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Clinical Validation of the Champagne Algorithm for Evoked Response Source Localization in Magnetoencephalography
Brain Topography ( IF 2.3 ) Pub Date : 2021-06-11 , DOI: 10.1007/s10548-021-00850-4
Abhishek S Bhutada 1 , Chang Cai 1 , Danielle Mizuiri 1 , Anne Findlay 1 , Jessie Chen 1 , Ashley Tay 1 , Heidi E Kirsch 1, 2 , Srikantan S Nagarajan 1
Affiliation  

Magnetoencephalography (MEG) is a robust method for non-invasive functional brain mapping of sensory cortices due to its exceptional spatial and temporal resolution. The clinical standard for MEG source localization of functional landmarks from sensory evoked responses is the equivalent current dipole (ECD) localization algorithm, known to be sensitive to initialization, noise, and manual choice of the number of dipoles. Recently many automated and robust algorithms have been developed, including the Champagne algorithm, an empirical Bayesian algorithm, with powerful abilities for MEG source reconstruction and time course estimation (Wipf et al. 2010; Owen et al. 2012). Here, we evaluate automated Champagne performance in a clinical population of tumor patients where there was minimal failure in localizing sensory evoked responses using the clinical standard, ECD localization algorithm. MEG data of auditory evoked potentials and somatosensory evoked potentials from 21 brain tumor patients were analyzed using Champagne, and these results were compared with equivalent current dipole (ECD) fit. Across both somatosensory and auditory evoked field localization, we found there was a strong agreement between Champagne and ECD localizations in all cases. Given resolution of 8mm voxel size, peak source localizations from Champagne were below 10mm of ECD peak source localization. The Champagne algorithm provides a robust and automated alternative to manual ECD fits for clinical localization of sensory evoked potentials and can contribute to improved clinical MEG data processing workflows.



中文翻译:

脑磁图诱发反应源定位香槟算法的临床验证

由于其出色的空间和时间分辨率,脑磁图 (MEG) 是一种用于感觉皮层的非侵入性脑功能映射的稳健方法。根据感觉诱发反应对功能性标志进行 MEG 源定位的临床标准是等效电流偶极子 (ECD) 定位算法,该算法对初始化、噪声和偶极子数量的手动选择很敏感。最近开发了许多自动化和鲁棒的算法,包括 Champagne 算法,一种经验贝叶斯算法,具有强大的 MEG 源重建和时间进程估计能力(Wipf et al. 2010; Owen et al. 2012)。这里,我们在肿瘤患者的临床人群中评估了自动香槟的性能,其中使用临床标准 ECD 定位算法定位感觉诱发反应的失败最小。使用 Champagne 分析了来自 21 名脑肿瘤患者的听觉诱发电位和体感诱发电位的 MEG 数据,并将这些结果与等效电流偶极 (ECD) 拟合进行了比较。在体感和听觉诱发场定位中,我们发现香槟和 ECD 定位在所有情况下都有很强的一致性。给定 8 毫米体素大小的分辨率,来自香槟的峰值源定位低于 ECD 峰值源定位的 10 毫米。

更新日期:2021-06-11
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