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: 8584207
• 负责人: Ranjan Maitra
• 金额: $ 20.07万
• 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 2015-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8584207
• 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

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）：本R21重新提交申请旨在提高使用功能性磁共振成像（fMRI）进行激活检测的准确性。在过去的二十年里，这种成像方式已经发展成为了解人类认知和运动功能的非侵入性工具。数据收集之后的数据分析产生激活图，该激活图突出显示体素或体积元素，其中存在响应于刺激或任务的大脑活动（范例）。不幸的是，由于扫描仪的可变性、MR信号潜在的固有不可靠性、受试者之间的可变性、受试者运动或由于神经刺激通过血液动力学滤器而导致的MR信号开始的几秒钟延迟，实验数据可能变化很大。结果可能是从一个扫描会话到下一个扫描会话的激活图的巨大差异，即使当相同的受试者被给予相同的范例。最近有很多工作来评估多种设置中激活图的可靠性。许多人已经将多个假设检验的结果以某种事后的方式合并，以提高激活检测的可靠性和一致性。为了解释激活的体素倾向于发生在 集群，一种常见的方法结合了伊辛模型，从统计物理学，其中每个体素被激活或不被激活，但与它的邻居的状态有一定的依赖性。几乎没有方法利用众所周知的信念，只有2-3%的体素是真正活跃的在一个典型的功能磁共振成像实验中，还没有方法结合了这两个预期的比例激活体素和空间背景。在激活标测图中需要精确的2-3%激活体素并不能准确表示我们的先验知识，即平均2-3%的体素被激活，并且会增加遗漏病理的机会，从而增加临床环境中误诊异常的机会。 该建议探索了通过约束伊辛模型的参数来改善激活检测的新方法，从而将真正活跃体素的先验预期比例限制在所需范围内。提出的具体目标是：1）研究将伊辛模型中激活体素的预期比例指定为先验值的方法; 2）开发一种计算上实用的方法来估计模型参数并在1）中引入的复杂性的背景下产生激活图。我们的建议将允许包括研究人员的不确定性的约束和解剖信息的空间背景下。 每一个eort的具体动机，并将有助于，如果成功的话，可靠一致的主题内的fMRI激活地图的发展，并确定异常激活跨学科。一系列来自真实的计算机模拟和存档的人类数据的运动任务实验和工作记忆实验中的创伤性脑损伤（TBI）患者和正常人的数据将被用来探索，发展和更新所建议的方法。还将开发并提供开放源码软件，沿着关于最佳做法和陷阱的详细教程，以促进功能磁共振成像从业人员早日采用。1