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

描述(申请人提供)：在这项申请中，我们提出了一种策略，以消除心脏和呼吸过程中的生理性噪声，作为功能磁共振成像(MRI)和功能连接MRI数据中的偏差和降低特异性的来源。如果没有专门的并行监测设备，就很难纠正这些噪声源，也很难确定纠正过程本身是否成功。因此，许多研究人员必然会忽略这些来源，或者使用非特定于这些来源的改进方法。由于这些来源在不同人群中的大小和可变性，这是在这一领域取得进展的关键障碍。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，使研究人员能够回溯校正他们的数据，其方式相当于用平行监测的噪声源校正他们的数据。这是一项重要的进步，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)过去的生理矫正经验仅限于那些能够接触到监测设备的调查人员。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，以便在我们的项目结束时，生理校正和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项目及其最新倡议--国际神经成像数据共享倡议--维护的大量公开可用的数据制作经过验证的生理学估计器。随着最近对公开可用的MRI数据的分析增加，该项目将极大地增加社区在生理校正方面的经验，并使分析和报告生理损坏的数据转变为分析和报告生理未损坏的数据。

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.