Disparity level identification using the voxel-wise Gabor model of fMRI data
Disparity level identification using the voxel-wise Gabor model of fMRI data
复制标题
使用 fMRI 数据的体素 Gabor 模型识别视差水平
DOI：
10.1002/hbm.24547
复制
发表时间：
2019
影响因子：
4.8
通讯作者：
Long Zhiying
中科院分区：
文献类型：
--
作者：
Li Yuan;Hou Chunping;Yao Li;Zhang Chuncheng;Zheng Hongna;Zhang Jiacai;Long Zhiying
感知差异是我们理解物理世界的直观基础。虽然许多电生理学研究已经揭示了猕猴大脑视觉区神经元的视差调节特性，但神经元群体对视差处理的反应很少被研究。许多使用功能性磁共振成像（fMRI）的视差研究揭示了人类大脑中的视差选择性视觉区域。然而，目前还不清楚如何使用fMRI技术来表征神经元群体差异调谐反应。在本研究中，我们构建了三个逐体素编码的Gabor模型来预测体素对新视差水平的响应，并使用解码方法从皮层中的群体响应中识别新视差水平。在三种编码模型中，使用细/粗视差来拟合体素对视差的响应的细-粗模型（FCM）优于单一模型和非交叉-交叉模型。此外，FCM在预测V3 A复合体中的体素反应和从V3 A复合体中的反应识别新差异方面表现出高准确性。我们的研究结果表明，FCM可以更好地表征体素的视差反应比其他两个模型和V3 A复合体是一个关键的视觉区域表示视差信息。
Perceiving disparities is the intuitive basis for our understanding of the physical world. Although many electrophysiology studies have revealed the disparity‐tuning characteristics of the neurons in the visual areas of the macaque brain, neuron population responses to disparity processing have seldom been investigated. Many disparity studies using functional magnetic resonance imaging (fMRI) have revealed the disparity‐selective visual areas in the human brain. However, it is unclear how to characterize neuron population disparity‐tuning responses using fMRI technique. In the present study, we constructed three voxel‐wise encoding Gabor models to predict the voxel responses to novel disparity levels and used a decoding method to identify the new disparity levels from population responses in the cortex. Among the three encoding models, the fine‐coarse model (FCM) that used fine/coarse disparities to fit the voxel responses to disparities outperformed the single model and uncrossed‐crossed model. Moreover, the FCM demonstrated high accuracy in predicting voxel responses in V3A complex and high accuracy in identifying novel disparities from responses in V3A complex. Our results suggest that the FCM can better characterize the voxel responses to disparities than the other two models and V3A complex is a critical visual area for representing disparity information.