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A standardized head-fixation system for performing large-scale, in vivo physiological recordings in mice

By PA Groblewski, D Sullivan, J Lecoq, SEJ de Vries, S Caldejon, Q L’Heureux, T Keenan, K Roll, C Slaughterback, A Williford, C Farrell

Posted 23 Jan 2020
bioRxiv DOI: 10.1101/2020.01.22.916007

BACKGROUND The Allen Institute recently built a set of high-throughput experimental pipelines to collect comprehensive in vivo surveys of physiological activity in the visual cortex of awake, head-fixed mice. Developing these large-scale, industrial-like pipelines posed many scientific, operational, and engineering challenges. NEW METHOD Our strategies for creating a cross-platform reference space to which all pipeline datasets were mapped required development of 1) a robust headframe, 2) a reproducible clamping system, and 3) data-collection systems that are built, and maintained, around precise alignment with a reference artifact. RESULTS When paired with our pipeline clamping system, our headframe exceeded deflection and reproducibility requirements. By leveraging our headframe and clamping system we were able to create a cross-platform reference space to which multi-modal imaging datasets could be mapped. COMPARISON WITH EXISTING METHODS Together, the Allen Brain Observatory headframe, surgical tooling, clamping system, and system registration strategy create a unique system for collecting large amounts of standardized in vivo datasets over long periods of time. Moreover, the integrated approach to cross-platform registration allows for multi-modal datasets to be collected within a shared reference space. CONCLUSIONS Here we report the engineering strategies that we implemented when creating the Allen Brain Observatory physiology pipelines. All of the documentation related to headframe, surgical tooling, and clamp design has been made freely available and can be readily manufactured or procured. The engineering strategy, or components of the strategy, described in this report can be tailored and applied by external researchers to improve data standardization and stability. ### Competing Interest Statement The authors have declared no competing interest.

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