A Sensitive and Specific Neural Signature for Picture-Induced Negative Affect.
A Sensitive and Specific Neural Signature for Picture-Induced Negative Affect.
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DOI：
10.1371/journal.pbio.1002180
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发表时间：
2015-06
期刊：
影响因子：
9.8
通讯作者：
Wager TD
中科院分区：
文献类型：
--
作者：
Chang LJ;Gianaros PJ;Manuck SB;Krishnan A;Wager TD
神经影像学已经识别出许多情绪的相关性，但尚未产生预测个体情绪体验强度的大脑表征。我们使用机器学习来识别对厌恶图像的情绪反应的敏感且特定的特征。该特征预测了交叉验证 (n = 121) 和测试 (n = 61) 样本中个体参与者的负面情绪强度（高低情绪 = 93.5% 准确度）。它对身体疼痛没有反应（情绪-疼痛 = 92% 的辨别准确度），这表明它不是普遍性唤醒或显着性的表现。该特征由跨越多个皮层和皮层下系统的中尺度模式组成，没有一个系统足以或足以预测经验。此外，它不能简化为传统“情绪相关”区域（例如杏仁核、脑岛）或静息状态网络（例如“显着性”、“默认模式”）的活动。总的来说，这项工作确定了负面情绪和疼痛的可微神经成分，为新的、基于大脑的情感过程分类法提供了基础。通过使用图像诱发人类参与者的负面情绪，这项研究利用神经影像学来开发和验证分布式大脑情绪特征，该特征可以预测新个体的负面情感体验的程度和类型。情绪是人类经验和行为的一个重要方面；然而，我们还不清楚它们在大脑中是如何处理的。我们已经确定了负面情绪的神经特征——分布在大脑中的神经激活模式，可以准确预测一个人在观看令人厌恶的图像后会感到多么负面。这种模式包含皮层和皮层下的多个大脑子网络。这种神经激活模式显着优于其他基于单个区域（例如杏仁核、岛叶和前扣带回）以及区域网络（例如边缘和“显着”网络）激活的大脑情绪指标。此外，没有一个子网络对于准确确定情感反应的强度和类型来说是必要或充分的。最后，这种模式似乎是图片引起的负面情绪所特有的，因为它对至少另一种令人厌恶的经历没有反应：痛苦的高温。总之，这些结果为广泛使用的负面情绪探针引起的感觉提供了神经生理学标记，并表明大脑成像有可能纯粹根据大脑活动的测量准确地揭示某人的感觉。
Neuroimaging has identified many correlates of emotion but has not yet yielded brain representations predictive of the intensity of emotional experiences in individuals. We used machine learning to identify a sensitive and specific signature of emotional responses to aversive images. This signature predicted the intensity of negative emotion in individual participants in cross validation (n =121) and test (n = 61) samples (high–low emotion = 93.5% accuracy). It was unresponsive to physical pain (emotion–pain = 92% discriminative accuracy), demonstrating that it is not a representation of generalized arousal or salience. The signature was comprised of mesoscale patterns spanning multiple cortical and subcortical systems, with no single system necessary or sufficient for predicting experience. Furthermore, it was not reducible to activity in traditional “emotion-related” regions (e.g., amygdala, insula) or resting-state networks (e.g., “salience,” “default mode”). Overall, this work identifies differentiable neural components of negative emotion and pain, providing a basis for new, brain-based taxonomies of affective processes. By using images to induce negative emotions in human participants, this study uses neuroimaging to develop and validate a distributed brain signature of emotion that can predict the magnitude and type of negative affective experience in new individuals. Emotions are an important aspect of human experience and behavior; yet, we do not have a clear understanding of how they are processed in the brain. We have identified a neural signature of negative emotion—a neural activation pattern distributed across the brain that accurately predicts how negative a person will feel after viewing an aversive image. This pattern encompasses multiple brain subnetworks in the cortex and subcortex. This neural activation pattern dramatically outperforms other brain indicators of emotion based on activation in individual regions (e.g., amygdala, insula, and anterior cingulate) as well as networks of regions (e.g., limbic and “salience” networks). In addition, no single subnetwork is necessary or sufficient for accurately determining the intensity and type of affective response. Finally, this pattern appears to be specific to picture-induced negative affect, as it did not respond to at least one other aversive experience: painful heat. Together, these results provide a neurophysiological marker for feelings induced by a widely used probe of negative affect and suggest that brain imaging has the potential to accurately uncover how someone is feeling based purely on measures of brain activity.
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影响因子：
48
作者：
Craddock, R. Cameron;Jbabdi, Saad;Yan, Chao-Gan;Vogelstein, Joshua T.;Castellanos, F. Xavier;Di Martino, Adriana;Kelly, Clare;Heberlein, Keith;Colcombe, Stan;Milham, Michael P.
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影响因子：
10.6
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Fu, Cynthia H. Y.;Mourao-Miranda, Janaina;Brammer, Michael J.
通讯作者：
Brammer, Michael J.
影响因子：
22.4
作者：
CAMPBELL, DT;FISKE, DW
通讯作者：
FISKE, DW
影响因子：
2.5
作者：
Critchley, HD
通讯作者：
Critchley, HD
影响因子：
25.8
作者：
Doehrmann, Oliver;Ghosh, Satrajit S.;Polli, Frida E.;Reynolds, Gretchen O.;Horn, Franziska;Keshavan, Anisha;Triantafyllou, Christina;Saygin, Zeynep M.;Whitfield-Gabrieli, Susan;Hofmann, Stefan G.;Pollack, Mark;Gabrieli, John D.
通讯作者：
Gabrieli, John D.