Disparity level identification using the voxel-wise Gabor model of fMRI data
Li, Yuan1; Hou, Chunping1; Yao, Li2,3,4; Zhang, Chuncheng5; Zheng, Hongna4; Zhang, Jiacai4; Long, Zhiying2,3
发表期刊HUMAN BRAIN MAPPING
ISSN1065-9471
2019-06-15
卷号40期号:9页码:2596-2610
通讯作者Long, Zhiying(friskying@163.com)
摘要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.
关键词disparity fMRI Gabor identify voxel-wise encoding model
DOI10.1002/hbm.24547
关键词[WOS]HORIZONTAL DISPARITY ; VISUAL-CORTEX ; 3D SHAPE ; FUNCTIONAL-ORGANIZATION ; BINOCULAR DISPARITY ; PARIETAL CORTEX ; DEPTH ; AREAS ; NEURONS ; REGIONS
收录类别SCI
语种英语
资助项目Key Program of National Natural Science Foundation of China[61731003] ; National Natural Science Foundation of China[61671067] ; National Natural Science Foundation of China[61471262] ; National Natural Science Foundation of China[61520106002] ; Fundamental Research Fund for the Central Universities[2017XTCX04] ; Interdiscipline Research Fund of Beijing Normal University ; Key Program of National Natural Science Foundation of China[61731003] ; National Natural Science Foundation of China[61671067] ; National Natural Science Foundation of China[61471262] ; National Natural Science Foundation of China[61520106002] ; Fundamental Research Fund for the Central Universities[2017XTCX04] ; Interdiscipline Research Fund of Beijing Normal University
项目资助者Key Program of National Natural Science Foundation of China ; National Natural Science Foundation of China ; Fundamental Research Fund for the Central Universities ; Interdiscipline Research Fund of Beijing Normal University
WOS研究方向Neurosciences & Neurology ; Radiology, Nuclear Medicine & Medical Imaging
WOS类目Neurosciences ; Neuroimaging ; Radiology, Nuclear Medicine & Medical Imaging
WOS记录号WOS:000467570300004
出版者WILEY
引用统计
被引频次:5[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/24578
专题脑图谱与类脑智能实验室_神经计算与脑机交互
通讯作者Long, Zhiying
作者单位1.Tianjin Univ, Sch Elect & Informat Engn, Tianjin, Peoples R China
2.Beijing Normal Univ, State Key Lab Cognit Neurosci & Learning, Beijing, Peoples R China
3.Beijing Normal Univ, IDG McGovern Inst Brain Res, Beijing, Peoples R China
4.Beijing Normal Univ, Coll Informat Sci & Technol, Beijing, Peoples R China
5.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
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GB/T 7714
Li, Yuan,Hou, Chunping,Yao, Li,et al. Disparity level identification using the voxel-wise Gabor model of fMRI data[J]. HUMAN BRAIN MAPPING,2019,40(9):2596-2610.
APA Li, Yuan.,Hou, Chunping.,Yao, Li.,Zhang, Chuncheng.,Zheng, Hongna.,...&Long, Zhiying.(2019).Disparity level identification using the voxel-wise Gabor model of fMRI data.HUMAN BRAIN MAPPING,40(9),2596-2610.
MLA Li, Yuan,et al."Disparity level identification using the voxel-wise Gabor model of fMRI data".HUMAN BRAIN MAPPING 40.9(2019):2596-2610.
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