Journal
NEUROIMAGE
Volume 102, Issue -, Pages 938-944Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2014.05.043
Keywords
Resting-state fMRI; Measurement error correction; Connectivity analysis; Shrinkage estimator; Empirical Bayes
Funding
- NIH from the National Institute of Neurological Disorders and Stroke [RO1 NS060910, RO1 NS085211, RO1 NS048527]
- NIH from the National Institute of Mental Health [RO1 MH095836, RO1 MH085328, RO1 MH078160]
- NIH from the National Institute of Biomedical Imaging and Bioengineering [RO1 EB016061, P41 EB015909]
- Johns Hopkins-National Institutes of Mental Health joint training program
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Resting-state functional magnetic resonance imaging (rs-fMRI) is used to investigate synchronous activations in spatially distinct regions of the brain, which are thought to reflect functional systems supporting cognitive processes. Analyses are often performed using seed-based correlation analysis, allowing researchers to explore functional connectivity between data in a seed region and the rest of the brain. Using scan-rescan rs-fMRI data, we investigate how well the subject-specific seed-based correlation map from the second replication of the study can be predicted using data from the first replication. We show that one can dramatically improve prediction of subject-specific connectivity by borrowing strength from the group correlation map computed using all other subjects in the study. Even more surprisingly, we found that the group correlation map provided a better prediction of a subject's connectivity than the individual's own data. While further discussion and experimentation are required to understand how this can be used in practice, results indicate that shrinkage-based methods that borrow strength from the population mean should play a role in rs-fMRI data analysis. (C) 2014 Elsevier Inc. All rights reserved.
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