Tracking brain arousal fluctuations for fMRI Big Data discovery

近年来，大型复杂功能磁共振的可用性迅速增长 人脑成像(FMRI)数据集。然而，此功能磁共振大数据的潜力目前受到以下限制 我们对影响功能磁共振信号的神经源的理解。觉醒的波动(即，在水平 觉醒和警觉)是已知的调节认知和行为过程并显示 神经精神障碍的显著改变。然而，由于绝大多数fMRI数据集缺乏 唤醒的神经生理或行为指标，功能磁共振大数据不容易被利用来理解 人脑在健康和疾病中的唤醒。最近的数据驱动方法试图填补这一空白，但已经 限制。此提案的总体目标是提高fMRI大数据的转型潜力 通过一种新的分析框架从fMRI数据中检测唤醒波动的人类神经科学 独自一人。我们将通过开发和传播建模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。 健康衰老和阿尔茨海默病的数据库，这两者都与觉醒改变有关 (具体目标2和3)。我们将利用这些数据库来确定大脑唤醒知识是如何 波动改善衰老和神经退行性疾病相关变化的神经成像生物标志物 人脑功能，以及觉醒本身在多大程度上构成了这些功能的生物标志物 各州。此外，这项研究还将进一步增加功能磁共振研究的可靠性和翻译潜力 广泛地说，通过提供解释这些主要神经(唤醒)状态变化的能力。 这些眼前的研究目标与我的长期研究目标形成了牢固的桥梁 通过发展和创新分析方法来理解大脑功能的原理 庞大而复杂的神经成像数据集。这一目标是由指导培训计划实现的，我将在 (1)发展尖端机器学习技术方面的专门知识；(2)将这些技术应用于多式联运 神经成像数据。这两位共同导师拥有互补的专业知识，分别与这两位保持一致 培训部分。目标1和2将跨越指导阶段和独立阶段的一部分，而目标 3(应用于阿尔茨海默病神经成像计划数据)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 计划中的数据获取，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)的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。

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.