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Emotion Regulation in Anxiety & Depression: A Novel Neurobehavioral Intervention

Emotion Regulation in Anxiety & Depression: A Novel Neurobehavioral Intervention
焦虑时的情绪调节
批准号:
8744326
负责人:
Amit Etkin
金额:
$23.98万
依托单位国家:
美国
项目类别:
财政年份:
2012
资助国家:
美国
项目状态:
已结题
起止时间:
2012-08-01 至 2017-06-30

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中文摘要
翻译
描述(由申请人提供):焦虑和情绪障碍是非常普遍的,往往是共病,当这样的时候,与发病率和残疾的增加。特别是,抑郁症背景下的焦虑预示着一系列治疗的结果更糟。焦虑和情绪障碍中过度的、无法控制的负面情绪的共性表明了情绪反应和调节的核心缺陷。我们和其他人已经描述了一个神经行为系统,包括前扣带回、杏仁核和外侧前额叶皮层,它们与情绪反应和调节有关。在焦虑和情绪障碍患者中,该回路内的活动和连接都受到干扰,这支持了以下假设,即情绪处理功能障碍是这些疾病的核心,并且现在可以使用经过验证的工具来客观地评估这种神经系统。这些发现为旨在改善情绪调节的新型干预方法奠定了坚实的科学基础,这些方法可以通过互联网提供,从而增加有效治疗的可用性。我们提议尝试一种新的网络 递送的基于神经可塑性的神经行为干预,其通过以下方式改善情绪调节:1)灌输对积极刺激的偏好,2)改善对情绪分心的抵抗力,以及3)更普遍地增强执行功能(例如,工作记忆、任务切换、抵抗干扰),因为这些是成功实施情绪调节(ER)所需的。 在R21阶段,30名无药物治疗的焦虑抑郁症患者(总Ham-D1 <$7 e16,Ham-D17焦虑子量表<$7)将接受60天的互联网培训。这一阶段的目的是优化培训和评估程序(症状,行为和功能磁共振成像),以及提供初步证据,将培训推广到与焦虑和抑郁相关的措施。在R33阶段,60名无药物治疗的焦虑抑郁症患者将随机接受60天的ER神经行为干预(如果R21指示,则进行调整)或类似参与但不提供情绪调节益处的在线任务的主动对照组。本阶段的目的是提供支持神经行为干预的优化版本的效用的初步证据,重点是效应量估计和大规模临床试验的招募和实施参数的描述。我们预见我们的互联网提供的,适应性神经行为培训ER适用于一系列精神疾病,特别是情绪或焦虑症。拟议研究的成功完成将为这种新的干预方法的实用性提供令人信服的初步证据,以神经科学对焦虑和抑郁核心缺陷的理解为指导,进行更大,更明确的临床试验。
英文摘要
DESCRIPTION (provided by applicant): Anxiety and mood disorders are highly prevalent, often co-morbid, and when so, are associated with increased morbidity and disability. In particular, anxiety in the context of depression predicts worse outcome with a range of treatments. The commonality of excessive, uncontrollable negative emotion across anxiety and mood disorders suggests a core deficit in emotional reactivity and regulation. We, and others, have delineated a neurobehavioral system, involving the anterior cingulate, amygdala, and lateral prefrontal cortex, that is involved in emotional reactivity and regulation. Both activity ad connectivity within this circuit are perturbed in patients with anxiety and mood disorders, supporting the hypothesis that dysfunction in the handling of emotion lies at the core of these disorders, and that validated tools are now available for objectively assessing this neural system. These findings constitute a robust scientific foundation for novel intervention approaches aimed at improving emotion regulation, which could be delivered over the internet, thereby also increasing availability of effective treatments. We propose to pilot a novel, internet delivered neuroplasticity-based neurobehavioral intervention, which improves emotion regulation by 1) instilling a bias towards positive stimuli, 2) improving resistance to emotional distraction, and 3) enhancing executive functioning more generally (e.g. working memory, task switching, resisting interference), as these are required for successful implementation of emotion regulation (ER). In the R21 phase, thirty medication-free patients with anxious depression (total Ham-D1¿7e16, Ham-D17 anxiety subscale ¿7) will be given 60 days of internet-delivered training. The aim of this phase is to optimize the training and assessment procedures (symptom, behavioral and fMRI), as well as to provide initial evidence for generalization of the training to measures relevant for anxiety and depression. In the R33 phase, sixty medication-free patients with anxious depression will be randomized to receive the 60-day ER neurobehavioral intervention (with adjustments if indicated by the R21) or an active control arm of online tasks that are similarly engaging but provide no emotion regulatory benefits. The aim of this phase is to provide initial evidence supporting the utility of an optimizd version of the neurobehavioral intervention, focusing on effect size estimation and delineation of recruitment and implementation parameters for a large-scale clinical trial. We foresee our internet-delivered, adaptive neurobehavioral training for ER as being applicable for a range of psychiatric disorders, and in particular for mood or anxiety disorders. Successful completion of the proposed study would provide compelling initial evidence for the utility of this novel intervention approach, guided by a neuroscientific understanding of core deficits in anxiety and depression, for a larger and more definitive clinical trial.
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Validating of Machine Learning-Based EEG Treatment Biomarkers in Depression
  • 批准号:
    10009501
  • 项目类别:
  • 资助金额:
    $98.81万
  • 财政年份:
    2020
  • 负责人:
    Amit Etkin
  • 依托单位:
Validating of Machine Learning-Based EEG Treatment Biomarkers in Depression
  • 批准号:
    10116492
  • 项目类别:
  • 资助金额:
    $118.41万
  • 财政年份:
    2020
  • 负责人:
    Amit Etkin
  • 依托单位:
Validating of Machine Learning-Based EEG Treatment Biomarkers in Depression
  • 批准号:
    10366060
  • 项目类别:
  • 资助金额:
    $101.65万
  • 财政年份:
    2020
  • 负责人:
    Amit Etkin
  • 依托单位:
Assessing an electroencephalography (EEG) biomarker of response to transcranial magnetic stimulation for major depression
海外基金