Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo 113-0033, Japan.
Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo 113-0033, Japan; Institute for AI and Beyond, The University of Tokyo, Tokyo 113-0033, Japan.
Neurosci Res. 2023 Aug;193:20-27. doi: 10.1016/j.neures.2023.03.003. Epub 2023 Mar 30.
Electrophysiological recordings using metal electrodes implanted into the brains have been widely utilized to evaluate neuronal circuit dynamics related to behavior and external stimuli. The most common method for identifying implanted electrode tracks in the brain tissue has been histological examination following postmortem slicing and staining of the brain tissue, which consumes time and resources and occasionally fails to identify the tracks because the brain preparations have been damaged during processing. Recent studies have proposed the use of a promising alternative method, consisting of computed tomography (CT) scanning that can directly reconstruct the three-dimensional arrangements of electrodes in the brains of living animals. In this study, we developed an open-source Python-based application that estimates the location of an implanted electrode from CT image sequences in a rat. After the user manually sets reference coordinates and an area from a sequence of CT images, this application automatically overlays an estimated location of an electrode tip on a histological template image; the estimates are highly accurate, with less than 135 µm of error, irrespective of the depth of the brain region. The estimation of an electrode location can be completed within a few minutes. Our simple and user-friendly application extends beyond currently available CT-based electrode localization methods and opens up the possibility of applying this technique to various electrophysiological recording paradigms.
使用植入大脑的金属电极进行电生理记录已被广泛用于评估与行为和外部刺激相关的神经元回路动力学。最常见的识别脑组织中植入电极轨迹的方法是在死后对脑组织进行切片和染色的组织学检查，这种方法既费时又费资源，而且偶尔还无法识别轨迹，因为在处理过程中脑组织已经受损。最近的研究提出了一种有前途的替代方法，包括计算机断层扫描（CT）扫描，可以直接重建活体动物大脑中电极的三维排列。在这项研究中，我们开发了一个基于 Python 的开源应用程序，该程序可以从大鼠的 CT 图像序列中估计植入电极的位置。在用户手动设置参考坐标和 CT 图像序列的一个区域后，该应用程序会自动将电极尖端的估计位置叠加在组织学模板图像上；无论大脑区域的深度如何，估计值的误差都小于 135µm，非常准确。电极位置的估计可以在几分钟内完成。我们的简单易用的应用程序超越了当前可用的基于 CT 的电极定位方法，并为将该技术应用于各种电生理记录范式开辟了可能性。
