The tools and techniques available for systems neuroscientists for neural recording and stimulation during behavior have become plentiful in the last decade. The tools for implementing these techniques in vivo, however, have not advanced respectively. The use of these techniques requires the removal of sections of skull tissue without damaging the underlying tissue, which is a very delicate procedure requiring significant training. Automating a part of the tissue removal processes would potentially enable more precise procedures to be performed, and it could democratize these procedres for widespread adoption by neuroscience lab groups. Here, we describe the ‘Craniobot’, a microsurgery platform that combines automated skull surface profiling with a computer numerical controlled (CNC) milling machine to perform a variety of microsurgical procedures in mice. Surface profiling by the Craniobot has micrometer precision, and the surface profiling information can be used to perform milling operations with relatively quick, allowing high throughput. We have used the Craniobot to perform skull thinning, small to large craniotomies, as well as drilling pilot holes for anchoring cranial implants. The Craniobot is implemented using open source and customizable machining practices and can be built with of the shelf parts for under $1000.
|Original language||English (US)|
|State||Published - 2018|
|Event||2018 Design of Medical Devices Conference, DMD 2018 - Minneapolis, United States|
Duration: Apr 9 2018 → Apr 12 2018
|Other||2018 Design of Medical Devices Conference, DMD 2018|
|Period||4/9/18 → 4/12/18|
Bibliographical noteFunding Information:
We thank Institute for Engineering for Medicine (IEM), MnDRIVE (Minnesota’s Discovery, Research, and InnoVation Economy), Department of Mechanical Engineering, University of Minnesota, The Lions Research Building/Mcguire Translational Research Facility, Minnesota Dental Research Center for Biomaterials and Biomechanics, and Unversity Imaging Center. SBK acknowledges ME department, MnDRIVE RSAM and McGovern Institute Neurotechnology (MINT) fund, National Institutes of Health (NIH) 1R21NS103098-01 grant.