Cognitive and Affective Neurosciences of Depression

描述：（由申请人提供）情感信息中断 处理（例如，对情感的关注、记忆和解释 信息）与抑郁症有关。新兴的情感领域 神经科学表明，情绪的变化有明显的生理相关性， 信息处理风格拟议的研究整合了基础研究 关于情感神经科学的认知-临床研究的结论， 抑郁信息处理，以了解大脑机制相关 与抑郁症中情绪信息处理的中断有关。 申请人Greg Siegle博士研究了信息处理， 抑郁症八年，并已开发出一种计算神经 网络模型，表明特定的认知，环境， 生物因素与抑郁症有关。进一步的培训 这项研究的下一步是必要的，其中涉及明确联系 观察到的认知现象和模型行为， 机制等短期目标包括使用认知、生理和 神经成像评估，以测试预测，使用模型生成， 关于各种因素的相互作用， 抑郁情绪信息处理长期目标包括利用这个 研究指导抑郁症患者进行针对性治疗， 他们独特的认知和生理特征，从而提高速度， 抑郁症干预的效果。 这项拟议中的研究涉及评估抑郁症患者的瞳孔扩张情况 和大脑活动（通过功能性磁共振成像; fMRI）， 情绪信息处理任务，以创建情绪的配置文件 抑郁症的信息处理中断。计算神经网络 相关大脑结构之间的相互作用建模将用于 在整个研究过程中产生并完善假设和实验设计。 这项研究将允许发展和完善的正式理论， 抑郁症个体差异的心理生物学机制 信息处理偏差这将导致R 01提案， 了解与治疗相关的大脑活动变化 萧条研究将在西方精神病研究所进行， 抑郁症心理生物学世界研究中心。 抑郁症专家的研究人员以及拟议的评估 技术将成为导师和导师。

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.