不同情绪载体的神经活动及其异同——脑成像研究的ALE元分析

doi:10.3724/SP.J.1042.2022.00536

摘要/Abstract

摘要：

情绪识别一直是学界关注的热点。虽然已有研究探讨了动态面孔表情、动态身体表情和声音情绪的脑机制, 但对每种情绪载体的整体认识相对不完善, 同时对不同情绪载体之间神经机制的共性和区别知之甚少。因此, 本研究首先通过三项独立的激活似然估计元分析来识别每种情绪模式的大脑激活区域, 然后进行对比分析以评估三种情绪载体之间共同的和独特的神经活动。结果显示, 动态面孔表情的大脑活动包括广泛的额叶、枕叶、颞叶和部分顶叶皮层以及海马、小脑、丘脑、杏仁核等皮层下区域; 动态身体表情的激活集中于颞/枕叶相关脑区以及小脑和海马; 声音情绪则引起了颞叶、额叶、杏仁核、尾状核和脑岛的激活。联合分析表明, 三种情绪载体跨模态激活了左侧颞中回和右侧颞上回。对比分析的结果证明了视觉刺激比听觉刺激更占优势, 动态面孔表情尤为突出, 同时动态身体表情也发挥着重要作用, 但声音情绪有其独特性。总之, 这些发现验证和拓展了三种情绪载体的现有神经模型, 揭示了情绪处理中心的、普遍性的区域, 但每种情绪载体又有自己可靠的特异性神经回路。

关键词: 情绪载体, 动态面孔表情, 动态身体表情, 声音情绪, ALE元分析

Abstract:

Emotion recognition has always been a hot topic in psychology. Although some studies have explored the brain mechanisms of dynamic facial expressions, dynamic bodily expressions and emotional voices, empirical studies have their own inevitable defects, which may lead to low statistical test power and effect size and inconsistent results. In addition, the existing meta-analyses of the three emotion carriers still have some deficiencies. Therefore, at present, the overall understanding of the three emotion carriers is relatively incomplete, and the commonness and differences of neural mechanisms among different emotion carriers were still poorly known. So, based on the background of high ecological validity, this study adopted the meta-analysis technique based on large-scale data synthesis method to overcome the above shortcomings. First, three separated activation likelihood estimation (ALE) meta-analyses were used to identify the brain regions activated by each emotion pattern, and then conjunction and contrast analysis of these activation maps were used to assess common and unique neural activity between the three emotion carriers. It is the first time that meta-analysis is used to explore the brain mechanism of dynamic bodily expressions, and it is also the first time that meta-analysis is used to explore the similarities and differences of neural activity among three emotion carriers: dynamic facial expressions, dynamic bodily expressions and emotional voices, and further improves the overall understanding of the neural mechanisms of dynamic facial expressions and emotional voices by previous meta-analyses. The results of single meta-analysis showed that the brain regions of dynamic facial expressions included superior frontal gyrus (SFG), middle frontal gyrus (MFG), inferior frontal gyrus (IFG), precentral gyrus (PG), inferior parietal lobule (IPL), middle occipital gyrus (MOG), inferior occipital gyrus (IOG), fusiform gyrus (FG), superior temporal gyrus (STG), middle temporal gyrus (MTG), inferior temporal gyrus (ITG), parahippocampal gyrus (PHG), cerebellum, amygdala, lentiform nucleus (LN) and insula. Dynamic bodily expressions caused activation of the middle occipital gyrus, inferior occipital gyrus, fusiform gyrus, superior temporal gyrus, middle temporal gyrus, inferior temporal gyrus, cuneus, lingual gyrus (LING), cerebellum, and parahippocampal gyrus. The activation of emotional voices was concentrated in the middle frontal gyrus, inferior frontal gyrus, precentral gyrus, superior temporal gyrus, middle temporal gyrus, heschl’s gyrus (HG), insula, amygdala and caudate nucleus (CN). Conjunction analysis suggested that the left middle temporal gyrus and the right superior temporal gyrus were activated by three emotion carrier across the modalities. The results of the contrast analysis proved that the visual stimuli was more advantaged than the auditory stimuli, especially the dynamic facial expressions, the dynamic bodily expressions also played an important role. However, the emotional voices had their own uniqueness. In sum, these findings validate, support, and extend the existing neural models of the three emotion carriers, revealing a central, universal region of the emotional processing, but with each emotion carrier relying on its own reliable specific neural circuits. This study provides consistent results across studies for researchers of emotional problems, and representative reference coordinate points for future region of interest (ROI) analysis, which is conducive to propose and test hypotheses of future researches, and also is conducive to the identification and neural regulation of patients with emotional disorders. Future researches should further validate and extend these findings to explore the neural mechanisms of emotional processing at different ages and their similarities and differences. In addition, it is necessary to study the brain mechanism of each emotion type and the similarity and difference of neural activity of each emotion in different carriers in the case of sufficient data. Examining the connection of different brain regions, and the different functions of a brain region also is necessary. Meanwhile, it is essential to focus on the neural basis of dynamic bodily expressions.

Key words: emotion carriers, dynamic facial expressions, dynamic bodily expressions, emotional voices, ALE meta-analysis

中图分类号:

B842

刘俊材, 冉光明, 张琪. (2022). 不同情绪载体的神经活动及其异同——脑成像研究的ALE元分析. 心理科学进展 , 30(3), 536-555.

LIU Juncai, RAN Guangming, ZHANG Qi. (2022). The neural activities of different emotion carriers and their similarities and differences: A meta-analysis of functional neuroimaging studies. Advances in Psychological Science, 30(3), 536-555.

图/表 6

参考文献 100

	注: *表示该文献被用于元分析, 剩余的元分析文献详见网络版附录。
[1]	曹林敬. (2018). 基于视觉和听觉激活模式的跨模态情绪表征机制研究 (硕士学位论文). 天津大学.
[2]	彭聃龄. (主编). (2019). 普通心理学.北京师范大学出版社.
[3]	张帆. (2018). 视觉和听觉通道情绪知觉神经机制的异同 (硕士学位论文). 西南大学,重庆.
[4]	张琪, 尹天子, 冉光明. (2015). 动态面孔表情优势效应的心理机制及神经基础. 心理科学进展, 23(9), 1514-1522.
[5]	周临舒, 赵怀阳, 蒋存梅. (2017). 音乐表演训练对神经可塑性的影响:元分析研究. 心理科学进展, 25(11), 1877-1887.
[6]	Alaerts, K., Woolley, D. G., Steyaert, J., Di Martino, A., Swinnen, S. P., & Wenderoth, N. (2013). Underconnectivity of the superior temporal sulcus predicts emotion recognition deficits in autism. Social Cognitive and Affective Neuroscience, 9(10), 1589-1600. doi: 10.1093/scan/nst156 URL
[7]	* Alba-Ferrara, L., Hausmann, M., Mitchell, R. L., & Weis, S. (2011). The neural correlates of emotional prosody comprehension: Disentangling simple from complex emotion. PLoS ONE, 6(12), e28701. https://doi.org/10.1371/journal.pone.0028701 doi: 10.1371/journal.pone.0028701 URL
[8]	* Arsalidou, M., Morris, D., & Taylor, M. J. (2011). Converging evidence for the advantage of dynamic facial expressions. Brain Topography, 24(2), 149-163. doi: 10.1007/s10548-011-0171-4 pmid: 21350872
[9]	Arsalidou, M., Vijayarajah, S., & Sharaev, M. (2020). Basal ganglia lateralization in different types of reward. Brain Imaging and Behavior, 14(6), 2618-2646. doi: 10.1007/s11682-019-00215-3 URL
[10]	Atkinson, A. P., Tunstall, M. L. & Dittrich, W. H. (2007). Evidence for distinct contributions of form and motion information to the recognition of emotions from body gestures. Cognition, 104(1), 59-72. pmid: 16831411
[11]	Atkinson, A. P., Vuong, Q. C., & Smithson, H. E. (2012). Modulation of the face- and body-selective visual regions by the motion and emotion of point-light face and body stimuli. NeuroImage, 59(2), 1700-1712. doi: 10.1016/j.neuroimage.2011.08.073 pmid: 21924368
[12]	* Aubé, W., Angulo-Perkins, A., Peretz, I., Concha, L., & Armony, J. L. (2015). Fear across the senses: Brain responses to music, vocalizations and facial expressions. Social Cognitive and Affective Neuroscience, 10(3), 399-407. doi: 10.1093/scan/nsu067 URL
[13]	Belin, P., Fecteau, S., & Bédard, C. (2004). Thinking the voice: Neural correlates of voice perception. Trends in Cognitive Sciences, 8(3), 129-135. doi: 10.1016/j.tics.2004.01.008 URL
[14]	Bestelmeyer, P. E. G., Maurage, P., Rouger, J., Latinus, M., & Belin, P. (2014). Adaptation to vocal expressions reveals multistep perception of auditory emotion. Journal of Neuroscience, 34(24), 8098-8105. doi: 10.1523/JNEUROSCI.4820-13.2014 pmid: 24920615
[15]	Binder, J. R., Frost, J. A., Hammeke, T. A., Cox, R. W., Rao, S. M., & Prieto, T. (1997). Human brain language areas identified by functional magnetic resonance imaging. Journal of Neuroscience, 17(1), 353-362. pmid: 8987760
[16]	* Brattico, E., Alluri, V., Bogert, B., Jacobsen, T., Vartiainen, N., Nieminen, S., & Tervaniemi, M. (2011). A functional MRI study of happy and sad emotions in music with and without lyrics. Frontiers in Psychology, 2, 308. https://doi.org/10.3389/fpsyg.2011.00308 doi: 10.3389/fpsyg.2011.00308 URL pmid: 22144968
[17]	Bukowski, H., & Lamm, C. (2018). Superior temporal sulcus. In V. Zeigler-Hill & T. K. Shackelford (Eds.), Encyclopedia of personality and individual differences (pp.1-5). Springer International Publishing.
[18]	Calvert, G. A., Brammer, M. J., & Iversen, S. D. (1998). Crossmodal identification. Trends in Cognitive Sciences, 2(7), 247-253. doi: 10.1016/S1364-6613(98)01189-9 pmid: 21244923
[19]	* Ceravolo, L. (2013). Neuroimaging and spatio-temporal dynamics of the localization and spatial attentional modulation by angry prosody (Unpublished doctorial dissertation). Université de Genève.
[20]	Christoff, K., & Gabrieli, J. D. E. (2000). The frontopolar cortex and human cognition: Evidence for a rostrocaudal hierarchical organization within the human prefrontal cortex. Psychobiology, 28(2), 168-186. doi: 10.3758/BF03331976 URL
[21]	de Gelder, B. (2006). Towards the neurobiology of emotional body language. Nature Reviews Neuroscience, 7, 242-249. doi: 10.1038/nrn1872 URL
[22]	de Gelder, B. (2009). Why bodies? Twelve reasons for including bodily expressions in affective neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences, 364(1535), 3475-3484. doi: 10.1098/rstb.2009.0190 URL
[23]	de Gelder, B. (2015). Emotions and the body. Oxford University Press.
[24]	de Gelder, B., de Borst, A. W., & Watson, R. (2014). The perception of emotion in body expressions. Wiley Interdisciplinary Reviews: Cognitive Science, 6(2), 149-158. doi: 10.1002/wcs.1335 URL
[25]	de Gelder, B., Meeren, H. K., Righart, R., van den Stock, J., van de Riet, W. A., & Tamietto, M. (2006). Beyond the face: Exploring rapid influences of context on face processing. Progress in Brain Research, 155, 37-48.
[26]	de Gelder, B., & Partan, S. (2009). The neural basis of perceiving emotional bodily expressions in monkeys. Neuroreport, 20(7), 642-646. doi: 10.1097/WNR.0b013e32832a1e56 URL
[27]	Downing, P. E., Jiang, Y., Shuman, M., & Kanwisher, N. (2001). A cortical area selective for visual processing of the human body. Science, 293(5539), 2470-2473. pmid: 11577239
[28]	Dricu, M., & Frühholz, S. (2016). Perceiving emotional expressions in others: Activation likelihood estimation meta-analyses of explicit evaluation, passive perception and incidental perception of emotions. Neuroscience & Biobehavioral Reviews, 71, 810-828. doi: 10.1016/j.neubiorev.2016.10.020 URL
[29]	Eickhoff, S. B., Laird, A. R., Fox, P. M., Lancaster, J. L., & Fox, P. T. (2017). Implementation errors in the GingerALE software: Description and recommendations. Human Brain Mapping, 38(1), 7-11. doi: 10.1002/hbm.23342 pmid: 27511454
[30]	Eickhoff, S. B., Laird, A. R., Grefkes, C., Wang, L. E., Zilles, K., & Fox, P. T. (2009). Coordinate-based activation likelihood estimation metaanalysis of neuroimaging data: A random- effects approach based on empirical estimates of spatial uncertainty. Human Brain Mapping, 30(9), 2907-2926. doi: 10.1002/hbm.20718 pmid: 19172646
[31]	* Fecteau, S., Belin, P., Joanette, Y., & Armony, J. L. (2007). Amygdala responses to nonlinguistic emotional vocalizations. Neuroimage, 36(2), 480-487. doi: 10.1016/j.neuroimage.2007.02.043 URL
[32]	* Foley, E., Rippon, G., Thai, N. J., Longe, O., & Senior, C. (2012). Dynamic facial expressions evoke distinct activation in the face perception network: A connectivity analysis study. Journal of Cognitive Neuroscience, 24(2), 507-520. doi: 10.1162/jocn_a_00120 URL
[33]	Friederici, A. D., & Alter, K. (2004). Lateralization of auditory language functions: A dynamic dual pathway model. Brain and Language, 89(2), 267-276. pmid: 15068909
[34]	Frühholz, S., & Grandjean, D. (2013). Multiple subregions in superior temporal cortex are differentially sensitive to vocal expressions: A quantitative meta-analysis. Neuroence & Biobehavioral Reviews, 37(1), 24-35.
[35]	Frühholz, S., Trost, W., & Kotz, S. A. (2016). The sound of emotions-towards a unifying neural network perspective of affective sound processing. Neuroscience & Biobehavioral Reviews, 68, 96-110. doi: 10.1016/j.neubiorev.2016.05.002 URL
[36]	Giese, M. A., & Poggio, T. (2003). Neural mechanisms for the recognition of biological movements. Nature Reviews Neuroscience, 4(3), 179-192. doi: 10.1038/nrn1057 URL
[37]	Goldberg, H., Christensen, A., Flash, T., Giese, M. A. & Malach, R. (2015). Brain activity correlates with emotional perception induced by dynamic avatars. NeuroImage, 122, 306-317. doi: 10.1016/j.neuroimage.2015.07.056 pmid: 26220746
[38]	* Grèzes, J., Pichon, S., & de Gelder, B. (2007). Perceiving fear in dynamic body expressions. NeuroImage, 35(2), 959-967. pmid: 17270466
[39]	Hasan, B. A. S., Valdes-Sosa, M., Gross, J., & Belin, P. (2016). “Hearing faces and seeing voices”: Amodal coding of person identity in the human brain. Scientific Reports, 6(1), 1-8. doi: 10.1038/s41598-016-0001-8 URL
[40]	Haxby, J. V., Hoffman, E. A., & Gobbini, M. I. (2000). The distributed human neural system for face perception. Trends in Cognitive Sciences, 4(6), 223-233. pmid: 10827445
[41]	Heberlein, A. S., & Atkinson, A. P. (2009). Neuroscientific evidence for simulation and shared substrates in emotion recognition: Beyond faces. Emotion Review, 1(2), 162-177. doi: 10.1177/1754073908100441 URL
[42]	Hickok, G., & Poeppel, D. (2007). The cortical organization of speech processing. Nature Reviews Neuroscience, 8(5), 393-402. doi: 10.1038/nrn2113 URL
[43]	Hodzic, A., Kaas, A., Muckli, L., Stirn, A., & Singer, W. (2009). Distinct cortical networks for the detection and identification of human body. NeuroImage, 45(4), 1264-1271. doi: 10.1016/j.neuroimage.2009.01.027 pmid: 19349239
[44]	Iacoboni, M., Woods, R. P., Brass, M., Bekkering, H., Mazziotta, J. C., & Rizzolatti, G. (1999). Cortical mechanisms of human imitation. Science, 286(5449), 2526-2528. pmid: 10617472
[45]	* Jastorff, J., Huang, Y. A., Giese, M. A., & Vandenbulcke, M. (2015). Common neural correlates of emotion perception in humans. Human Brain Mapping, 36(10), 4184-4201. doi: 10.1002/hbm.22910 pmid: 26219630
[46]	* Kilts, C. D., Egan, G., Gideon, D. A., Ely, T. D., & Hoffman, J. M. (2003). Dissociable neural pathways are involved in the recognition of emotion in static and dynamic facial expressions. NeuroImage, 18(1), 156-168. doi: 10.1006/nimg.2002.1323 URL
[47]	Klapper, A., Dotsch, R., van Rooij, I., & Wigboldus, D. H. J. (2016). Do we spontaneously form stable trustworthiness impressions from facial appearance? Journal of Personality and Social Psychology, 111(5), 655-664. pmid: 27762574
[48]	Krauss, R. M., Freyberg, R., & Morsella, E. (2002). Inferring speakers’ physical attributes from their voices. Journal of Experimental Social Psychology, 38(6), 618-625. doi: 10.1016/S0022-1031(02)00510-3 URL
[49]	Kret, M. E., Pichon, S., Grèzes, J., & de Gelder, B. (2011). Similarities and differences in perceiving threat from dynamic faces and bodies. An fMRI study. NeuroImage, 54(2), 1755-1762. doi: 10.1016/j.neuroimage.2010.08.012 pmid: 20723605
[50]	Kriegstein, K. V., & Giraud, A. L. (2004). Distinct functional substrates along the right superior temporal sulcus for the processing of voices. Neuroimage, 22(2), 948-955. pmid: 15193626
[51]	* Leitman, D. I., Wolf, D. H., Ragland, J. D., Laukka, P., Loughead, J., Valdez, J. N., ... Gur, R. C. (2010). “It’s not what you say, but how you say it”: A reciprocal temporo-frontal network for affective prosody. Frontiers in Human Neuroscience, 4, 19. https://doi.org/10.3389/fnhum.2010.00019 doi: 10.3389/fnhum.2010.00019 URL pmid: 20204074
[52]	Lin, H., Müller-Bardorff, M., Gathmann, B., Brieke, J., Mothes-Lasch, M., Bruchmann, M., ... Straube, T. (2020). Stimulus arousal drives amygdalar responses to emotional expressions across sensory modalities. Scientific Reports, 10(1), 1898. doi: 10.1038/s41598-020-58839-1 URL
[53]	Magnée, M. J., Stekelenburg, J. J., Kemner, C., & de Gelder, B. (2007). Similar facial electromyographic responses to faces, voices, and body expressions. Neuroreport, 18(4), 369-372. doi: 10.1097/WNR.0b013e32801776e6 URL
[54]	Maguinness, C., Roswandowitz, C., & von Kriegstein, K. (2018). Understanding the mechanisms of familiar voiceidentity recognition in the human brain. Neuropsychologia, 116, 179-193. doi: S0028-3932(18)30140-4 pmid: 29614253
[55]	Mier, D., Eisenacher, S., Rausch, F., Englisch, S., Gerchen, M. F., Zamoscik, V., ... Kirsch, P. (2016). Aberrant activity and connectivity of the posterior superior temporal sulcus during social cognition in schizophrenia. European Archives of Psychiatry and Clinical Neuroscience, 267(7), 597-610. doi: 10.1007/s00406-016-0737-y URL
[56]	* Mitterschiffthaler, M. T., Fu, C. H. Y., Dalton, J. A., Andrew, C. M., & Williams, S. C. R. (2007). A functional MRI study of happy and sad affective states induced by classical music. Human Brain Mapping, 28(11), 1150-1162. pmid: 17290372
[57]	Moro, V., Urgesi, C., Pernigo, S., Lanteri, P., Pazzaglia, M., & Aglioti, S. M. (2008). The neural basis of body form and body action agnosia. Neuron, 60(2), 235-246. doi: 10.1016/j.neuron.2008.09.022 URL
[58]	Morrison, I. (2016). ALE meta-analysis reveals dissociable networks for affective and discriminative aspects of touch. Human Brain Mapping, 37(4), 1308-1320. doi: 10.1002/hbm.23103 pmid: 26873519
[59]	* Numminen-Kontti, T. (2014). Personality affects musical emotion processing: An fMRI-study (Unpublished master’s thesis). University of Helsinki.
[60]	Olson, I. R., Plotzker, A., & Ezzyat, Y. (2007). The enigmatic temporal pole: A review of findings on social and emotional processing. Brain, 130(7), 1718-1731. doi: 10.1093/brain/awm052 URL
[61]	* Peelen, M. V., Atkinson, A. P., & Vuilleumier, P. (2010). Supramodal representations of perceived emotions in the human brain. Journal of Neuroscience, 30(30), 10127-10134. doi: 10.1523/JNEUROSCI.2161-10.2010 URL
[62]	Pelphrey, K. A., Morris, J. P., Michelich, C. R., Allison, T., & McCarthy, G. (2005). Functional anatomy of biological motion perception in posterior temporal cortex: An fMRI study of eye, mouth and hand movements. Cerebral Cortex, 15(12), 1866-1876. pmid: 15746001
[63]	Pisanski, K., Cartei, V., Mcgettigan, C., Raine, J., & Reby, D. (2016). Voice modulation: A window into the origins of human vocal control? Trends in Cognitive Sciences, 20, 304-318. doi: 10.1016/j.tics.2016.01.002 URL
[64]	* Poyo Solanas, M., Vaessen, M., & de Gelder, B. (2020). Computation-based feature representation of body expressions in the human brain. Cereb Cortex, 30(12), 6376-6390. doi: 10.1093/cercor/bhaa196 pmid: 32770200
[65]	Ran, G., & Zhang, Q. (2018). The neural correlates of attachment style during emotional processing: An activation likelihood estimation meta-analysis. Attachment & Human Development, 20(6), 626-633.
[66]	* Regenbogen, C., Schneider, D. A., Gur, R. E., Schneider, F., Habel, U., & Kellermann, T. (2012). Multimodal human communication-targeting facial expressions, speech content and prosody. NeuroImage, 60(4), 2346-2356. doi: 10.1016/j.neuroimage.2012.02.043 pmid: 22487549
[67]	Ritchie, K. L., Palermo, R., & Rhodes, G. (2017). Forming impressions of facial attractiveness is mandatory. Scientific Reports, 7(1), 1-8. doi: 10.1038/s41598-016-0028-x URL
[68]	Rizzolatti, G., & Craighero, L. (2004). The mirror-neuron system. Annual Review of Neuroscience, 27(1), 169-192. doi: 10.1146/neuro.2004.27.issue-1 URL
[69]	Roswandowitz, C., Schelinski, S., & von Kriegstein, K. (2017). Developmental phonagnosia: Linking neural mechanisms with the behavioural phenotype. Neuroimage, 155, 97-112. doi: S1053-8119(17)30172-6 pmid: 28254454
[70]	Sarkheil, P., Goebel, R., Schneider, F., & Mathiak, K. (2013). Emotion unfolded by motion: A role for parietal lobe in decoding dynamic facial expressions. Social Cognitive and Affective Neuroscience, 8(8), 950-957. doi: 10.1093/scan/nss092 pmid: 22962061
[71]	* Sato, W., Kochiyama, T., & Yoshikawa, S. (2010). Amygdala activity in response to forward versus backward dynamic facial expressions. Brain Research, 1315, 92-99. doi: 10.1016/j.brainres.2009.12.003 URL
[72]	* Sato, W., Toichi, M., Uono, S., & Kochiyama, T. (2012). Impaired social brain network for processing dynamic facial expressions in autism spectrum disorders. BMC Neuroscience, 13(1), 1-17. doi: 10.1186/1471-2202-13-1 URL
[73]	Scherer, K. R. (1995). Expression of emotion in voice and music. Journal of Voice, 9(3), 235-248. pmid: 8541967
[74]	Scherer, K. R., Johnstone, T., & Klasmeyer, G. (2003). Vocal expression of emotion. In R. J. Davidson, H. H. Goldsmith & K. Scherer (Eds.), Handbook of the affective sciences (pp.433-456). Oxford University Press.
[75]	Schirmer, A. (2018). Is the voice an auditory face? An ALE meta-analysis comparing vocal and facial emotion processing. Social Cognitive and Affective Neuroscience, 13(1), 1-13. doi: 10.1093/scan/nsx142 pmid: 29186621
[76]	Schirmer, A., & Kotz, S. A. (2006). Beyond the right hemisphere: Brain mechanisms mediating vocal emotional processing. Trends in Cognitive Sciences, 10(1), 24-30. pmid: 16321562
[77]	Schwarzlose, R. F., Baker, C. I., & Kanwisher, N. (2005). Separate face and body selectivity on the fusiform gyrus. Journal of Neuroscience, 25(47), 11055-11059. pmid: 16306418
[78]	Sereno, M. I., Diedrichsen, J., Tachrount, M., Testa-Silva, G., d'Arceuil, H., & Zeeuw, C. D. (2020). The human cerebellum has almost 80% of the surface area of the neocortex. Proceedings of the National Academy of Sciences, 117(32), 19538-19543. doi: 10.1073/pnas.2002896117 URL
[79]	Sinke, C. B., Kret, M. E., & de Gelder, B. (2012). Body language: Embodied perception of emotion. In: Berglund, B., Rossi, G. B., Townsend, J. T., & Pendrill, L. R. (Eds.), Measuring with persons: Theory, methods and implementation areas (pp. 335-352). Psychology Press/Taylor & Francis.
[80]	Sokolov, A. A., Gharabaghi, A., Tatagiba, M. S., & Pavlova, M. (2010). Cerebellar engagement in an action observation network. Cereb Cortex, 20(2), 486-491. doi: 10.1093/cercor/bhp117 pmid: 19546157
[81]	Sperduti, M., Delaveau, P., Fossati, P., & Nadel, J. (2011). Different brain structures related to self- and external- agency attribution: A brief review and meta-analysis. Brain Structure and Function, 216(2), 151-157. doi: 10.1007/s00429-010-0298-1 pmid: 21212978
[82]	Spreng, R. N., & Grady, C. L. (2010). Patterns of brain activity supporting autobiographical memory, prospection, and theory of mind, and their relationship to the default mode network. Journal of Cognitive Neuroscience, 22(6), 1112-1123. doi: 10.1162/jocn.2009.21282 URL
[83]	Stevenson, R. A., & James, T. W. (2009). Audiovisual integration in human superior temporal sulcus: Inverse effectiveness and the neural processing of speech and object recognition. Neuroimage, 44(3), 1210-1223. doi: 10.1016/j.neuroimage.2008.09.034 pmid: 18973818
[84]	Stoodley, C. J., & Schmahmann, J. D. (2010). Evidence for topographic organization in the cerebellum of motor control versus cognitive and affective processing. Cortex, 46(7), 831-844. doi: 10.1016/j.cortex.2009.11.008 URL
[85]	Tamietto, M., Cauda, F., Celeghin, A., Diano, M., Costa, T., Cossa, F. M., ... de Gelder, B. (2015). Once you feel it, you see it: Insula and sensory-motor contribution to visual awareness for fearful bodies in parietal neglect. Cortex, 62, 56-72. doi: 10.1016/j.cortex.2014.10.009 pmid: 25465122
[86]	* Trautmann, S. A., Fehr, T., & Herrmann, M. (2009). Emotions in motion: Dynamic compared to static facial expressions of disgust and happiness reveal more widespread emotion- specific activations. Brain Research, 1284, 100-115. doi: 10.1016/j.brainres.2009.05.075 pmid: 19501062
[87]	Turkeltaub, P. E., Eickhoff, S. B., Laird, A. R., Fox, M., Wiener, M., & Fox, P. (2012). Minimizing within-experiment and within-group effects in activation likelihood estimation meta-analyses. Human Brain Mapping, 33(1), 1-13. doi: 10.1002/hbm.21186 pmid: 21305667
[88]	van de Riet, W. A. C., Grezes, J., & de Gelder, B. (2009). Specific and common brain regions involved in the perception of faces and bodies and the representation of their emotional expressions. Social Neuroscience, 4(2), 101-120. doi: 10.1080/17470910701865367 pmid: 19255912
[89]	Waters, K., & Terzopoulos, D. (1990, January). A physical model of facial tissue and muscle articulation. In Proceedings of the First Conference on Visualization in Biomedical Computing (pp.77-78). IEEE Computer Society.
[90]	Watson, R., Latinus, M., Charest, I., Crabbe, F., & Belin, P. (2014). People-selectivity, audiovisual integration and heteromodality in the superior temporal sulcus. Cortex, 50, 125-136. doi: 10.1016/j.cortex.2013.07.011 URL
[91]	* Wicker, B., Keysers, C., Plailly, J., Royet, J.-P., Gallese, V., & Rizzolatti, G. (2003). Both of us disgusted in my insula: The common neural basis of seeing and feeling disgust. Neuron, 40(3), 655-664. pmid: 14642287
[92]	Witteman, J., van Heuven, V. J. P., & Schiller, N. O. (2012). Hearing feelings: A quantitative meta-analysis on the neuroimaging literature of emotional prosody perception. Neuropsychologia, 50(12), 2752-2763. doi: 10.1016/j.neuropsychologia.2012.07.026 URL
[93]	Xu, J., Lyu, H., Li, T., Xu, Z., Fu, X., Jia, F., ... Hu, Q. (2019). Delineating functional segregations of the human middle temporal gyrus with resting-state functional connectivity and coactivation patterns. Human Brain Mapping, 40(18), 1-13. doi: 10.1002/hbm.v40.1 URL
[94]	Yalpe, Z., & Arsalidou, M. (2018). N-back working memory task: Meta-analysis of normative fMRI studies with children. Child Development, 89(6), 2010-2022. doi: 10.1111/cdev.2018.89.issue-6 URL
[95]	Yang, D. Y.-J., Rosenblau, G., Keifer, C., & Pelphrey, K. A. (2015). An integrative neural model of social perception, action observation, and theory of mind. Neuroscience & Biobehavioral Reviews, 51, 263-275. doi: 10.1016/j.neubiorev.2015.01.020 URL
[96]	Yarkoni, T. (2009). Big correlations in little studies: Inflated fMRI correlations reflect low statistical power - Commentary on Vul et al., (2009). Perspectives on Psychological Science, 4(3), 294-298. doi: 10.1111/j.1745-6924.2009.01127.x pmid: 26158966
[97]	Yarkoni, T., Poldrack, R. A., Nichols, T. E., van Essen, D. C., & Wager, T. D. (2011). Large-scale automated synthesis of human functional neuroimaging data. Nature Methods, 8(8), 665-670. doi: 10.1038/nmeth.1635 pmid: 21706013
[98]	Zieber, N., Kangas. A., Hock, A., & Bhatt, R. S. (2014). Infants’s perception of emotion from body movements. Child Development, 85(2), 675-684. doi: 10.1111/cdev.12134 URL
[99]	Zinchenko, O., Yaple, Z. A., & Arsalidou, M. (2018). Brain responses to dynamic facial expressions: A normative meta-analysis. Frontiers in Human Neuroscience, 12, 227. https://doi.org/10.3389/fnhum.2018.00227 doi: 10.3389/fnhum.2018.00227 URL pmid: 29922137
[100]	Zuberer, A., Schwarz, L., Kreifelts, B., Wildgruber, D., Erb, M., Fallgatter, A., ... Ethofer, T. (2020). Neural basis of impaired emotion recognition in adult attention deficit hyperactivity disorder. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging. https://doi.org/10.1016/j.bpsc.2020.11.013

文献信息第一作者/年份	样本量 (男)	年龄	坐标空间	成像手段	实验对比	提取坐标数
动态面孔情绪
Arsalidou et al., 2011	24(6)	26.50 ± 4.60	T	fMRI	Dh>Sh	2
	15(2)	26.30 ± 4.50	T	fMRI	Dh>Da, D>S	7
杜经纶, 2007	13(0)	21-26	T	fMRI	Dh>Dn, Ds₁>Dn	18
杜经纶等, 2007	26(0)	21-50	T	fMRI	Dh>Dn, Ds₁>Dn	29
Foley et al., 2012a	14(6)	28.30 ± 3.67	T	fMRI	Da>Sa, Dh>Sh, D>S (a, h, n)	142
Foley et al., 2012b	14(6)	28.30 ± 3.67	T	fMRI	Da>Sa, Dh>Sh, Da>Dn, Dh>Dn, D>S (a, h, n)	171
Hennenlotter et al., 2005	12(6)	24.50	T	fMRI	Dh>Dn	20
Hurlemann et al., 2008	14(7)	25.40 ± 2.40	M	fMRI	DE>Dn (a, h), Da>Dn, Dh>Dn, Dh>Da	41
Johnston et al., 2013	28(15)	23.93 ± 4.70	M	fMRI	D>S (f, s₂, n)	4
Kessler et al., 2011	30(16)	23 ± 3.70	T	fMRI	D>S (f, h, s₁, d)	12
Kilts et al., 2003	13(9)	24.50	T	PET	Dn>Sn, Da>Dn, Da>Sa, Dh>Dn, Dh>Sh, Da>Dh, Dh>Da	60
Krautheim et al., 2018	178(89)	24.04 ± 3.25	M	fMRI	Dh>Da	32
Krautheim et al., 2019	178(89)	24.04 ± 3.25	M	fMRI	Da>Dn, Dh>Dn	35
Krautheim et al., 2020	178(89)	24.04 ± 3.25	M	fMRI	DE>Dn (a, h)	12
LaBar et al., 2003	10(5)	21-30	M	fMRI	DE>SE (a, f), Df>Sf, Da>Sa, Df>Da, Da>Df	51
Liang et al., 2019	20(10)	19-25	M	fMRI	Dh>Da, Dh>Df, Da>Dh, Da>Df, Df>Da, Df>Dh	28
Liu, 2018	14(3)	23.14 ± 4.22	T	fMRI	Dd>Dn, Df>Dn	5
Pelphrey et al., 2007	8(6)	24.10 ± 5.60	M	fMRI	D>S (a, f, n)	6
Pentón et al., 2010	13(8)	33.70	M	fMRI	Df>Sf	21
Prochnow et al., 2013	15(8)	19.55	T	fMRI	DE>Dn(a, f, s₁, d, s₂)	9
Rahko et al., 2010	27(9)	14.50	T	fMRI	Dh>Dn, Dn>Dh, Df>Dn, Dn>Df, Dh>Df, Df>Dh	46
Rymarczyk et al., 2018	46(25)	23.80 ± 2.50	M	fMRI	Dh>Sh, Da>Sa, Dn>Sn, DE>SE (a, h), Dh>Dn, Da>Dn, DE>Dn (a, h), D>S (a, h, n)	275
文献信息第一作者/年份	样本量 (男)	年龄	坐标空间	成像手段	实验对比	提取坐标数
Rymarczyk et al., 2019	46(25)	23.80 ± 2.50	M	fMRI	Dd>Sd, Df>Sf, Dn>Sn, DE>SE (d, f), Dd>Dn, Df>Dn, DE>Dn (d, f), D>S (d, f, n)	120
Sato et al., 2004	22(10)	26.50	M	fMRI	Dh>Sh, Dh>Dn, Df>Sf, Df>Dn	56
Sato et al., 2010	21(11)	22.30 ± 2	M	fMRI	D>S (a, h)	3
Sato et al., 2012	13(12)	24.30 ± 3.40	M	fMRI	D>S (f, h, n)	20
Sato et al., 2016	35(22)	25.68	M	fMRI	D>S (f, h, n)	16
Sato et al., 2019	51(25)	22.50 ± 4.50	M	fMRI	DE>Dn (a, h)	41
Schultz et al., 2009	10(6)	-	M	fMRI	DE>Dn (s₂, a), D>S (s₂, a)	19
Schultz et al., 2013	26(14)	26.60	M	fMRI	DE>Dn (s₂, a), D>S (s₂, a)	7
Skiba et al., 2019	24(12)	25.90 ± 5.50	M	fMRI	DE>Dn (a, h, s₁)	13
Trautmann et al., 2009	16(0)	21.60 ± 2.30	T	fMRI	Dd>Dn, Dh>Dn	59
van der Gaag et al., 2007	17(8)	23.30	M	fMRI	Dn>DE (h, d, f), DE>Dn, Dd>Dn, Df>Dn, Dh>Dn	36
王鹏飞, 2012	16(9)	24-26	M	fMRI	DE>Dn (a, f)	13
Wicker et al., 2003	14(14)	20-27	T	fMRI	Dd>Dn, Dh>Dn	23
Willinger et al., 2019	33(9)	27.40 ± 5.20	M	fMRI	DN>Dn (s₁, d), DP>Dn (h, s₂), DN>DP	16
姚志剑等, 2007	26(0)	34.60 ±11.15	T	fMRI	Dh>Dn, Ds₁>Dn	13
动态身体情绪
Engelen et al., 2018	17(5)	23 ± 2.20	T	fMRI	Da>Dn	12
Grèzes et al., 2007	16(10)	25	M	fMRI	D>S (f, n)	28
Jastorff et al., 2015	16(8)	25	M	fMRI	DE>Dn (a, h, f, s₁)	23
Jessen, 2012	18(10)	25.90 ± 3.90	M	fMRI	DE>Dn (a, f, n), Da>Dn, Df>Dn	12
Jessen et al., 2015	17(9)	25.90 ± 3.90	M	fMRI	DE>Dn (f, a), Da>Dn, Df>Dn	8
Liang et al., 2019	20(10)	19-25	M	fMRI	Dh>Da, Dh>Df, Da>Dh, Da>Df, Df>Dh, Df>Da	30
Peelen et al., 2007	18(8)	26	T	fMRI	DE>Dn (a, d, f, h, s₁), Dn>DE (a, d, f, h, s₁), Da>Dn, Dd>Dn, Df>Dn, Dh>Dn, Ds₁>Dn	35
Peelen et al., 2010	18(8)	26	T	fMRI	Dh>DN (a, d, f, s₁)	3
Pichon et al., 2008	16(9)	18-26	M	fMRI	Da>Sa, Dn>Sn	33
Pichon et al., 2009	16(8)	24.55	M	fMRI	DN>Dn (a, f), Df>Dn, Da>Dn	95
Pichon et al., 2012	16(8)	24.55	M	fMRI	DN>Dn (a, f)	16
Poyo Solanas et al., 2020	13(3)	25.80	T	fMRI	DE>Dn (a, f, h)	13
Ross et al., 2019	26(11)	21.28 ± 2.11	M	fMRI	Da>Dn, Dh>Dn	13
	18(9)	14.82 ± 1.88	M	fMRI	Da>Dn, Dh>Dn	11
	25(12)	9.55 ± 1.46	M	fMRI	Da>Dn, Dh>Dn	7
van de Vliet et al., 2018	12(5)	54.30 ± 8	M	fMRI	DE>Dn (a, f, h, s₂)	27
声音情绪
Agnew, 2018	16	23-49	M	fMRI	d>n, f>n	16
Alba-Ferrara et al., 2011	19(19)	24.80 ± 8.79	M	fMRI	E>n (h, a, s₁)	12
Aubé et al., 2015	47(27)	26.40 ± 4.80	M	fMRI	f>n, h>n	4
Bach et al., 2008	16(8)	26 ± 3.90	M	fMRI	E>n (a, f), a>f	17
Beaucousin, 2007	23(11)	23.30 ± 3	M	fMRI	E>n (a, h, s₁)	20
Brattico et al., 2011	15(9)	23.90 ± 2.90	M	fMRI	s₁>h, h>s₁	6
Brück et al., 2011	24(12)	23.30	M	fMRI	E>n (h, a), h>a, a>h	8
Buchanan et al., 2000	10(10)	22-40	M	fMRI	s1>h	1
Castelluccio, 2014	8(3)	22.68 ± 3.84	T	fMRI	a>n	14
Castelluccio et al., 2015	8(3)	22.68 ± 3.84	T	fMRI	a>n	8
Ceravolo, 2013	14(3)	23.07 ± 3.95	M	fMRI	a>n, n>a	39
Ceravolo et al., 2016	14(6)	23.07 ± 3.95	M	fMRI	a>n	11
文献信息第一作者/年份	样本量 (男)	年龄	坐标空间	成像手段	实验对比	提取坐标数
Dietrich et al., 2008	16(8)	25 ± 3.30	M	fMRI	E>n	4
Eigsti et al., 2012	11(7)	13.70 ± 2.60	T	fMRI	a>n	1
Ethofer et al., 2007	10(6)	24	M	fMRI	E>n (a, f, h)	7
Ethofer et al., 2009	24(12)	26.30	M	fMRI	a>n	9
Ethofer et al., 2011	22(9)	26.30 ± 7.70	M	fMRI	E>n (a, h, s₁)	23
Fecteau et al., 2005	16(10)	22.60 ± 2.70	T	fMRI	E>n (h, f, s₁)	1
Fecteau et al., 2007	14(8)	23.40 ± 3	M	fMRI	E>n (h, f, s₁), N>n (f, s₁), h>n, N>h (f, s₁), h>N (f, s₁)	20
Frühholz et al., 2011	17(3)	25.52 ± 5.08	M	fMRI	a>n	97
Frühholz et al., 2014	13(6)	23.85 ± 3.69	M	fMRI	a>n	59
Gandour et al., 2003	20(10)	27.05	T	fMRI	E>n (a, h, s₁)	14
Goerlich-Dobre et al., 2014	22(9)	24.80 ± 5.30	M	fMRI	E>n (a, s₂)	6
Grandjean et al., 2005	15(8)	24.40 ± 4.60	M	fMRI	a>n	15
Jessen, 2012	18(10)	25.90 ± 3.90	M	fMRI	E>n (a, f), a>n, f>n, n>E (a, f), n>f	12
Jessen et al., 2015	17(8)	25.90 ± 3.90	M	fMRI	E>n (a, f), a>n, f>n	14
Johnstone et al., 2006	40(22)	18-50	M	fMRI	h>a, a>h	6
Kang et al., 2009	28(14)	29.90 ± 2.90	M	fMRI	h>n, s>n	16
	14(7)	30.40 ± 3.70	M	fMRI	h>n, s>n	13
Kanske et al., 2010	22(12)	24.70 ± 2.70	M	fMRI	N>n	3
Koelsch et al., 2013	18(9)	23.78 ± 3.54	M	fMRI	h>f, f>h	35
Korb et al., 2015	18(8)	27	M	fMRI	a>n, n>a	56
Kotz et al., 2003	12(4)	22-29	T	fMRI	h>n, a>n	35
Kotz et al., 2015	20(10)	26.90 ± 3.40	M	fMRI	h>a, a>h	11
Leitman et al., 2010	20(20)	28 ± 5	T	fMRI	E>n (a, f, h)	14
Liu, 2018	14(3)	23.14 ± 4.22	T	fMRI	d>n, f>n, s₁>n	6
Mitchell et al., 2003	13(13)	32.20 ± 0.93	T	fMRI	E>n (h, s₁), n>E (h, s₁)	8
Mitterschiffthaler et al., 2007	16(8)	29.50 ± 5.50	M	fMRI	h>n, s₁>n, n>h+s₁	20
Morris et al., 1999	6(6)	32.70	M	PET	E>n (h, f, s₁), n>E (h, f, s₁)	13
Numminen-Kontti, 2014	31(9)	27.60 ± 6.90	M	fMRI	h>s₁, h>f, s₁>h, s₁>f, f>h, f>s₁	71
Park et al., 2015	24(10)	19.63	M	fMRI	E>n (h, f, s₁)	2
Peelen et al., 2010	18(8)	26	T	fMRI	h>N (a, d, f, s₁)	2
Péron et al., 2015	15(3)	25.12 ± 4.95	M	fMRI	a>n	14
Phillips et al., 1998	6(6)	37	T	fMRI	f>n, d>n, f>d, d>f	43
Regenbogen et al., 2012	27(14)	34.07 ± 9.82	M	fMRI	E>n (d, f, h, s1)	44
Rosenblau et al., 2016	20(15)	32	M	fMRI	E>n (a, d, f, h, s1, s₂)	13
Salimpoor et al., 2011	10(5)	20.80 ± 1.90	M	fMRI	h>n	7
Sander et al., 2005	15(7)	24.40 ± 4.60	M	fMRI	a>n	16
Seydell-Greenwald et al., 2020	20(8)	21.75 ± 3.50	T	fMRI	E>n (h, a, s₁)	9
Smith et al., 2015	17(10)	26.50 ± 5.95	M	fMRI	a>n, s1>n	4
Wildgruber et al., 2002	12(6)	21-33	M	fMRI	E>n (h, s₁)	20
Wildgruber et al., 2005	10(5)	21-33	M	fMRI	E>n (h, a, f, d, s₁)	17
Witteman, 2014	19(6)	24.92 ± 5.65	M	fMRI	E>n (a, s₂), a>n, s₂>n	9
Wittfoth et al., 2009	20(10)	24.90 ± 3.70	M	fMRI	h>a, a>h, E>n (h, a)	7

文献信息第一作者/年份	样本量 (男)	年龄	坐标空间	成像手段	实验对比	提取坐标数
动态面孔情绪
Arsalidou et al., 2011	24(6)	26.50 ± 4.60	T	fMRI	Dh>Sh	2
	15(2)	26.30 ± 4.50	T	fMRI	Dh>Da, D>S	7
杜经纶, 2007	13(0)	21-26	T	fMRI	Dh>Dn, Ds₁>Dn	18
杜经纶等, 2007	26(0)	21-50	T	fMRI	Dh>Dn, Ds₁>Dn	29
Foley et al., 2012a	14(6)	28.30 ± 3.67	T	fMRI	Da>Sa, Dh>Sh, D>S (a, h, n)	142
Foley et al., 2012b	14(6)	28.30 ± 3.67	T	fMRI	Da>Sa, Dh>Sh, Da>Dn, Dh>Dn, D>S (a, h, n)	171
Hennenlotter et al., 2005	12(6)	24.50	T	fMRI	Dh>Dn	20
Hurlemann et al., 2008	14(7)	25.40 ± 2.40	M	fMRI	DE>Dn (a, h), Da>Dn, Dh>Dn, Dh>Da	41
Johnston et al., 2013	28(15)	23.93 ± 4.70	M	fMRI	D>S (f, s₂, n)	4
Kessler et al., 2011	30(16)	23 ± 3.70	T	fMRI	D>S (f, h, s₁, d)	12
Kilts et al., 2003	13(9)	24.50	T	PET	Dn>Sn, Da>Dn, Da>Sa, Dh>Dn, Dh>Sh, Da>Dh, Dh>Da	60
Krautheim et al., 2018	178(89)	24.04 ± 3.25	M	fMRI	Dh>Da	32
Krautheim et al., 2019	178(89)	24.04 ± 3.25	M	fMRI	Da>Dn, Dh>Dn	35
Krautheim et al., 2020	178(89)	24.04 ± 3.25	M	fMRI	DE>Dn (a, h)	12
LaBar et al., 2003	10(5)	21-30	M	fMRI	DE>SE (a, f), Df>Sf, Da>Sa, Df>Da, Da>Df	51
Liang et al., 2019	20(10)	19-25	M	fMRI	Dh>Da, Dh>Df, Da>Dh, Da>Df, Df>Da, Df>Dh	28
Liu, 2018	14(3)	23.14 ± 4.22	T	fMRI	Dd>Dn, Df>Dn	5
Pelphrey et al., 2007	8(6)	24.10 ± 5.60	M	fMRI	D>S (a, f, n)	6
Pentón et al., 2010	13(8)	33.70	M	fMRI	Df>Sf	21
Prochnow et al., 2013	15(8)	19.55	T	fMRI	DE>Dn(a, f, s₁, d, s₂)	9
Rahko et al., 2010	27(9)	14.50	T	fMRI	Dh>Dn, Dn>Dh, Df>Dn, Dn>Df, Dh>Df, Df>Dh	46
Rymarczyk et al., 2018	46(25)	23.80 ± 2.50	M	fMRI	Dh>Sh, Da>Sa, Dn>Sn, DE>SE (a, h), Dh>Dn, Da>Dn, DE>Dn (a, h), D>S (a, h, n)	275
文献信息第一作者/年份	样本量 (男)	年龄	坐标空间	成像手段	实验对比	提取坐标数
Rymarczyk et al., 2019	46(25)	23.80 ± 2.50	M	fMRI	Dd>Sd, Df>Sf, Dn>Sn, DE>SE (d, f), Dd>Dn, Df>Dn, DE>Dn (d, f), D>S (d, f, n)	120
Sato et al., 2004	22(10)	26.50	M	fMRI	Dh>Sh, Dh>Dn, Df>Sf, Df>Dn	56
Sato et al., 2010	21(11)	22.30 ± 2	M	fMRI	D>S (a, h)	3
Sato et al., 2012	13(12)	24.30 ± 3.40	M	fMRI	D>S (f, h, n)	20
Sato et al., 2016	35(22)	25.68	M	fMRI	D>S (f, h, n)	16
Sato et al., 2019	51(25)	22.50 ± 4.50	M	fMRI	DE>Dn (a, h)	41
Schultz et al., 2009	10(6)	-	M	fMRI	DE>Dn (s₂, a), D>S (s₂, a)	19
Schultz et al., 2013	26(14)	26.60	M	fMRI	DE>Dn (s₂, a), D>S (s₂, a)	7
Skiba et al., 2019	24(12)	25.90 ± 5.50	M	fMRI	DE>Dn (a, h, s₁)	13
Trautmann et al., 2009	16(0)	21.60 ± 2.30	T	fMRI	Dd>Dn, Dh>Dn	59
van der Gaag et al., 2007	17(8)	23.30	M	fMRI	Dn>DE (h, d, f), DE>Dn, Dd>Dn, Df>Dn, Dh>Dn	36
王鹏飞, 2012	16(9)	24-26	M	fMRI	DE>Dn (a, f)	13
Wicker et al., 2003	14(14)	20-27	T	fMRI	Dd>Dn, Dh>Dn	23
Willinger et al., 2019	33(9)	27.40 ± 5.20	M	fMRI	DN>Dn (s₁, d), DP>Dn (h, s₂), DN>DP	16
姚志剑等, 2007	26(0)	34.60 ±11.15	T	fMRI	Dh>Dn, Ds₁>Dn	13
动态身体情绪
Engelen et al., 2018	17(5)	23 ± 2.20	T	fMRI	Da>Dn	12
Grèzes et al., 2007	16(10)	25	M	fMRI	D>S (f, n)	28
Jastorff et al., 2015	16(8)	25	M	fMRI	DE>Dn (a, h, f, s₁)	23
Jessen, 2012	18(10)	25.90 ± 3.90	M	fMRI	DE>Dn (a, f, n), Da>Dn, Df>Dn	12
Jessen et al., 2015	17(9)	25.90 ± 3.90	M	fMRI	DE>Dn (f, a), Da>Dn, Df>Dn	8
Liang et al., 2019	20(10)	19-25	M	fMRI	Dh>Da, Dh>Df, Da>Dh, Da>Df, Df>Dh, Df>Da	30
Peelen et al., 2007	18(8)	26	T	fMRI	DE>Dn (a, d, f, h, s₁), Dn>DE (a, d, f, h, s₁), Da>Dn, Dd>Dn, Df>Dn, Dh>Dn, Ds₁>Dn	35
Peelen et al., 2010	18(8)	26	T	fMRI	Dh>DN (a, d, f, s₁)	3
Pichon et al., 2008	16(9)	18-26	M	fMRI	Da>Sa, Dn>Sn	33
Pichon et al., 2009	16(8)	24.55	M	fMRI	DN>Dn (a, f), Df>Dn, Da>Dn	95
Pichon et al., 2012	16(8)	24.55	M	fMRI	DN>Dn (a, f)	16
Poyo Solanas et al., 2020	13(3)	25.80	T	fMRI	DE>Dn (a, f, h)	13
Ross et al., 2019	26(11)	21.28 ± 2.11	M	fMRI	Da>Dn, Dh>Dn	13
	18(9)	14.82 ± 1.88	M	fMRI	Da>Dn, Dh>Dn	11
	25(12)	9.55 ± 1.46	M	fMRI	Da>Dn, Dh>Dn	7
van de Vliet et al., 2018	12(5)	54.30 ± 8	M	fMRI	DE>Dn (a, f, h, s₂)	27
声音情绪
Agnew, 2018	16	23-49	M	fMRI	d>n, f>n	16
Alba-Ferrara et al., 2011	19(19)	24.80 ± 8.79	M	fMRI	E>n (h, a, s₁)	12
Aubé et al., 2015	47(27)	26.40 ± 4.80	M	fMRI	f>n, h>n	4
Bach et al., 2008	16(8)	26 ± 3.90	M	fMRI	E>n (a, f), a>f	17
Beaucousin, 2007	23(11)	23.30 ± 3	M	fMRI	E>n (a, h, s₁)	20
Brattico et al., 2011	15(9)	23.90 ± 2.90	M	fMRI	s₁>h, h>s₁	6
Brück et al., 2011	24(12)	23.30	M	fMRI	E>n (h, a), h>a, a>h	8
Buchanan et al., 2000	10(10)	22-40	M	fMRI	s1>h	1
Castelluccio, 2014	8(3)	22.68 ± 3.84	T	fMRI	a>n	14
Castelluccio et al., 2015	8(3)	22.68 ± 3.84	T	fMRI	a>n	8
Ceravolo, 2013	14(3)	23.07 ± 3.95	M	fMRI	a>n, n>a	39
Ceravolo et al., 2016	14(6)	23.07 ± 3.95	M	fMRI	a>n	11
文献信息第一作者/年份	样本量 (男)	年龄	坐标空间	成像手段	实验对比	提取坐标数
Dietrich et al., 2008	16(8)	25 ± 3.30	M	fMRI	E>n	4
Eigsti et al., 2012	11(7)	13.70 ± 2.60	T	fMRI	a>n	1
Ethofer et al., 2007	10(6)	24	M	fMRI	E>n (a, f, h)	7
Ethofer et al., 2009	24(12)	26.30	M	fMRI	a>n	9
Ethofer et al., 2011	22(9)	26.30 ± 7.70	M	fMRI	E>n (a, h, s₁)	23
Fecteau et al., 2005	16(10)	22.60 ± 2.70	T	fMRI	E>n (h, f, s₁)	1
Fecteau et al., 2007	14(8)	23.40 ± 3	M	fMRI	E>n (h, f, s₁), N>n (f, s₁), h>n, N>h (f, s₁), h>N (f, s₁)	20
Frühholz et al., 2011	17(3)	25.52 ± 5.08	M	fMRI	a>n	97
Frühholz et al., 2014	13(6)	23.85 ± 3.69	M	fMRI	a>n	59
Gandour et al., 2003	20(10)	27.05	T	fMRI	E>n (a, h, s₁)	14
Goerlich-Dobre et al., 2014	22(9)	24.80 ± 5.30	M	fMRI	E>n (a, s₂)	6
Grandjean et al., 2005	15(8)	24.40 ± 4.60	M	fMRI	a>n	15
Jessen, 2012	18(10)	25.90 ± 3.90	M	fMRI	E>n (a, f), a>n, f>n, n>E (a, f), n>f	12
Jessen et al., 2015	17(8)	25.90 ± 3.90	M	fMRI	E>n (a, f), a>n, f>n	14
Johnstone et al., 2006	40(22)	18-50	M	fMRI	h>a, a>h	6
Kang et al., 2009	28(14)	29.90 ± 2.90	M	fMRI	h>n, s>n	16
	14(7)	30.40 ± 3.70	M	fMRI	h>n, s>n	13
Kanske et al., 2010	22(12)	24.70 ± 2.70	M	fMRI	N>n	3
Koelsch et al., 2013	18(9)	23.78 ± 3.54	M	fMRI	h>f, f>h	35
Korb et al., 2015	18(8)	27	M	fMRI	a>n, n>a	56
Kotz et al., 2003	12(4)	22-29	T	fMRI	h>n, a>n	35
Kotz et al., 2015	20(10)	26.90 ± 3.40	M	fMRI	h>a, a>h	11
Leitman et al., 2010	20(20)	28 ± 5	T	fMRI	E>n (a, f, h)	14
Liu, 2018	14(3)	23.14 ± 4.22	T	fMRI	d>n, f>n, s₁>n	6
Mitchell et al., 2003	13(13)	32.20 ± 0.93	T	fMRI	E>n (h, s₁), n>E (h, s₁)	8
Mitterschiffthaler et al., 2007	16(8)	29.50 ± 5.50	M	fMRI	h>n, s₁>n, n>h+s₁	20
Morris et al., 1999	6(6)	32.70	M	PET	E>n (h, f, s₁), n>E (h, f, s₁)	13
Numminen-Kontti, 2014	31(9)	27.60 ± 6.90	M	fMRI	h>s₁, h>f, s₁>h, s₁>f, f>h, f>s₁	71
Park et al., 2015	24(10)	19.63	M	fMRI	E>n (h, f, s₁)	2
Peelen et al., 2010	18(8)	26	T	fMRI	h>N (a, d, f, s₁)	2
Péron et al., 2015	15(3)	25.12 ± 4.95	M	fMRI	a>n	14
Phillips et al., 1998	6(6)	37	T	fMRI	f>n, d>n, f>d, d>f	43
Regenbogen et al., 2012	27(14)	34.07 ± 9.82	M	fMRI	E>n (d, f, h, s1)	44
Rosenblau et al., 2016	20(15)	32	M	fMRI	E>n (a, d, f, h, s1, s₂)	13
Salimpoor et al., 2011	10(5)	20.80 ± 1.90	M	fMRI	h>n	7
Sander et al., 2005	15(7)	24.40 ± 4.60	M	fMRI	a>n	16
Seydell-Greenwald et al., 2020	20(8)	21.75 ± 3.50	T	fMRI	E>n (h, a, s₁)	9
Smith et al., 2015	17(10)	26.50 ± 5.95	M	fMRI	a>n, s1>n	4
Wildgruber et al., 2002	12(6)	21-33	M	fMRI	E>n (h, s₁)	20
Wildgruber et al., 2005	10(5)	21-33	M	fMRI	E>n (h, a, f, d, s₁)	17
Witteman, 2014	19(6)	24.92 ± 5.65	M	fMRI	E>n (a, s₂), a>n, s₂>n	9
Wittfoth et al., 2009	20(10)	24.90 ± 3.70	M	fMRI	h>a, a>h, E>n (h, a)	7

脑区	半球	BA区	中心坐标	体积 /mm³	ALE值 (×10^-3)	脑区	半球	BA区	中心坐标	体积 /mm³	ALE值 (×10^-3)
脑区	半球	BA区	X, Y, Z	体积 /mm³	ALE值 (×10^-3)	脑区	半球	BA区	X, Y, Z	体积 /mm³	ALE值 (×10^-3)
动态效应						情绪效应
面孔∩身体						面孔∩身体
颞上回	右	13	56, -44, 16	648	14.73	颞中回	左	37	-48, -66, 4	2232	25.80
颞上回	左	22	-58, -44, 14	472	15.18	颞上回	左	39	-48, -48, 8		23.95
小脑	右		38, -50, -18	336	10.58	枕下回	左	19	-44, -72, -6		21.23
梭状回	左	37	40, 44, -18		9.40	颞下回	右	37	48, -64, 2	1056	29.41
						颞下回	右	37	44, -68, -4		25.77
正性情绪效应						梭状回	右	37	36, -44, -16	520	30.48
面孔∩身体						颞上回	右	41	42, -38, 6	456	16.33
颞下回	右		44, -68, 0	160	14.13	海马旁回	右	34	24, 0, -12	432	19.56
颞中回	右	37	46, -64, 2		13.89	梭状回	右	37	38, -50, -16	40	16.57
						豆状核	右		18, -4, -6	24	12.03
负性情绪效应						面孔∩声音
面孔∩身体						颞上回	右	41	44, -34, 6	1608	33.12
颞下回	右	37	48, -64, 2	624	24.46	脑岛	右	22	42, -26, 0		24.56
枕下回	右	19	44, -72, -6		15.32	脑岛	右	13	44, -18, -2		19.87
颞下回	左	39	48, -48, 6	528	19.72	额下回	右	45	50, 20, 6	1424	27.33
颞中回	左	37	48, -48, 4	272	18.42	额下回	右	13	44, 24, 4		25.55
						脑岛	右	13	48, 12, 2		25.35
						杏仁核	左		-20, -8, -10	768	29.74
						颞中回	左	21	-54, -44, 6	120	21.10
						脑岛	右	13	58, -34, 20	8	17.19
						身体∩声音
						颞上回	右	41	42, -38, 6	424	16.33
						颞中回	左	21	-52, -46, 8	56	16.85

脑区	半球	BA区	中心坐标	体积 /mm³	ALE值 (×10^-3)	脑区	半球	BA区	中心坐标	体积 /mm³	ALE值 (×10^-3)
脑区	半球	BA区	X, Y, Z	体积 /mm³	ALE值 (×10^-3)	脑区	半球	BA区	X, Y, Z	体积 /mm³	ALE值 (×10^-3)
动态效应						情绪效应
面孔∩身体						面孔∩身体
颞上回	右	13	56, -44, 16	648	14.73	颞中回	左	37	-48, -66, 4	2232	25.80
颞上回	左	22	-58, -44, 14	472	15.18	颞上回	左	39	-48, -48, 8		23.95
小脑	右		38, -50, -18	336	10.58	枕下回	左	19	-44, -72, -6		21.23
梭状回	左	37	40, 44, -18		9.40	颞下回	右	37	48, -64, 2	1056	29.41
						颞下回	右	37	44, -68, -4		25.77
正性情绪效应						梭状回	右	37	36, -44, -16	520	30.48
面孔∩身体						颞上回	右	41	42, -38, 6	456	16.33
颞下回	右		44, -68, 0	160	14.13	海马旁回	右	34	24, 0, -12	432	19.56
颞中回	右	37	46, -64, 2		13.89	梭状回	右	37	38, -50, -16	40	16.57
						豆状核	右		18, -4, -6	24	12.03
负性情绪效应						面孔∩声音
面孔∩身体						颞上回	右	41	44, -34, 6	1608	33.12
颞下回	右	37	48, -64, 2	624	24.46	脑岛	右	22	42, -26, 0		24.56
枕下回	右	19	44, -72, -6		15.32	脑岛	右	13	44, -18, -2		19.87
颞下回	左	39	48, -48, 6	528	19.72	额下回	右	45	50, 20, 6	1424	27.33
颞中回	左	37	48, -48, 4	272	18.42	额下回	右	13	44, 24, 4		25.55
						脑岛	右	13	48, 12, 2		25.35
						杏仁核	左		-20, -8, -10	768	29.74
						颞中回	左	21	-54, -44, 6	120	21.10
						脑岛	右	13	58, -34, 20	8	17.19
						身体∩声音
						颞上回	右	41	42, -38, 6	424	16.33
						颞中回	左	21	-52, -46, 8	56	16.85

脑区	半球	BA区	中心坐标	体积 /mm³	P值	脑区	半球	BA区	中心坐标	体积 /mm³	P值
脑区	半球	BA区	X, Y, Z	体积 /mm³	P值	脑区	半球	BA区	X, Y, Z	体积 /mm³	P值
动态效应						正性情绪效应
身体>面孔						身体>面孔
颞上回	左	22	-57.9, -44.5, 15.5	264	0.0045	枕中回	右	37	39.7, -64.5, 5.5	1008	0.0004
情绪效应						面孔>声音
面孔>身体						小脑	右		42, -62, -20	2440	0.0003
小脑	右		36, -64, -22	1264	0.0000	梭状回	右	37	42, -57.5, -18.5		0.0005
梭状回	右	37	46, -60, -14		0.0004	梭状回	右	37	42, -63.3, -14.7		0.0006
小脑	右		41.1, -61.6, -18.6		0.0005	枕中回	右	37	48, -62, -6		0.0008
小脑	右		36, -62, -14		0.0006	梭状回	右	37	43.4, -63.7, -8.9		0.0009
中央前回	右	44	48, 10, 8	720	0.0006	梭状回	右	19	45, -67, -8.3		1.0000
海马旁回	右	28	20, -14, -16	680	0.0022	颞下回	右		52.2, -64, 1.8		0.0015
身体>面孔						枕下回	右	19	40.7, -73, -8.1		0.0016
舌回	左	17	-17.1, -91.3, 2.9	1936	0.0000	枕下回	右	19	43.1, -70.6, -2		0.0018
楔叶	左	17	-15.6, -96, -1.2		0.0002	声音>面孔
舌回	左	17	-10.2, -95.5, -3.1		0.0002	脑岛	左	13	-42, -14, -2	1344	0.0008
枕下回	左	17	-16, -92, -6		0.0003	颞上回	左	22	-52, -10, -2		0.0009
舌回	左	18	-16, -86, -8		0.0025	颞上回	左	22	-49, -5, -2		0.0013
枕中回	右	37	-41.1, -64.6, 5.6	1800	0.0000	颞上回	左	22	-46.5, -9, -1.5		0.0014
面孔>声音						颞上回	左	22	-48, -14, -2		0.0015
梭状回	左	37	-42.5, -62.8, -5.4	5304	1.0000	脑岛	左	13	-44, -10, 4		0.0040
小脑	左		-39.6, -50.8, -22		0.0001	颞上回	左	22	-47, -16, 2		0.0045
梭状回	右	37	43.8, -64.1, -8.5	5272	0.0034	颞上回	左	22	-52, -2, 4		0.0050
中央前回	右	6	47.3, 1.6, 47.2	1392	0.0000	颞上回	右	22	49.5, -15, 8	296	1.0000
中央前回	右	4	52, -4, 46		0.0001	脑岛	右	13	47.3, -14.7, 12		0.0025
海马旁回	左	34	-18, -10, -18	1256	0.0000	身体>声音
杏仁核	左		-22, -10, -18		0.0001	枕中回	右	37	42.2, -64.5, 3.7	1568	0.0016
海马旁回	右	34	15.7, -2.2, -17.3	1192	0.0001	负性情绪效应
额上回	右	6	3.4, 4.6, 66.6	888	0.0002	身体>面孔
梭状回	右	37	37.4, -43.6, -13.1	696	0.0000	楔叶	左	17	-15.2, -94.1, -0.3	2616	0.0000
梭状回	右	37	35.3, -0.8, -14.7		0.0001	枕中回	右	37	42.4, -65.1, 5.4	1840	0.0000
顶下小叶	右	40	53, -26, 26	576	0.0001	小脑	右		36, -46, -18	416	0.0023
顶下小叶	右	40	52, -30, 24		0.0005	面孔>声音
颞上回	右	22	46, -30, -2	304	0.0038	梭状回	右	19	44.8, -67.9, -7.2	2056	0.0000
海马旁回	右	19	41, -38, 0		0.0053	颞中回	右	37	53.5, -61, 1		0.0005
颞上回	右	22	44, -20, -6	208	0.0022	颞中回	右	37	50, -63, 5.5		0.0019
声音>面孔						中央前回	右	6	48.5, 2.7, 45.3	1072	0.0002
颞上回	右	41	58.4, -18.2, 5.1	7944	0.0000	中央前回	右	6	49.5, -2, 45.3		0.0003
颞上回	右	22	50, -10, 6.7		0.0007	颞中回	左	22	-54, -50, 6	864	0.0005
颞上回	左	22	-49.8, -21, 3.2	5440	0.0000	杏仁核	左		-22, -6, -16	728	0.0008
脑区	半球	BA区	中心坐标	体积 /mm³	P值	脑区	半球	BA区	中心坐标	体积 /mm³	P值
脑区	半球	BA区	X, Y, Z	体积 /mm³	P值	脑区	半球	BA区	X, Y, Z	体积 /mm³	P值
颞上回	左	21	-62, -19, -2		0.0001	颞中回	右	22	50, -37.3, 1.3	664	0.0007
脑岛	左	13	-40, -12, -3		0.0020	颞中回	右	21	59, -45, 8		0.0008
颞上回	左	22	-60, -6, 4		0.0059	额下回	左	47	-36, 20, -10	544	0.0021
身体>声音						额下回	左	47	-34, 24, -14		0.0025
颞下回	左	37	-46.3, -69.3, -0.5	3624	0.0000	颞中回	左	37	-47, -66, 5	232	0.002
颞中回	右	37	42.9, -65.2, 3.2	3288	0.0000	颞中回	左	37	-48, -62, 6		0.0022
楔叶	左	17	-16.5, -93.7, 1	2712	0.0000	声音>面孔
舌回	左	18	-14.7, -84.7, -8		0.0001	颞上回	右		58.1, -19.4, 4.2	7888	0.0000
梭状回	右	37	38.4, -45, -16.1	1384	0.0000	颞上回	右	22	56, -6, 0		0.0001
豆状核	右		18, 2, -8	384	0.0003	颞上回	右	22	57.1, -2.9, -2		0.0002
声音>身体						颞上回	左	22	-46.5, -20.4, 3.6	3696	0.0000
颞上回	左	22	-46, -15.3, 8	4176	0.0002	颞横回	左	41	-38, -28, 8		0.0001
颞上回	左	22	-54.2, -11.6, 9.3		0.0001	颞上回	左	22	-58, -18, 4		0.0003
颞上回	左	22	-62, -22, 4		0.0006	颞上回	左	41	-34, -34, 14		0.0020
颞上回	右	22	50, -16, 8	3384	0.0002	身体>声音
颞上回	右	22	52, -12, 6		0.0003	颞下回	左		-45.1, -70.5, 1.1	3080	0.0000
中央前回	右	6	56, -4, 6		0.0006	颞中回	左	37	-48.2, -66, 5.6		0.0001
中央前回	右	44	54, 6, 8		0.0014	颞上回	左	22	-52, -50, 10		0.0002
颞中回	右	21	62, -26, -2		0.0019	颞上回	左	39	-48, -52, 10		0.0010
中央前回	右	44	52, 12, 4		0.0027	颞中回	左	21	-50, -46.7, 7.7		0.0011
颞上回	左	22	-48, 3.3, -1.3	384	0.0004	颞中回	左	39	-50, -56, 8		0.0013
颞上回	左	22	-52, 4, 2		0.0006	梭状回	左	19	-44, -74, -10		0.0018
						颞中回	左	39	-46, -54, 6		0.0020
						颞下回	右		43.6, -66.5, 1.9	2960	0.0000
						楔叶	左	17	-15.1, -94.4, -0.4	2616	0.0000
						梭状回	右	37	37.6, -44.8, -16.5	1344	0.0000
						声音>身体
						颞上回	右	41	58, -20, 8	3000	0.0005
						颞横回	右	41	54.6, -19, 11.4		0.0006
						颞上回	右	22	63.3, -19.3, 4		0.0011
						中央前回	左	6	-46, -8, 7	2168	0.0002
						颞横回	左	41	-47.3, -20.3, 9.3		0.0004
						脑岛	左	13	-42.7, -19, 6		0.0005
						颞上回	左	22	-51, -13.3, 8.7		0.0008