Resting state fMRI in the Human Connectome Project

S.M. Smith; Christian Beckmann; J. Andersson; E.J. Auerbach; J. Bijsterbosch; G. Douaud; E. Duff; D.A. Feinberg; L. Griffanti; M.P. Harms; M. Kelly; T. Laumann; K.L. Miller; S. Moeller; S. Petersen; J. Power; G. Salimi-Khorshidi; A.Z. Snyder; A.T. Vu; Mark W. Woolrich; J. Xu; E. Yacoub; K. Uǧurbil; D.C. Van Essen; M.F. Glasser

doi:10.1016/j.neuroimage.2013.05.039

Resting state fMRI in the Human Connectome Project

S.M. Smith, Christian Beckmann, J. Andersson, E.J. Auerbach, J. Bijsterbosch, G. Douaud, E. Duff, D.A. Feinberg, L. Griffanti, M.P. Harms, M. Kelly, T. Laumann, K.L. Miller, S. Moeller, S. Petersen, J. Power, G. Salimi-Khorshidi, A.Z. Snyder, A.T. Vu, Mark W. WoolrichJ. Xu, E. Yacoub, K. Uǧurbil, D.C. Van Essen, M.F. Glasser

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

1133 Citations (Scopus)

1419 Downloads (Pure)

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. © 2013 Elsevier Inc.

Original language	English
Pages (from-to)	144-168
Journal	NeuroImage
Volume	80
DOIs	https://doi.org/10.1016/j.neuroimage.2013.05.039
Publication status	Published - 2013

Access to Document

10.1016/j.neuroimage.2013.05.039

Cite this

Smith, S. M., Beckmann, C., 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

@article{a43b19f980b241ad8d2cd48692e8cffe,

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. {\textcopyright} 2013 Elsevier Inc.",

author = "S.M. Smith and Christian Beckmann and J. Andersson and E.J. Auerbach and J. Bijsterbosch and G. Douaud and E. Duff and D.A. Feinberg and L. Griffanti and M.P. Harms and M. Kelly and T. Laumann and K.L. Miller and S. Moeller and S. Petersen and J. Power and G. Salimi-Khorshidi and A.Z. Snyder and A.T. Vu and Woolrich, {Mark W.} and J. Xu and E. Yacoub and K. Uǧurbil and {Van Essen}, D.C. and M.F. Glasser",

year = "2013",

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

language = "English",

volume = "80",

pages = "144--168",

journal = "NeuroImage",

issn = "1053-8119",

publisher = "Academic Press Inc.",

}

Smith, SM, Beckmann, C, 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

TY - JOUR

T1 - Resting state fMRI in the Human Connectome Project

AU - Smith, S.M.

AU - Beckmann, Christian

AU - Andersson, J.

AU - Auerbach, E.J.

AU - Bijsterbosch, J.

AU - Douaud, G.

AU - Duff, E.

AU - Feinberg, D.A.

AU - Griffanti, L.

AU - Harms, M.P.

AU - Kelly, M.

AU - Laumann, T.

AU - Miller, K.L.

AU - Moeller, S.

AU - Petersen, S.

AU - Power, J.

AU - Salimi-Khorshidi, G.

AU - Snyder, A.Z.

AU - Vu, A.T.

AU - Woolrich, Mark W.

AU - Xu, J.

AU - Yacoub, E.

AU - Uǧurbil, K.

AU - Van Essen, D.C.

AU - Glasser, M.F.

PY - 2013

Y1 - 2013

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. © 2013 Elsevier Inc.

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. © 2013 Elsevier Inc.

U2 - 10.1016/j.neuroimage.2013.05.039

DO - 10.1016/j.neuroimage.2013.05.039

M3 - Article

SN - 1053-8119

VL - 80

SP - 144

EP - 168

JO - NeuroImage

JF - NeuroImage

ER -

Resting state fMRI in the Human Connectome Project

Abstract

Access to Document

Fingerprint

Cite this