A new method for estimating population receptive field topography in visual cortex
A new method for estimating population receptive field topography in visual cortex
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DOI：
10.1016/j.neuroimage.2013.05.026
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发表时间：
2013-11-01
期刊：
影响因子：
5.7
通讯作者：
Keliris, Georgios A.
中科院分区：
文献类型：
--
作者：
Lee, Sangkyun;Papanikolaou, Amalia;Keliris, Georgios A.
我们引入了一种利用功能磁共振成像 (fMRI) 测量视觉群体感受野 (pRF) 的新方法。 pRF 结构被建模为一组权重，可以通过求解线性模型来估计，该线性模型使用刺激协议和规范的血流动力学响应函数来预测血氧水平相关 (BOLD) 信号。该方法不会对特定的 pRF 形状做出先验假设，因此是一种以公正的方式揭示不同空间位置的底层 pRF 结构的有用工具。我们表明，我们的方法比之前描述的方法（Dumoulin 和 Wandell，2008）更准确，后者直接拟合二维各向同性高斯 pRF 模型来预测 fMRI 时间序列。我们证明直接拟合模型不能完全捕获实际的 pRF 形状，并且当 pRF 位于刺激空间边界附近时，可能容易出现 pRF 中心误定位。定量比较表明，我们的方法通过实现 BOLD 信号的更高解释方差，优于 pRF 中心建模中的直接拟合方法。对于直接拟合各向同性高斯模型、各向异性高斯模型和各向同性高斯模型的差异来说，情况都是如此。重要的是，我们的模型还能够探索各种 pRF 属性，例如环绕抑制、感受野中心伸长、方向、位置和大小。此外，所提出的方法对于监测视觉通路损伤的受试者视觉区域的 pRF 特性特别有吸引力，因为很难预测重组的 pRF 可能采取什么形状。最后，这里提出的方法比直接拟合方法在计算时间上更有效，直接拟合方法需要在极大的搜索空间中搜索一组参数。相反，该方法使用 pRF 拓扑来约束后续建模需要搜索的空间。 (C) 2013 Elsevier Inc. 保留所有权利。
We introduce a new method for measuring visual population receptive fields (pRF) with functional magnetic resonance imaging (fMRI). The pRF structure is modeled as a set of weights that can be estimated by solving a linear model that predicts the Blood Oxygen Level-Dependent (BOLD) signal using the stimulus protocol and the canonical hemodynamic response function. This method does not make a priori assumptions about the specific pRF shape and is therefore a useful tool for uncovering the underlying pRF structure at different spatial locations in an unbiased way. We show that our method is more accurate than a previously described method (Dumoulin and Wandell, 2008) which directly fits a 2-dimensional isotropic Gaussian pRF model to predict the fMRI time-series. We demonstrate that direct-fit models do not fully capture the actual pRF shape, and can be prone to pRF center mislocalization when the pRF is located near the border of the stimulus space. A quantitative comparison demonstrates that our method outperforms the direct-fit methods in the pRF center modeling by achieving higher explained variance of the BOLD signal. This was true for direct-fit isotropic Gaussian, anisotropic Gaussian, and difference of isotropic Gaussians model. Importantly, our model is also capable of exploring a variety of pRF properties such as surround suppression, receptive field center elongation, orientation, location and size. Additionally, the proposed method is particularly attractive for monitoring pRF properties in the visual areas of subjects with lesions of the visual pathways, where it is difficult to anticipate what shape the reorganized pRF might take. Finally, the method proposed here is more efficient in computation time than direct-fit methods, which need to search for a set of parameters in an extremely large searching space. Instead, this method uses the pRF topography to constrain the space that needs to be searched for the subsequent modeling. (C) 2013 Elsevier Inc. All rights reserved.