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

将自然任务期间的大脑活动与抑郁谱上的个体表型联系起来

• 批准号: 10238174
• 负责人: Emily Suzanne Finn
• 金额: $ 24.9万
• 依托单位: DARTMOUTH COLLEGE
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10238174
• 关键词: Address; Affect; Anxiety; Area; Attenuated; Award; Base of the Brain; Behavior; Behavioral; Behavioral Research; Biological Markers; Brain; Brain imaging; Categories; Clinical; Clip; Cognition; Combined Modality Therapy; Communities; Complex; Data; Data Analyses; Detection; Diagnosis; Dimensions; Disease; Early Intervention; Emotional; Emotions; Engineering; Environment; Event; Face; Faculty; Family; Film; Functional Magnetic Resonance Imaging; Funding; Future; Glean; Goals; Health; Heterogeneity; Human; Hyperactivity; Individual; Individual Differences; Intramural Research Program; Lead; Learning; Link; Literature; Longitudinal Studies; Machine Learning; Magnetic Resonance Imaging; Measures; Mental Depression; Mental Processes; Mental disorders; Methods; Modeling; Mood Disorders; National Institute of Mental Health; Nature; Neurobiology; Neurosciences; Normalcy; Pathology; Patients; Pattern; Personality; Phenotype; Population; Populations at Risk; Prognosis; Property; Psychiatrist; Psychiatry; Research; Research Domain Criteria; Research Personnel; Resources; Rest; Risk; Sampling; Scanning; Senior Scientist; Social Environment; Societies; Stimulus; Stress Tests; Symptoms; System; Techniques; Time; Training; Translating; United States National Institutes of Health; Universities; Variant; Work; anxious; base; behavioral outcome; behavioral response; brain behavior; career; career development; cognitive neuroscience; data acquisition; design; diagnostic biomarker; emotional stimulus; healthy volunteer; individual variation; innovation; insight; interest; large scale data; member; mood symptom; movie; neuroimaging; novel; novel strategies; outcome prediction; programs; public health relevance; relating to nervous system; response; skills; tool; trait

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.

项目总结/摘要 许多精神疾病，特别是抑郁症和其他精神疾病， 情绪障碍-最好被概念化为表型谱的远端，这表明， 描述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，功能磁共振成像有潜力从大脑功能预测个人行为，但进展 由于过度依赖小组研究而受到阻碍（即，患者与对照组）和有限的范例 (i.e.,要么是高度控制的任务，有可能是人为的，要么是在另一个极端，休息状态，这是完全 不受约束且难以解释）。自然主义的功能磁共振成像，其中受试者做复杂的，从事的任务，如 看电影或听故事，提供了一种更接近模仿现实世界认知的替代方案， 使研究人员能够从单个人的扫描中提取更丰富，更有意义的信息。因此，这些 范式是大脑“压力测试”的有希望的候选者，该测试将引出大脑活动模式，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 发展从自然的fMRI数据预测表型的方法;（2）设计和进行fMRI研究 使用有针对性的电影刺激来引出感兴趣的个体差异，特别是与抑郁症相关的特征; 以及（3）将新开发的范式和分析扩展到对处于风险中的人群的纵向研究， 抑郁症和/或其他情绪障碍。将研究几种创新的数据分析方法。 核心假设是，这些范例引起的大脑活动在一个特定的时间内会因个体而异。 连续的，多维的空间，与表型强度协变，这些关系将是强大的 足以预测看不见的个体中的表型，并且修饰的（例如，模式的衰减）版本 在症状出现之前，通过这些范例可以检测到与疾病相关的风险。 PI的长期目标是成为一个独立的NIH资助的教师在一个研究密集型 大学，研究项目探索个体差异的基本认知神经科学， 人格和认知，以及开发精神病学的翻译应用。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 学习技术，以预测个体受试者的行为，从功能磁共振成像数据;（2）进行神经成像， 对临床和高危人群进行抑郁和情绪障碍的行为研究;（3）获得 成功的独立研究生涯所必需的专业技能。职业生涯的环境 发展将发生的是精神卫生的国家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，包括相关的课程， 研讨会，最先进的MRI数据采集和分析设施，计算能力和专业知识， 来自神经科学、工程学、机器学习和精神病学资深科学家的指导。