Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Protocol
  • Published:

Assembly and operation of an open-source, computer numerical controlled (CNC) robot for performing cranial microsurgical procedures

Nature Protocols volume 15, pages 1992–2023 (2020)Cite this article

Subjects

Abstract

Cranial microsurgery is an essential procedure for accessing the brain through the skull that can be used to introduce neural probes that measure and manipulate neural activity. Neuroscientists have typically used tools such as high-speed drills adapted from dentistry to perform these procedures. As the number of technologies available for neuroscientists has increased, the corresponding cranial microsurgery procedures to deploy them have become more complex. Using a robotic tool that automatically performs these procedures could standardize cranial microsurgeries across neuroscience laboratories and democratize the more challenging procedures. We have recently engineered a robotic surgery platform that utilizes principles of computer numerical control (CNC) machining to perform a wide variety of automated cranial procedures. Here, we describe how to adapt, configure and use an inexpensive desktop CNC mill equipped with a custom-built surface profiler for performing CNC-guided microsurgery on mice. Detailed instructions are provided to utilize this ‘Craniobot’ for performing circular craniotomies for coverslip implantation, large craniotomies for implanting transparent polymer skulls for cortex-wide imaging access and skull thinning for intact skull imaging. The Craniobot can be set up in <2 weeks using parts that cost <$1,500, and we anticipate that the Craniobot could be easily adapted for use in other small animals.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Overview.
Fig. 2: Craniobot hardware.
Fig. 3: Craniobot control electronics.
Fig. 4: Step-by-step illustration of the surface profiler assembly process.
Fig. 5: Craniobot software GUI.
Fig. 6: Step-by-step illustration of the CNC mill assembly process.
Fig. 7: Surface profiler performance.
Fig. 8: Benchtop testing of Craniobot function on a plastic tube and key steps during surface profiling.
Fig. 9: Surface-profile-guided machining.
Fig. 10: See-Shell assembly and implantation.
Fig. 11: Chronic implantation using the Craniobot.

Similar content being viewed by others

Data availability

All data shown here have been sourced and modified from previous articles describing the Craniobot19 and See-Shells23. Full data are available as supplementary datasets accompanying these articles.

Code availability

We have made the control software available with this article as Supplementary Software 1. Furthermore, a MATLAB version of the same is available at our GitHub repository: www.github.com/bsbrl. We will post updated versions of the software at this location.

References

  1. Jun, J. J. et al. Fully integrated silicon probes for high-density recording of neural activity. Nature 551, 232 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Shobe, J. L., Claar, L. D., Parhami, S., Bakhurin, K. I. & Masmanidis, S. C. Brain activity mapping at multiple scales with silicon microprobes containing 1,024 electrodes. J. Neurophysiol. 114, 2043–2052 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Scholvin, J. et al. Close-packed silicon microelectrodes for scalable spatially oversampled neural recording. IEEE Trans. Biomed. Eng. 63, 120–130 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Berényi, A. et al. Large-scale, high-density (up to 512 channels) recording of local circuits in behaving animals. J. Neurophysiol. 111, 1132–1149 (2014).

    Article  PubMed  Google Scholar 

  5. Voigts, J., Siegle, J., Pritchett, D. L. & Moore, C. I. The flexDrive: an ultra-light implant for optical control and highly parallel chronic recording of neuronal ensembles in freely moving mice. Front. Syst. Neurosci. 7, 8 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Wentz, C. T. et al. A wirelessly powered and controlled device for optical neural control of freely-behaving animals. J. Neural Eng. 8, 046021 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Kim, T. I. et al. Injectable, cellular-scale optoelectronics with applications for wireless optogenetics. Science 340, 211–216 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Jeong, J. W. et al. Wireless optofluidic systems for programmable in vivo pharmacology and optogenetics. Cell 162, 662–674 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. McCall, J. G. et al. Preparation and implementation of optofluidic neural probes for in vivo wireless pharmacology and optogenetics. Nat. Protoc. 12, 219–237 (2017).

    Article  CAS  PubMed  Google Scholar 

  10. Dzirasa, K., Fuentes, R., Kumar, S., Potes, J. M. & Nicolelis, M. A. L. Chronic in vivo multi-circuit neurophysiological recordings in mice. J. Neurosci. Methods 195, 36–46 (2011).

    Article  PubMed  Google Scholar 

  11. Stringer, C. et al. Spontaneous behaviors drive multidimensional, brainwide activity. Science 364, 255 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. Sofroniew, N. J., Flickinger, D., King, J. & Svoboda, K. A large field of view two-photon mesoscope with subcellular resolution for in vivo imaging. Elife 5, e14472 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. Stirman, J. N., Smith, I. T., Kudenov, M. W. & Smith, S. L. Wide field-of-view, multi-region, two-photon imaging of neuronal activity in the mammalian brain. Nat. Biotechnol. 34, 857–862 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Jeong, D. C., Tsai, P. S. & Kleinfeld, D. All-optical osteotomy to create windows for transcranial imaging in mice. Opt. Express 21, 23160 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  15. Kim, T. H. et al. Long-term optical access to an estimated one million neurons in the live mouse cortex. Cell Rep. 17, 3385–3394 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Pak, N. et al. Closed-loop, ultraprecise, automated craniotomies. J. Neurophysiol. 113, 3943–3953 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  17. Pohl, B. M., Schumacher, A. & Hofmann, U. G. Towards an automated, minimal invasive, precision craniotomy on small animals. In 2011 5th International IEEE/EMBS Conference on Neural Engineering, NER 2011 302–305 (IEEE, Piscataway, NJ, USA, 2011).

  18. Loschak, P. et al. Cranial drilling tool with retracting drill bit upon skull penetration. J. Med. Devices 6, 017522 (2012).

    Article  Google Scholar 

  19. Ghanbari, L. et al. Craniobot: a computer numerical controlled robot for cranial microsurgeries. Sci. Rep. 9, 1023 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. Kodandaramaiah, S. B. et al. Multi-neuron intracellular recording in vivo via interacting autopatching robots. eLife 7, e24656 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Allen, B. D. et al. Automated in vivo patch-clamp evaluation of extracellular multielectrode array spike recording capability. J. Neurophysiol. 120, 2182–2200 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Shih, A. Y., Mateo, C., Drew, P. J., Tsai, P. S. & Kleinfeld, D. A polished and reinforced thinned-skull window for long-term imaging of the mouse brain. J. Vis. Exp. 3742 (2012).

  23. Ghanbari, L. et al. Cortex-wide neural interfacing via transparent polymer skulls. Nat. Commun. 10, 1500 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Hultman, R. et al. Brain-wide electrical spatiotemporal dynamics encode depression vulnerability. Cell 173, 166–180 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Sych, Y., Chernysheva, M., Sumanovski, L. T. & Helmchen, F. High-density multi-fiber photometry for studying large-scale brain circuit dynamics. Nat. Methods 16, 553–560 (2019).

    Article  CAS  PubMed  Google Scholar 

  26. Kim, C. K. et al. Simultaneous fast measurement of circuit dynamics at multiple sites across the mammalian brain. Nat. Methods 13, 325–328 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Siegle, J. H. et al. Open Ephys: an open-source, plugin-based platform for multichannel electrophysiology. J. Neural Eng. 14, 045003 (2017).

    Article  PubMed  Google Scholar 

  28. Mathis, A. et al. DeepLabCut: markerless pose estimation of user-defined body parts with deep learning. Nat. Neurosci. 21, 1281–1289 (2018).

    Article  CAS  PubMed  Google Scholar 

  29. Ghosh, K. K. et al. Miniaturized integration of a fluorescence microscope. Nat. Methods 8, 871–878 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Cai, D. J. et al. A shared neural ensemble links distinct contextual memories encoded close in time. Nature 534, 115–118 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Skocek, O. et al. High-speed volumetric imaging of neuronal activity in freely moving rodents. Nat. Methods 15, 429–432 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. de Groot, A. et al. NINscope: a versatile miniscope for multi-region circuit investigations. eLife 9, e49987 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Srinivasan, S. et al. Miniaturized microscope with flexible light source input for neuronal imaging and manipulation in freely behaving animals. Biochem. Biophys. Res. Commun. 517, 520–524 (2019).

    Article  CAS  PubMed  Google Scholar 

  34. Liang, B., Zhang, L., Moffitt, C., Li, Y. & Lin, D. T. An open-source automated surgical instrument for microendoscope implantation. J. Neurosci. Methods 311, 83–88 (2019).

    Article  PubMed  Google Scholar 

  35. Khaw, I. et al. Flat-field illumination for quantitative fluorescence imaging. Opt. Express 26, 15276–15288 (2018).

    Article  CAS  PubMed  Google Scholar 

  36. Adam, Y. et al. Voltage imaging and optogenetics reveal behaviour-dependent changes in hippocampal dynamics. Nature 569, 413–417 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Holtmaat, A. et al. Long-term, high-resolution imaging in the mouse neocortex through a chronic cranial window. Nat. Protoc. 4, 1128 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Drew, P. J. et al. Chronic imaging and manipulation of cells and vessels through a polished and reinforced thinned-skull. Nat. Methods 7, 981–984 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Alieva, M., Ritsma, L., Giedt, R. J., Weissleder, R. & van Rheenen, J. Imaging windows for long-term intravital imaging. Intravital 3, e29917 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Kawakami, M. & Yamamura, K. I. Cranial bone morphometric study among mouse strains. BMC Evol. Biol. 8, 73 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Dana, H. et al. Thy1-GCaMP6 transgenic mice for neuronal population imaging in vivo. PLoS One 9, e108697 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  42. Feng, G. et al. Imaging neuronal subsets in transgenic mice expressing multiple spectral variants of GFP. Neuron 28, 41–51 (2000).

    Article  CAS  PubMed  Google Scholar 

  43. Kozlowski, C. & Weimer, R. M. An automated method to quantify microglia morphology and application to monitor activation state longitudinally in vivo. PLoS One 7, e31814 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

S.B.K. acknowledges funds from the Mechanical Engineering Department, College of Science and Engineering, MnDRIVE RSAM initiative of the University of Minnesota, Minnesota Department of Higher Education and NIH 1R21NS103098-01, 1R01NS111028, 1R34NS111654, 1R21NS112886 and 1R21 NS111196. L.G. was supported by the University of Minnesota Informatics Institute’s (UMII) graduate fellowship. We thank Dr. Eric Yttri, Alan Lai and Mark Nicholas of the Yttri laboratory at Carnegie Mellon University for useful feedback during beta-testing of the Craniobot. We also thank Luiz Bueno and Dr. York Winter of labmaker.org, who provided insights and suggested improvements to streamline hardware assembly. We also thank Dr. Spencer Smith (@Labrigger) for discussions on automated technologies for cranial microsurgeries and the strategies for wide adoption of such technologies, which partially motivated the documentation of this protocol.

Author information

Author notes

  1. These authors contributed equally: Leila Ghanbari, Daniel Sousa Schulman.

Authors and Affiliations

  1. Department of Biomedical Engineering, University of Minnesota Twin Cities, Minneapolis, MN, USA

    Mathew L. Rynes & Suhasa B. Kodandaramaiah

  2. Department of Mechanical Engineering, University of Minnesota Twin Cities, Minneapolis, MN, USA

    Leila Ghanbari, Daniel Sousa Schulman, Samantha Linn, Judith Dominguez, Zahra S. Navabi, Peter Sherman & Suhasa B. Kodandaramaiah

  3. Schools of Medicine, University of Minnesota Medical School, Minneapolis, MN, USA

    Michael Laroque

Authors
  1. Mathew L. Rynes
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Leila Ghanbari
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Daniel Sousa Schulman
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Samantha Linn
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Michael Laroque
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Judith Dominguez
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Zahra S. Navabi
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Peter Sherman
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Suhasa B. Kodandaramaiah
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

M.L.R., L.G., D.S.S., M.L., P.S. and S.B.K. developed the hardware systems. D.S.S. and L.G. developed the Python-based software GUI. M.L.R., S.L. and S.B.K. tested the complete system. M.L.R., L.G., D.S.S., S.L., M.L., J.D., Z.S.N., P.S. and S.B.K. wrote the manuscript.

Corresponding author

Correspondence to Suhasa B. Kodandaramaiah.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Protocols thanks Jasmin Hefendehl and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Related links

Key references using this protocol

Ghanbari, L. et al. Sci. Rep. 9, 1023 (2019): https://doi.org/10.1038/s41598-018-37073-w

Ghanbari, L. et al. Nat. Commun. 10, 1500 (2019): https://doi.org/10.1038/s41467-019-09488-0

Supplementary information

Supplementary Video 1

Video demonstrating the use of the Craniobot to perform a 3-mm-diameter circular craniotomy centered at 2 mm to the right of and 2 mm posterior to Bregma.

Reporting Summary

Supplementary Software 1

Setup file for installing and using the Craniobot control software.

Supplementary Software 2

Setup file for installing and using the Arduino microcontroller.

Supplementary Data 1

A compressed file archive consisting of all computer-aided design (CAD) files of custom-fabricated components needed for assembly of the Craniobot and the surface profiler.

Supplementary Data 2

A file containing the code for the microcontroller to regulate the switching circuit.

Supplementary Data 3

A compressed file archive consisting of .csv files for performing craniotomy over the whole dorsal cortex and a rectangular craniotomy.

Supplementary Data 4

A compressed file archive consisting of all CAD files required for assembly of the See-Shell implant.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rynes, M.L., Ghanbari, L., Schulman, D.S. et al. Assembly and operation of an open-source, computer numerical controlled (CNC) robot for performing cranial microsurgical procedures. Nat Protoc 15, 1992–2023 (2020). https://doi.org/10.1038/s41596-020-0318-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41596-020-0318-4

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing