Multimodal Imaging Laboratory, Department of Radiology, University of California, San Diego.
Hum Brain Mapp. 2013 Mar;34(3):665-83. doi: 10.1002/hbm.21461. Epub 2011 Nov 18.
Retinotopy constrained source estimation (RCSE) is a method for noninvasively measuring the time courses of activation in early visual areas using magnetoencephalography (MEG) or electroencephalography (EEG). Unlike conventional equivalent current dipole or distributed source models, the use of multiple, retinotopically mapped stimulus locations to simultaneously constrain the solutions allows for the estimation of independent waveforms for visual areas V1, V2, and V3, despite their close proximity to each other. We describe modifications that improve the reliability and efficiency of this method. First, we find that increasing the number and size of visual stimuli results in source estimates that are less susceptible to noise. Second, to create a more accurate forward solution, we have explicitly modeled the cortical point spread of individual visual stimuli. Dipoles are represented as extended patches on the cortical surface, which take into account the estimated receptive field size at each location in V1, V2, and V3 as well as the contributions from contralateral, ipsilateral, dorsal, and ventral portions of the visual areas. Third, we implemented a map fitting procedure to deform a template to match individual subject retinotopic maps derived from functional magnetic resonance imaging (fMRI). This improves the efficiency of the overall method by allowing automated dipole selection, and it makes the results less sensitive to physiological noise in fMRI retinotopy data. Finally, the iteratively reweighted least squares (IRLS) method was used to reduce the contribution from stimulus locations with high residual error for robust estimation of visual evoked responses.
视网膜约束源估计 (RCSE) 是一种使用脑磁图 (MEG) 或脑电图 (EEG) 无创测量早期视觉区域激活时间过程的方法。与传统的等效电流偶极子或分布式源模型不同,使用多个、视网膜映射的刺激位置同时约束解,可以估计视觉区域 V1、V2 和 V3 的独立波形,尽管它们彼此非常接近。我们描述了改进该方法可靠性和效率的修改。首先,我们发现增加视觉刺激的数量和大小会导致对噪声不太敏感的源估计。其次,为了创建更准确的正向解决方案,我们已经明确地对单个视觉刺激的皮质点扩展进行了建模。偶极子表示为皮质表面上的扩展补丁,它考虑了在 V1、V2 和 V3 中的每个位置的估计感受野大小,以及来自视觉区域的对侧、同侧、背侧和腹侧部分的贡献。第三,我们实施了地图拟合程序,将模板变形以匹配从功能磁共振成像 (fMRI) 得出的个体受试者的视网膜图。这通过允许自动偶极子选择提高了整体方法的效率,并使结果对 fMRI 视网膜图数据中的生理噪声不太敏感。最后,使用迭代重加权最小二乘法 (IRLS) 方法来减少具有高残余误差的刺激位置的贡献,以稳健地估计视觉诱发电响应。