GridLoc: An automatic and unsupervised localization method for high-density ECoG grids

GridLoc:一种用于高密度脑电图网格的自动、无监督定位方法

阅读:1

Abstract

Precise localization of electrodes is essential in the field of high-density (HD) electrocorticography (ECoG) brain signal analysis in order to accurately interpret the recorded activity in relation to functional anatomy. Current localization methods for subchronically implanted HD electrode grids involve post-operative imaging. However, for situations where post-operative imaging is not available, such as during acute measurements in awake surgery, electrode localization is complicated. Intra-operative photographs may be informative, but not for electrode grids positioned partially or fully under the skull. Here we present an automatic and unsupervised method to localize HD electrode grids that does not require post-operative imaging. The localization method, named GridLoc, is based on the hypothesis that the anatomical and vascular brain structures under the ECoG electrodes have an effect on the amplitude of the recorded ECoG signal. More specifically, we hypothesize that the spatial match between resting-state high-frequency band power (45-120 Hz) patterns over the grid and the anatomical features of the brain under the electrodes, such as the presence of sulci and larger blood vessels, can be used for adequate HD grid localization. We validate this hypothesis and compare the GridLoc results with electrode locations determined with post-operative imaging and/or photographs in 8 patients implanted with HD-ECoG grids. Locations agreed with an average difference of 1.94 ± 0.11 mm, which is comparable to differences reported earlier between post-operative imaging and photograph methods. The results suggest that resting-state high-frequency band activity can be used for accurate localization of HD grid electrodes on a pre-operative MRI scan and that GridLoc provides a convenient alternative to methods that rely on post-operative imaging or intra-operative photographs.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。