Resting-state fMRI in the Human Connectome Project

Stephen M. Smith, Christian F. Beckmann, Jesper Andersson, Edward J. Auerbach, Janine Bijsterbosch, Gwenaëlle Douaud, Eugene Duff, David A. Feinberg, Ludovica Griffanti, Michael P. Harms, Michael Kelly, Timothy Laumann, Karla L. Miller, Steen Moeller, Steve Petersen, Jonathan Power, Gholamreza Salimi-Khorshidi, Abraham Z. Snyder, An T. Vu, Mark W. WoolrichJunqian Xu, Essa Yacoub, Kamil Uǧurbil, David C. Van Essen, Matthew F. Glasser

Research output: Contribution to journalArticlepeer-review

1029 Scopus citations


Resting-state functional magnetic resonance imaging (rfMRI) allows one to study functional connectivity in the brain by acquiring fMRI data while subjects lie inactive in the MRI scanner, and taking advantage of the fact that functionally related brain regions spontaneously co-activate. rfMRI is one of the two primary data modalities being acquired for the Human Connectome Project (the other being diffusion MRI). A key objective is to generate a detailed in vivo mapping of functional connectivity in a large cohort of healthy adults (over 1000 subjects), and to make these datasets freely available for use by the neuroimaging community. In each subject we acquire a total of 1. h of whole-brain rfMRI data at 3. T, with a spatial resolution of 2. ×. 2. ×. 2. mm and a temporal resolution of 0.7. s, capitalizing on recent developments in slice-accelerated echo-planar imaging. We will also scan a subset of the cohort at higher field strength and resolution. In this paper we outline the work behind, and rationale for, decisions taken regarding the rfMRI data acquisition protocol and pre-processing pipelines, and present some initial results showing data quality and example functional connectivity analyses.

Original languageEnglish (US)
Pages (from-to)144-168
Number of pages25
StatePublished - Oct 15 2013

Bibliographical note

Funding Information:
We are very grateful: to Natalie Voets, Sonia Bishop, David Cole, Nicola Filippini, Alejo Nevado and Chris Summerfield (Oxford) and Deanna Barch and Nick Bloom (WashU) for help with the FMRIB multiband motion piloting; to Erin Reid and Donna Dierker (WashU), for helping with the FIX training (hand-labelling of ICA components); and to David Flitney (Oxford), for creating the Melview ICA component viewing and labelling tool. We are grateful for funding via the following NIH grants: 1U54MH091657-01 , P30-NS057091 , P41-RR08079/EB015894 , and F30-MH097312 .


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