Abstract
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.
Original language | English (US) |
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Pages (from-to) | 938-944 |
Number of pages | 7 |
Journal | NeuroImage |
Volume | 102 |
Issue number | P2 |
DOIs |
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State | Published - Nov 5 2014 |
Keywords
- Connectivity analysis
- Empirical Bayes
- Measurement error correction
- Resting-state fMRI
- Shrinkage estimator
ASJC Scopus subject areas
- Neurology
- Cognitive Neuroscience