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: 8866477
• 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 2017-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8866477
• 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; Methods; 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; personalized medicine; programs; prospective; reconstruction; translational approach

DESCRIPTION (provided by applicant): 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 clinicl 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.

描述（由申请人提供）：静息状态功能磁共振成像（R-fMRI）和内在功能连接（iFC）映射为发育和精神功能神经成像提供了动力，但对参与者运动和预处理策略对R-fMRI结果的影响的担忧已经开始阻碍该领域的进展。面对过多的选项，回顾运动修正和干扰信号回归，研究人员不确定如何进行。最近发表的一种方法，多回波独立分量回归（ME-ICR），提供了一种原则性和客观的方法来识别和去除R-fMRI数据中与运动相关的伪影和其他不感兴趣的信号。这种新方法利用了多回波（ME）采集的原理，可用于区分fMRI信号强度的非BOLD变化（与运动、生理节律和扫描仪相关噪声等干扰源有关）和感兴趣的BOLD信号的变化，后者表现出对回波时间（TE）的依赖。该原理应用于ME-ICR中，根据独立分量分析（ICA）识别的信号分量的TE依赖性对其进行排序，从而将感兴趣的TE依赖性BOLD信号与不感兴趣的TE依赖性非BOLD信号分离开来。当在35名健康成人样本中进行验证时，ME-ICR表现出与传统分析相比，时间信噪比增加、运动伪影的成功去除、iFC图的特异性增加以及统计推断的改进。虽然引人注目，但该方法不仅需要改变分析过程，还需要改变数据采集（从单次拍摄到ME）。实验室将不愿意采用ME-ICR方法，因为没有确凿的证据证明其适用于临床人群。在临床人群中获取新的ME数据集可能需要数年时间。幸运的是，我们的实验室从2009年就开始使用ME序列了。因此，我们建议将ME- icr应用于我们独特的大型ME- R-fMRI扫描数据库，该数据库收集了600多名患有多动症、自闭症的儿童和成人，以及典型的比较。我们将研究ME-ICR对R-fMRI测量中年龄和诊断相关差异估计的影响，以及它们的重测信度。通过评估ME-ICR的效度和信度