Linking brain activity during naturalistic tasks to individual phenotypes on the depression spectrum

项目摘要/摘要 许多精神疾病，特别是抑郁症和其他疾病，而不是健康和病理之间的二分法 情绪障碍--最好将其概念化为表型谱的另一端，这表明 确定大脑功能和行为的个体差异将有助于我们进一步了解疾病。 现有的工作表明，功能磁共振成像具有根据大脑功能预测个人行为的潜力，但进展 由于过度依赖群体研究(即患者与对照)和有限的范例而受到阻碍 (即，要么是有人工风险的高度受控任务，要么是完全处于静止状态的另一个极端 不受约束且难以解释)。自然主义功能磁共振成像，受试者做复杂的、引人入胜的任务，如 看电影或听故事，提供了一种更接近真实世界认知的选择，可能 允许研究人员从单个人的扫描中提取更丰富、更有意义的信息。因此，这些 范例有望成为大脑“压力测试”的候选者，这种测试将引发大脑活动的模式，从而预测 现在或将来的行为。该项目的具体目标是：(1)利用现有的大规模数据集 开发从自然功能磁共振成像数据中预测表型的方法；(2)设计和进行功能磁共振成像研究 使用有针对性的电影刺激来提取感兴趣的个体差异，特别是与抑郁相关的特征； 以及(3)将新开发的范例和分析扩展到对高危人群的纵向研究 抑郁和/或其他情绪障碍。将研究几种创新的数据分析方法。 中心假设是，这些范例引起的大脑活动在不同的个体之间会有所不同 与表型强度相关的连续的多维空间，这些关系将是强大的 足以预测看不见的个体的表型，以及模式的修改(例如，减弱)版本 在症状出现之前，将通过这些范例在有风险的人中检测到与疾病相关的疾病。 PI的长期目标是成为美国国立卫生研究院资助的研究密集型大学的独立教员 大学的一个研究项目，探索个体差异的基本认知神经科学 个性和认知，以及开发精神病学的翻译应用程序。为了实现这一目标， 该奖项的培训目标是提高PI在以下方面的技能：(1)应用机器 从功能磁共振成像数据预测个体受试者行为的学习技术；(2)进行神经成像和 临床和高危人群中抑郁和情绪障碍的行为研究；以及(3)获得 成功的独立研究事业所必需的专业技能。职业生涯所处的环境 将开展的是国家精神卫生研究所的校内研究计划， 充满活力的社区，拥有出色的资源来支持拟议的项目，包括相关课程和 研讨会、用于MRI数据采集和分析的最先进设施、计算能力和专业知识以及 神经科学、工程学、机器学习和精神病学方面的资深科学家的指导。

PROJECT SUMMARY / ABSTRACT Rather than a dichotomy between health and pathology, many mental illnesses—especially depression and other mood disorders—are best conceptualized as the far end of a phenotypic spectrum, suggesting that characterizing individual differences in brain function and behavior will help further our understanding of disease. Existing work suggests that fMRI has the potential to predict individual behaviors from brain function, yet progress has been hindered by an overreliance on group studies (i.e., patients versus controls) and limited paradigms (i.e., either highly controlled tasks that risk being artificial, or at the other extreme, resting state, which is entirely unconstrained and difficult to interpret). Naturalistic fMRI, in which subjects do complex, engaging tasks such as watching films or listening to stories, offers an alternative that more closely mimics real-world cognition and may allow researchers to extract richer, more meaningful information from a single individual’s scan. As such, these paradigms are promising candidates for brain “stress tests” that would elicit patterns of brain activity that predict present or future behaviors. The specific aims of this project are: (1) to leverage existing large-scale datasets to develop methods to predict phenotypes from naturalistic fMRI data; (2) to design and conduct an fMRI study using targeted film stimuli to draw out individual variability of interest, specifically in traits related to depression; and (3) to extend the newly developed paradigms and analyses to a longitudinal study of a population at risk for depression and/or other mood disorders. Several innovative approaches to data analysis will be investigated. The central hypothesis is that brain activity evoked by these paradigms will vary across individuals in a continuous, multidimensional space that covaries with phenotype strength, that these relationships will be strong enough to predict phenotypes in unseen individuals, and that modified (e.g., attenuated) versions of patterns associated with illness will be detectable via these paradigms in those at risk before the emergence of symptoms. The long-term goal of the PI is to become an independent NIH-funded faculty member at a research-intensive university, with a research program exploring the basic cognitive neuroscience of individual differences in personality and cognition, as well as developing translational applications for psychiatry. To reach this goal, the training objectives for this award are to enhance the PI’s skills in the following areas: (1) applying machine learning techniques to predict individual-subject behavior from fMRI data; (2) conducting neuroimaging and behavioral research on depression and mood disorders with clinical and at-risk populations; and (3) gaining professional skills essential for a successful independent research career. The environment in which the career development will take place is the Intramural Research Program of the National Institute of Mental Health, a vibrant community with outstanding resources to support the proposed project, including relevant courses and seminars, state-of-the-art facilities for MRI data acquisition and analysis, computing power, and expertise and guidance from senior scientists in neuroscience, engineering, machine learning and psychiatry.