Topology-preserving smoothing of retinotopic maps.
Topology-preserving smoothing of retinotopic maps.
复制标题
视网膜映射的拓扑保持光滑化。
DOI：
10.1371/journal.pcbi.1009216
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发表时间：
2021-08
影响因子：
4.3
通讯作者：
Wang Y
中科院分区：
文献类型：
--
作者：
Tu Y;Ta D;Lu ZL;Wang Y
视网膜定位，即，视网膜上的视觉输入与皮质视觉区神经元激活之间的映射是视觉神经科学的中心课题之一。对于人类观察者，映射是通过分析功能磁共振成像（fMRI）信号的皮质反应，以缓慢移动的视觉刺激在视网膜上。虽然从神经生理学中众所周知映射是拓扑的（即，邻域连通性的拓扑结构被保留）在每个视觉区域内，由于fMRI的低信噪比和空间分辨率，从现有技术方法导出的视网膜拓扑图通常不是拓扑图。拓扑条件的违反在对应于中央凹的邻域的皮质区域中最严重（例如，在人类连接组计划（HCP）数据集中偏心度<1度），显著妨碍视网膜定位图的准确分析。本研究的目的是直接模拟拓扑条件，并产生拓扑保持和光滑的视网膜拓扑图。具体地说，我们采用Beltrami系数（一种拟共形映射的度量）定义拓扑条件，建立了一个数学模型，将拓扑光滑量化为一个约束优化问题，并阐述了一种有效的数值方法来解决该问题.然后将该方法同时应用于HCP数据集中的V1、V2和V3。仿真和真实的视网膜验光数据的实验表明，该方法可以生成拓扑和光滑的视网膜验光图。由于功能磁共振成像的低信噪比和空间分辨率，从最先进的方法获得的人类观察者的视网膜定位图通常不是拓扑的。所提出的拓扑平滑方法可以从视网膜功能磁共振成像数据中同时生成V1，V2和V3中的拓扑保持和平滑视网膜拓扑图。
Retinotopic mapping, i.e., the mapping between visual inputs on the retina and neuronal activations in cortical visual areas, is one of the central topics in visual neuroscience. For human observers, the mapping is obtained by analyzing functional magnetic resonance imaging (fMRI) signals of cortical responses to slowly moving visual stimuli on the retina. Although it is well known from neurophysiology that the mapping is topological (i.e., the topology of neighborhood connectivity is preserved) within each visual area, retinotopic maps derived from the state-of-the-art methods are often not topological because of the low signal-to-noise ratio and spatial resolution of fMRI. The violation of topological condition is most severe in cortical regions corresponding to the neighborhood of the fovea (e.g., < 1 degree eccentricity in the Human Connectome Project (HCP) dataset), significantly impeding accurate analysis of retinotopic maps. This study aims to directly model the topological condition and generate topology-preserving and smooth retinotopic maps. Specifically, we adopted the Beltrami coefficient, a metric of quasiconformal mapping, to define the topological condition, developed a mathematical model to quantify topological smoothing as a constrained optimization problem, and elaborated an efficient numerical method to solve the problem. The method was then applied to V1, V2, and V3 simultaneously in the HCP dataset. Experiments with both simulated and real retinotopy data demonstrated that the proposed method could generate topological and smooth retinotopic maps. Retinotopic maps of human observers derived from state-of-the-art methods are often not topological because of the low signal-to-noise ratio and spatial resolution of fMRI. The proposed topological smoothing method can generate topology-preserving and smooth retinotopic maps in V1, V2, and V3 simultaneously from retinotopy fMRI data.
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影响因子：
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作者：
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