Proc Natl Acad Sci U S A 107(10): 4734-4739

PMC: PMC2842060

PMID: 20176931

Toward discovery science of human brain function

Randy Buckner+45 authors

Supplementary Material

Supporting Information:

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^{Department of Radiology, New Jersey Medical School, Newark, NJ 07103;}

^{Phyllis Green and Randolph Cōwen Institute for Pediatric Neuroscience, New York University Child Study Center, NYU Langone Medical Center, New York, NY 10016;}

^{FMRIB Centre, Oxford University, Oxford OX3 9DU, UK;}

^{Howard Hughes Medical Institute, Harvard University, Cambridge, MA 02138;}

^{School of Psychology, University of Wales, Bangor, UK;}

^{Danish Research Centre for Magnetic Resonance, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark;}

^{Mood and Anxiety Disorders Program, National Institute of Mental Health/National Institutes of Health, Department of Health and Human Services, Bethesda, MD 20892;}

^{Behavioral Neuroscience Department, Oregon Health & Science University, Portland, OR 97239;}

^{Department of Diagnostic Radiology, Yale University School of Medicine, New Haven, CT 06511;}

^{Division of Clinical Research, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY 10962;}

^{Biophysics Research Institute, Medical College of Wisconsin, Milwaukee, WI 53226;}

^{Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland;}

^{Donders Institute for Brain, Cognition, and Behavior, Center for Neuroscience, Radboud University Nijmegen Medical Center, 6500 HB Nijmegen, The Netherlands;}

^{Biophysics Research Institute, Medical College of Wisconsin, Milwaukee, WI 53226;}

^{Institute of Neuroscience, National Yang-Ming University, Taiwan;}

^{Imaging Institute, The Cleveland Clinic, Cleveland, OH 44195;}

^{Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC, 27710;}

^{Department of Cognitive Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany;}

^{Department of Psychiatry and Department of Neurology, Emory University School of Medicine, Atlanta, GA 30322;}

^{Centre for Advanced Imaging, University of Queensland, Brisbane, Australia;}

^{Department of Psychology, University of Michigan, Ann Arbor, MI 48109;}

^{Laboratory for Neurocognitive and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, 21205;}

^{Department of Psychiatry, Oregon Health & Science University, Portland, OR 97239;}

^{F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD 21205;}

^{Functional MRI Laboratory, University of Michigan, Ann Arbor, MI 48109;}

^{McDonnell Center for Higher Brain Functions, Washington University School of Medicine, St. Louis, MO 63110;}

^{Departments of Neurology and Neuroradiology, Klinikum Rechts der Isar, Technische Universität München, 81675 Munich, Germany;}

^{Institute of Psychology and Department of Radiology, Leiden University Medical Center, Leiden University, Leiden, The Netherlands;}

^{Center for Brain Health and School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, TX 75080;}

^{Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110;}

^{Department of Neurology, Charité Univesitaetsmedizin-Berlin, 10117 Berlin, Germany;}

^{School of Kinesiology, University of Michigan, Ann Arbor, MI 48109;}

^{Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213;}

^{Department of Psychiatry, Klinikum Rechts der Isar, Technische Universität München, D-81675 Munich, Germany;}

^{Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhong-Da Hospital, Southeast University, Nanjing 210009, China;}

^{Department of Psychiatry, Institute of Clinical Medicine and Department of Public Health Science, Institute of Health Science, University of Oulu, Oulu 90014, Finland;}

^{Berlin NeuroImaging Center, 10099 Berlin, Germany;}

^{Department of Psychiatry, Otto-von-Guericke University of Magdeburg, Magdeburg 39106, Germany;}

^{Laboratory for Higher Brain Function, Institute of Psychology, Chinese Academy of Sciences, Beijing 100864, China;}

^{Department of Brain and Cognitive Sciences, Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Boston, MA 02139;}

^{Department of Psychiatry, University of Western Ontario, London, ON N6A3H8, Canada;}

^{Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria; and}

^{State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China}

^{To whom correspondence should be addressed. E-mail:}gro.cmuyn@mahlim.leahcim.

Edited^* by Marcus E. Raichle, Washington University, St. Louis, MO, and approved January 20, 2010 (received for review October 14, 2009)

Author contributions: B.B.B., R.L.B., J.S.H., R.K., A.V., Y.Z., F.X.C., and M.P.M. designed research; B.B.B., M.M., XN.Z., S.G., C.K., S.M.S., C.F.B., J.S.A., R.L.B., S.C., A.-M.D., M.E., D.F., M.H., M.J.H., J.S.H., V.J.K., R.K., SJ.L., CP.L., M.J.L., C.E.M., D.M., K.H.M., D.S.M., H.S.M., K.M., C.S.M., S.M., B.J.N., J.J.P., S.J.P., S.E.P., V.R., S.A.R., B.R., B.L.S., S.S., R.D.S., G.S., C.S., GJ.T., J.M.V., A.V., M.W., L.W., XC.W., S.W.-G., P.W., C.W., Y.Z., HY.Z., F.X.C., and M.P.M. performed research; S.M.S., C.F.B., R.L.B., S.C., A.-M.D., M.E., D.F., M.H., M.J.H., J.S.H., V.J.K., R.K., SJ.L., CP.L., M.J.L., C.E.M., D.M., K.H.M., D.S.M., H.S.M., K.M., C.S.M., S.M., B.J.N., J.J.P., S.J.P., S.E.P., V.R., S.A.R., B.R., B.L.S., S.S., R.D.S., G.S., C.S., GJ.T., J.M.V., A.V., M.W., L.W., XC.W., S.W.-G., P.W., C.W., Y.Z., HY.Z., B.B.B., F.X.C., and M.P.M. contributed new reagents/analytic tools; B.B.B., M.M., XN.Z., S.G., C.K., F.X.C., and M.P.M. analyzed data; and B.B.B., M.M., XN.Z., C.K., J.S.A., F.X.C., and M.P.M. wrote the paper.

Edited^* by Marcus E. Raichle, Washington University, St. Louis, MO, and approved January 20, 2010 (received for review October 14, 2009)

Freely available online through the PNAS open access option.

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 www.nitrc.org/projects/fcon_1000/.

Keywords: database, neuroimaging, open access, reproducibility, resting state

Abstract

Much like the challenge of decoding the human genome, the complexities of mapping human brain function pose a challenge to the functional neuroimaging community. As demonstrated by the 1000 Genomes Project (1), the accumulation and sharing of large-scale datasets for data mining is necessary for the first phase of discovery science.

Although the neuroimaging community has traditionally focused on hypothesis-driven task-based approaches, resting-state functional MRI (R-fMRI) has recently emerged as a powerful tool for discovery science. 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 (2 –5). A single R-fMRI scan (as brief as 5 min) can be used to interrogate a multitude of functional circuits simultaneously, without the requirement of selecting a priori hypotheses (6). Building on the term “connectome,” coined to describe the comprehensive map of structural connections in the human brain (7), we use “functional connectome” to describe the collective set of functional connections in the human brain.

Buttressed by moderate to high test–retest reliability (8 –10) and replicability (11, 12), as well as widespread access, R-fMRI has overcome initial skepticism (13) regarding the validity of examining such an apparently unconstrained state (5, 8, 14). Recent R-fMRI studies have identified putative biomarkers of neuropsychiatric illness (12, 15 –18), provided insight into the development of functional networks in the maturing and aging brain (19 –22), demonstrated a shared intrinsic functional architecture (23) between humans and nonhuman primates (24, 25), and delineated the effects of sleep (26), anesthesia (27), and pharmacologic agents on R-fMRI measures (28, 29). Given the many sources of variability inherent in fMRI, the remaining challenge is to demonstrate the feasibility and utility of adopting a high-throughput model for R-fMRI, commensurate with the scale used by human genetics studies to have the power to detect both single gene and combinatorial genetic and environmental effects on complex phenotypes.

Accordingly, the 1000 Functional Connectomes Project was formed to aggregate existing R-fMRI data from collaborating centers throughout the world and to provide an initial demonstration of the ability to pool functional data across centers. As of December 11, 2009, the repository includes data from 1,414 healthy adult participants contributed by 35 laboratories (Table S1). The intent is to expand this open resource as additional data are made available.

Here we provide an initial demonstration of the feasibility of pooling R-fMRI datasets across centers. Specifically, we (i) establish the presence of a universal functional architecture in the brain, consistently detectable across centers; (ii) investigate the influence of center on R-fMRI measures; (iii) explore the potential impact of demographic variables (e.g., age, sex) on R-fMRI measures; and (iv) demonstrate the use of an intersubject variance–based method for identifying putative boundaries between functional networks.

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Acknowledgments

We thank David Kennedy and www.nitrc.org for supporting the 1000 Functional Connectomes Project data release, Avi Snyder for providing helpful insights and advice concerning project goals, and Cameron Craddock for helpful advice on this study. Financial support for the 1000 Functional Connectomes project was provided by grants from the National Institutes of Mental Health (R01MH083246; and R01MH081218 to F.X.C. and M.P.M.), National Institute on Drug Abuse (R03DA024775;, to C.K.; R01DA016979, to F.X.C.), Autism Speaks, National Institute of Neurological Disorders and Stroke (R01NS049176, to B.B.), and the Howard Hughes Medical Institute (to J.S.A. and R.L.B.), as well as gifts to the NYU Child Study Center from the Stavros Niarchos Foundation, Leon Levy Foundation, Joseph P. Healy, Linda and Richard Schaps, and Jill and Bob Smith and an endowment provided by Phyllis Green and Randolph Cōwen. NITRC is funded by the National Institutes of Health's Blueprint for Neurosciences Research (neuroscienceblueprint.nih.gov) (Contract N02-EB-6-4281, to TCG, Inc.).

Acknowledgments

Footnotes

The authors declare no conflict of interest.

Data deposition: All data used in this work were released on December 11, 2009 via www.nitrc.org/projects/fcon_1000/.

*This Direct Submission article had a prearranged editor.

This article contains supporting information online at www.pnas.org/cgi/content/full/0911855107/DCSupplemental.

Footnotes

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