Accuracy of SEEG Source Localization: A Pilot Study Using Corticocortical Evoked Potentials.

IF 2.3 4区 医学 Q3 CLINICAL NEUROLOGY
Benjamin C Cox, Rachel J Smith, Ismail Mohamed, Jenna V Donohue, Mahtab Rostamihosseinkhani, Jerzy P Szaflarski, Rebekah J Chatfield
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引用次数: 0

Abstract

Introduction: EEG source localization is an established technique for localizing scalp EEG in medically refractory epilepsy but has not been adequately studied with intracranial EEG (iEEG). Differences in sensor location and spatial sampling may affect the accuracy of EEG source localization with iEEG. Corticocortical evoked potentials can be used to evaluate EEG source localization algorithms for iEEG given the known source location.

Methods: We recorded 205 sets of corticocortical evoked potentials using low-frequency single-pulse electrical stimulation in four patients with iEEG. Averaged corticocortical evoked potentials were analyzed using 11 distributed source algorithms and compared using the Wilcoxon signed-rank test ( P < 0.05). We measured the localization error from stimulated electrodes and the spatial dispersion of each solution.

Results: Minimum norm, standard low-resolution electromagnetic tomography (sLORETA), LP Norm, sLORETA-weighted accurate minimum norm (SWARM), exact LORETA (eLORETA), standardized weighted LORETA (swLORETA), and standardized shrinking LORETA-FOCUSS (ssLOFO) had the least localization error (13.3-15.7 mm) and were superior to focal underdetermined system solver (FOCUSS), logistic autoregressive average (LAURA, and LORETA, 17.9-21.7, P < 0.001). The FOCUSS solution had the smallest spatial dispersion (7.4 mm), followed by minimum norm, L1 norm, LP norm, and SWARM (20.8-28.3 mm). Gray matter stimulations had less localization error than white matter (median differences 3.1-6.1 mm) across all algorithms except SWARM, LORETA, and logistic autoregressive average. A multivariate linear regression showed that distance from the source to sensors and gray/white matter stimulation had a significant effect on localization error for some algorithms but not SWARM, minimum norm, focal underdetermined system solver, logistic autoregressive average, and LORETA.

Conclusions: Our study demonstrated that minimum norm, L1 norm, LP norm, and SWARM localize iEEG corticocortical evoked potentials well with lower localization error and spatial dispersion. Larger studies are needed to confirm these findings.

SEEG源定位的准确性:皮质-皮质诱发电位的初步研究。
脑电图源定位是医学上难治性癫痫患者头皮脑电图定位的一种成熟技术,但尚未在颅内脑电图(iEEG)中得到充分的研究。传感器位置和空间采样的差异会影响EEG源定位的准确性。皮质-皮质诱发电位可用于评价已知脑电源定位算法。方法:采用低频单脉冲电刺激,记录4例脑电图患者205组皮质-皮质诱发电位。采用11种分布式源算法分析皮质皮质平均诱发电位,并采用Wilcoxon符号秩检验进行比较(P < 0.05)。我们测量了受激电极的定位误差和每个溶液的空间色散。结果:最小norm、标准低分辨率电断层扫描(sLORETA)、LP norm、sLORETA加权精确最小norm (SWARM)、精确LORETA (eLORETA)、标准化加权LORETA (swLORETA)和标准化缩小LORETA- focus (ssLOFO)的定位误差最小(13.3 ~ 15.7 mm),优于焦点欠定系统求解器(FOCUSS)、logistic自回归平均(LAURA, LORETA, 17.9 ~ 21.7, P < 0.001)。FOCUSS溶液的空间弥散最小(7.4 mm),其次是minimum norm、L1 norm、LP norm和SWARM (20.8 ~ 28.3 mm)。除了SWARM、LORETA和logistic自回归平均算法外,所有算法中灰质刺激的定位误差都小于白质(中位数差3.1-6.1 mm)。多元线性回归表明,源到传感器的距离和灰质/白质刺激对某些算法的定位误差有显著影响,而SWARM、最小范数、焦点欠定系统解算器、logistic自回归平均和LORETA对定位误差没有显著影响。结论:最小范数、L1范数、LP范数和SWARM对脑电图皮层诱发电位具有较好的定位误差和较低的空间离散性。需要更大规模的研究来证实这些发现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Clinical Neurophysiology
Journal of Clinical Neurophysiology 医学-临床神经学
CiteScore
4.60
自引率
4.20%
发文量
198
审稿时长
6-12 weeks
期刊介绍: ​The Journal of Clinical Neurophysiology features both topical reviews and original research in both central and peripheral neurophysiology, as related to patient evaluation and treatment. Official Journal of the American Clinical Neurophysiology Society.
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