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由多个视网膜拓扑映射刺激位置约束的MEG/EEG视觉诱发电位的源估计。

Source estimates for MEG/EEG visual evoked responses constrained by multiple, retinotopically-mapped stimulus locations.

作者信息

Hagler Donald J, Halgren Eric, Martinez Antigona, Huang Mingxiong, Hillyard Steven A, Dale Anders M

机构信息

University of California, San Diego, USA.

出版信息

Hum Brain Mapp. 2009 Apr;30(4):1290-309. doi: 10.1002/hbm.20597.

Abstract

Studying the human visual system with high temporal resolution is a significant challenge due to the limitations of the available, noninvasive measurement tools. MEG and EEG provide the millisecond temporal resolution necessary for answering questions about intracortical communication involved in visual processing, but source estimation is ill-posed and unreliable when multiple; simultaneously active areas are located close together. To address this problem, we have developed a retinotopy-constrained source estimation method to calculate the time courses of activation in multiple visual areas. Source estimation was disambiguated by: (1) fixing MEG/EEG generator locations and orientations based on fMRI retinotopy and surface tessellations constructed from high-resolution MRI images; and (2) solving for many visual field locations simultaneously in MEG/EEG responses, assuming source current amplitudes to be constant or varying smoothly across the visual field. Because of these constraints on the solutions, estimated source waveforms become less sensitive to sensor noise or random errors in the specification of the retinotopic dipole models. We demonstrate the feasibility of this method and discuss future applications such as studying the timing of attentional modulation in individual visual areas.

摘要

由于现有非侵入性测量工具的局限性,以高时间分辨率研究人类视觉系统是一项重大挑战。脑磁图(MEG)和脑电图(EEG)提供了回答有关视觉处理中皮质内通信问题所需的毫秒级时间分辨率,但当多个同时活跃的区域靠得很近时,源估计是不适定且不可靠的。为了解决这个问题,我们开发了一种视网膜拓扑约束源估计方法,以计算多个视觉区域的激活时间进程。源估计通过以下方式消除歧义:(1)基于功能磁共振成像(fMRI)视网膜拓扑和由高分辨率磁共振成像(MRI)图像构建的表面细分来固定MEG/EEG发生器的位置和方向;(2)假设源电流幅度在整个视野中恒定或平滑变化,在MEG/EEG响应中同时求解多个视野位置。由于对解的这些约束,估计的源波形对传感器噪声或视网膜拓扑偶极子模型规范中的随机误差变得不太敏感。我们证明了该方法的可行性,并讨论了未来的应用,如研究单个视觉区域中注意力调制的时间。

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