• Bharat B Biswal, Maarten Mennes, Xi-Nian Zuo, Suril Gohel, Clare Kelly, Steve M Smith, Christian F Beckmann, Jonathan S Adelstein, Randy L Buckner, Stan Colcombe, Anne-Marie Dogonowski, Monique Ernst, Damien Fair, Michelle Hampson, Matthew J Hoptman, James S Hyde, Vesa J Kiviniemi, Rolf Kötter, Shi-Jiang Li, Ching-Po Lin, Mark J Lowe, Clare Mackay, David J Madden, Kristoffer H Madsen, Daniel S Margulies, Helen S Mayberg, Katie McMahon, Christopher S Monk, Stewart H Mostofsky, Bonnie J Nagel, James J Pekar, Scott J Peltier, Steven E Petersen, Valentin Riedl, Serge A R B Rombouts, Bart Rypma, Bradley L Schlaggar, Sein Schmidt, Rachael D Seidler, Greg J Siegle, Christian Sorg, Gao-Jun Teng, Juha Veijola, Arno Villringer, Martin Walter, Lihong Wang, Xu-Chu Weng, Susan Whitfield-Gabrieli, Peter Williamson, Christian Windischberger, Yu-Feng Zang, Hong-Ying Zhang, F Xavier Castellanos, and Michael P Milham.
• Department of Radiology, New Jersey Medical School, Newark, NJ 07103, USA.
• Proc. Natl. Acad. Sci. U.S.A. 2010 Mar 9; 107 (10): 4734-9.

AbstractAlthough 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 www.nitrc.org/projects/fcon_1000/.

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