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

doi:10.1016/j.dcn.2023.101222

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

Research output: Contribution to journal › Article › peer-review

5 Scopus citations

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.

Original language	English
Article number	101222
Journal	Developmental Cognitive Neuroscience
Volume	60
DOIs	https://doi.org/10.1016/j.dcn.2023.101222
State	Published - Apr 2023

Bibliographical note

Publisher Copyright:
© 2023 The Authors

Funding

Critical philanthropic support was provided by the Abracadbra Foundation (Stephen and Patricia Sharp). Multiple NIMH grants supported the development of these cohorts: 1R01-MH59105 (Nigg); R01-MH63146 (Nigg); R01-MH070004 (Friderici); 1R01MH86654 (Nigg-Fair); R01MH099064 (Nigg); 1R37MH59105 (Nigg); 2R56MH086654 (Nigg); R01MH115357 (Fair-Nigg); 2R37MH59105 (Nigg); R01 MH096773 (Fair); K23 MH108656 (Karalunas). An integrated SQL data base at OHSU has housed all the data and is supported by the Oregon Clinical and Translational Research Institute funded by a grant from the National Center for Advancing Translational Sciences ( NCATS ), National Institutes of Health , through Grant Award Number UL1TR002369 . The material in this paper does not necessarily reflect the opinion of the National Institutes of Health or the National Institute of Mental Health. Critical philanthropic support was provided by the Abracadbra Foundation (Stephen and Patricia Sharp). Multiple NIMH grants supported the development of these cohorts: 1R01-MH59105 (Nigg); R01-MH63146 (Nigg); R01-MH070004 (Friderici); 1R01MH86654 (Nigg-Fair); R01MH099064 (Nigg); 1R37MH59105 (Nigg); 2R56MH086654 (Nigg); R01MH115357 (Fair-Nigg); 2R37MH59105 (Nigg); R01 MH096773 (Fair); K23 MH108656 (Karalunas). An integrated SQL data base at OHSU has housed all the data and is supported by the Oregon Clinical and Translational Research Institute funded by a grant from the National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, through Grant Award Number UL1TR002369. The material in this paper does not necessarily reflect the opinion of the National Institutes of Health or the National Institute of Mental Health.

Funders	Funder number
Abracadbra Foundation
National Institutes of Health (NIH)	UL1TR002369
National Institutes of Health (NIH)
National Institute of Mental Health	K23 MH108656, R01-MH070004, 1R37MH59105, R01-MH63146, 2R56MH086654, R01 MH096773, R01MH099064, R01MH115357
National Institute of Mental Health
National Center for Advancing Translational Sciences (NCATS)
Oregon Clinical and Translational Research Institute

Keywords

Attention-deficit/hyperactivity disorder
Case-control longitudinal
Design
Genetic and epigenetic array
Neuroimaging
Public dataset
Pyschophysiological

ASJC Scopus subject areas

Cognitive Neuroscience

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.dcn.2023.101222

Cite this

Nigg, J. T., Karalunas, S. L., Mooney, M. A., Wilmot, B., Nikolas, M. A., Martel, M. M., Tipsord, J., Nousen, E. K., Schmitt, C., Ryabinin, P., Musser, E. D., Nagel, B. J., & Fair, D. A. (2023). The Oregon ADHD-1000: A new longitudinal data resource enriched for clinical cases and multiple levels of analysis. Developmental Cognitive Neuroscience, 60, Article 101222. https://doi.org/10.1016/j.dcn.2023.101222

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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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note = "Publisher Copyright: {\textcopyright} 2023 The Authors",

year = "2023",

month = apr,

doi = "10.1016/j.dcn.2023.101222",

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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 2023, 'The Oregon ADHD-1000: A new longitudinal data resource enriched for clinical cases and multiple levels of analysis', Developmental Cognitive Neuroscience, vol. 60, 101222. https://doi.org/10.1016/j.dcn.2023.101222

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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.

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

Abstract

Bibliographical note

Funding

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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Cite this