Petro Nathan M, Livermore Cooper L, Springer Seth D, Okelberry Hannah J, John Jason A, Glesinger Ryan, Horne Lucy K, Embury Christine M, Spooner Rachel K, Taylor Brittany K, Picci Giorgia, Wilson Tony W
Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE 68010, United States.
College of Medicine, University of Nebraska Medical Center, Omaha, NE 68198, United States.
Soc Cogn Affect Neurosci. 2025 May 21;20(1). doi: 10.1093/scan/nsaf047.
Facial expressions are ubiquitous and highly reliable social cues. Decades of research has shown that affective faces undergo facilitated processing across a distributed brain network. However, few studies have examined the multispectral brain dynamics underlying affective face processing, which is surprising given the multiple brain regions and rapid temporal dynamics thought to be involved. Herein, we used magnetoencephalography to derive dynamic functional maps of angry, neutral, and happy face processing in healthy adults. We found stronger theta oscillations shortly after the onset of affective relative to neutral faces (0-250 ms), within distributed ventral visual and parietal cortices, and the anterior hippocampus. Early gamma oscillations (100-275 ms) were strongest for angry faces in the inferior parietal lobule, temporoparietal junction, and presupplementary motor cortex. Finally, beta oscillations (175-575 ms) were stronger for neutral relative to affective expressions in the middle occipital and fusiform cortex. These results are consistent with the literature in regard to the critical brain regions, and delineate a distributed network where multispectral oscillatory dynamics support affective face processing through the rapid merging of low-level visual inputs to interpret the emotional meaning of each facial expression.
面部表情是无处不在且高度可靠的社会线索。数十年的研究表明，带有情感的面孔在一个分布式脑网络中会经历加速处理。然而，很少有研究考察过情感面孔处理背后的多光谱脑动力学，鉴于人们认为涉及多个脑区且时间动态变化迅速，这令人惊讶。在此，我们使用脑磁图来推导健康成年人中愤怒、中性和快乐面孔处理的动态功能图谱。我们发现，与中性面孔相比，在情感面孔出现后不久（0 - 250毫秒），在分布式腹侧视觉和顶叶皮层以及前海马体中，θ振荡更强。早期伽马振荡（100 - 275毫秒）在顶下小叶、颞顶交界区和辅助运动前皮层中对愤怒面孔最强。最后，在枕中皮层和梭状皮层中，与情感表情相比，中性表情的β振荡（175 - 575毫秒）更强。这些结果在关键脑区方面与文献一致，并描绘了一个分布式网络，其中多光谱振荡动力学通过快速合并低级视觉输入来支持情感面孔处理，以解读每个面部表情的情感意义。
