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Automated detection of datasets with artefactual signal in large n studies using Shannon entropy

John McGonigle, Andrea L. Malizia, Majid Mirmehdi, Automated detection of datasets with artefactual signal in large n studies using Shannon entropy. 16th Annual Meeting of the Organization for Human Brain Mapping. June 2010. PDF, 517 Kbytes.

Abstract

The recent public release of more than 1200 resting state BOLD MRI (R-fMRI) datasets as part of the 1000 Functional Connectomes Project provides the community with the opportunity to apply and test analysis techniques on a much larger number of subjects than may be available locally. With the potential to examine data from many sources comes the issue of how the characteristics of this data vary between site, and also between studies at the same site. Here we explore the use of examining standard deviation and Shannon entropy distributions for common voxels in a subset of the released data with similar parameters in order to automatically identify outlier datasets. Removal of these is prudent before carrying out further data-driven methods, as are often used in the analysis of R-fMRI.

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