Validity and Reliability of New Standard for Resting fMRI Data

静息功能磁共振成像数据新标准的有效性和可靠性

• 批准号: 8748494
• 负责人: Anne Marie Clare Kelly
• 金额: $ 8.48万
• 依托单位: NEW YORK UNIVERSITY SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-07-01 至 2016-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8748494
• 关键词: Address; Adopted; Adoption; Adult; Age; Attention deficit hyperactivity disorder; Autistic Disorder; Biological Markers; Brain; Brain Mapping; Capital; Child; Clinical; Computer software; Data; Data Set; Databases; Dependence; Development; Diagnosis; Disease; Excision; Exhibits; Face; Freedom; Frequencies; Functional Magnetic Resonance Imaging; Goals; Grant; Health; Human; Individual; Infant; International; Investments; Mainstreaming; Maps; Measures; Medicine; Methods; Metric; Morphologic artifacts; Motion; Neurodegenerative Disorders; Neurodevelopmental Disorder; Noise; Paper; Participant; Patients; Physiological; Population; Positioning Attribute; Procedures; PubMed; Publishing; Relative (related person); Reporter; Research Personnel; Resources; Rest; Sampling; Scanning; Seeds; Seminal; Signal Transduction; Solutions; Sorting - Cell Movement; Source; Specificity; Techniques; Testing; Time; Transcend; United States National Institutes of Health; Validity and Reliability; Work; aged; autism spectrum disorder; base; blood oxygen level dependent; brain behavior; comparison group; cost; data acquisition; data sharing; frontier; improved; independent component analysis; interest; neuroimaging; novel; novel strategies; programs; prospective; public health relevance; reconstruction; translational approach

SUMMARY Resting state fMRI (R-fMRI) and intrinsic functional connectivity (iFC) mapping have energized developmental and psychiatric functional neuroimaging, but concerns about the impact of participant motion and preprocessing strategies on R-fMRI findings have begun to stall progress in the field. Faced with a plethora of options for retrospective motion correction and nuisance signal regression, researchers are unsure of how to proceed. A recently published method, multi-echo independent components regression (ME-ICR), offers a principled and objective approach to identifying and removing motion-related artifact and other signals of no- interest from R-fMRI data. This novel method capitalizes on the principle that multiecho (ME) acquisitions can be used to differentiate non-BOLD changes in fMRI signal intensity (related to nuisance sources such as motion, physiological rhythms, and scanner-related noise) from changes in BOLD signals of interest, which exhibit a dependence on echo time (TE). This principle is applied in ME-ICR to sort signal components identified using independent components analysis (ICA) according to their TE dependence, thus separating TE-dependent BOLD signals of interest from TE-independent non-BOLD signals of no interest. When validated in a sample of 35 healthy adults, ME-ICR exhibited increased temporal signal-to-noise, successful removal of motion artifacts, increased specificity of iFC maps, and improved statistical inference, relative to conventional analyses. Although compelling, the approach requires not only a change in analytic procedures but also in data acquisition (from single-shot to ME). Labs will be reluctant to adopt ME-ICR method in the absence of conclusive evidence of its applicability to clinical populations. Efforts to acquire new ME datasets in clinical populations could take years. Providentially, our lab has used an ME sequence since 2009. We thus propose to apply ME-ICR to our uniquely large database of ME R-fMRI scans collected from over 600 children and adults with ADHD, autism, as well as typical comparisons. We will examine the impact of ME-ICR on estimates of age- and diagnosis-related differences in R-fMRI measures, and on their test-retest reliability. By assessing the validity and reliability of ME-ICR in a large sample of existing data, the proposed study will accelerate the demonstration of its utility, and ultimately, adoption, for developmental and psychiatric R-fMRI studies. This proposal is timely and potentially transformational, since the methodological advances offered by ME-ICR would permit the field to transcend present challenges, thus enabling R-fMRI to fulfill its promise as a translational approach that faithfully reveals macro-scale brain function in health and disease. In addition, by permitting valid statistical inference at the individual subject level, ME-ICR can move neuroimaging toward the goals of personalized medicine. Finally, by proposing sharing of the ME data, the scientific impact of this project will be greatly enhanced at no additional cost.

总结 静息态功能磁共振成像（R-fMRI）和内在功能连接（iFC）映射激发了发育 和精神功能性神经影像学，但对参与者运动的影响， R-fMRI结果的预处理策略已经开始阻碍该领域的进展。面对过多的 回顾性运动校正和滋扰信号回归的选项，研究人员不确定如何 继续.最近公布的方法，多回波独立成分回归（ME-ICR），提供了一个 原则和客观的方法来识别和消除运动相关的伪影和其他信号， 从R-fMRI数据来看。这种新方法利用了多回波（ME）采集可以 用于区分fMRI信号强度的非BOLD变化（与干扰源有关， 运动、生理节律和与扫描仪相关的噪声）， 表现出对回波时间（TE）的依赖性。在ME-ICR中应用该原理对信号分量进行分选 根据它们的TE依赖性使用独立分量分析（伊卡）识别，从而分离 从不感兴趣的TE无关非BOLD信号中提取感兴趣的TE相关BOLD信号。验证时 在35名健康成年人的样本中，ME-ICR表现出增加的时间信噪比，成功去除了 运动伪影，iFC图的特异性增加，统计推断改善，相对于传统的 分析。虽然引人注目，但这种方法不仅需要改变分析程序，还需要改变数据 采集（从单次采集到ME）。实验室不愿意在缺乏以下条件的情况下采用ME-ICR方法： 其适用于临床人群的确凿证据。在临床中获取新ME数据集的努力 人口增长可能需要数年时间。幸运的是，我们的实验室自2009年以来一直使用ME序列。因此，我们建议 将ME-ICR应用于我们独特的大型MER-fMRI扫描数据库，该数据库从600多名儿童中收集， 成人多动症，自闭症，以及典型的比较。我们将研究ME-ICR对估计的影响 年龄和诊断相关的差异，在R-fMRI措施，并对他们的重测信度。通过评估 ME-ICR在大样本现有数据中的有效性和可靠性，拟议的研究将加速 证明其实用性，并最终通过，为发展和精神病R-fMRI研究。这 由于ME-ICR提供的方法学进步， 将允许该领域超越目前的挑战，从而使R-fMRI能够实现其作为一种 翻译方法，忠实地揭示了健康和疾病中的宏观大脑功能。另外通过 允许在个体受试者水平上进行有效的统计推断，ME-ICR可以将神经成像推向 个性化医疗的目标。最后，通过建议共享ME数据， 该项目将在不增加费用的情况下得到大大加强。