Enhancing the PESTICA Toolkit: Open-Source Physiologic Noise Detection and Remova

增强 PESTICA 工具包：开源生理噪声检测和消除

• 批准号: 8440746
• 负责人: Mark J Lowe
• 金额: $ 7.4万
• 依托单位: CLEVELAND CLINIC LERNER COM-CWRU
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-04-01 至 2015-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8440746
• 关键词: Affect; Algorithms; Cardiac; Chest; Communities; Computer software; Data; Data Analyses; Data Set; Detection; Development; Disease; Echo-Planar Imaging; Equipment; Functional Magnetic Resonance Imaging; Future; Goals; Image; Image Analysis; Individual; Informatics; International; Letters; Libraries; Magnetic Resonance Imaging; Measures; Methods; Monitor; Motion; Noise; Physiologic Monitoring; Physiologic pulse; Physiological; Population; Process; Reporting; Research; Research Design; Research Personnel; Resources; Respiration; Respiratory Process; Rest; Running; Signal Transduction; Software Tools; Source; Special Equipment; Specificity; Spin Labels; Structure; Testing; Training; United States National Institutes of Health; Validation; Weight; blood oxygen level dependent; data acquisition; data sharing; experience; improved; independent component analysis; neuroimaging; open source; respiratory; success; tool; web site

DESCRIPTION (provided by applicant): In this application, we propose a strategy to remove physiologic noise from the cardiac and respiratory processes as sources of bias and reduced specificity in functional magnetic resonance imaging (MRI) and functional connectivity MRI data. These noise sources are difficult to correct for without special equipment for parallel monitoring and it is difficult to ascertain the success of the correction process itself. As a result, many researchers necessarily ignore these sources or use amelioration methods that are not specific to these sources. Due to the size and variability of these sources across populations, this represents a critical barrier to progress in the field. However, recent progress in physiologic estimation and correction by the investigators has produced a tool to enable researchers to retrospectively correct their data, in a manner equivalent to correcting their data with parallel monitored noise sources. This is an important advance, but only a small set of researchers are currently using these tools. The reasons are twofold: 1) the tool is not easy to use and requires additional software and 2) past experience with physiologic correction is limited to those investigators who have access to monitoring equipment. To counter these problems, we propose to improve the integration of our tools with the Analysis of Functional NeuroImages (AFNI) library such that at the conclusion of our project physiologic correction is as easy to apply as volumetric motion correction currently is. In addition, we propose to produce validated physiologic estimators for the bulk of the publicly-available data maintained by the Functional Connectomes Project and its most recent initiative, the International Neuroimaging Data-sharing Initiative. With the recent increase in analyses of publicly-available MRI data, this project will dramatically increase community experience with physiologic correction and enable a shift from the analysis and reporting of physiologic- corrupted data to the analysis and reporting of physiologic-uncorrupted data.

描述（由申请人提供）：在本申请中，我们提出了一种从心脏和呼吸过程中去除生理噪声的策略，该生理噪声是功能磁共振成像（MRI）和功能连接MRI数据中偏倚和特异性降低的来源。这些噪声源在没有用于并行监测的特殊设备的情况下难以校正，并且难以确定校正过程本身的成功。因此，许多研究人员必然会忽略这些来源或使用不特定于这些来源的改进方法。由于这些来源在人群中的规模和变异性，这是该领域取得进展的关键障碍。然而，最近的进展，在生理估计和校正的研究人员已经产生了一种工具，使研究人员能够回顾性地纠正他们的数据，在某种程度上相当于纠正他们的数据与并行监测噪声源。这是一个重要的进步，但目前只有一小部分研究人员在使用这些工具。原因有二：1）该工具不易使用，需要额外的软件; 2）过去的生理矫正经验仅限于能够使用监测设备的研究者。为了解决这些问题，我们建议改进我们的工具与功能神经影像分析（AFNI）库的集成，以便在我们的项目结束时，生理校正与体积运动校正一样容易应用。此外，我们建议为功能性连接体项目及其最近的倡议国际神经影像数据共享倡议所维护的大量公开数据产生经验证的生理估计值。随着最近对公开可用MRI数据的分析增加，该项目将显著增加生理校正的社区经验，并实现从分析和报告生理损坏数据到分析和报告生理未损坏数据的转变。