Babajani-Feremi Abbas, Pourmotabbed Haatef, Schraegle William A, Calley Clifford S, Clarke Dave F, Papanicolaou Andrew C
Department of Neurology, University of Florida, Gainesville, FL, United States.
Magnetoencephalography (MEG) Lab, The Norman Fixel Institute of Neurological Diseases, University of Florida Health, Gainesville, FL, United States.
Front Neurosci. 2023 Jun 2;17:1151885. doi: 10.3389/fnins.2023.1151885. eCollection 2023.
INTRODUCTION: The single equivalent current dipole (sECD) is the standard clinical procedure for presurgical language mapping in epilepsy using magnetoencephalography (MEG). However, the sECD approach has not been widely used in clinical assessments, mainly because it requires subjective judgements in selecting several critical parameters. To address this limitation, we developed an automatic sECD algorithm (AsECDa) for language mapping. METHODS: The localization accuracy of the AsECDa was evaluated using synthetic MEG data. Subsequently, the reliability and efficiency of AsECDa were compared to three other common source localization methods using MEG data recorded during two sessions of a receptive language task in 21 epilepsy patients. These methods include minimum norm estimation (MNE), dynamic statistical parametric mapping (dSPM), and dynamic imaging of coherent sources (DICS) beamformer. RESULTS: For the synthetic single dipole MEG data with a typical signal-to-noise ratio, the average localization error of AsECDa was less than 2 mm for simulated superficial and deep dipoles. For the patient data, AsECDa showed better test-retest reliability (TRR) of the language laterality index (LI) than MNE, dSPM, and DICS beamformer. Specifically, the LI calculated with AsECDa revealed excellent TRR between the two MEG sessions across all patients (Cor = 0.80), while the LI for MNE, dSPM, DICS-event-related desynchronization (ERD) in the alpha band, and DICS-ERD in the low beta band ranged lower (Cor = 0.71, 0.64, 0.54, and 0.48, respectively). Furthermore, AsECDa identified 38% of patients with atypical language lateralization (i.e., right lateralization or bilateral), compared to 73%, 68%, 55%, and 50% identified by DICS-ERD in the low beta band, DICS-ERD in the alpha band, MNE, and dSPM, respectively. Compared to other methods, AsECDa's results were more consistent with previous studies that reported atypical language lateralization in 20-30% of epilepsy patients. DISCUSSION: Our study suggests that AsECDa is a promising approach for presurgical language mapping, and its fully automated nature makes it easy to implement and reliable for clinical evaluations.
引言：单等效电流偶极子（sECD）是癫痫术前语言映射中使用脑磁图（MEG）的标准临床程序。然而，sECD方法在临床评估中尚未得到广泛应用，主要是因为在选择几个关键参数时需要主观判断。为了解决这一局限性，我们开发了一种用于语言映射的自动sECD算法（AsECDa）。 方法：使用合成MEG数据评估AsECDa的定位准确性。随后，在21例癫痫患者接受性语言任务的两个阶段记录的MEG数据中，将AsECDa的可靠性和效率与其他三种常见的源定位方法进行比较。这些方法包括最小范数估计（MNE）、动态统计参数映射（dSPM）和相干源动态成像（DICS）波束形成器。 结果：对于具有典型信噪比的合成单偶极子MEG数据，对于模拟的浅表和深部偶极子，AsECDa的平均定位误差小于2毫米。对于患者数据，AsECDa在语言偏侧指数（LI）的重测可靠性（TRR）方面优于MNE、dSPM和DICS波束形成器。具体而言，用AsECDa计算的LI在所有患者的两个MEG阶段之间显示出极好的TRR（Cor = 0.80），而MNE、dSPM、α波段的DICS事件相关去同步（ERD）和低β波段的DICS-ERD的LI较低（分别为Cor = 0.71、0.64、0.54和0.48）。此外，AsECDa识别出38%的非典型语言偏侧化患者（即右侧偏侧化或双侧偏侧化），相比之下，低β波段的DICS-ERD、α波段的DICS-ERD、MNE和dSPM分别识别出73%、68%、55%和50%。与其他方法相比，AsECDa的结果与先前报道20%-30%癫痫患者存在非典型语言偏侧化的研究更为一致。 讨论：我们的研究表明，AsECDa是一种有前景的术前语言映射方法，其完全自动化的特性使其易于实施且在临床评估中可靠。
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