Implementation and dissemination of cloud-based retrospective hemodynamic analysis tools to enhance HCP data interpretation

实施和传播基于云的回顾性血流动力学分析工具，以增强 HCP 数据解释

• 批准号: 10509534
• 负责人: Blaise deBonneval Frederick
• 金额: $ 74.88万
• 依托单位: MCLEAN HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10509534
• 关键词: Address; Adolescent; Age; BRAIN initiative; Blood; Blood Circulation; Blood flow; Brain; Cardiac; Code; Computer software; Computers; Data; Data Analyses; Data Set; Development; Dissemination and Implementation; Documentation; Excision; Frequencies; Functional Magnetic Resonance Imaging; Funding; Glean; Human; Image; Infrastructure; Institution; Lead; Learning; Manuals; Maps; Measurement; Measures; Methods; Modeling; Motivation; Neurons; Neurosciences Research; Noise; Pathologic; Performance; Perfusion; Phase; Physiologic pulse; Physiological; Procedures; Process; Protocols documentation; Publishing; Research; Research Personnel; Resources; Rest; Running; Scanning; Signal Transduction; Source; Specificity; Structure; Techniques; Testing; Time; Training; Universities; base; blood oxygen level dependent; cerebral hemodynamics; cerebrovascular; cloud based; cluster computing; cognitive development; cohort; connectome; cost; cost effective; denoising; flexibility; heart rate variability; hemodynamics; improved; insight; non-invasive monitor; novel; open source; symposium; tool; virtual; web services

Summary Functional Magnetic Resonance Imaging data has been a mainstay of neuroscience research for more than two decades, as it allows rapid, continuous, noninvasive monitoring of neuronal function. However, a substantial portion of the fMRI signal arises from purely physiological cerebral hemodynamic signals in the low and cardiac frequency bands. Historically, these have simply been considered noise sources that complicate fMRI analysis. We have developed two novel, retrospective analyses to separate the neuronal and hemodynamic portions of BOLD fMRI data to not only model and remove low and cardiac frequency systemic noise, but to make use of this “noise” to characterize bulk and pulsatile blood flow with high precision. These techniques have been extensively tested and validated and have shown great flexibility in processing fMRI data from a number of sources. The first technique, Regressor Interpolation at Progressive Time Delays (“RIPTiDe”), isolates and characterizes the low frequency global hemodynamic signal in fMRI data[2]; as this is a bloodborne signal, RIPTiDe can be used to measure blood arrival time and rCBV throughout the brain in normal and pathological circulation[3-8]. without a separate perfusion scan. Moreover, this generates voxelwise noise regressors which remove confound signal without generating the spurious correlations arising from global signal regression[10], substantially increasing the specificity of resting state and task fMRI analyses for detecting neuronal, rather than hemodynamic[10, 11]. The second technique, “happy”, allows retrospective extraction of the plethysmogram signal from multiband fMRI data[12], allowing R-R interval and heart rate variability measurements even in subjects where no plethysmogram was recorded (or failed to record), and mapping and/or removing of the cardiac pulsation waveform as it moves through the brain. These two tools together both enhance and extend existing datasets by removing a substantial proportion of previously intractable in-band noise, while providing new, entirely separate windows into cerebral hemodynamics and autonomic function from existing data. Both have been released in the open source “rapidtide” package. This project will take these tools to the next level, by improving their documentation, capabilities and reliability, optimizing them for retrospective analysis of the multiple HCP and ABCD datasets, and making them available to the widest audience by developing a cloud-based platform to allow users from institutions of all sizes (not only large, well-funded universities with extensive computing infrastructure) to use this software.

总结 功能性磁共振成像数据已经成为神经科学研究的支柱超过两年 几十年来，因为它可以快速，连续，无创监测神经元功能。然而， 一部分fMRI信号来自于低和心脏中的纯生理脑血流动力学信号， 频段。从历史上看，这些被简单地认为是使fMRI分析复杂化的噪声源。 我们开发了两种新颖的回顾性分析，以分离神经元和血流动力学部分， BOLD fMRI数据不仅可以建模和去除低频和心脏频率的系统噪声，而且可以利用 这种“噪声”以高精度表征大体积和脉动血流。这些技术 经过广泛的测试和验证，并在处理来自许多fMRI数据方面表现出极大的灵活性。 源第一种技术，渐进时间延迟的回归插值（“RIPTiDe”），分离和 表征了fMRI数据中的低频全局血液动力学信号[2];由于这是血液传播信号， RIPTiDe可用于测量正常和病理性脑出血的血液到达时间和rCBV 循环[3-8]。没有单独的灌注扫描。此外，这生成逐体素噪声回归子， 去除混杂信号而不产生由全局信号回归引起的伪相关[10]， 显著增加静息状态和任务fMRI分析的特异性，用于检测神经元，而不是 血流动力学[10，11]。第二种技术，“快乐”，允许回顾性提取体积描记图 来自多频带fMRI数据的信号[12]，即使在 未记录体积描记图（或未能记录）的受试者，以及标测和/或移除 心脏脉动波形，因为它通过大脑移动。这两个工具一起增强和扩展了 现有的数据集，通过去除相当大比例的以前难以处理的带内噪声，同时提供 新的，完全独立的窗口，从现有的数据到脑血流动力学和自主神经功能。两 已在开源“rapidtide”包中发布。 该项目将通过改进这些工具的文档、功能和可靠性，将它们提升到一个新的水平， 优化它们，以便对多个HCP和ABCD数据集进行回顾性分析，并使其可用 通过开发一个基于云的平台，允许来自各种规模的机构（不仅是 拥有广泛计算基础设施的大型、资金充足的大学）使用该软件。