Department of Psychology, University of Innsbruck, Innsbruck, Austria.
Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin, Berlin, Germany.
Hum Brain Mapp. 2024 Apr;45(5):e26667. doi: 10.1002/hbm.26667.
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.
情绪调节是个体调节情绪反应以应对不同环境需求的过程,例如,通过重新评估情绪情境。在这里,我们测试了在情绪调节任务中,重新评估相关神经网络的静息状态有效连接是否可以预测成功调节高强度和低强度负性情绪。在三个扫描阶段,使用 7T 超高磁场强度采集了 28 名参与者的任务基和静息态功能磁共振成像(rs-fMRI)数据。我们在受情绪强度调制的脑区的 rs-fMRI 数据上使用了谱动态因果建模(spDCM)。我们发现了与高强度和低强度刺激都相关的共同连接模式。在与低强度刺激相关的研究中,我们从额叶区域到颞叶区域发现了独特的有效连接模式。对于高强度刺激,重新评估的成功可以通过 vlPFC 内的额外连接以及从颞叶到额叶区域的连接来预测。在静息状态下预测重新评估成功的连接模式通常对于低强度刺激更为明显,这表明在重新评估相对容易实施时,刻板模式可能反映了准备状态,可能具有更大的作用。对于高强度刺激则相反,这可能需要超出静息状态连接模式所反映的资源更灵活的招募。静息状态有效连接作为成功重新评估的有力预测指标出现,揭示了高强度和低强度刺激的共享和独特网络动态。这些模式标志着与警觉、注意力、自我意识和目标导向认知处理相关的特定预备状态,特别是在重新评估以减轻外部刺激的情绪影响时。我们的研究结果对于理解与情感障碍相关的大脑连接的心理病理改变具有潜在意义。