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

Authors :
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
Source :
Developmental Cognitive Neuroscience, Vol 60, Iss , Pp 101222- (2023)
Publication Year :
2023
Publisher :
Elsevier, 2023.

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.

Details

Language :
English
ISSN :
18789293
Volume :
60
Issue :
101222-
Database :
Directory of Open Access Journals
Journal :
Developmental Cognitive Neuroscience
Publication Type :
Academic Journal
Accession number :
edsdoj.2bfd63dd63724d2093ae383b0435b892
Document Type :
article
Full Text :
https://doi.org/10.1016/j.dcn.2023.101222