Resting State and Task-Evoked Neural Bases of Rumination and Affective Dysfunction

 描述(申请人提供)：[抑郁症是一种常见的疾病，对公众健康有重大影响。]最近的研究将默认模式网络(DMN)中的[抑郁与功能异常]联系在一起，DMN是一个区域网络，通常在休息时活跃，在认知任务中失活。[抑郁症与DMN连接性增加有关]在静息状态功能磁共振成像期间，当个体观看负面图像时，相对失败地抑制DMN。此外，抑郁的人表现出对负面图像的杏仁核反应增强和延长。[这导致了一种理论，即DMN的异常可能会干扰抑郁症患者大脑区域的招募，以调节杏仁核。][抑郁症患者的这些神经标记物也与沉思有关，这是一种涉及重复的负面想法的不适应应对方式。]这项工作建议通过在静息状态扫描期间和参与者观看负图时对个体的fMRI数据进行跨模式比较来测试这一模型。两个主要的假设将被检验：1)安静状态DMN[功能]将预测负面图像观看时情绪的自上而下调节减少，2)安静状态DMN[功能]增加，负面图像观看时自上而下情绪调节减少，将由增加的特质[沉思]预测。对负像观看过程中自上而下情绪调节的评估将是多通道的，将重点放在图像偏移后6-12s的时间段，捕捉到从负像中“恢复”，同时控制最初6 S对图像的反应活动(“反应性”)。测量将包括杏仁核活动，杏仁核-前额叶连接，以及上眉皱肌的肌电图，这是一种可靠的负面情绪体验指标。[此外，多变量模式分析(MVPA)将用于对负性图像和中性图像进行分类，作为神经反应的替代衡量标准。]DMN[功能]将通过两种方式进行评估。首先，将使用基于相关性的方法计算DMN的两个主要节点(内侧前额叶皮质和后扣带回皮质)之间的功能连接性。其次，[认知控制网络上DMN支配地位的衡量标准将按照其他研究人员之前使用的方法计算。为了评估静息状态DMN功能如何预测自上而下的情绪调节，这些DMN指标将回归到上述所有情绪调节指标上。为了评估这些措施如何对应于特征孵化，将使用标准化测量(反省反应量表；RRS)[并回归到上述结果衡量标准]来评估孵化。这项工作将阐明DMN活动、情绪调节的[神经关联]和[沉思]与情感障碍的风险和治疗的关系。

 DESCRIPTION (provided by applicant): [Depression is a common disorder with a major public health impact.] Recent research has linked [depression with abnormal function] in the default-mode network (DMN), a network of regions that is typically active at rest, and deactivates during cognitive tasks. [Depression has been linked with increased DMN connectivity] during resting-state fMRI, and a relative failure to suppress DMN when individuals view negative images. In addition, depressed individuals demonstrate enhanced and prolonged amygdala responses to negative images. [This has led to the theory that DMN abnormalities might interfere with recruitment of brain regions to regulate amygdala in depressed individuals.] [These neural markers in depressed individuals have also been related to brooding rumination, a type of maladaptive coping involving repetitive negative thoughts.] This work proposes to test this model using cross-modal comparison of fMRI data in individuals during a resting-state scan and while participants are viewing negative images. Two main hypotheses will be tested: 1) resting-state DMN [function] will predict decreased top-down regulation of emotion during viewing of negative images, and 2) increased resting-state DMN [function], and decreased top-down emotion regulation during negative-image-viewing, will be predicted by increased trait [brooding]. Assessment of top-down emotion regulation during negative-image-viewing will be multimodal and will focus on the time period 6-12s after image offset, capturing "recovery" from the negative image, while controlling for initial 6 s of activity in response to the image ("reactivity"). Measures will include amygdala activity, amygdala-prefrontal connectivity, and electromyography of the corrugator supercilii, a muscle that is a reliable indicator of the experience of negative emotion. [In addition, multivariate pattern analysis (MVPA) will be used to classify negative vs. neutral images as an alternative measure of neural responses.] DMN [function] will be assessed in two ways. First, functional connectivity between two major nodes of the DMN (medial prefrontal cortex and posterior cingulate cortex) will be calculated using correlation-based methods. Second, [a metric of DMN dominance over a cognitive control network will be calculated following methods used previously by other researchers. In order to assess how resting-state DMN function predicts top-down emotion regulation, these DMN metrics will be regressed onto all of the above metrics of emotion regulation. In order to assess how these measures correspond to trait brooding, brooding will be assessed using a standardized measure (the Ruminative Responses Scale; RRS) [and also regressed onto the outcome measures above]. This work will elucidate the relationship of DMN activity, [neural correlates of] emotion regulation and [brooding] with implications for risk and treatment of affective disorders.