Abstracts

Rationale: Mouse models are beneficial to understanding neural networks and epilepsy given a wide array of transgenic mice and cell-selective techniques. However, instrumentation of mice for neurophysiological studies is difficult. Often surgery is prolonged with experimental error arising from non-concurrent and variable implantations. Methods: We describe a method for the rapid, reproducible and customizable instrumentation of mice for chronic electrophysiological experiments. We constructed a head-plate that conforms to the mouse skull surface using script-based computer aided design. This head-plate was then modified to enable the friction-fit addition of instrumentation prior to surgery and printed at high-resolution resin-based 3D printing. Using this approach, we describe an easily customized head-plate with four dural screws, EMG electrodes, a cannula hole and two independent micro-drives for hippocampal depth electrodes. Results: Implantation of the head-plate reliably takes less than 40 minutes, enabling a cohort of eight mice to be implanted in one day. Good quality recordings were obtained after surgical recovery and the head-plate was stable for at least four weeks. Depth electrode placement was found to be accurate. Conclusions: While a similar approach with microelectrodes have been used in rats before, and related methods exist for targeting tetrodes to one brain region, we do not know of similar head-plates for mice, nor a strictly source-code and easily reconfigurable technique. 3D-printing and friction-fit pre-assembly of mouse head-plates offers a rapid, easily reconfigurable, consistent, and cost-effective way to implant larger numbers of mice in a highly reproducible way, reducing surgical time and mitigating experimental error. Further variations of the head-plate are being developed to enable large scale network recordings. Funding: Research reported in this publication was supported by the National Institute Of Neurological Disorders And Stroke of the National Institutes of Health under Award Number K08NS105929.