Functional connectomics from resting-state fMRI

Stephen M. Smith, Diego Vidaurre, Christian F. Beckmann, Matthew F. Glasser, Mark Jenkinson, Karla L. Miller, Thomas E. Nichols, Emma C. Robinson, Gholamreza Salimi-Khorshidi, Mark W. Woolrich, Deanna M. Barch, Kamil Uǧurbil, David C. Van Essen

Research output: Contribution to journalReview article

376 Scopus citations

Abstract

Spontaneous fluctuations in activity in different parts of the brain can be used to study functional brain networks. We review the use of resting-state functional MRI (rfMRI) for the purpose of mapping the macroscopic functional connectome. After describing MRI acquisition and image-processing methods commonly used to generate data in a form amenable to connectomics network analysis, we discuss different approaches for estimating network structure from that data. Finally, we describe new possibilities resulting from the high-quality rfMRI data being generated by the Human Connectome Project and highlight some upcoming challenges in functional connectomics.

Original languageEnglish (US)
Pages (from-to)666-682
Number of pages17
JournalTrends in Cognitive Sciences
Volume17
Issue number12
DOIs
StatePublished - Dec 1 2013

Keywords

  • Connectomics
  • Network modelling
  • Resting-state fMRI

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    Smith, S. M., Vidaurre, D., Beckmann, C. F., Glasser, M. F., Jenkinson, M., Miller, K. L., Nichols, T. E., Robinson, E. C., Salimi-Khorshidi, G., Woolrich, M. W., Barch, D. M., Uǧurbil, K., & Van Essen, D. C. (2013). Functional connectomics from resting-state fMRI. Trends in Cognitive Sciences, 17(12), 666-682. https://doi.org/10.1016/j.tics.2013.09.016