Development of a Power Calculation Tool for Neuroimaging Studies

神经影像研究功率计算工具的开发

• 批准号: 7589260
• 负责人: Satoru Hayasaka
• 金额: $ 16万
• 依托单位: WAKE FOREST UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-20 至 2010-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7589260
• 关键词: Accounting; Address; Alzheimer' s Disease; Area; Brain; Communities; Computational Technique; Country; Data; Data Analyses; Data Set; Development; Gold; Grant; Investigation; Knowledge; Literature; Magnetic Resonance Imaging; Maps; Mental disorders; Methods; Modality; Modeling; Nature; Neurologic; Numbers; Outcome; Outcome Study; Performance; Procedures; Process; Public Health; Rate; Research Personnel; Resources; Sample Size; Scanning; Schizophrenia; Signal Transduction; Software Design; Software Tools; Standards of Weights and Measures; Three-Dimensional Image; Three-Dimensional Imaging; Time; Today; United States National Institutes of Health; base; cost; design; graphical user interface; neuroimaging; simulation; size; statistics; theories; tool; user-friendly

DESCRIPTION (provided by applicant): Determining the right number of subjects in neuroimaging studies is an important process as in any other biomedical studies. However, because of the 3D nature of neuroimaging data, calculating power and sample sizes remains to be a major challenge. Despite some pioneering attempts, to this date, there is no effective method for investigators to calculate power for neuroimaging studies. To address this issue, the PI of this project has developed a power calculation method specifically designed for neuroimaging data analyses. This method, based on random field theory (RFT), can model signals in neuroimaging data theoretically without time consuming simulations and resampling procedures. Furthermore, this method can visualize local variability of power in different areas of the brain in the form of power and sample size maps. Such maps can be a useful study planning tool for investigators to determine where signals are likely to be detected with how many subjects. Based on this RFT-based method, we will develop a user-friendly tool for power calculation for neuroimaging studies, and fully characterize its performance. In Specific Aim 1, we will focus on the actual development of a power calculation software tool with a graphical user interface (GUI). In Specific Aim 2, we will characterize the performance of our power calculation framework so that users will be able to appropriately interpret the resulting power maps. In particular, gold standard power maps will be generated by a resampling process from multiple functional and structural MRI data sets. These gold standard power maps will be compared to power maps generated based on mock pilot data (randomly-selected subset of the full data) with different sample sizes to determine the relationship between the accuracy and the size of pilot data. In Specific Aim 3, we will develop and implement some methodological frameworks in addition to the existing RFT-based method in order to increase the utility of our tool. Specifically, we will implement power calculation based on FDR (false discovery rate) method, which is a popular alternative to RFT-based methods in neuroimaging data analyses. In summary, the proposed tool is the first power analysis tool specialized for neuroimaging studies accounting for massive multiple comparisons, and can greatly simplify the study planning process. The potential use of this tool is not limited to functional neuroimaging, but also a variety of other neuroimaging modalities. Moreover, accurate sample size estimates can help reduce unnecessary scans and under-powered studies, saving time and costs associated with neuroimaging studies. PUBLIC HEALTH RELEVANCE: In recent years, neuroimaging has become an essential part of investigations of the brain, and increased our understanding of various neurological and psychiatric disorders, such as Alzheimer's disease and schizophrenia. The proposed project will help investigators of such studies to allocate time and resources more efficiently, by accurately determining the number of subjects required for their studies.

描述（由申请人提供）：确定神经影像学研究中受试者的正确数量与任何其他生物医学研究一样是一个重要的过程。然而，由于神经成像数据的3D性质，计算能力和样本量仍然是一个主要的挑战。尽管有一些开创性的尝试，但到目前为止，研究人员还没有有效的方法来计算神经影像学研究的功效。为了解决这一问题，本项目的PI开发了一种专为神经成像数据分析设计的功效计算方法。该方法基于随机场理论（RFT），可以从理论上对神经成像数据中的信号进行建模，而无需耗时的仿真和重新建模过程。此外，该方法可以以功率和样本大小图的形式可视化大脑不同区域中功率的局部变化。这样的地图可以是一个有用的研究规划工具，研究人员确定信号可能被检测到多少受试者。基于这种基于RFT的方法，我们将开发一个用户友好的工具，用于神经成像研究的功率计算，并充分表征其性能。在具体目标1中，我们将重点关注具有图形用户界面（GUI）的功率计算软件工具的实际开发。在具体目标2中，我们将描述我们的功率计算框架的性能，以便用户能够适当地解释生成的功率图。特别是，将通过多个功能和结构MRI数据集的重建过程生成金标准功率图。将这些黄金标准功效图与基于模拟试验数据（随机选择的完整数据子集）生成的功效图进行比较，以确定准确度与试验数据大小之间的关系。在具体目标3中，除了现有的基于RFT的方法之外，我们还将开发和实施一些方法框架，以提高我们工具的实用性。具体来说，我们将实现基于FDR（错误发现率）方法的功效计算，FDR方法是神经成像数据分析中基于RFT方法的流行替代方法。总之，所提出的工具是第一个专门用于神经影像学研究的功效分析工具，可以进行大量的多重比较，并且可以大大简化研究计划过程。该工具的潜在用途不仅限于功能性神经成像，还包括各种其他神经成像方式。此外，准确的样本量估计可以帮助减少不必要的扫描和动力不足的研究，节省与神经影像学研究相关的时间和成本。公共卫生相关性：近年来，神经影像学已成为大脑研究的重要组成部分，并增加了我们对各种神经和精神疾病的理解，如阿尔茨海默病和精神分裂症。拟议的项目将帮助这些研究的研究人员更有效地分配时间和资源，准确地确定研究所需的受试者人数。