PDF(Pubmed)
Abstract：
Emotion regulation is a process by which individuals modulate their emotional responses to cope with different environmental demands, for example, by reappraising the emotional situation. Here, we tested whether effective connectivity of a reappraisal-related neural network at rest is predictive of successfully regulating high- and low-intensity negative emotions in an emotion-regulation task. Task-based and resting-state functional magnetic resonance imaging (rs-fMRI) data of 28 participants were collected using ultra-high magnetic field strength at 7 Tesla during three scanning sessions. We used spectral dynamic causal modeling (spDCM) on the rs-fMRI data within brain regions modulated by emotion intensity. We found common connectivity patterns for both high- and low-intensity stimuli. Distinctive effective connectivity patterns in relation to low-intensity stimuli were found from frontal regions connecting to temporal regions. Reappraisal success for high-intensity stimuli was predicted by additional connections within the vlPFC and from temporal to frontal regions. Connectivity patterns at rest predicting reappraisal success were generally more pronounced for low-intensity stimuli, suggesting a greater role of stereotyped patterns, potentially reflecting preparedness, when reappraisal was relatively easy to implement. The opposite was true for high-intensity stimuli, which might require a more flexible recruitment of resources beyond what is reflected in resting state connectivity patterns. Resting-state effective connectivity emerged as a robust predictor for successful reappraisal, revealing both shared and distinct network dynamics for high- and low-intensity stimuli. These patterns signify specific preparatory states associated with heightened vigilance, attention, self-awareness, and goal-directed cognitive processing, particularly during reappraisal for mitigating the emotional impact of external stimuli. Our findings hold potential implications for understanding psychopathological alterations in brain connectivity related to affective disorders.
摘要：
情绪调节是个人调节其情绪反应以应对不同环境需求的过程，例如,通过重新评估情绪状况。这里,我们测试了在情绪调节任务中,与重新评价相关的神经网络在静止状态下的有效连接是否可以预测成功调节高和低强度负面情绪.在三个扫描过程中，使用7特斯拉的超高磁场强度收集了28名参与者的基于任务和静息状态的功能磁共振成像（rs-fMRI）数据。我们对受情绪强度调节的大脑区域内的rs-fMRI数据使用了频谱动态因果模型（spDCM）。我们发现了高强度和低强度刺激的共同连接模式。从连接到颞区的额叶区域发现了与低强度刺激有关的独特有效连接模式。通过vlPFC内以及从颞叶到额叶区域的其他连接，可以预测高强度刺激的重新评估成功。对于低强度刺激，休息时预测重新评估成功的连通性模式通常更明显，表明刻板印象模式的作用更大，可能反映了准备，当重新评估相对容易实施时。高强度刺激的情况正好相反，这可能需要比静息状态连接模式所反映的更灵活的资源招募。静息状态有效连通性成为成功重新评估的可靠预测指标，揭示高和低强度刺激的共享和不同的网络动态。这些模式意味着与提高警惕相关的特定准备状态，注意,自我意识,和目标导向的认知过程，特别是在重新评估以减轻外部刺激的情绪影响时。我们的发现对理解与情感障碍相关的大脑连接的心理病理学改变具有潜在意义。
