SHIELD: Skull-shaped hemispheric implants enabling large-scale-electrophysiology datasets in the mouse brain

Corbett Bennett, Ben Ouellette, Tamina K Ramirez, Alex Cahoon, Hannah Cabasco, Hannah Belski, Ryan Gillis, Conor Grasso, Robert Howard, Tye Johnson, Henry Loeffler, Heston Smith, David Sullivan, Allison Williford, Shiella Caldejon, Severine Durand, Samuel D Gale, Alan Guthrie, Vivian Ha, Warren Han, Ben Hardcastle, Ethan McBride, Chris Mochizuki, Arjun Sridhar, Lucas Suarez, Jackie Swapp, Josh Wilkes, Colin Farrell, Peter Groblewski, Shawn Olsen
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Abstract

To understand the neural basis of behavior, it is essential to measure spiking dynamics across many interacting brain regions. While new technology, such as Neuropixels probes, facilitates multi-regional recordings, significant surgical and procedural hurdles remain for these experiments to achieve their full potential. Here, we describe a novel 3D-printed cranial implant for electrophysiological recordings from distributed areas of the mouse brain. The skull-shaped implant is designed with customizable insertion holes, allowing targeting of dozens of cortical and subcortical structures in single mice. We demonstrate the procedure's high success rate, implant biocompatibility, lack of adverse effects on behavior training, compatibility with optical imaging and optogenetics, and repeated high-quality Neuropixels recordings over multiple days. To showcase the scientific utility of this new methodology, we use multi-probe recordings to reveal how alpha rhythms organize spiking activity across visual and sensorimotor networks. Overall, this methodology enables powerful large-scale electrophysiological measurements for the study of distributed computation in the mouse brain.
SHIELD:颅骨状半球植入物,可在小鼠大脑中建立大规模电生理数据集
为了理解行为的神经基础,测量许多相互作用的大脑区域的脉冲动力学是必要的。虽然神经像素探针等新技术促进了多区域记录,但这些实验要充分发挥其潜力,仍存在重大的手术和程序障碍。在这里,我们描述了一种新的3d打印颅骨植入物,用于从小鼠大脑的分布区域进行电生理记录。这种头骨形状的植入物设计有可定制的插入孔,可以针对单个小鼠的数十个皮层和皮层下结构。我们证明了该方法的高成功率,植入物的生物相容性,对行为训练没有不良影响,与光学成像和光遗传学的兼容性,以及在多天内重复高质量的神经像素记录。为了展示这种新方法的科学效用,我们使用多探针记录来揭示α节律如何在视觉和感觉运动网络中组织尖峰活动。总的来说,这种方法为研究小鼠大脑中的分布式计算提供了强大的大规模电生理测量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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