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)
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Freund, Michelle
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Nathan Kline Institute for Psychiatric Research
United States
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Dickie, Erin W; Ameis, Stephanie H; Shahab, Saba et al. (2018) Personalized Intrinsic Network Topography Mapping and Functional Connectivity Deficits in Autism Spectrum Disorder. Biol Psychiatry 84:278-286
Milham, Michael P; Craddock, R Cameron; Son, Jake J et al. (2018) Assessment of the impact of shared brain imaging data on the scientific literature. Nat Commun 9:2818
Zhao, Yihong; Zheng, Zhi-Liang; Castellanos, F Xavier (2017) Analysis of alcohol use disorders from the Nathan Kline Institute-Rockland Sample: Correlation of brain cortical thickness with neuroticism. Drug Alcohol Depend 170:66-73
Potvin, Olivier; Dieumegarde, Louis; Duchesne, Simon et al. (2017) Freesurfer cortical normative data for adults using Desikan-Killiany-Tourville and ex vivo protocols. Neuroimage 156:43-64
Zuo, Xi-Nian; He, Ye; Betzel, Richard F et al. (2017) Human Connectomics across the Life Span. Trends Cogn Sci 21:32-45
Van Dam, Nicholas T; O'Connor, David; Marcelle, Enitan T et al. (2017) Data-Driven Phenotypic Categorization for Neurobiological Analyses: Beyond DSM-5 Labels. Biol Psychiatry 81:484-494
Hoogman, Martine; Bralten, Janita; Hibar, Derrek P et al. (2017) Subcortical brain volume differences in participants with attention deficit hyperactivity disorder in children and adults: a cross-sectional mega-analysis. Lancet Psychiatry 4:310-319
Potvin, Olivier; Dieumegarde, Louis; Duchesne, Simon et al. (2017) Normative morphometric data for cerebral cortical areas over the lifetime of the adult human brain. Neuroimage 156:315-339
Koyama, Maki S; O'Connor, David; Shehzad, Zarrar et al. (2017) Differential contributions of the middle frontal gyrus functional connectivity to literacy and numeracy. Sci Rep 7:17548
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 26:2341-2352

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