Attention deficit hyperactivity disorder (ADHD) is a common disorder often leading to poor outcomes. Although it is now known that genes play a role in ADHD and that brain alterations, observed on MRI, are associated with ADHD, how genetic effects are implemented in the brain to shape ADHD is not known. It is likely that there are distinct ways this can happen, that is, heterogeneous etiology in ADHD. These etiologies include a combination of genetic and environmental influences, but the present proposal focuses on the genetic influences. It then attempts to identify genetic biotypes of ADHD that are validated in brain and cognition. This study adopts a systems perspective in that it will bring together (a) systemic analysis of brain connectivity using functional and structural MRI scanning, and (b) gene-pathway analyses based on biologically related gene groups.
In Aim 1, existing genetic databases will be extended with Baysian methods, and gene pathways will be prioritized by informatics methods using publically available genome-wide datasets and related to ADHD. A new cohort will be augmented and genotyped, to achieve the necessary sample size at substantially reduced cost. The Omni 2.5 chip will be used to assay common SNPs and copy number variants, and the Omni Exome chip will be used to assay rare variants. Then, pathways associated with ADHD will be replicated in a new cohort, creating an authoritative set of gene-pathway findings. From the surviving set of gene pathways, profiles or biotypes of the ADHD and control participants will be created using a form of analysis called modularity analysis. This method comes from graph theory community detection methods.
In Aim 2, these biotypes will be validated with neurocognitive measures, and with functional and structural MRI connectivity analyses. The focus in Aim 2 will be on the genetic influences on well-established neural correlates of ADHD and to understand these in relation to biotypes. Thus, connectivity in specific neural circuits will be studied.
In Aim 3, the focus shifts to a newer perspective of ADHD as involving disruptions in brain organization or maturation at the level of whole brain assembly. The pathways scores identified in Aim 1 will compete to explain variation with specific, well defined metrics of brain efficiency and organization from functional and structural MRI data. Biotypes will also be compared on these brain-organization metrics. CNV and QTL analyses will be included in Aim 3 as well to gain converging information on brain metrics and gene pathways. Finally, biotype-MRI effects will be tested for cross-validation in an independent, similar-sized cohort through collaborative arrangements. If successful, the project will break new ground in describing the relation of genetic and neural alterations in ADHD, will move the field past single-SNP gene analyses in ADHD, and will help provide a way forward to characterize biological subtypes of the syndrome.

Public Health Relevance

In attention deficit/hyperactivity disorder (ADHD) both brain and gene effects are known, but how they relate to each other is not known. Combining this information with new mathematical tools, we seek to clarify how ADHD might be caused biologically, and to identify new, biologically-defined subtypes of ADHD. Doing so can help explain its heterogeneous causes, presentations, and outcomes.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Research Project (R01)
Project #
Application #
Study Section
Child Psychopathology and Developmental Disabilities Study Section (CPDD)
Program Officer
Friedman-Hill, Stacia
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Oregon Health and Science University
Schools of Medicine
United States
Zip Code
Kong, Xiang-Zhen; Mathias, Samuel R; Guadalupe, Tulio et al. (2018) Mapping cortical brain asymmetry in 17,141 healthy individuals worldwide via the ENIGMA Consortium. Proc Natl Acad Sci U S A 115:E5154-E5163
Kim, Daniel Seung; Burt, Amber A; Ranchalis, Jane E et al. (2017) Sequencing of sporadic Attention-Deficit Hyperactivity Disorder (ADHD) identifies novel and potentially pathogenic de novo variants and excludes overlap with genes associated with autism spectrum disorder. Am J Med Genet B Neuropsychiatr Genet 174:381-389
Dosenbach, Nico U F; Koller, Jonathan M; Earl, Eric A et al. (2017) Real-time motion analytics during brain MRI improve data quality and reduce costs. Neuroimage 161:80-93
Kamradt, Jaclyn M; Nigg, Joel T; Friderici, Karen H et al. (2017) Neuropsychological performance measures as intermediate phenotypes for attention-deficit/hyperactivity disorder: A multiple mediation analysis. Dev Psychopathol 29:259-272
Martel, Michelle M; Nigg, Joel T; Schimmack, Ulrich (2017) Psychometrically Informed Approach to Integration of Multiple Informant Ratings in Adult ADHD in a Community-Recruited Sample. Assessment 24:279-289
Nigg, Joel T; Elmore, Alexis L; Natarajan, Neil et al. (2016) Variation in an Iron Metabolism Gene Moderates the Association Between Blood Lead Levels and Attention-Deficit/Hyperactivity Disorder in Children. Psychol Sci 27:257-69
Bottomly, Daniel; McWeeney, Shannon K; Wilmot, Beth (2016) HitWalker2: visual analytics for precision medicine and beyond. Bioinformatics 32:1253-5
Nigg, Joel T; Nagel, Bonnie J (2016) Commentary: Risk taking, impulsivity, and externalizing problems in adolescent development--commentary on Crone et al. 2016. J Child Psychol Psychiatry 57:369-70
Mooney, Michael A; McWeeney, Shannon K; Faraone, Stephen V et al. (2016) Pathway analysis in attention deficit hyperactivity disorder: An ensemble approach. Am J Med Genet B Neuropsychiatr Genet 171:815-26
Nigg, Joel T (2016) Where Do Epigenetics and Developmental Origins Take the Field of Developmental Psychopathology? J Abnorm Child Psychol 44:405-19

Showing the most recent 10 out of 25 publications