Genetic Networks Influencing Gray Matter Changes in Persistent ADHD

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 DESCRIPTION (provided by applicant): Attention Deficit Hyperactivity Disorder (ADHD) refers to an early-onset neurobehavioral disorder. Its persistence into adulthood has just been recognized in 15% to 65% of cases, and this large group of patients suffers from higher rates of unemployment, relationship ⁄marital difficulties, risk taking behavior, accidents and legal violations. The goal of this proposal is to improve our understanding of the neuropathology of persistent ADHD. Three genetic pathways have shown genetic associations with ADHD symptom load, disease severity and neuropsychological performance: the dopamine/noradrenaline pathway, the serotonin pathway and the neurite outgrowth network. Gray matter reduction in the brain of patients with ADHD has been repeatedly reported, and it likely involves many complex networks beyond the fronto-striatal circuit. Yet the exact genetic variants, and their integrated effect on the brain structural deficits and persistence into adulthood, are still unknown. We will systemically integrate brain structure, common genetic variants and symptoms associated with children and adults with ADHD, to reveal genetic effects on the neural substrate of ADHD symptoms which persist into adulthood. We will first extract gray matter (GM) networks in adults' structural brain images, and evaluate their associations with the two symptom domains (inattentive and hyperactive/impulsive) of ADHD. We will then analyze datasets of children and adolescents to identify GM networks related to the two symptom domains in children. Through comparing ADHD symptom related networks in adults and children, we will be able to identify the GM reduction patterns common to children and adults with ADHD, which carry indicative and predictive power for the persistence of ADHD. Second, we will analyze Single Nucleotide Polymorphisms (SNPs) from the three genetic pathways in conjunction with GM networks associated with ADHD symptoms, and we will determine whether unaffected siblings carry intermediate genetic profile risks and GM network deficits. Finally, we will leverage two independent datasets, (1) children diagnosed with ADHD and with follow-up information in adulthood, and 2) adults with ADHD and age-matched healthy controls) to replicate the initial results and to evaluate the classification and prediction power n persistence of ADHD based on GM abnormalities and genetic factors identified. At the end of this project, we have identified the patterns of gray matter showing significant association with ADHD symptoms in both childhood and adulthood. This will allow us to disentangle the neural changes (partially) responsible for the persistent form of ADHD. More importantly, we will identify the connections between genetic variations from the three pathways and such neurobiological anomalies, and improve our understanding of neuropathology of persistent ADHD. The derived GM features and genetic factors provide biological metrics for persistent ADHD, which will help to predict whether a child with ADHD will continue to have symptoms in adulthood, thus aid early prevention, diagnosis, and treatment.