Faced with the challenge of charting brain function and the origins of mental illness across the lifespan, the functional neuroimaging community is following the precedent set by molecular genetics in turning to discovery science. Once a distant goal, the 1000 Functional Connectomes Project (FCP) demonstrated the feasibility of conducting discovery science for human brain function. Capitalizing on the ease of data-sharing with resting state fMRI (R-fMRI), the FCP pooled datasets from over 1200 individuals independently collected at sites throughout the world. The FCP dataset immediately demonstrated the power of large-scale R-fMRI investigations, revealing widespread differences in the brain's intrinsic functional architecture related to sex and age that are not easily detected with typical sample sizes (e.g., n = 20-100). The FCP effort represented the inauguration of effective, open data-sharing, with researchers who once struggled to obtain 20-30 datasets for analyses suddenly granted access to over 1200 datasets for methods innovation and data mining. Building on the initial success of the FCP, we recently called for the establishment of prospective data-sharing, using the pilot NKI-Rockland Sample, a phenotypically rich, life-span sample, as our prototype. With more than 300 phenotypic variables obtained across 26 psychiatric, behavioral, and cognitive assessment tools, the NKI- Rockland Sample exemplifies they type of data collection model ideally suited to allow neuroimaging methods to employ the data mining and discovery approach applied successfully in molecular genetics. Neuroimagers can now identify brain-behavior relationships and delineate their dynamic trajectories across the lifespan. Having established the feasibility of generating phenotypically rich datasets and openly sharing them with the scientific community on a prospective basis, the proposed work aims to generate a more carefully constructed lifespan sample, larger in scale, maximally representative of the community and sampled using appropriate strategies for the delineation of normative trajectories for metrics of the brain's intrinsic functional architecture. Specifically, we propose to recruit 1000 participants (ages 6-85) over a four-year period, densely sampling early developmental and advanced aging periods, where age-related gradients of changes are maximal and model-fitting techniques are most prone to artifactual results. Recruitment and enrollment strategies will be carefully controlled to maximize the community representativeness of the sample and minimize biases commonly encountered with opportunistic recruiting. Seventy percent of the collected datasets will be randomly selected for discovery, while the remaining 30% will serve to rigorously test hypotheses generated during the discovery phase. Consistent with the model established for the pilot NKI-Rockland Sample, data generated as part of this proposal will be shared prospectively, on a weekly basis, via the FCP and the associated International Neuroimaging Data-sharing Initiative (INDI).

Public Health Relevance

The brain imaging community is rapidly advancing toward the goal of discovering markers of psychiatric illness in the brain. Central to the ultimate attainment of this goal is the development of normative measures of brain function across the lifespan, like the growth curves used by pediatricians. We propose to generate a large set of imaging data, from which the percentiles of human brain function measures can be derived across the lifespan;simultaneously the data will be shared with the broader scientific community on a weekly basis.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
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Special Emphasis Panel (ZMH1-ERB-L (04))
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Freund, Michelle
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Nathan Kline Institute for Psychiatric Research
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Zhao, Yihong; Castellanos, F Xavier (2016) Annual Research Review: Discovery science strategies in studies of the pathophysiology of child and adolescent psychiatric disorders--promises and limitations. J Child Psychol Psychiatry 57:421-39
Yang, Zhi; Zuo, Xi-Nian; McMahon, Katie L et al. (2016) Genetic and Environmental Contributions to Functional Connectivity Architecture of the Human Brain. Cereb Cortex 26:2341-52
Shehzad, Zarrar; Kelly, Clare; Reiss, Philip T et al. (2014) A multivariate distance-based analytic framework for connectome-wide association studies. Neuroimage 93 Pt 1:74-94
Zuo, Xi-Nian; Xing, Xiu-Xia (2014) Test-retest reliabilities of resting-state FMRI measurements in human brain functional connectomics: a systems neuroscience perspective. Neurosci Biobehav Rev 45:100-18
Yang, Zhen; Craddock, R Cameron; Margulies, Daniel S et al. (2014) Common intrinsic connectivity states among posteromedial cortex subdivisions: Insights from analysis of temporal dynamics. Neuroimage 93 Pt 1:124-37
Di Martino, A; Yan, C-G; Li, Q et al. (2014) The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Mol Psychiatry 19:659-67
Yang, Zhi; Chang, Catie; Xu, Ting et al. (2014) Connectivity trajectory across lifespan differentiates the precuneus from the default network. Neuroimage 89:45-56
Mennes, Maarten; Biswal, Bharat B; Castellanos, F Xavier et al. (2013) Making data sharing work: the FCP/INDI experience. Neuroimage 82:683-91
Craddock, R Cameron; Jbabdi, Saad; Yan, Chao-Gan et al. (2013) Imaging human connectomes at the macroscale. Nat Methods 10:524-39
Yan, Chao-Gan; Craddock, R Cameron; Zuo, Xi-Nian et al. (2013) Standardizing the intrinsic brain: towards robust measurement of inter-individual variation in 1000 functional connectomes. Neuroimage 80:246-62

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