生态研究中脑电信号预处理与源定位的评价。

Frontiers in neuroimaging Pub Date : 2025-03-31 eCollection Date: 2025-01-01 DOI:10.3389/fnimg.2025.1479569
Carlos Gomez-Tapia, Bojan Bozic, Luca Longo
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引用次数: 0

摘要

脑电图(EEG)源定位(SL)已显示出多种应用潜力,从癫痫和发作焦点定位到精神障碍评估。然而,在现实世界中,只有脑电图信号,没有特定的解剖信息,其神经生理学合理性仍然存在问题。本研究探讨了当将已建立的预处理和源定位方法应用于没有结构磁共振成像(MRI)或数字化电极位置的自然脑电图数据时,是否可以产生神经生理学上合理的激活模式。方法:经过验证的方法被聚合成一个端到端管道,其中包括自动预处理、用于源估计的eLORETA以及从ICBM 2009c非线性对称模板及其相应的CerebrA图谱中导出的共享前向模型。该管道使用两个不同的数据集进行验证:比较静息和自然视频观看状态的健康脑网络(HBN)数据集和比较不同认知工作量水平的多会话和多任务脑电图认知数据集(COGBCI)。验证方法侧重于重构源激活是否通过排列测试表现出预期的神经生理模式。结果:研究结果显示静息状态和观看视频任务之间存在显著差异,观看视频时后脑区更活跃,与已知的视觉加工通路一致。认知负荷分析同样显示,随着任务难度的增加,大脑的渐进式激活也会增加,并映射到与执行功能相关的区域。讨论:这些结果证明,既定的源定位方法可以在没有受试者特定信息的情况下产生神经生理学上合理的激活模式,突出了将这些方法应用于中长度自然脑电图数据的优势和局限性。本研究表明,在个体结构成像不可用或不切实际的研究环境中,基于模板的源分析是可行的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evaluation of EEG pre-processing and source localization in ecological research.

Introduction: Electroencephalography (EEG) source localization (SL) has shown potential for various applications, from epilepsy and seizure focus localization to psychiatric disorder evaluation. However, questions remain about its neurophysiological plausibility in real-world settings where only EEG signals are available without subject-specific anatomical information. This study investigates whether established pre-processing and source localization methods can produce neurophysiologically plausible activation patterns when applied to naturalistic EEG data without structural magnetic resonance imaging (MRI) or digitized electrode positions.

Methods: Proven methods are aggregated into an end-to-end pipeline that includes automatic pre-processing, eLORETA for source estimation, and a shared forward model derived from the ICBM 2009c Nonlinear Symmetric template and its corresponding CerebrA atlas. The pipeline is validated using two distinct datasets: the Healthy Brain Network (HBN) dataset comparing resting and naturalistic video-watching states and the multi-session and multi-task EEG cognitive dataset (COGBCI) comparing different cognitive workload levels. The validation approach focuses on whether the reconstructed source activations exhibit expected neurophysiological patterns via permutation testing.

Results: Findings revealed significant differences between resting state and video-watching tasks, with greater activation in posterior regions during video-watching, consistent with known visual processing pathways. The cognitive workload analysis similarly showed progressive activation increases with task difficulty, mapping to regions associated with executive function.

Discussion: These results prove that established source localization methods can produce neurophysiologically plausible activation patterns without subject-specific information, highlighting the strengths and limitations of applying these methods to mid-length naturalistic EEG data. This research demonstrates the viability of template-based source analysis for research settings where individual structural imaging is unavailable or impractical.

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