Event Abstract

Methods for Correcting Artifacts in FMRI Time Series

  • 1 VCU, Biostatistics, United States

A major problem in the initial stages of neural signal analysis is the identification and compensating artifacts, many of which reflect physiological processes. In functional MRI data the major sources of artifacts are head motion, changes in signal strength with breathing, and pulse. We would like to estimate the size of these effects across all the measures, but we usually don’t have accurate independent measures of these effects; furthermore these effects  generally do not closely track external measures of breathing or pulse. I introduce a method of constructing synthetic controls to provide a first step to identify artifacts in fMRI or other neural time series data. Synthetic controls are differences of little biological significance, which however differ in their relation to anticipated (but unmeasured) artifacts. For a typical MRI scan, with alternating planes of excitation, differences between grey matter voxels in adjacent planes may play this role. The large number of such differences turns out to have very strong systematic patterns summarized by a very few principal components, which are almost orthogonal to the predictors derived from the experimental design. These factors in turn predict typically 50% of variance in voxels not used for the construction of the factors. These factors may be further improved by an iterative constrained fitting procedure. This procedure seems to improve the S/N ratio of the data by a factor of two.  

Keywords: Clinical Neuroscience

Conference: 4th INCF Congress of Neuroinformatics, Boston, United States, 4 Sep - 6 Sep, 2011.

Presentation Type: Poster Presentation

Topic: Clinical neuroscience

Citation: Reimers M (2011). Methods for Correcting Artifacts in FMRI Time Series. Front. Neuroinform. Conference Abstract: 4th INCF Congress of Neuroinformatics. doi: 10.3389/conf.fninf.2011.08.00021

Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters.

The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated.

Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed.

For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions.

Received: 17 Oct 2011; Published Online: 19 Oct 2011.

* Correspondence: Dr. Mark Reimers, VCU, Biostatistics, Richmond, United States, reimersm@msu.edu