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首页> 美国卫生研究院文献>Cognitive Neurodynamics >Decoding the different states of visual attention using functional and effective connectivity features in fMRI data
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Decoding the different states of visual attention using functional and effective connectivity features in fMRI data

机译：使用fMRI数据中的功能和有效连接功能解码视觉注意力的不同状态

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The present paper concentrates on the impact of visual attention task on structure of the brain functional and effective connectivity networks using coherence and Granger causality methods. Since most studies used correlation method and resting-state functional connectivity, the task-based approach was selected for this experiment to boost our knowledge of spatial and feature-based attention. In the present study, the whole brain was divided into 82 sub-regions based on Brodmann areas. The coherence and Granger causality were applied to construct functional and effective connectivity matrices. These matrices were converted into graphs using a threshold, and the graph theory measures were calculated from it including degree and characteristic path length. Visual attention was found to reveal more information during the spatial-based task. The degree was higher while performing a spatial-based task, whereas characteristic path length was lower in the spatial-based task in both functional and effective connectivity. Primary and secondary visual cortex (17 and 18 Brodmann areas) were highly connected to parietal and prefrontal cortex while doing visual attention task. Whole brain connectivity was also calculated in both functional and effective connectivity. Our results reveal that Brodmann areas of 17, 18, 19, 46, 3 and 4 had a significant role proving that somatosensory, parietal and prefrontal regions along with visual cortex were highly connected to other parts of the cortex during the visual attention task. Characteristic path length results indicated an increase in functional connectivity and more functional integration in spatial-based attention compared with feature-based attention. The results of this work can provide useful information about the mechanism of visual attention at the network level.
机译：本文着重研究视觉注意任务对大脑功能和有效连接网络结构的影响，采用相干和格兰杰因果关系方法。由于大多数研究都使用了相关方法和静止状态功能连接，因此本实验选择了基于任务的方法，以增强我们对空间和基于特征的注意力的认识。在本研究中，根据布罗德曼地区将整个大脑分为82个子区域。相干性和格兰杰因果关系被用于构建功能和有效的连通性矩阵。使用阈值将这些矩阵转换为图形，并根据其计算图形理论度量，包括程度和特征路径长度。发现视觉注意力在基于空间的任务期间揭示了更多信息。在执行基于空间的任务时，该程度较高，而在基于功能的任务和有效连通性方面，基于空间的任务的特征路径长度较低。初级和次级视觉皮层（17和18个Brodmann区域）在执行视觉注意任务时与顶叶和前额叶皮层高度相关。在功能和有效连接性方面也计算了全脑连接性。我们的研究结果表明，布罗德曼地区的17、18、19、46、3和4具有重要作用，证明在视觉注意任务中，体感，顶叶和额叶前部区域以及视觉皮层与皮质的其他部分高度连接。特征路径长度结果表明，与基于功能的注意相比，基于空间的注意增加了功能连接性，并实现了更多的功能集成。这项工作的结果可以提供有关网络级别的视觉注意力机制的有用信息。

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