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

描述（由申请人提供）：在本申请中，我们提出了一种策略，以消除来自心脏和呼吸过程的生理噪声，作为功能磁共振成像（MRI）和功能连接MRI数据的偏差和降低特异性的来源。如果没有特殊的平行监测设备，这些噪声源很难纠正，而且很难确定纠正过程本身是否成功。因此，许多研究人员必然忽略这些来源或使用不针对这些来源的改进方法。由于这些来源在人群中的规模和可变性，这是该领域取得进展的一个关键障碍。然而，研究人员在生理估计和校正方面的最新进展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，其方式相当于用平行监测的噪声源纠正他们的数据。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。原因有两个：1)该工具不容易使用，需要额外的软件；2)过去的生理校正经验仅限于那些有机会使用监测设备的研究人员。为了解决这些问题，我们建议改进我们的工具与功能神经图像分析（AFNI）库的集成，这样在我们的项目结束时，生理校正就像目前的体积运动校正一样容易应用。此外，我们建议为功能连接体项目及其最近的倡议——国际神经成像数据共享倡议——维护的大量公开可用数据生产经过验证的生理估计器。随着最近对可公开获得的MRI数据的分析的增加，该项目将极大地增加生理校正的社区经验，并使生理损坏数据的分析和报告转变为生理未损坏数据的分析和报告。

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. PUBLIC HEALTH RELEVANCE: Physiologic noise from cardiac and respiratory processes obscures useful signals in functional magnetic resonance imaging (MRI) data. The size of this noise varies randomly between individuals and systematically between disease populations and states. However it is difficult to correct for its presence without special equipment so many researchers necessarily ignore it, making it a critical barrier to progress in the field. This projct will remove that barrier by providing this correction for public neuroimaging data and a tool to correct researchers' and clinicians' data without special equipment.