ADHD is a major public health problem with severe long term outcomes. Existing treatments do not achieve long term change, which has increased the urgency of discovering biomarkers, biological bases of heterogeneity, and developmental specificity of effects. The proposed project would test focused theories of aberrant developmental organization of brain networks in ADHD in a longitudinal design. It would also seek to identify subtypes of ADHD based on brain networks. Two latest-generation methods will be utilized: Diffusion tensor imaging (DTI) and functional connectivity MRI (fcMRI). Use of these methods is directly in line with the NIH Human Connectome guidelines, so the data set will also serve as a resource in the eventual creation of a child connectome. A total of 490 children age 7-10 would be enrolled prior to data loss to attrition and motion artifact. They will be followed annually in three waves, yielding an age based accelerated longitudinal design spanning ages 7-12 years. 70% of the sample will have ADHD (all subtypes) and 30% will be typically developing comparison youth. Community- based recruitment is emphasized so that some treatment naove scans will be obtained. Clinical change, treatment use, and psychosocial moderators will be tracked. Selected cognitive measures will be used to cross validate clinical findings and determine whether mechanistic suppositions about brain circuits hold at the cognitive level of analysis. The major innovations in this project are: (a) evaluates ADHD as a dynamic entity rather than a static phenomenon, (b) the first longitudinal study of functional connectivity or DTI in ADHD, (c) combines functional and structural connectivity measures in ADHD, (d) introduces sophisticated usage of graph theory in order to determine biological subtypes of ADHD based on brain networks. The major significance is that it will enable new discoveries about brain networks in ADHD that can enable significant advances in describing neurobiological markers of liability (those that persist regardless of symptom change) and markers of course of illness (those that are acutely sensitive to phenotypic change or predict outcome or treatment response). Those data can inform new models of assessment, individualized treatments, and treatment monitoring. If the project is successful it will represent an important step forward in mapping integrative, paradigmatic models of ADHD mechanism using the newest insights about brain development.
ADHD is a costly disorder. Understanding of causes has been hampered by inconsistent findings on brain imaging due to the variability that ADHD exhibits across cases and across time. The present study will identify specific brain networks that are altered in ADHD and track these changes over time. The results are intended to identify clear markers of ADHD in the brain that endure over time, as well as to identify subtypes of ADHD based on brain network involvement.
|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|
|Feczko, E; Balba, N M; Miranda-Dominguez, O et al. (2018) Subtyping cognitive profiles in Autism Spectrum Disorder using a Functional Random Forest algorithm. Neuroimage 172:674-688|
|Karalunas, Sarah L; Hawkey, Elizabeth; Gustafsson, Hanna et al. (2018) Overlapping and Distinct Cognitive Impairments in Attention-Deficit/Hyperactivity and Autism Spectrum Disorder without Intellectual Disability. J Abnorm Child Psychol 46:1705-1716|
|Dickie, Erin W; Ameis, Stephanie H; Shahab, Saba et al. (2018) Personalized Intrinsic Network Topography Mapping and Functional Connectivity Deficits in Autism Spectrum Disorder. Biol Psychiatry 84:278-286|
|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|
|Grayson, David S; Fair, Damien A (2017) Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature. Neuroimage 160:15-31|
|Di Martino, Adriana; O'Connor, David; Chen, Bosi et al. (2017) Enhancing studies of the connectome in autism using the autism brain imaging data exchange II. Sci Data 4:170010|
|Cary, Robert P; Ray, Siddharth; Grayson, David S et al. (2017) Network Structure among Brain Systems in Adult ADHD is Uniquely Modified by Stimulant Administration. Cereb Cortex 27:3970-3979|
|Musser, Erica D; Nigg, Joel T (2017) Emotion Dysregulation Across Emotion Systems in Attention Deficit/Hyperactivity Disorder. J Clin Child Adolesc Psychol :1-13|
|Costa Dias, Taciana G; Iyer, Swathi P; Carpenter, Samuel D et al. (2015) Characterizing heterogeneity in children with and without ADHD based on reward system connectivity. Dev Cogn Neurosci 11:155-74|
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