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

Cleveland Clinic Lerner Com-Cwru, Cleveland OH

Investigators

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Abstract

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.