Toward discovery science of human brain function

Biswal, Bharat B., Mennes, Maarten, Zuo, Xi-Nian, Gohel, Suril, Kelly, Clare, Smith, Steve M., Beckmann, Christina F., Adelstein, Jonathan S., Buckner, Randy L., Colcombe, Stan, Dogonowski, Anne-Marie, Ernst, Monique, Fair, Damien, Hampson, Michelle, Hoptman, Matthew J., Hyde, James S., Kiviniemi, Vesa J., Kotter, Rolf, Li, Shi-Jiang, Lin, Ching-Po, Lowe, Mark J., Mackay, Clare, Madden, david J., Madsen, Kristoffer H., Margulies, Daniel S., Mayberg, Helen S., McMahon, Katie, Monk, Chirstopher S., Mostofsky, Stewart H., Nagel, Bonnie J., Pekar, James J., Peltier, Scott J., Petersen, Steven E., Riedl, Valentin, Rombouts, Serge A. R. B., Rypma, Bart, Schlaggar, Bradley L., Schmidt, Sein, Seidler, Rachael D., Siegle, Greg J., Sorg, Christian, Teng, Gao-Jun, Veijola, Juha, Villringer, Arno, Walter, Martin, Wang, Lihong, Weng, Xu-Chu, Whitfield-Gabrieli, Susan, Williamson, Peter, Windischberger, Christina, Zang, Yu-Feng, Zhang, Hong-Ying, Castellanos, F. Xavier and Milham, Michael P. (2010) Toward discovery science of human brain function. Proceedings of the National Academy of Sciences of the United States of America, 107 10: 4734-4739. doi:10.1073/pnas.0911855107

Author Biswal, Bharat B.
Mennes, Maarten
Zuo, Xi-Nian
Gohel, Suril
Kelly, Clare
Smith, Steve M.
Beckmann, Christina F.
Adelstein, Jonathan S.
Buckner, Randy L.
Colcombe, Stan
Dogonowski, Anne-Marie
Ernst, Monique
Fair, Damien
Hampson, Michelle
Hoptman, Matthew J.
Hyde, James S.
Kiviniemi, Vesa J.
Kotter, Rolf
Li, Shi-Jiang
Lin, Ching-Po
Lowe, Mark J.
Mackay, Clare
Madden, david J.
Madsen, Kristoffer H.
Margulies, Daniel S.
Mayberg, Helen S.
McMahon, Katie
Monk, Chirstopher S.
Mostofsky, Stewart H.
Nagel, Bonnie J.
Pekar, James J.
Peltier, Scott J.
Petersen, Steven E.
Riedl, Valentin
Rombouts, Serge A. R. B.
Rypma, Bart
Schlaggar, Bradley L.
Schmidt, Sein
Seidler, Rachael D.
Siegle, Greg J.
Sorg, Christian
Teng, Gao-Jun
Veijola, Juha
Villringer, Arno
Walter, Martin
Wang, Lihong
Weng, Xu-Chu
Whitfield-Gabrieli, Susan
Williamson, Peter
Windischberger, Christina
Zang, Yu-Feng
Zhang, Hong-Ying
Castellanos, F. Xavier
Milham, Michael P.
Title Toward discovery science of human brain function
Journal name Proceedings of the National Academy of Sciences of the United States of America   Check publisher's open access policy
ISSN 0027-8424
Publication date 2010-03-01
Sub-type Article (original research)
DOI 10.1073/pnas.0911855107
Volume 107
Issue 10
Start page 4734
End page 4739
Total pages 6
Editor Nicholas R. Cozzarelli
Place of publication United States
Publisher National Academy of Sciences
Language eng
Abstract Although it is being successfully implemented for exploration of the genome, discovery science has eluded the functional neuroimaging community. The core challenge remains the development of common paradigms for interrogating the myriad functional systems in the brain without the constraints of a priori hypotheses. Resting-state functional MRI (R-fMRI) constitutes a candidate approach capable of addressing this challenge. Imaging the brain during rest reveals large-amplitude spontaneous low-frequency (<0.1 Hz) fluctuations in the fMRI signal that are temporally correlated across functionally related areas. Referred to as functional connectivity, these correlations yield detailed maps of complex neural systems, collectively constituting an individual's “functional connectome.” Reproducibility across datasets and individuals suggests the functional connectome has a common architecture, yet each individual's functional connectome exhibits unique features, with stable, meaningful interindividual differences in connectivity patterns and strengths. Comprehensive mapping of the functional connectome, and its subsequent exploitation to discern genetic influences and brain–behavior relationships, will require multicenter collaborative datasets. Here we initiate this endeavor by gathering R-fMRI data from 1,414 volunteers collected independently at 35 international centers. We demonstrate a universal architecture of positive and negative functional connections, as well as consistent loci of inter-individual variability. Age and sex emerged as significant determinants. These results demonstrate that independent R-fMRI datasets can be aggregated and shared. High-throughput R-fMRI can provide quantitative phenotypes for molecular genetic studies and biomarkers of developmental and pathological processes in the brain. To initiate discovery science of brain function, the 1000 Functional Connectomes Project dataset is freely accessible at
Keyword Database
Open access
Resting state
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2011 Collection
Centre for Advanced Imaging Publications
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Citation counts: TR Web of Science Citation Count  Cited 1134 times in Thomson Reuters Web of Science Article | Citations
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Created: Sun, 28 Mar 2010, 10:01:41 EST