Tracking brain arousal fluctuations for fMRI Big Data discovery

跟踪大脑唤醒波动以发现功能磁共振成像大数据

• 批准号: 9982966
• 负责人: Catherine Elizabeth Chang
• 金额: $ 20.25万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-04-01 至 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9982966
• 关键词: Address; Advanced Development; Aging; Alzheimer' s Disease; Alzheimer’s disease biomarker; Arousal; Behavior; Behavioral; Big Data; Biological Availability; Biological Markers; Brain; Brain Diseases; Clinical; Clinical Research; Cognitive; Communities; Complex; Data; Data Analyses; Data Discovery; Data Science; Data Set; Databases; Detection; Diagnosis; Dimensions; Disease; Disease Progression; Electroencephalography; Environment; Extramural Activities; Functional Magnetic Resonance Imaging; Goals; Growth; Health; Human; Intramural Research Program; Knowledge; Longevity; Machine Learning; Measures; Mental disorders; Mentors; Methods; Modeling; Neurodegenerative Disorders; Neurosciences; Neurosciences Research; Outcome; Participant; Phase; Physiologic Monitoring; Physiological; Process; Property; Research; Research Personnel; Resources; Signal Transduction; Software Tools; Source; Space Models; Techniques; Training; United States National Institutes of Health; Universities; Visit; Wakefulness; Work; age related; aged; alertness; base; behavior measurement; big-data science; brain dysfunction; career; clinical database; cohort; data acquisition; dimensional analysis; experience; flexibility; healthy aging; high dimensionality; human data; imaging study; improved; indexing; innovation; learning strategy; mild cognitive impairment; multimodality; nervous system disorder; neuroimaging; neuroimaging marker; neurophysiology; neuropsychiatric disorder; novel; rapid growth; recurrent neural network; relating to nervous system; specific biomarkers; statistical learning; statistics; symposium; tool; usability; visual tracking

Recent years have seen rapid growth in the availability of large, complex functional magnetic resonance imaging (fMRI) datasets of the human brain. However, the potential of this fMRI Big Data is presently limited by our understanding of the neural sources that contribute to fMRI signals. Fluctuations in arousal (i.e., in the level wakefulness and alertness) are known to modulate cognitive and behavioral processes and to display prominent alterations in neuropsychiatric disorders. Yet, since the vast majority of fMRI datasets lack neurophysiological or behavioral indices of arousal, fMRI Big Data cannot be readily harnessed to understand human brain arousal in health and disease. Recent data-driven approaches attempt to fill this gap but have limitations. The overall goal of this proposal is to increase the transformative potential of fMRI Big Data for human neuroscience through a novel analytic framework for detecting arousal fluctuations from fMRI data alone. We will accomplish this goal by developing and disseminating tools for modeling arousal fluctuations based on powerful statistical learning methods (Specific Aim 1). We will apply these models to large fMRI databases of healthy aging and Alzheimer’s Disease, both of which are associated with altered arousal (Specific Aims 2 and 3). We will capitalize on these databases to determine how knowledge of brain arousal fluctuations improves neuroimaging biomarkers of aging- and neurodegenerative disease-related changes in human brain function, and the extent to which arousal itself constitutes an informative biomarker of these states. This research would, moreover, increase the reliability and translational potential of fMRI studies more broadly by providing the ability to account for these major neural (arousal) state changes. These immediate research goals form a strong bridge with my long-term research objective of understanding principles of brain function by developing and innovatively adapting methods for the analysis of large and complex neuroimaging datasets. This objective is enabled by the mentored training plan, where I will (i) develop expertise in cutting-edge machine learning techniques and (ii) apply these techniques to multimodal neuroimaging data. The two co-mentors have complementary expertise that align, respectively, with these two training components. Aims 1 and 2 will span the mentored phase and part of the independent phase, while Aim 3 (application to the Alzheimer’s Disease Neuroimaging Initiative data) will be performed in the independent phase. The mentored environment of the NIH Intramural Research Program provides the resources for all planned data acquisition, as well as a rich community of neuroscience investigators and seminars. Interaction with the extramural (Columbia University) co-mentor will occur through frequent video conferences and several visits, with opportunities to engage with the Columbia data science community.

近年来，大型、复杂的功能性磁共振成像的可用性迅速增长。 人类大脑的功能磁共振成像（fMRI）数据集。然而，这种fMRI大数据的潜力目前受到限制， 我们对功能磁共振成像信号的神经源的理解。觉醒的波动（即，水平 清醒和警觉）被认为调节认知和行为过程，并显示 神经精神疾病的显著改变。然而，由于绝大多数功能磁共振成像数据集缺乏 神经生理学或行为指标的唤醒，功能磁共振成像大数据不能很容易地利用来了解 人类大脑在健康和疾病中的觉醒。最近的数据驱动方法试图填补这一空白，但 局限性。该提案的总体目标是增加fMRI大数据的变革潜力， 人类神经科学通过一种新的分析框架，从fMRI数据中检测唤醒波动 一个人我们将通过开发和传播模拟唤醒波动的工具来实现这一目标 基于强大的统计学习方法（具体目标1）。我们将把这些模型应用于大型功能磁共振成像 健康老龄化和老年痴呆症的数据库，这两种疾病都与唤醒改变有关。 （具体目标2和3）。我们将利用这些数据库来确定大脑唤醒的知识 波动改善了衰老和神经退行性疾病相关变化的神经影像学生物标志物， 人类大脑功能，以及唤醒本身构成这些信息生物标志物的程度 states.此外，这项研究将进一步提高功能磁共振成像研究的可靠性和转化潜力。 通过提供解释这些主要神经（唤醒）状态变化的能力来广泛地实现。 这些近期的研究目标与我的长期研究目标形成了一个牢固的桥梁， 通过开发和创新性地调整分析方法来理解大脑功能的原理， 大型和复杂的神经成像数据集。这一目标是通过指导培训计划实现的，在该计划中，我将 (i)开发尖端机器学习技术的专业知识，并（ii）将这些技术应用于多模式 神经成像数据。这两位共同导师具有互补的专业知识，分别与这两个 培训内容。目标1和2将跨越辅导阶段和独立阶段的一部分， 3（应用于阿尔茨海默病神经影像学倡议数据）将在独立的 相位NIH校内研究计划的指导环境为所有人提供了资源。 有计划的数据采集，以及丰富的神经科学研究人员和研讨会社区。相互作用 与校外（哥伦比亚大学）共同导师将通过频繁的视频会议和几个 访问，并有机会与哥伦比亚数据科学界接触。

项目成果

All-night functional magnetic resonance imaging sleep studies.

• DOI: 10.1016/j.jneumeth.2018.09.019
• 发表时间: 2019-03-15
• 期刊: Journal of neuroscience methods
• 影响因子: 3
• 作者: Moehlman TM;de Zwart JA;Chappel-Farley MG;Liu X;McClain IB;Chang C;Mandelkow H;Özbay PS;Johnson NL;Bieber RE;Fernandez KA;King KA;Zalewski CK;Brewer CC;van Gelderen P;Duyn JH;Picchioni D
• 通讯作者: Picchioni D