Cognitive and Affective Neurosciences of Depression

描述：(申请人提供)情绪信息中断 处理(例如，对情绪的关注、记忆和解释 信息)与抑郁症有牵连。新兴的情感领域 神经科学表明，情绪与情绪有明显的生理关联 信息处理风格。建议的研究整合了基础研究。 从认知-临床研究的结论谈情感神经科学 抑郁信息处理以了解相关的大脑机制 抑郁症患者的情绪信息处理中断。 申请人Greg Siegle博士，博士，曾在 抑郁障碍八年了，并开发了一种计算神经 网络模型建议将特定的认知、环境、 而生物因素与抑郁症有关。进一步的培训是 对于这项研究的下一步来说是必要的，这涉及到明确的联系 观察到的认知现象和对潜在生物的模拟行为 机械装置。短期目标包括使用认知、生理和 神经成像评估，以测试使用该模型生成的预测， 关于可能导致不同特征的因素的相互作用 抑郁情绪信息处理。长期目标包括利用这一点 研究指导抑郁症患者进行有针对性的治疗 他们独特的认知和生理特征，从而提高速度和 抑郁干预的有效性。 这项拟议的研究涉及评估抑郁症患者的瞳孔扩张情况。 和大脑活动(通过功能磁共振成像；功能磁共振成像) 创建情感档案的情感信息处理任务 抑郁症患者的信息处理障碍。计算神经网络 相关大脑结构之间的相互作用的建模将用于 在整个研究过程中生成和参考假设和实验设计。 这项研究将使一个正式的理论得以发展和完善 抑郁症个体差异的心理生物学机制 信息处理偏向。它将导致R01提案，涉及 了解与治疗有关的脑活动的变化 抑郁症。研究将在西方精神病学研究所进行， 世界抑郁症心理生物学研究中心。 研究抑郁症和拟议评估的专家 技术将充当导师和训导者。

DESCRIPTION: (provided by applicant) Disruptions of emotional information processing (e.g., attention to, memory for, and interpretation of emotional information) have been implicated in depression. The emerging field of affective neuroscience suggests there are distinct physiological correlates of emotional information processing styles. The proposed research integrates basic research on affective neuroscience with conclusions from cognitive-clinical research regarding depressive information processing to understand brain mechanisms associated with disruptions of emotional information processing in depression. The applicant, Dr. Greg Siegle, Ph.D., has examined information processing in depressive disorders for eight years, and has developed a computational neural network model that suggests a convergence of specific cognitive, environmental, and biological factors are associated with depression. Further training is necessary for the next step in this research, which involves clearly linking observed cognitive phenomena and model behaviors to underlying biological mechanisms. Short-term goals include using cognitive, physiological, and neuroimaging assessment to test predictions, generated using the model, regarding interactions of factors that could lead to distinct profiles of depressive emotional information processing. Long-term goals involve using this research to direct depressed individuals to focused treatments that target their particular cognitive and physiological profiles, thus improving speed and efficacy of depression interventions. The proposed research involves assessing depressed individuals' pupil dilation and brain activity (via functional magnetic resonance imaging; fMRI) during emotional information processing tasks to create profiles of emotional information processing disruptions in depression. Computational neural network modeling of interactions between relevant brain structures will be used to generate and refme hypotheses and experimental design throughout the study. This research will allow development and refinement of a formal theory of psychobiological mechanisms underlying individual differences in depressive information processing biases. It will lead to an R01 proposal involving understanding changes in brain activity associated with treatment for depression. Research will be conducted at the Western Psychiatric Institute and Clinic, a world center for research on the psychobiology of depression. Researchers who are expert in depression as well as the proposed assessment technologies will serve as mentors and preceptors.