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基于视觉任务的脑功能网络连通性:与视觉信息处理相关的脑区在任务状态下被显著激活。

Brain functional network connectivity based on a visual task: visual information processing-related brain regions are significantly activated in the task state.

作者信息

Yang Yan-Li, Deng Hong-Xia, Xing Gui-Yang, Xia Xiao-Luan, Li Hai-Fang

机构信息

School of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, Shanxi Province, China.

出版信息

Neural Regen Res. 2015 Feb;10(2):298-307. doi: 10.4103/1673-5374.152386.

Abstract

It is not clear whether the method used in functional brain-network related research can be applied to explore the feature binding mechanism of visual perception. In this study, we investigated feature binding of color and shape in visual perception. Functional magnetic resonance imaging data were collected from 38 healthy volunteers at rest and while performing a visual perception task to construct brain networks active during resting and task states. Results showed that brain regions involved in visual information processing were obviously activated during the task. The components were partitioned using a greedy algorithm, indicating the visual network existed during the resting state. Z-values in the vision-related brain regions were calculated, confirming the dynamic balance of the brain network. Connectivity between brain regions was determined, and the result showed that occipital and lingual gyri were stable brain regions in the visual system network, the parietal lobe played a very important role in the binding process of color features and shape features, and the fusiform and inferior temporal gyri were crucial for processing color and shape information. Experimental findings indicate that understanding visual feature binding and cognitive processes will help establish computational models of vision, improve image recognition technology, and provide a new theoretical mechanism for feature binding in visual perception.

摘要

目前尚不清楚功能脑网络相关研究中所使用的方法是否可用于探索视觉感知的特征捆绑机制。在本研究中,我们调查了视觉感知中颜色和形状的特征捆绑。从38名健康志愿者在静息状态和执行视觉感知任务时收集功能磁共振成像数据,以构建静息和任务状态下活跃的脑网络。结果表明,参与视觉信息处理的脑区在任务期间明显被激活。使用贪婪算法对组件进行划分,表明视觉网络在静息状态下存在。计算了与视觉相关脑区的Z值,证实了脑网络的动态平衡。确定了脑区之间的连通性,结果表明枕叶和舌回是视觉系统网络中稳定的脑区,顶叶在颜色特征和形状特征的捆绑过程中起着非常重要的作用,梭状回和颞下回对于处理颜色和形状信息至关重要。实验结果表明,了解视觉特征捆绑和认知过程将有助于建立视觉计算模型,改进图像识别技术,并为视觉感知中的特征捆绑提供新的理论机制。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b95/4392680/2b33534cf6e2/NRR-10-298-g011.jpg

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