March 13, 2019
Suhasa Kodandaramaiah from the University of Minnesota, Twin Cities, has shared the following about Craniobot, a computer numerical controlled robot for cranial microsurgeries.
The palette of tools available for neuroscientists to measure and manipulate the brain during behavioral experiments has greatly expanded in the previous decade. In many cases, using these tools requires removing sections of the skull to access the brain. The procedure to remove the sub-millimeter thick mouse skull precisely without damaging the underlying brain can be technically challenging and often takes significant skill and practice. This presents a potential obstacle for neuroscience labs wishing to adopt these technologies in their research. To overcome this challenge, a team at the University of Minnesota led by Mathew Rynes and Leila Ghanbari (equal contribution) created the ‘Craniobot,’ a cranial microsurgery platform that combines automated skull surface profiling with a computer numerical controlled (CNC) milling machine to perform a variety of cranial microsurgical procedures on mice. The Craniobot can be built from off-the-shelf components for a little over $1000 and the team has demonstrated its capability to perform small to large craniotomies, skull thinning procedures and for drilling pilot holes for installing bone anchor screws.
Read more about the Craniobot here. Software package for controlling the craniobot can be found on Github.
Ghanbari, L., Rynes, M. L., Hu, J., Schulman, D. S., Johnson, G. W., Laroque, M., . . . Kodandaramaiah, S. B. (2019). Craniobot: A computer numerical controlled robot for cranial microsurgeries. Scientific Reports, 9(1). doi:10.1038/s41598-018-37073-w
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.
August 29, 2018
In a recent bioRxiv preprint, Scott Owen and Anatol Kreitzer share PhotometryBox, an open-source solution for electronic control of fiber-based fluorescence measurements.
Fluorescence measurements from deep-brain structures through optical fibers (fiber photometry) represent a versatile, powerful, and rapidly growing neuroscience technique. A typical fiber photometry system consists of three
parts: (1) an implant with an optical fiber that is cemented to the skull, (2) optical components for generation of fluorescence excitation light and detection of emission light, and (3) electronic components for controlling light sources and acquiring signals. Excellent technical solutions are available for implants and optical components; however, currently available electronic control systems are not optimized for these experiments. The most commonly used electronic components are either over-engineered or unnecessarily inflexible. To address these issues, Owen et al have developed an open-source, low-cost solution for the electronic components. This system is based on a programmable microcontroller (MBED LPC1768) and can be assembled in ~1 hour (less than a day for an inexperienced user with limited soldering experience). The total estimated cost is about $650, less than one tenth the price of the most commonly used commercially available systems.
The design, development and implementation of this project is described in a manuscript now available on bioRxiv, while details regarding parts, construction and use are available on Hackaday.
Read more on bioRxiv
or check out the Hackaday page.
July 23, 2018
OpenBehavior has been covering open-source neuroscience projects for a few years, and we are always thrilled to see projects that are well documented and can be easily reproduced by others. To further this goal, we have formed a collaboration with Hackaday.io, who have provided a home for OpenBehavior on their site. This can be found at: https://hackaday.io/OpenBehavior, where we currently have 36 projects listed ranging from electrophysiology to robotics to behavior. We are excited about this collaboration because it provides a straightforward way for people to document their projects with instructions, videos, images, data, etc. Check it out, see what’s there, and if you want your project linked to the OpenBehavior page simply tag it as “OPENBEHAVIOR” or drop us a line at the Hackaday page.
Note: This collaboration between OpenBehavior and Hackaday.io is completely non-commercial, meaning that we don’t pay Hackaday.io for anything, nor do we receive any payments from them. It’s simply a way to further our goal of promoting open-source neuroscience tools and their goal of growing their science and engineering community.
June 15, 2018
In a recent preprint on BioRxiv, Alessio Buccino and colleagues from the University of Oslo provide a step-by-step guide for setting up an open source, low cost, and adaptable system for combined behavioral tracking, electrophysiology, and closed-loop stimulation. Their setup integrates Bonsai and Open Ephys with multiple modules they have developed for robust real-time tracking and behavior-based closed-loop stimulation. In the preprint, they describe using the system to record place cell activity in the hippocampus and medial entorhinal cortex, and present a case where they used the system for closed-loop optogenetic stimulation of grid cells in the entorhinal cortex as examples of what the system is capable of. Expanding the Open Ephys system to include animal tracking and behavior-based closed-loop stimulation extends the availability of high-quality, low-cost experimental setup within standardized data formats.
Read more on BioRxiv, or on GitHub!
Buccino A, Lepperød M, Dragly S, Häfliger P, Fyhn M, Hafting T (2018). Open Source Modules for Tracking Animal Behavior and Closed-loop Stimulation Based on Open Ephys and Bonsai. BioRxiv. http://dx.doi.org/10.1101/340141
June 12, 2018
In a recent publication in the Frontiers in Systems Neuroscience, Solari and colleagues of the Hungarian Academy of Sciences and Semmelweis University have shared the following about a behavioral setup for temporally controlled rodent behavior. This arrangement allows for training of head-fixed animals with calibrated sound stimuli, precisely timed fluid and air puff presentations as reinforcers. It combines microcontroller-based behavior control with a sound delivery system for acoustic stimuli, fast solenoid valves for reinforcement delivery and a custom-built sound attenuated chamber, and is shown to be suitable for combined behavior, electrophysiology and optogenetics experiments. This system utilizes an optimal open source setup of both hardware and software through using Bonsai, Bpod and OpenEphys.
Read more here!
Solari N, Sviatkó K, Laszlovszky T, Hegedüs P and Hangya B (2018). Open Source Tools for Temporally Controlled Rodent Behavior Suitable for Electrophysiology and Optogenetic Manipulations. Front. Syst. Neurosci. 12:18. doi: 10.3389/fnsys.2018.00018
An interesting summary of recent methods for monitoring behavior in rodents was published this week in Nature.The article mentions Lex Kravitz and his lab’s efforts on the Feeding Experimentation Device (FED) and also OpenBehavior. Check it out: https://www.nature.com/articles/d41586-018-02403-5
December 20, 2017
StimDuino, an inexpensive Arduino-controlled stimulus isolator that allows for highly accurate, reproducible automated setting of stimulation currents. The automatic stimulation patterns are software-controlled and the parameters are set from Matlab-coded simple, intuitive and user-friendly graphical user interface. StimDuino-generated automation of the input-output relationship assessment eliminates need for the current intensity manually adjusting, improves stimulation reproducibility, accuracy and allows on-site and remote control of the stimulation parameters for both in vivo and in vitro applications.
Sheinin, A., Lavi, A., & Michaelevski, I. (2015). StimDuino: An Arduino-based electrophysiological stimulus isolator. Journal of Neuroscience Methods, 243, 8-17. doi:10.1016/j.jneumeth.2015.01.016