Toward discovery science of human brain function

Bharat B. Biswal(Rutgers, The State University of New Jersey), Maarten Mennes(NYU Langone Health), Xi‐Nian Zuo(NYU Langone Health), Suril Gohel(Rutgers, The State University of New Jersey), Clare Kelly(NYU Langone Health), Steve M. Smith(University of Oxford), Christian F. Beckmann(University of Oxford), Jonathan S. Adelstein(NYU Langone Health), Randy L. Buckner(Howard Hughes Medical Institute), Stan Colcombe(Bangor University), Anne-Marie Dogonowski(Hvidovre Hospital), Monique Ernst(National Institutes of Health), Damien A. Fair(Oregon Health & Science University), Michelle Hampson(Yale University), Matthew J. Hoptman(Nathan Kline Institute for Psychiatric Research), James S. Hyde(Medical College of Wisconsin), Vesa Kiviniemi(Oulu University Hospital), Rolf Kötter(Radboud University Nijmegen), Shi‐Jiang Li(Medical College of Wisconsin), Ching‐Po Lin(National Yang Ming Chiao Tung University), Mark J. Lowe(Cleveland Clinic), Clare E. Mackay(University of Oxford), David J. Madden(Duke Medical Center), Kristoffer H. Madsen(Hvidovre Hospital), Daniel S. Margulies(Max Planck Institute for Human Cognitive and Brain Sciences), Helen S. Mayberg(Emory University), Katie L. McMahon(The University of Queensland), Christopher S. Monk(University of Michigan), Stewart H. Mostofsky(Kennedy Krieger Institute), Bonnie J. Nagel(Oregon Health & Science University), James J. Pekar(Kennedy Krieger Institute), Scott Peltier(University of Michigan), Steven E. Petersen(Hope Center for Neurological Disorders), Valentin Riedl(TUM Klinikum), Serge A.R.B. Rombouts(Leiden University Medical Center), Bart Rypma(The University of Texas at Dallas), Bradley L. Schlaggar(Washington University in St. Louis), Sein Schmidt(Charité - Universitätsmedizin Berlin), Rachael D. Seidler(University of Michigan), Greg J. Siegle(University of Pittsburgh), Christian Sorg(TUM Klinikum), Gao‐Jun Teng(Zhongda Hospital Southeast University), Juha Veijola(University of Oulu), Arno Villringer(Charité - Universitätsmedizin Berlin), Martin Walter(Otto-von-Guericke-Universität Magdeburg), Lihong Wang(Duke Medical Center), Xu-Chu Weng(Chinese Academy of Sciences), Susan Whitfield‐Gabrieli(Harvard–MIT Division of Health Sciences and Technology), Peter Williamson(Western University), Christian Windischberger(Medical University of Vienna), Yu‐Feng Zang(Beijing Normal University), Hong-Ying Zhang(Zhongda Hospital Southeast University), F. Xavier Castellanos(Nathan Kline Institute for Psychiatric Research), Michael P. Milham(NYU Langone Health)
Proceedings of the National Academy of Sciences
February 22, 2010
Cited by 3,086Open Access
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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/.


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