Aydin Ümit, Vorwerk Johannes, Küpper Philipp, Heers Marcel, Kugel Harald, Galka Andreas, Hamid Laith, Wellmer Jörg, Kellinghaus Christoph, Rampp Stefan, Wolters Carsten Hermann
Institute for Biomagnetism and Biosignalanalysis, Westfälische Wilhelms-Universität Münster, Münster, Germany.
Institute for Biomagnetism and Biosignalanalysis, Westfälische Wilhelms-Universität Münster, Münster, Germany; Department of Neurology, Klinikum Osnabrück, Osnabrück, Germany.
PLoS One. 2014 Mar 26;9(3):e93154. doi: 10.1371/journal.pone.0093154. eCollection 2014.
To increase the reliability for the non-invasive determination of the irritative zone in presurgical epilepsy diagnosis, we introduce here a new experimental and methodological source analysis pipeline that combines the complementary information in EEG and MEG, and apply it to data from a patient, suffering from refractory focal epilepsy. Skull conductivity parameters in a six compartment finite element head model with brain anisotropy, constructed from individual MRI data, are estimated in a calibration procedure using somatosensory evoked potential (SEP) and field (SEF) data. These data are measured in a single run before acquisition of further runs of spontaneous epileptic activity. Our results show that even for single interictal spikes, volume conduction effects dominate over noise and need to be taken into account for accurate source analysis. While cerebrospinal fluid and brain anisotropy influence both modalities, only EEG is sensitive to skull conductivity and conductivity calibration significantly reduces the difference in especially depth localization of both modalities, emphasizing its importance for combining EEG and MEG source analysis. On the other hand, localization differences which are due to the distinct sensitivity profiles of EEG and MEG persist. In case of a moderate error in skull conductivity, combined source analysis results can still profit from the different sensitivity profiles of EEG and MEG to accurately determine location, orientation and strength of the underlying sources. On the other side, significant errors in skull modeling are reflected in EEG reconstruction errors and could reduce the goodness of fit to combined datasets. For combined EEG and MEG source analysis, we therefore recommend calibrating skull conductivity using additionally acquired SEP/SEF data.
为提高术前癫痫诊断中刺激性区域非侵入性测定的可靠性，我们在此引入一种新的实验和方法学源分析流程，该流程结合了脑电图（EEG）和脑磁图（MEG）中的互补信息，并将其应用于一名难治性局灶性癫痫患者的数据。利用体感诱发电位（SEP）和场（SEF）数据，在一个校准程序中估计由个体磁共振成像（MRI）数据构建的具有脑各向异性的六室有限元头部模型中的颅骨电导率参数。这些数据在单次采集中测量，然后再采集进一步的自发性癫痫活动数据。我们的结果表明，即使对于单个发作间期棘波，容积传导效应也比噪声占主导，并且在进行精确的源分析时需要考虑这一点。虽然脑脊液和脑各向异性会影响这两种模态，但只有EEG对颅骨电导率敏感，电导率校准显著减小了两种模态在特别是深度定位方面的差异，强调了其在结合EEG和MEG源分析中的重要性。另一方面，由于EEG和MEG不同的敏感性特征导致的定位差异仍然存在。在颅骨电导率存在适度误差的情况下，联合源分析结果仍可从EEG和MEG不同的敏感性特征中受益，以准确确定潜在源的位置、方向和强度。另一方面，颅骨建模中的显著误差会反映在EEG重建误差中，并可能降低对联合数据集的拟合优度。因此，对于联合EEG和MEG源分析，我们建议使用额外采集的SEP/SEF数据校准颅骨电导率。
