Improved method for retinotopy constrained source estimation of visual-evoked responses
Improved method for retinotopy constrained source estimation of visual-evoked responses
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DOI：
10.1002/hbm.21461
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发表时间：
2013-03-01
影响因子：
4.8
通讯作者：
Dale, Anders M.
中科院分区：
文献类型：
--
作者：
Hagler, Donald J., Jr.;Dale, Anders M.
视网膜约束源估计（RCSE）是一种利用脑磁图（MEG）或脑电图（EEG）无创测量早期视觉区激活时程的方法。与传统的等效电流偶极或分布式源模型不同，使用多个视网膜定位映射的刺激位置来同时约束解决方案允许估计视觉区域V1、V2和V3的独立波形，尽管它们彼此非常接近。我们描述的修改，提高这种方法的可靠性和效率。首先，我们发现，增加视觉刺激的数量和大小的结果在源估计，是不太容易受到噪音。其次，为了创建更准确的正向解，我们明确地对个体视觉刺激的皮层点扩散进行了建模。偶极子被表示为皮层表面上的扩展补丁，它考虑到在V1，V2和V3中的每个位置处的估计的感受野大小，以及来自视觉区的对侧，同侧，背侧和腹侧部分的贡献。第三，我们实现了一个地图拟合程序变形模板，以匹配来自功能性磁共振成像（fMRI）的个别受试者视网膜定位图。这通过允许自动偶极子选择来提高整个方法的效率，并且它使得结果对fMRI视网膜病变数据中的生理噪声不太敏感。最后，采用迭代重新加权最小二乘（IRLS）方法来减少高残留误差刺激位置的贡献，以实现视觉诱发反应的鲁棒估计。^Brain Mapp，2013. (c)2011 Wiley Periodicals，Inc.
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. Hum Brain Mapp, 2013. (c) 2011 Wiley Periodicals, Inc.