An open-source application to identify the three-dimensional locations of electrodes implanted into the rat brain from computed tomography images.

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.