Humans use binocular disparity to extract depth information from two-dimensional retinal images in a process called stereopsis. Previous studies usually introduce the standard univariate analysis to describe the correlation between disparity level and brain activity within a given brain region based on functional magnetic resonance imaging (fMRI) data. Recently, multivariate pattern analysis has been developed to extract activity patterns across multiple voxels for deciphering categories of binocular disparity. However, the functional connectivity (FC) of patterns based on regions of interest or voxels and their mapping onto disparity category perception remain unknown. The present study extracted functional connectivity patterns for three disparity conditions (crossed disparity, uncrossed disparity, and zero disparity) at distinct spatial scales to decode the binocular disparity. Results of 27 subjects’ fMRI data demonstrate that FC features are more discriminatory than traditional voxel activity features in binocular disparity classification. The average binary classification of the whole brain and visual areas are respectively 87% and 79% at single subject level, and thus above the chance level (50%). Our research highlights the importance of exploring functional connectivity patterns to achieve a novel understanding of 3D image processing.

中文翻译：

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使用功能连接模式从fMRI数据解码3维图像中的视差类别

人类利用双眼视差从称为“立体视”的过程中从二维视网膜图像中提取深度信息。先前的研究通常引入标准单变量分析，以基于功能磁共振成像（fMRI）数据描述给定大脑区域内视差水平与大脑活动之间的相关性。最近，已经开发了多变量模式分析来提取跨多个体素的活动模式，以解密双目视差的类别。然而，基于关注区域或体素的模式的功能连接性（FC）及其在视差类别感知上的映射仍然未知。本研究提取了三种差异条件（交叉差异，非交叉差异，和零视差）在不同的空间尺度上解码双目视差。27位受试者的fMRI数据的结果表明，在双眼视差分类中，FC特征比传统体素活动特征更具歧视性。全脑和视觉区域的平均二值分类在单个受试者水平分别为87％和79％，因此高于机会水平（50％）。我们的研究强调了探索功能连接模式以实现对3D图像处理的新颖理解的重要性。全脑和视觉区域的平均二值分类在单个受试者水平分别为87％和79％，因此高于机会水平（50％）。我们的研究强调了探索功能连接模式以实现对3D图像处理的新颖理解的重要性。全脑和视觉区域的平均二值分类在单个受试者水平分别为87％和79％，因此高于机会水平（50％）。我们的研究强调了探索功能连接模式以实现对3D图像处理的新颖理解的重要性。