Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Japan.
Center of Biomedical Physics and Information Technology, Nagoya Institute of Technology, Nagoya 466-8555, Japan.
Phys Med Biol. 2024 Feb 22;69(5). doi: 10.1088/1361-6560/ad25c3.
. Electroencephalograms (EEGs) are often used to monitor brain activity. Several source localization methods have been proposed to estimate the location of brain activity corresponding to EEG readings. However, only a few studies evaluated source localization accuracy from measured EEG using personalized head models in a millimeter resolution. In this study, based on a volume conductor analysis of a high-resolution personalized human head model constructed from magnetic resonance images, a finite difference method was used to solve the forward problem and to reconstruct the field distribution.. We used a personalized segmentation-free head model developed using machine learning techniques, in which the abrupt change of electrical conductivity occurred at the tissue interface is suppressed. Using this model, a smooth field distribution was obtained to address the forward problem. Next, multi-dipole fitting was conducted using EEG measurements for each subject (= 10 male subjects, age: 22.5 ± 0.5), and the source location and electric field distribution were estimated.For measured somatosensory evoked potential for electrostimulation to the wrist, a multi-dipole model with lead field matrix computed with the volume conductor model was found to be superior than a single dipole model when using personalized segmentation-free models (6/10). The correlation coefficient between measured and estimated scalp potentials was 0.89 for segmentation-free head models and 0.71 for conventional segmented models. The proposed method is straightforward model development and comparable localization difference of the maximum electric field from the target wrist reported using fMR (i.e. 16.4 ± 5.2 mm) in previous study. For comparison, DUNEuro based on sLORETA was (EEG: 17.0 ± 4.0 mm). In addition, somatosensory evoked magnetic fields obtained by Magnetoencephalography was 25.3 ± 8.5 mm using three-layer sphere and sLORETA.. For measured EEG signals, our procedures using personalized head models demonstrated that effective localization of the somatosensory cortex, which is located in a non-shallower cortex region. This method may be potentially applied for imaging brain activity located in other non-shallow regions.
脑电图（EEG）常用于监测大脑活动。已经提出了几种源定位方法来估计对应于 EEG 读数的大脑活动的位置。然而，只有少数研究使用毫米分辨率的个性化头模型评估从测量的 EEG 中源定位的准确性。在这项研究中，基于从磁共振图像构建的高分辨率个性化人头模型的容积导体分析，使用有限差分方法解决正向问题并重建场分布。我们使用了一种使用机器学习技术开发的个性化无分割头模型，其中抑制了电导率在组织界面处的突然变化。使用该模型，获得了平滑的场分布来解决正向问题。接下来，对每个被试（= 10 名男性，年龄：22.5±0.5）进行 EEG 测量的多偶极拟合，并估计源位置和电场分布。对于腕部电刺激的体感诱发电位测量，使用个性化无分割模型，带场矩阵的多偶极模型优于单偶极模型（6/10）。无分割头模型的头皮电位测量值和估计值之间的相关系数为 0.89，而常规分割模型为 0.71。与以前使用 fMRI 报告的目标手腕的最大电场的定位差异（即 16.4±5.2mm）相比，所提出的方法是直接的模型开发和可比的。相比之下，基于 DUNEuro 的 sLORETA 为（EEG：17.0±4.0mm）。此外，使用三层球和 sLORETA 获得的体感诱发电磁场为 25.3±8.5mm。对于测量的 EEG 信号，我们使用个性化头模型的程序表明，可以有效地定位位于非浅层皮层区域的体感皮层。该方法可能潜在地应用于对位于其他非浅层区域的大脑活动进行成像。
