Although ADHD represents one of the most common psychiatric disorders of youth, the biological underpinnings remain largely unknown. The prevalence of ADHD in the general population is estimated at 3- 9%, and the risk of recurrence in a sibling is increased 3-5 times that of siblings of healthy controls. The high prevalence rate coupled with the increased risk for several co-morbid conditions including reading disabilities (~20%), disruptive behavior disorders (~60%), anxiety/mood disorders (40%), and substance abuse/dependence (~40%) underscores the morbidity of this disorder and the potential costs to the individual and society. Symptom clusters of inattention and/or hyperactivity and impulsivity are diagnostic of the illness, but appear partially independent. Given the heterogeneous nature of ADHD, identification of biomarkers that are heritable phenotypes that more closely reflect gene expression (i.e., endophenotypes) is essential to further our understanding of the biology of this disorder. Recently, 'converging methods'approaches or 'multiple level of analysis'approaches are advocated in the investigation of complex neurodevelopmental disorders such as ADHD The availability of non-invasive, non-radioactive neuroimaging techniques and sophisticated data analytic approaches used in combination with neurophysiologic (EEG), neurocognitive and family genetic data holds the promise of greatly improving our ability to identify these biomarkers. The principal goal of the work proposed in this project is to use these tools to identify neuromorphometric and neurochemical measures that, in concert with electrophysiologic and neurocognitive measures, define meaningful endophenotypes in ADHD. In addition, the extent to which these measures are shared among siblings and therefore are familial will be explored as a means of identifying these endophenotypes. Using these tools, Sibling Pairs (30 pairs concordant and 30 pairs discordant for ADHD) selected from the ADHD Genetic Study (AGS) (Loo PI) at UCLA, and a sample of healthy control children will be examined. The AGS study population is a large group of multiplex families with phenotypic and genotype data available for access. We propose that key behavioral phenotypic dimensions of ADHD including: EEG metrics and working memory deficits are associated with specific brain neuromorphometric and neurochemical measures. Furthermore, we will examine the extent to which the neurobiologic findings underlying these phenotypic variations will be correlated among siblings, suggesting that they may be useful phenotypes for future investigations. Taken together, these studies will provide the basis for genetic studies to delineate specific gene >brain>behavioral pathways and lay the foundation for early detection and better intervention in ADHD. Although attention deficit/hyperactivity disorder affects a significant number of children and adults, and despite a great deal of research in this field, the underlying biology remains poorly understood. In this proposal, we plan to combine cutting edge neuroimaging techniques with electrophysiologic and neurocognitive measures, in order to delineate potential biomarkers of ADHD. The combination of these techniques will provide a powerful approach to unraveling this complex neurodevelopmental disorder.
Although attention deficit/hyperactivity disorder affects a significant number of children and adults, and despite a great deal of research in this field, the underlying biology remains poorly understood. In this proposal, we plan to combine cutting edge neuroimaging techniques with electrophysiologic and neurocognitive measures, in order to delineate potential biomarkers of ADHD. The combination of these techniques will provide a powerful approach to unraveling this complex neurodevelopmental disorder.
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| Lawrence, Katherine E; Levitt, Jennifer G; Loo, Sandra K et al. (2013) White matter microstructure in subjects with attention-deficit/hyperactivity disorder and their siblings. J Am Acad Child Adolesc Psychiatry 52:431-440.e4 |
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| Alger, Jeffry R (2010) Quantitative proton magnetic resonance spectroscopy and spectroscopic imaging of the brain: a didactic review. Top Magn Reson Imaging 21:115-28 |
