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

基本信息

批准号：
10415111
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
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-07-01 至 2023-09-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10415111
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 translational applications

项目摘要

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.

项目摘要 /摘要而不是健康与病理学之间的二分法，而是许多精神疾病，尤其是抑郁症和其他情绪障碍 - 最好将其概念化为表型光谱的远端，表明表征脑功能和行为的个体差异将有助于我们进一步理解疾病。现有工作表明fMRI有可能从大脑功能中预测个人行为，但进展对小组研究的过度依赖（即患者与对照组）和有限的范例受到了阻碍（即，高度控制的任务是人造的，或者处于另一个极端的静止状态，这完全是不受约束且难以解释）。自然主义的fMRI，受试者执行复杂，引人入胜的任务，例如看电影或听故事，提供了一种更紧密地模仿现实世界认知的替代方法允许研究人员从一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”的有前途的候选人，这将引起大脑活动的模式，以预测现在或将来的行为。该项目的具体目的是：（1）利用现有的大规模数据集开发从自然主义功能磁共振成像数据中预测表型的方法；（2）设计和进行fMRI研究使用靶向膜刺激来提出感兴趣的个体变异性，特别是在与抑郁有关的特征中；（3）将新开发的范例扩展到对有风险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。中心假设是，这些范式引起的大脑活动会因某人的个人而异与表型强度协变量的连续，多维空间，这些关系将是牢固的足以预测看不见的个体的表型，并修改了模式的（例如，减弱）版本在出现症状之前，与疾病相关的人将通过这些范式检测到有风险的人。 PI的长期目标是成为一名研究密集型NIH资助的独立教师大学，通过一项研究计划，探讨了个体差异的基本认知神经科学个性和认知，以及为精神病学的转化应用。为了达到这个目标，该奖项的培训目标是在以下领域提高PI的技能：（1）应用机器从fMRI数据中预测个人受试者行为的学习技术；（2）进行神经影像学和临床和高危人群的抑郁症和情绪障碍的行为研究；（3）获得成功独立研究职业至关重要的专业技能。职业的环境发展将进行开发是国家心理健康研究所的壁内研究计划充满活力的社区，拥有杰出的资源来支持拟议项目，包括相关课程和 SEMIAR，MRI数据获取和分析，计算能力和专业知识的最先进设施，神经科学，工程，机器学习和精神病学高级科学家的指导。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Is it time to put rest to rest?

DOI：
10.1016/j.tics.2021.09.005
发表时间：
2021-12
期刊：
Trends in cognitive sciences
影响因子：
19.9
作者：
Finn ES
通讯作者：
Finn ES

Functional connectivity during frustration: a preliminary study of predictive modeling of irritability in youth.

DOI：
10.1038/s41386-020-00954-8
发表时间：
2021-06
期刊：
Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology
影响因子：
0
作者：
Scheinost D;Dadashkarimi J;Finn ES;Wambach CG;MacGillivray C;Roule AL;Niendam TA;Pine DS;Brotman MA;Leibenluft E;Tseng WL
通讯作者：
Tseng WL

Movie-watching outperforms rest for functional connectivity-based prediction of behavior.