Blenkmann Alejandro O, Solbakk Anne-Kristin, Ivanovic Jugoslav, Larsson Pål Gunnar, Knight Robert T, Endestad Tor
Department of Psychology, University of Oslo, Oslo, Norway.
RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo, Oslo, Norway.
Front Neuroinform. 2022 Oct 6;16:788685. doi: 10.3389/fninf.2022.788685. eCollection 2022.
Intracranial electrodes are implanted in patients with drug-resistant epilepsy as part of their pre-surgical evaluation. This allows the investigation of normal and pathological brain functions with excellent spatial and temporal resolution. The spatial resolution relies on methods that precisely localize the implanted electrodes in the cerebral cortex, which is critical for drawing valid inferences about the anatomical localization of brain function. Multiple methods have been developed to localize the electrodes, mainly relying on pre-implantation MRI and post-implantation computer tomography (CT) images. However, they are hard to validate because there is no ground truth data to test them and there is no standard approach to systematically quantify their performance. In other words, their validation lacks standardization. Our work aimed to model intracranial electrode arrays and simulate realistic implantation scenarios, thereby providing localization algorithms with new ways to evaluate and optimize their performance.
We implemented novel methods to model the coordinates of implanted grids, strips, and depth electrodes, as well as the CT artifacts produced by these. We successfully modeled realistic implantation scenarios, including different sizes, inter-electrode distances, and brain areas. In total, ∼3,300 grids and strips were fitted over the brain surface, and ∼850 depth electrode arrays penetrating the cortical tissue were modeled. Realistic CT artifacts were simulated at the electrode locations under 12 different noise levels. Altogether, ∼50,000 thresholded CT artifact arrays were simulated in these scenarios, and validated with real data from 17 patients regarding the coordinates' spatial deformation, and the CT artifacts' shape, intensity distribution, and noise level. Finally, we provide an example of how the simulation platform is used to characterize the performance of two cluster-based localization methods.
We successfully developed the first platform to model implanted intracranial grids, strips, and depth electrodes and realistically simulate thresholded CT artifacts and their noise. These methods provide a basis for developing more complex models, while simulations allow systematic evaluation of the performance of electrode localization techniques. The methods described in this article, and the results obtained from the simulations, are freely available via open repositories. A graphical user interface implementation is also accessible via the open-source iElectrodes toolbox.
颅内电极被植入耐药性癫痫患者体内，作为其术前评估的一部分。这使得能够以出色的空间和时间分辨率研究正常和病理性脑功能。空间分辨率依赖于精确将植入电极定位在大脑皮层的方法，这对于得出关于脑功能解剖定位的有效推论至关重要。已经开发了多种方法来定位电极，主要依靠植入前的磁共振成像（MRI）和植入后的计算机断层扫描（CT）图像。然而，它们难以验证，因为没有地面真值数据来测试它们，也没有系统量化其性能的标准方法。换句话说，它们的验证缺乏标准化。我们的工作旨在对颅内电极阵列进行建模并模拟现实的植入场景，从而为定位算法提供评估和优化其性能的新方法。
我们实施了新颖的方法来对植入的格栅、条带和深度电极的坐标以及由它们产生的CT伪影进行建模。我们成功模拟了现实的植入场景，包括不同的尺寸、电极间距和脑区。总共在脑表面拟合了约3300个格栅和条带，并对约850个穿透皮质组织的深度电极阵列进行了建模。在12种不同噪声水平下，在电极位置模拟了逼真的CT伪影。在这些场景中总共模拟了约50000个阈值化CT伪影阵列，并根据17名患者的真实数据对坐标的空间变形以及CT伪影的形状、强度分布和噪声水平进行了验证。最后，我们提供了一个示例，展示了模拟平台如何用于表征两种基于聚类的定位方法的性能。
我们成功开发了首个对植入的颅内格栅、条带和深度电极进行建模并逼真模拟阈值化CT伪影及其噪声的平台。这些方法为开发更复杂的模型提供了基础，而模拟允许对电极定位技术的性能进行系统评估。本文所述方法以及从模拟中获得的结果可通过开放存储库免费获取。通过开源的iElectrodes工具箱也可获得图形用户界面实现。
