Surface based electrode localization and standardized regions of interest for intracranial EEG.
Surface based electrode localization and standardized regions of interest for intracranial EEG.
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DOI：
10.1002/hbm.23876
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发表时间：
2018-03
影响因子：
4.8
通讯作者：
Zaghloul KA
中科院分区：
文献类型：
--
作者：
Trotta MS;Cocjin J;Whitehead E;Damera S;Wittig JH Jr;Saad ZS;Inati SK;Zaghloul KA
从抗药性癫痫患者的硬膜下电极捕捉到的颅内记录为临床医生和研究人员提供了一种强大的工具，可以高空间和高时间精度地检查人类大脑中的神经活动。然而，无论是在个人内部还是在个人之间，解释这些信号都面临两大挑战。植入后的解剖扭曲使准确识别电极位置变得困难。此外，由于每个植入物都涉及独特的配置，以标准化的方式比较个体之间的神经活动仅限于皮质叶或脑回等广泛的解剖区域。我们在这里介绍了一种半自动的方法，将硬膜下电极接触定位到每个人独特的表面解剖结构，并使用基于表面的感兴趣区域网格(ROI)来收集来自相似解剖位置的跨个体电极数据，从而解决这些挑战。我们的定位算法只使用术后CT和术前MRI，建立在以前基于弹簧的优化方法的基础上，通过直接在大脑表面引入手动识别的锚点来约束最终的电极位置。该算法的精度为2 mm。我们的基于表面的ROI方法包括选择具有不同空间分辨率的灵活数量的ROI。ROI是跨个体注册的，以代表相同的解剖位置，同时考虑到每个大脑表面的独特曲率。因此，这种基于ROI的方法能够从空间上精确的解剖区域进行组级统计测试。
Intracranial recordings captured from subdural electrodes in patients with drug resistant epilepsy offer clinicians and researchers a powerful tool for examining neural activity in the human brain with high spatial and temporal precision. There are two major challenges, however, to interpreting these signals both within and across individuals. Anatomical distortions following implantation make accurately identifying the electrode locations difficult. In addition, because each implant involves a unique configuration, comparing neural activity across individuals in a standardized manner has been limited to broad anatomical regions such as cortical lobes or gyri. We address these challenges here by introducing a semi-automated method for localizing subdural electrode contacts to the unique surface anatomy of each individual, and by using a surface-based grid of regions of interest (ROIs) to aggregate electrode data from similar anatomical locations across individuals. Our localization algorithm, which uses only a postoperative CT and preoperative MRI, builds upon previous spring-based optimization approaches by introducing manually identified anchor points directly on the brain surface to constrain the final electrode locations. This algorithm yields an accuracy of 2 mm. Our surface-based ROI approach involves choosing a flexible number of ROIs with different spatial resolutions. ROIs are registered across individuals in order to represent identical anatomical locations while accounting for the unique curvature of each brain surface. This ROI based approach therefore enables group level statistical testing from spatially precise anatomical regions.
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影响因子：
5.7
作者：
Fischl, B;Sereno, MI;Dale, AM
通讯作者：
Dale, AM
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影响因子：
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