The smoothing artifact of spatially constrained canonical correlation analysis in functional MRI

Authors:
Dietmar Cordes;Mingwu Jin;Tim Curran;Rajesh Nandy
Affiliations:
Department of Physics, Ryerson University, Toronto, ON, Canada and Department of Psychology and Neuroscience and Department of Radiology, University of Colorado, Boulder, CO;Department of Radiology, University of Colorado, Denver, CO and Department of Physics, University of Texas, Arlington, TX;Department of Psychology and Neuroscience, University of Colorado, Boulder, CO;Departments of Biostatistics and Psychology, UCLA, Los Angeles, CA
Venue:
Journal of Biomedical Imaging - Special issue on Advanced Signal Processing Methods for Biomedical Imaging
Year:
2012

Citing 1
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Extending local canonical correlation analysis to handle general linear contrasts for fMRI data

Journal of Biomedical Imaging - Special issue on Mathematical Methods for Images and Surfaces 2011

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Abstract

A wide range of studies show the capacity of multivariate statistical methods for fMRI to improve mapping of brain activations in a noisy environment. An advanced method uses local canonical correlation analysis (CCA) to encompass a group of neighboring voxels instead of looking at the single voxel time course. The value of a suitable test statistic is used as a measure of activation. It is customary to assign the value to the center voxel; however, this is a choice of convenience and without constraints introduces artifacts, especially in regions of strong localized activation. To compensate for these deficiencies, different spatial constraints in CCA have been introduced to enforce dominance of the center voxel. However, even if the dominance condition for the center voxel is satisfied, constrained CCA can still lead to a smoothing artifact, often called the "bleeding artifact of CCA", in fMRI activation patterns. In this paper a new method is introduced to measure and correct for the smoothing artifact for constrained CCA methods. It is shown that constrained CCA methods corrected for the smoothing artifact lead to more plausible activation patterns in fMRI as shown using data from a motor task and a memory task.