权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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：
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
项目类别：
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

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)：这份R21重新提交的申请是关于使用功能磁共振成像(FMRI)提高激活检测的准确性。在过去的二十年里，这种成像方式已经演变成一种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了一张激活图，它突出显示了体素或体积元素，其中有大脑活动对刺激或任务(范式)的反应。不幸的是，由于扫描仪的可变性、MR信号潜在的内在不可靠性、受试者之间的可变性、受试者的运动或由于神经刺激通过血流动力学后部而导致的MR信号开始的几秒延迟，实验数据可能变化很大。结果可能是每次扫描过程中激活图的巨大差异，即使相同的受试者使用相同的范例。最近已经有很多工作来评估激活图在多个环境中的可靠性。许多人以一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。为了解释这样一个事实，即激活的体素往往出现在星团是一种常见的方法，它结合了统计物理学中的伊辛模型，其中每个体素要么被激活，要么不被激活，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%的体素是真正活跃的，并且还没有方法同时考虑对激活体素比例和空间背景的期望。在激活图中要求2%-3%的激活体素并不能准确地表达我们的先验知识，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。该建议探索了通过约束Ising模型的参数来改进激活检测的新方法，从而使真正活跃的体素的先验预期比例被限制在期望的范围内。提出的具体目标是：1)研究将Ising模型中激活的体素的期望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1)所介绍的复杂性的背景下生成激活图。我们的建议将允许将研究人员关于约束的不确定性和空间背景下的解剖信息包括在内。每个报告都有明确的动机，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。一系列来自现实的计算机模拟和关于创伤性脑损伤(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被用来探索、开发和更新所建议的方法。还将开发和提供开放源码软件以及关于最佳做法和陷阱的详细教程，以便利功能磁共振从业人员及早采用。1