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Functional connectomics from resting-state fMRI

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperReviewResearchpeer-review

  • Stephen M. Smith, University of Oxford
  • ,
  • Diego Vidaurre
  • Christian F. Beckmann, University of Oxford, Radboud University Nijmegen, University of Twente
  • ,
  • Matthew F. Glasser, Washington University St. Louis
  • ,
  • Mark Jenkinson, University of Oxford
  • ,
  • Karla L. Miller, University of Oxford
  • ,
  • Thomas E. Nichols, University of Oxford, The University of Warwick
  • ,
  • Emma C. Robinson, University of Oxford
  • ,
  • Gholamreza Salimi-Khorshidi, University of Oxford
  • ,
  • Mark W. Woolrich, University of Oxford
  • ,
  • Deanna M. Barch, Washington University St. Louis
  • ,
  • Kamil Uǧurbil, University of Minnesota
  • ,
  • David C. Van Essen, Washington University St. Louis

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
JournalTrends in Cognitive Sciences
Pages (from-to)666-682
Number of pages17
Publication statusPublished - 1 Dec 2013
Externally publishedYes

    Research areas

  • Connectomics, Network modelling, Resting-state fMRI

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