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

基本信息

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
项目类别：
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

项目摘要

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 重新提交申请旨在提高使用功能磁共振成像 (fMRI) 的激活检测的准确性。在过去的二十年里，这种成像方式已经发展成为一种了解人类认知和运动功能的非侵入性工具。数据收集和数据分析会生成一个激活图，突出显示体素或体积元素，其中存在响应刺激或任务（范例）的大脑活动。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网络的通过而导致 MR 信号出现几秒的延迟，实验数据可能会有很大差异。通过血流动力学过滤器进行刺激。结果可能是从一个扫描会话到下一个扫描会话的激活图存在巨大差异，即使同一受试者接受相同的范式也是如此。最近有很多工作来评估多种设置下激活图的可靠性。许多人以某种事后的方式整合了多个假设检验的结果，以提高激活检测的可靠性和一致性。考虑到激活体素往往出现在簇，一种常见的方法结合了来自统计物理学的伊辛模型，其中每个体素要么被激活，要么未被激活，但在一定程度上依赖于其邻居的状态。几乎没有方法利用众所周知的信念，即在典型的 fMRI 实验中只有 2-3% 的体素真正活跃，而且还没有方法将这种对激活体素比例和空间背景的期望结合起来。要求激活图中恰好有 2-3% 的激活体素并不能准确表示我们的先验知识，即平均有 2-3% 的体素被激活，并且会增加遗漏病理的机会，从而在临床环境中误诊异常。该提案探索了通过约束 Ising 模型的参数来改进激活检测的新方法，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）开发一种计算实用的方法来估计模型参数并在上下文中生成激活图1) 中介绍的复杂性。我们的建议将允许纳入研究人员关于空间环境中的约束和解剖信息的不确定性。每项努力都有明确的动机，如果成功，将有助于开发可靠一致的受试者内 fMRI 激活图，并识别受试者之间的激活异常。来自真实计算机模拟的一系列数据以及创伤性脑损伤（TBI）患者和正常受试者的运动任务实验和工作记忆实验的存档人类数据将用于探索、开发和完善建议的方法。还将开发并提供开源软件以及有关最佳实践和陷阱的详细教程，以促进功能磁共振成像从业者的早期采用。 1