gas anesthesia mask, and warm water bed. While the design is compact enough to work with a variety of microscope stages, the use of 3D printed components makes this design customizable.

https://www.frontiersin.org/files/Articles/184541/fnins-10-00169-HTML/image_m/fnins-10-00169-g004.jpg

Read more about the TRIO Platform in Frontiers in Neuroscience!

The design files and list of commercially available build components are provided here.


Live Mouse Tracker

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!


PsiBox: Automated Operant Conditioning in the Mouse Home Cage

November 30, 2018

Nikolas Francis and Patrick Kanold of the University of Maryland share their design for Psibox, a platform for automated operant conditioning in the mouse home cage, in Frontiers in Neural Circuits.


The ability to collect behavioral data from large populations of subjects is advantageous for advancing behavioral neuroscience research. However, few cost-effective options are available for collecting large sums of data especially for operant behaviors. Francis and Kanold have developed and shared Psibox,  an automated operant conditioning system. It incorporates three modules for central control , water delivery, and home cage interface, all of which can be customized with different parts. The system was validated for training mice in a positive reinforcement auditory task and can be customized for other tasks as well. The full, low-cost system allows for quick training of groups of mice in an operant task with little day-to-day experimenter involvement.

Learn how to set up your own Psibox system here!


Francis, NA., Kanold, PO., (2017). Automated operant conditioning in the mouse home cage. Front. Neural Circuits.

FreemoVR: virtual reality for freely moving animals

November 14, 2018

John Stowers and colleagues from the Straw Lab at the University of Frieburg have developed and shared FreemoVR, a virtual reality set-up for unrestrained animals.


Virtual reality (VR) systems can help to mimic nature in behavioral paradigms, which help us to understand behavior and brain function. Typical VR systems require that animals are movement restricted, which limits natural responses. The FreemoVR system was developed to address these issues and allows for virtual reality to be integrated with freely moving behavior. This system can be used with a number of different species including mice, zebrafish, and Drosophila. FreemoVR has been validated to investigate several behavior in tests of height-aversion, social interaction, and visuomotor responses in unrestrained animals.

 

Read more on the Straw Lab site, Nature Methods paper, or access the software on Github.


Multi-channel Fiber Photometry

October 24, 2018

Qingchun Guo and colleagues share their cost-effective, multi-channel fiber photometry system in Biomedical Optics Express.


Fiber photometry is a viable tool for recording in vivo calcium activity in freely behaving animals. In combination with genetically encoded calcium indicators, this tool can be used to measure neuronal and population activity from a genetically defined subset of neurons. Guo and colleagues have developed a set-up to allow for recording from multiple brain regions, or multiple animals, simultaneously with the use of a galvano-mirror system. This creative and simple solution reduces the number of detectors necessary for multi-channel data collection. This expands the ability of researchers to collect calcium imaging data from many subjects in a cost-effective way.

Read more here!


OpenFeeder

October 17, 2018

In the journal HardwareX, Jinook Oh and colleagues share their design for OpenFeeder, an automatic feeder for animal experiments.


Automatic delivery of precisely measured food amounts is important when studying reward and feeding behavior. Commercially available devices are often designed with specific species and food types in mind, limiting the ways that they can be used. This open-source automatic feeding design can easily be customized for food types from seeds to pellets to fit the needs of any species. OpenFeeder integrates plexiglass tubes, Arduino Uno, a motor driver, and piezo sensor to reliably deliver accurate amounts of food, and can also be built using 3D printed parts.

Read more from HardwareX.

Or check out the device on Open Science Framework and Github.