Wang Xu, Song Yiying, Zhen Zonglei, Liu Jia
State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China.
School of Psychology, Beijing Normal University, Beijing, 100875, China.
Hum Brain Mapp. 2016 May;37(5):1930-40. doi: 10.1002/hbm.23145. Epub 2016 Feb 25.
Face perception is essential for daily and social activities. Neuroimaging studies have revealed a distributed face network (FN) consisting of multiple regions that exhibit preferential responses to invariant or changeable facial information. However, our understanding about how these regions work collaboratively to facilitate facial information processing is limited. Here, we focused on changeable facial information processing, and investigated how the functional integration of the FN is related to the performance of facial expression recognition. To do so, we first defined the FN as voxels that responded more strongly to faces than objects, and then used a voxel-based global brain connectivity method based on resting-state fMRI to characterize the within-network connectivity (WNC) of each voxel in the FN. By relating the WNC and performance in the "Reading the Mind in the Eyes" Test across participants, we found that individuals with stronger WNC in the right posterior superior temporal sulcus (rpSTS) were better at recognizing facial expressions. Further, the resting-state functional connectivity (FC) between the rpSTS and right occipital face area (rOFA), early visual cortex (EVC), and bilateral STS were positively correlated with the ability of facial expression recognition, and the FCs of EVC-pSTS and OFA-pSTS contributed independently to facial expression recognition. In short, our study highlights the behavioral significance of intrinsic functional integration of the FN in facial expression processing, and provides evidence for the hub-like role of the rpSTS for facial expression recognition. Hum Brain Mapp 37:1930-1940, 2016. © 2016 Wiley Periodicals, Inc.
面部感知对于日常和社交活动至关重要。神经影像学研究揭示了一个分布式面部网络（FN），它由多个对不变或可变面部信息表现出优先反应的区域组成。然而，我们对于这些区域如何协同工作以促进面部信息处理的理解是有限的。在此，我们聚焦于可变面部信息处理，并研究了FN的功能整合与面部表情识别表现之间的关系。为此，我们首先将FN定义为对面部反应比对物体反应更强的体素，然后使用基于静息态功能磁共振成像的基于体素的全脑连接方法来表征FN中每个体素的网络内连接性（WNC）。通过将参与者在“读心术（眼睛）测试”中的WNC与表现相关联，我们发现右侧后颞上沟（rpSTS）中WNC较强的个体在识别面部表情方面表现更好。此外，rpSTS与右侧枕叶面部区域（rOFA）、早期视觉皮层（EVC）以及双侧STS之间的静息态功能连接（FC）与面部表情识别能力呈正相关，并且EVC-pSTS和OFA-pSTS的FC对面部表情识别有独立贡献。简而言之，我们的研究突出了FN内在功能整合在面部表情处理中的行为意义，并为rpSTS在面部表情识别中的枢纽样作用提供了证据。《人类大脑图谱》37:1930 - 1940, 2016。© 2016威利期刊公司。
