Shrinkage prediction of seed-voxel brain connectivity using resting state fMRI

Research output: Contribution to journalComment/debatepeer-review

20 Scopus citations


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 languageEnglish (US)
Pages (from-to)938-944
Number of pages7
Issue numberP2
StatePublished - Nov 5 2014


  • Connectivity analysis
  • Empirical Bayes
  • Measurement error correction
  • Resting-state fMRI
  • Shrinkage estimator

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience


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