Toward discovery science of human brain function

Biswal BB, Mennes M, Zuo XN, Gohel S, Kelly C, Smith SM, Beckmann CF, Adelstein JS, Buckner RL, Colcombe S, Dogonowski AM, Ernst M, Fair D, Hampson M, Hoptman MJ, Hyde JS, Kiviniemi VJ, Kotter R, Li SJ, Lin CP, Lowe MJ, Mackay C, Madden DJ, Madsen KH, Margulies DS, Mayberg HS, Mcmahon K, Monk CS, Mostofsky SH, Nagel BJ, Pekar JJ, Peltier SJ, Petersen SE, Riedl V, Rombouts SARB, Rypma B, Schlaggar BL, Schmidt S, Seidler RD, Siegle GJ, Sorg C, Teng GJ, Veijola J, Villringer A, Walter M, Wang L, Weng XC, Whitfield-Gabrieli S, Williamson P, Windischberger C, Zang YF, Zhang HY, Castellanos FX, Milham MP (2010)

Publication Type: Journal article

Publication year: 2010

Journal

Book Volume: 107

Pages Range: 4734-4739

Journal Issue: 10

DOI: 10.1073/pnas.0911855107

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/.

Authors with CRIS profile

Involved external institutions

University of Medicine and Dentistry of New Jersey (UMDNJ) United States (USA) (US) New York University (NYU) United States (USA) (US) University of Oxford United Kingdom (GB) Harvard University United States (USA) (US) University of Wales United Kingdom (GB) University of Copenhagen Denmark (DK) National Institute of Mental Health Information Resource Center United States (USA) (US) Oregon Health and Science University (OSHU) United States (USA) (US) Yale University United States (USA) (US) Nathan S. Kline Institute for Psychiatric Research (NKI) United States (USA) (US) Medical College of Wisconsin (MCW) United States (USA) (US) Oulu University Hospital Finland (FI) Radboud University Nijmegen Netherlands (NL) Cleveland Clinic United States (USA) (US) Duke University United States (USA) (US) Max-Planck-Institut für Kognitions- und Neurowissenschaften / Max Planck Institute for Human Cognitive and Brain Sciences Germany (DE) Emory University United States (USA) (US) University of Queensland Australia (AU) University of Michigan United States (USA) (US) Kennedy Krieger Institute United States (USA) (US) Washington University United States (USA) (US) Technische Universität München (TUM) Germany (DE) Leiden University Netherlands (NL) University of Texas at Dallas (UTD / UT Dallas) United States (USA) (US) Charité - Universitätsmedizin Berlin Germany (DE) University of Pittsburgh United States (USA) (US) Southeast University / 东南大学 China (CN) Oulun Yliopisto / University of Oulo Finland (FI) Otto-von-Guericke-Universität Magdeburg Germany (DE) Chinese Academy of Sciences (CAS) / 中国科学院 China (CN) Massachusetts Institute of Technology (MIT) United States (USA) (US) Western University Canada (CA) Medizinische Universität Wien Austria (AT) Beijing Normal University China (CN)

How to cite

APA:

Biswal, B.B., Mennes, M., Zuo, X.-N., Gohel, S., Kelly, C., Smith, S.M.,... Milham, M.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. https://doi.org/10.1073/pnas.0911855107

MLA:

Biswal, Bharat B., et al. "Toward discovery science of human brain function." Proceedings of the National Academy of Sciences of the United States of America 107.10 (2010): 4734-4739.

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