TY - JOUR
T1 - The Oregon ADHD-1000
T2 - A new longitudinal data resource enriched for clinical cases and multiple levels of analysis
AU - Nigg, Joel T.
AU - Karalunas, Sarah L.
AU - Mooney, Michael A.
AU - Wilmot, Beth
AU - Nikolas, Molly A.
AU - Martel, Michelle M.
AU - Tipsord, Jessica
AU - Nousen, Elizabeth K.
AU - Schmitt, Colleen
AU - Ryabinin, Peter
AU - Musser, Erica D.
AU - Nagel, Bonnie J.
AU - Fair, Damien A.
N1 - Publisher Copyright:
© 2023 The Authors
PY - 2023/4
Y1 - 2023/4
N2 - 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.
AB - 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.
KW - Attention-deficit/hyperactivity disorder
KW - Case-control longitudinal
KW - Design
KW - Genetic and epigenetic array
KW - Neuroimaging
KW - Public dataset
KW - Pyschophysiological
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U2 - 10.1016/j.dcn.2023.101222
DO - 10.1016/j.dcn.2023.101222
M3 - Article
C2 - 36848718
AN - SCOPUS:85148873692
SN - 1878-9293
VL - 60
JO - Developmental Cognitive Neuroscience
JF - Developmental Cognitive Neuroscience
M1 - 101222
ER -