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 articlepeer-review

436 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 2013

Bibliographical note

Funding Information:
The authors are grateful for funding via the following grants: 1U54MH091657-01 (NIH Blueprint for Neuroscience Research), P30-NS057091, P41-RR08079/EB015894, F30-MH097312 (NIH), and 098369/Z/12/Z (Wellcome Trust). They thank their many colleagues within the WU–Minn HCP Consortium for their invaluable contributions in generating the publicly available HCP data and in implementing the many procedures needed to acquire, analyse, visualise, and share these datasets.

Keywords

  • Connectomics
  • Network modelling
  • Resting-state fMRI

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