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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不同状态的视觉注意力

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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：本文专注于使用连贯和格兰杰因果关系方法对脑功能和有效连通网络结构的影响。由于大多数研究使用了相关方法和休息状态功能连接，因此选择了基于任务的方法，为此实验提升了我们对基于空间和特征的注意的了解。在本研究中，全部大脑基于Brodmann地区分为82个子区域。相干和格兰杰因果关系被应用于构建功能和有效的连通基质。使用阈值将这些矩阵转换为曲线图，并且从其包括度和特征路径长度来计算图形理论措施。发现视觉关注在基于空间的任务期间揭示更多信息。在执行基于空间的任务的同时，该程度越高，而在功能性和有效的连接中，在基于空间的任务中的特征路径长度较低。初级和次级视觉皮质（17和18 Brodmann地区）高度连接到顶视和前额叶皮质，同时进行视觉关注任务。整个脑连接也在功能性和有效的连接中计算。我们的研究结果表明，17,18,19,46,3和4的Brodmann地区证明躯体感觉，前额平均区域以及可视皮质的躯体传感器，在视觉注意事项期间高度连接到皮质的其他部分。特征路径长度结果表明，与基于特征的注意力相比，基于空间的关注的功能连接和更功能集成的增加。这项工作的结果可以提供有关网络级别的视觉注意机制的有用信息。

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