Nigg JT, Karalunas SL, Mooney MA, Wilmot B, Nikolas MA, Martel MM, Tipsord J, Nousen EK, Schmitt C, Ryabinin P, Musser ED, Nagel BJ, Fair DA. The Oregon ADHD-1000: A new longitudinal data resource enriched for clinical cases and multiple levels of analysis.
Dev Cogn Neurosci 2023;
60:101222. [PMID:
36848718 PMCID:
PMC9984785 DOI:
10.1016/j.dcn.2023.101222]
[Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 01/31/2023] [Accepted: 02/20/2023] [Indexed: 02/27/2023] Open
Abstract
The fields of developmental psychopathology, developmental neuroscience, and behavioral genetics are increasingly moving toward a data sharing model to improve reproducibility, robustness, and generalizability of findings. This approach is particularly critical for understanding attention-deficit/hyperactivity disorder (ADHD), which has unique public health importance given its early onset, high prevalence, individual variability, and causal association with co-occurring and later developing problems. A further priority concerns multi-disciplinary/multi-method datasets that can span different units of analysis. Here, we describe a public dataset using a case-control design for ADHD that includes: multi-method, multi-measure, multi-informant, multi-trait data, and multi-clinician evaluation and phenotyping. It spans > 12 years of annual follow-up with a lag longitudinal design allowing age-based analyses spanning age 7-19 + years with a full age range from 7 to 21. Measures span genetic and epigenetic (DNA methylation) array data; EEG, functional and structural MRI neuroimaging; and psychophysiological, psychosocial, clinical and functional outcomes data. The resource also benefits from an autism spectrum disorder add-on cohort and a cross sectional case-control ADHD cohort from a different geographical region for replication and generalizability. Datasets allowing for integration from genes to nervous system to behavior represent the "next generation" of researchable cohorts for ADHD and developmental psychopathology.
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