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. Woolrich; Junqian Xu; Essa Yacoub; Kamil Uǧurbil; David C. Van Essen; Matthew F. Glasser

doi:10.1016/j.neuroimage.2013.05.039

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

Radiology

Research output: Contribution to journal › Article › peer-review

1106 Scopus citations

Abstract

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 language	English (US)
Pages (from-to)	144-168
Number of pages	25
Journal	NeuroImage
Volume	80
DOIs	https://doi.org/10.1016/j.neuroimage.2013.05.039
State	Published - 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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10.1016/j.neuroimage.2013.05.039

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Smith, S. M., Beckmann, C. F., Andersson, J., Auerbach, E. J., Bijsterbosch, J., Douaud, G., Duff, E., Feinberg, D. A., Griffanti, L., Harms, M. P., Kelly, M., Laumann, T., Miller, K. L., Moeller, S., Petersen, S., Power, J., Salimi-Khorshidi, G., Snyder, A. Z., Vu, A. T., ... Glasser, M. F. (2013). Resting-state fMRI in the Human Connectome Project. NeuroImage, 80, 144-168. https://doi.org/10.1016/j.neuroimage.2013.05.039

Smith, SM, Beckmann, CF, Andersson, J, Auerbach, EJ, Bijsterbosch, J, Douaud, G, Duff, E, Feinberg, DA, Griffanti, L, Harms, MP, Kelly, M, Laumann, T, Miller, KL, Moeller, S, Petersen, S, Power, J, Salimi-Khorshidi, G, Snyder, AZ, Vu, AT, Woolrich, MW, Xu, J, Yacoub, E , Uǧurbil, K, Van Essen, DC & Glasser, MF 2013, 'Resting-state fMRI in the Human Connectome Project', NeuroImage, vol. 80, pp. 144-168. https://doi.org/10.1016/j.neuroimage.2013.05.039

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title = "Resting-state fMRI in the Human Connectome Project",

abstract = "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.",

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

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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doi = "10.1016/j.neuroimage.2013.05.039",

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T1 - Resting-state fMRI in the Human Connectome Project

AU - Smith, Stephen M.

AU - Beckmann, Christian F.

AU - Andersson, Jesper

AU - Auerbach, Edward J.

AU - Bijsterbosch, Janine

AU - Douaud, Gwenaëlle

AU - Duff, Eugene

AU - Feinberg, David A.

AU - Griffanti, Ludovica

AU - Harms, Michael P.

AU - Kelly, Michael

AU - Laumann, Timothy

AU - Miller, Karla L.

AU - Moeller, Steen

AU - Petersen, Steve

AU - Power, Jonathan

AU - Salimi-Khorshidi, Gholamreza

AU - Snyder, Abraham Z.

AU - Vu, An T.

AU - Woolrich, Mark W.

AU - Xu, Junqian

AU - Yacoub, Essa

AU - Uǧurbil, Kamil

AU - Van Essen, David C.

AU - Glasser, Matthew F.

N1 - 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 .

PY - 2013/10/15

Y1 - 2013/10/15

N2 - 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.

AB - 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.

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Resting-state fMRI in the Human Connectome Project

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