Developmental Cognitive Neuroscience (Apr 2023)

The Oregon ADHD-1000: A new longitudinal data resource enriched for clinical cases and multiple levels of analysis

  • Joel T. Nigg,
  • Sarah L. Karalunas,
  • Michael A. Mooney,
  • Beth Wilmot,
  • Molly A. Nikolas,
  • Michelle M. Martel,
  • Jessica Tipsord,
  • Elizabeth K. Nousen,
  • Colleen Schmitt,
  • Peter Ryabinin,
  • Erica D. Musser,
  • Bonnie J. Nagel,
  • Damien A. Fair

Journal volume & issue
Vol. 60
p. 101222

Abstract

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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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