Craniobot: A computer numerical controlled robot for cranial microsurgeries

Leila Ghanbari, Mathew L. Rynes, Jia Hu, Daniel S. Schulman, Gregory W. Johnson, Michael Laroque, Gabriella M. Shull, Suhasa Bangalore Kodandaramaiah

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

Over the last few decades, a plethora of tools has been developed for neuroscientists to interface with the brain. Implementing these tools requires precisely removing sections of the skull to access the brain. These delicate cranial microsurgical procedures need to be performed on the sub-millimeter thick bone without damaging the underlying tissue and therefore, require significant training. Automating some of these procedures would not only enable more precise microsurgical operations, but also facilitate widespread use of advanced neurotechnologies. Here, we introduce 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 utilizes a low-force contact sensor to profile the skull surface and uses this information to perform precise milling operations within minutes. We have used the Craniobot to perform intact skull thinning and open small to large craniotomies over the dorsal cortex.

Original languageEnglish (US)
Article number1023
JournalScientific reports
Volume9
Issue number1
DOIs
StatePublished - Dec 1 2019

Bibliographical note

Funding Information:
S.B.K. acknowledges funds from the Mechanical Engineering Department, College of Science and Engineering, MnDRIVE RSAM initiative of the University of Minnesota, McGovern Institute Neurotechnology (MINT) fund, National Institutes of Health (NIH) 1R21NS103098-01 and 3R21 NS103098-01S1. LG was supported by the University of Minnesota Informatics Institute (UMII) Graduate Research Fellowship. G.S. was supported by the NSF IGERT Neural Engineering traineeship. We would also like to acknowledge Dr. Mark Sanders and Jason Mitchell at the UMN University Imaging Center where all 2P intensity imaging experiments were conducted. Metrology characterizations were conducted in the Minnesota Nano Center, which is supported by the National Science Foundation through the National Nano Coordinated Infrastructure Network (NNCI) under Award Number ECCS-1542202. Research reported in this publication was supported by the National Center for Advancing Translational Sciences of the National Institutes of Health Award Number UL1TR000114. We would like to thank Bonita Van Heel at Minnesota Dental Research Center for Biomaterials and Biomechanics for help with micro-CT scanning experiments. We would also like to thank Joseph Wang and Alana Tillery for useful comments on the manuscript.

Publisher Copyright:
© 2019, The Author(s).

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