The NKI Rockland Sample II: An Open Resource of Multimodal Brain, Physiology & Behavior Data from a Community Lifespan Sample

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ABSTRACT. The present competing renewal application proposes to create the next generation NKI-Rockland Sample (NKI-RS) initiative. The NKI-Rockland Sample (NKI-RS) has served as a beacon for lifespan connectomics research, providing a model for accelerating the pace of psychiatric discovery science. More than 200 publications have used NKI-RS, generated largely by independent investigators and in major journals. Since 2011, it has generated and publicly shared (on a quarterly basis) a large-scale (N > 1400), deeply phenotyped, community-ascertained, cross-sectional, lifespan sample (ages 6–85 years old) with advanced connectomics- focused neuroimaging (i.e., diffusion MRI, resting state fMRI [R-fMRI]) and genetic samples. Recently launched large- scale efforts, such as the HCP Lifespan Studies and the NIH ABCD Study are working to bring to scale human connectome mapping and brain function across the lifespan, using ‘battle-tested’ imaging technologies and strategies. These ongoing studies are less focused on mental health. Moreover, technologies and ideas continue to evolve - often too rapidly to permit timely testing and inclusion in ongoing research. The overarching goal of the present proposal is to create the next generation NKI-RS initiative that will once again extend the vanguard in the study of lifespan connectomics by enriching and expanding the landscape for neuroscientific advancement and biomarker discovery. Three major themes have guided the design of the proposed NKI-RS-II lifespan resource: 1) multimodal measurement integration across functional domains (e.g., fMRI, EEG, mobile brain/body imaging [MoBI] framework), 2) ecological sampling (e.g., wearables, sensors, smartphones apps), and 3) enhanced physiological phenotyping for cardiovascular fitness and obesity. Specifically, in a community- ascertained lifespan sample (N=600; ages 9-75; M: F = 1:1; age range selected to maximize data yield and tolerability), the proposed work aims to: 1) Generate and share large-scale multimodal MRI/EEG imaging data complemented by comprehensive phenotyping of cognition, behavior, and psychiatric status, from human and sensor-based informants; 2) Optimize brain-age prediction across the lifespan using multimodal data (R-fMRI, Naturalistic Viewing fMRI [NV-fMRI], dMRI, T2/T1, R-EEG, NV-EEG) and relate deviations from chronological age to dimensions of psychopathology and cognitive performance; and 3) Identify the relationship of modifiable health risk factors (e.g., fitness, obesity, physical activity, substance use) to deviations between predicted brain age and chronological age across the lifespan. Consistent with the model established by the previously funded NKI-RS initiatives, all data will be shared prospectively, on a quarterly basis, via the International Neuroimaging Data-sharing Initiative (INDI) and the NIMH Data Archive (NDA).