Pellegrino Giovanni, Xu Min, Alkuwaiti Abdulla, Porras-Bettancourt Manuel, Abbas Ghada, Lina Jean-Marc, Grova Christophe, Kobayashi Eliane
Neurology and Neurosurgery Department, Montreal Neurological Institute, McGill University, Montreal, QC, Canada.
Department of Geriatrics, Zhongnan Hospital of Wuhan University, Wuhan, China.
Front Neurol. 2020 Jun 2;11:479. doi: 10.3389/fneur.2020.00479. eCollection 2020.
Magnetoencephalography source imaging (MSI) of interictal epileptiform discharges (IED) is a useful presurgical tool in the evaluation of drug-resistant frontal lobe epilepsy (FLE) patients. Yet, failures in MSI can arise related to artifacts and to interference of background activity. Independent component analysis (ICA) is a popular denoising procedure but its clinical application remains challenging, as the selection of multiple independent components (IC) is controversial, operator dependent, and time consuming. We evaluated whether selecting only one IC of interest based on its similarity with the average IED field improves MSI in FLE. MSI was performed with the equivalent current dipole (ECD) technique and two distributed magnetic source imaging (dMSI) approaches: minimum norm estimate (MNE) and coherent Maximum Entropy on the Mean (cMEM). MSI accuracy was evaluated under three conditions: (1) ICA of continuous data (Cont_ICA), (2) ICA at the time of IED (IED_ICA), and (3) without ICA (No_ICA). Localization performance was quantitatively measured as actual distance of the source maximum in relation to the focus (Dmin), and spatial dispersion (SD) for dMSI. After ICA, ECD Dmin did not change significantly ( > 0.200). For both dMSI techniques, ICA application worsened the source localization accuracy. We observed a worsening of both MNE Dmin ( < 0.05, consistently) and MNE SD ( < 0.001, consistently) for both ICA approaches. A similar behaviour was observed for cMEM, for which, however, Cont_ICA seemed less detrimental. We demonstrated that a simplified ICA approach selecting one IC of interest in combination with distributed magnetic source imaging can be detrimental. More complex approaches may provide better results but would be rather difficult to apply in real-world clinical setting. In a broader perspective, caution should be taken in applying ICA for source localization of interictal activity. To ensure optimal and useful results, effort should focus on acquiring good quality data, minimizing artifacts, and determining optimal candidacy for MEG, rather than counting on data cleaning techniques.
发作间期癫痫样放电（IED）的脑磁图源成像（MSI）是评估耐药性额叶癫痫（FLE）患者术前情况的一种有用工具。然而，MSI可能会因伪迹和背景活动干扰而失败。独立成分分析（ICA）是一种常用的去噪方法，但其临床应用仍然具有挑战性，因为多个独立成分（IC）的选择存在争议、依赖操作人员且耗时。我们评估了基于与平均IED场的相似性仅选择一个感兴趣的IC是否能改善FLE患者的MSI。采用等效电流偶极子（ECD）技术和两种分布式磁源成像（dMSI）方法进行MSI：最小范数估计（MNE）和相干平均最大熵（cMEM）。在三种情况下评估MSI准确性：（1）连续数据的ICA（Cont_ICA），（2）IED发作时的ICA（IED_ICA），以及（3）不进行ICA（No_ICA）。将定位性能定量测量为源最大值相对于病灶的实际距离（Dmin），以及dMSI的空间离散度（SD）。ICA后，ECD的Dmin没有显著变化（>0.200）。对于两种dMSI技术，应用ICA都会降低源定位准确性。我们观察到，对于两种ICA方法，MNE的Dmin（始终<0.05）和MNE的SD（始终<0.001）都有所恶化。cMEM也观察到类似的情况，不过Cont_ICA似乎危害较小。我们证明，一种简化的ICA方法，即结合分布式磁源成像选择一个感兴趣的IC可能会有不利影响。更复杂的方法可能会提供更好的结果，但在实际临床环境中应用会相当困难。从更广泛的角度来看，在将ICA应用于发作间期活动的源定位时应谨慎。为确保获得最佳且有用的结果，应致力于获取高质量数据、尽量减少伪迹以及确定MEG的最佳候选者，而不是依赖数据清理技术。
