Statistical Methods for Improved Activation Detection in fMRI Studies

改进功能磁共振成像研究中激活检测的统计方法

• 批准号: 8703694
• 负责人: Ranjan Maitra
• 金额: $ 17.44万
• 依托单位: IOWA STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-01 至 2017-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8703694
• 关键词: Accounting; Address; Adoption; Algorithms; Anatomy; Archives; Belief; Beryllium; Brain; Cerebrum; Clinical; Cognitive; Computer Simulation; Computer software; Computing Methodologies; Data; Data Analyses; Data Collection; Data Set; Dependence; Detection; Development; Diagnosis; Diagnostic; Ensure; Functional Magnetic Resonance Imaging; Goals; Human; Human Characteristics; Image; Intervention; Knowledge; Location; Maps; Markov Chains; Methodology; Methods; Modeling; Motion; Motor; Noise; Pathology; Patients; Pattern; Physics; Process; Research; Research Personnel; Scanning; Series; Short-Term Memory; Signal Transduction; Simulate; Solutions; Specific qualifier value; Speed; Statistical Methods; Statistical Models; Stimulus; Testing; To specify; Traumatic Brain Injury; Uncertainty; Variant; Work; computerized data processing; expectation; hemodynamics; human data; imaging modality; improved; novel strategies; open source; public health relevance; relating to nervous system; research study; response; sound; tool; Accounting; Address; Adoption; Algorithms; Anatomy; Archives; Belief; Beryllium; Brain; Cerebrum; Clinical; Cognitive; Computer Simulation; Computer software; Computing Methodologies; Data; Data Analyses; Data Collection; Data Set; Dependence; Detection; Development; Diagnosis; Diagnostic; Ensure; Functional Magnetic Resonance Imaging; Goals; Human; Human Characteristics; Image; Intervention; Knowledge; Location; Maps; Markov Chains; Methodology; Methods; Modeling; Motion; Motor; Noise; Pathology; Patients; Pattern; Physics; Process; Research; Research Personnel; Scanning; Series; Short-Term Memory; Signal Transduction; Simulate; Solutions; Specific qualifier value; Speed; Statistical Methods; Statistical Models; Stimulus; Testing; To specify; Traumatic Brain Injury; Uncertainty; Variant; Work; computerized data processing; expectation; hemodynamics; human data; imaging modality; improved; novel strategies; open source; public health relevance; relating to nervous system; research study; response; sound; tool

DESCRIPTION (provided by applicant): This R21 resubmission application is on improving the accuracy of activation detection using functional Magnetic Resonance Imaging (fMRI). Over the past two decades this imaging modality has evolved into a noninvasive tool for understanding human cognitive and motor functions. Data collection followed by data analysis produces an activation map that highlights voxels, or volume elements, where there is brain activity in response to a stimulus or task (a paradigm). Unfortunately, the experimental data can vary greatly because of scanner variability, potential inherent unreliability of the MR signal, between-subject variability, subject motion or the several-seconds delay in the onset of the MR signal as a result of the passage of the neural stimulus through the hemodynamic lter. The result can be vast differences in activation maps from one scanning session to the next, even when the same subject is administered the same paradigm. There has been much recent work to assess reliability of activation maps in multiple settings. Many have incorporated results on multiple hypothesis tests in a somewhat post hoc manner to improve the reliability and consistency in activation detection. To account for the fact that activated voxels tend to occur in clusters, a common approach incorporates the Ising model, from statistical physics, where each voxel is either activated or not, but with some dependence on the states of its neighbors. Almost no methods take advantage of the well-known belief that only 2-3% of the voxels are truly active in a typical fMRI experiment, and no method has yet incorporated both this expectation on the proportion of activated voxels and the spatial context. Requiring exactly 2-3% activated voxels in the activation maps is not an accurate representation of our prior knowledge that 2-3% of voxels are activated on average and would increase the chance of missing pathologies and hence mis-diagnosing anomalies in a clinical setting. This proposal explores new approaches to improving activation detection by constraining the parameters of the Ising model so the a priori expected proportion of truly active voxels is restricted to the desired range. The specific aims proposed are: 1) to investigate approaches to specify the expected proportion of activated voxels in the Ising model to be the a priori value and 2) to develop a computationally practical approach to estimate the model parameters and produce activation maps in the context of the complexities introduced in 1). Our proposal will allow inclusion of researcher uncertainty about the constraint and anatomic information in the spatial context. Each e ort is specifically motivated and will contribute, if successful, to the development of reliably consistent within-subject fMRI activation maps and also to identify anomalies in activation across subjects. A range of data from realistic computer simulations and archived human data on motor task experiments and working memory experiments in traumatic brain injury (TBI) patients and normal subjects will be used to explore, develop and re ne the suggested approaches. Open-source software, along with detailed tutorials on best practices and pitfalls, will also be developed and made available in order to facilitate early adoption by practitioners in fMRI. 1

描述（由申请人提供）：此R21重新提交应用程序是使用功能磁共振成像（fMRI）提高激活检测的准确性。在过去的二十年中，这种成像方式已演变为一种无创的工具，用于理解人类的认知和运动功能。数据收集随后数据分析产生一个激活图，该激活图突出显示体体元素或体积元素，其中大脑活动响应刺激或任务（范式）。不幸的是，由于扫描仪的可变性，MR信号的潜在固有不可靠性，受试者之间的可变性，受试者运动或MR信号发作的几秒钟，由于神经刺激通过血液动力学的效果，MR信号发作的延迟。结果可能是从一个扫描会话到下一个的激活图上的巨大差异，即使同一主题是相同的范式。最近有很多工作来评估多个设置中激活图的可靠性。许多人以某种后事后方式将结果纳入了多个假设检验，以提高激活检测的可靠性和一致性。考虑到激活的体素倾向于发生的事实 群集是一种常见的方法，结合了统计物理学的Ising模型，其中每个体素要么被激活，但对其邻居的状态有所依赖。几乎没有方法可以利用众所周知的信念，即只有2-3％的体素在典型的fMRI实验中真正活跃，并且尚未在活化体素和空间环境的比例上纳入这种期望。在激活图中需要准确需要2-3％的活化体素并不是我们先前知道的，即2-3％的体素平均被激活，并且会增加缺失的病理学的机会，从而增加临床环境中的异常诊断。 该提案通过约束ISING模型的参数来探讨改善激活检测的新方法，因此真正活跃体素的先验预期比例仅限于所需范围。提出的具体目的是：1）研究方法以指定Ising模型中激活体素的预期比例为先验值，而2）开发一种计算实用方法，以估计模型参数并在1中引入的复杂性的上下文中产生激活图。我们的建议将允许在空间环境中纳入有关约束和解剖信息的不确定性。 每个E ORT都是专门的动机，如果成功的话，将为可靠的受试者内部FMRI激活图的发展做出贡献，并确定跨受试者激活中的异常情况。一系列来自现实的计算机模拟和归档人类数据的数据，这些数据将用于运动任务实验和创伤性脑损伤（TBI）患者的工作记忆实验和正常受试者，用于探索，开发和重新研究建议的方法。开源软件以及有关最佳实践和陷阱的详细教程，还将开发并提供，以促进fMRI的从业人员的早期采用。 1