Brain Imaging Research Center, Department of Psychiatry, University of Arkansas for Medical Sciences, Little Rock, AR, United States of America.
Department of Psychiatry, University of Utah School of Medicine, Salt Lake City, UT, United States of America.
PLoS One. 2022 Aug 30;17(8):e0273376. doi: 10.1371/journal.pone.0273376. eCollection 2022.
In this study, we merged methods from engineering control theory, machine learning, and human neuroimaging to critically test the putative role of the dorsal anterior cingulate cortex (dACC) in goal-directed performance monitoring during an emotion regulation task. Healthy adult participants (n = 94) underwent cued-recall and re-experiencing of their responses to affective image stimuli with concurrent functional magnetic resonance imaging and psychophysiological response recording. During cued-recall/re-experiencing trials, participants engaged in explicit self-regulation of their momentary affective state to match a pre-defined affective goal state. Within these trials, neural decoding methods measured affect processing from fMRI BOLD signals across the orthogonal affective dimensions of valence and arousal. Participants' affective brain states were independently validated via facial electromyography (valence) and electrodermal activity (arousal) responses. The decoded affective states were then used to contrast four computational models of performance monitoring (i.e., error, predicted response outcome, action-value, and conflict) by their relative abilities to explain emotion regulation task-related dACC activation. We found that the dACC most plausibly encodes action-value for both valence and arousal processing. We also confirmed that dACC activation directly encodes affective arousal and also likely encodes recruitment of attention and regulation resources. Beyond its contribution to improving our understanding of the roles that the dACC plays in emotion regulation, this study introduced a novel analytical framework through which affect processing and regulation may be functionally dissociated, thereby permitting mechanistic analysis of real-world emotion regulation strategies, e.g., distraction and reappraisal, which are widely employed in cognitive behavioral therapy to address clinical deficits in emotion regulation.
在这项研究中，我们融合了工程控制理论、机器学习和人类神经影像学的方法，批判性地检验了背侧前扣带皮层（dACC）在情绪调节任务中目标导向表现监测中的作用。健康成年参与者（n=94）接受了情绪图像刺激的提示回忆和重新体验，同时进行功能磁共振成像和心理生理反应记录。在提示回忆/重新体验试验中，参与者通过明确的自我调节来匹配预定义的情绪目标状态，从而调节他们的即时情绪状态。在这些试验中，神经解码方法从 fMRI BOLD 信号测量了跨效价和唤醒度的正交情绪维度的情绪处理。通过面部肌电图（效价）和皮肤电活动（唤醒）反应，独立验证了参与者的情绪大脑状态。然后，使用解码的情绪状态通过相对能力来对比四种性能监测的计算模型（即错误、预测反应结果、动作值和冲突），以解释情绪调节任务相关的 dACC 激活。我们发现，dACC 最合理地编码了效价和唤醒处理的动作值。我们还证实，dACC 激活直接编码了情感唤醒，也可能编码了注意力和调节资源的招募。除了对理解 dACC 在情绪调节中所起的作用有贡献之外，这项研究还通过一种新颖的分析框架引入了一种方法，通过该方法可以对情绪处理和调节进行功能分离，从而允许对现实世界中的情绪调节策略（例如分心和再评价）进行机制分析，这些策略在认知行为疗法中被广泛用于解决情绪调节的临床缺陷。
