Characterizing mechanistic heterogeneity across ADHD and Autism

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DESCRIPTION (provided by applicant): Progress in establishing the etiology of psychiatric illness is limited by the absence of objective biological measures able to detect and discriminate between disorders. This problem is particularly important in developmental disorders, where early identification could eventually assist in prevention of lifelong impairments. The two earlies onsets, most common and costly developmental disorders in child psychiatry are attention deficit hyperactivity disorder (ADHD) and Autism spectrum disorders (ASD). A recent 2011 study, surveying the years 1997-2008, has now verified that 1 in 6 children have a developmental disability, a 17% increase over the past decade driven largely by increases in ASD and ADHD. These developments highlight the need for innovative approaches to address the underlying cause of these disorders. It is likely that the clinical heterogeneity and the imprecise nature of their nosological distinctions represent fundamentally confounding factors limiting a better understanding of their etiology, prevention, and treatment. Interestingly, an emerging observation regarding brain imaging in ASD and ADHD is that they often have the same atypical functional brain signatures. However, because these two syndromes are almost exclusively studied separately, it is difficult to determine atypical brain function that is common compared to what is distinct for each disorder. If we are to improve our understanding regarding the underlying etiology of these disorders, it will be necessary to study these populations simultaneously. With that said, simply comparing groups of children based on their DSM diagnosis is unlikely to suffice. The behavioral and biological heterogeneity within each syndrome further complicates the meaning of any given group difference found in brain imaging. Thus, progress in our understanding requires not only examining these disorders in the same studies, but also identifying how brain signatures relate to distinct behavioral components (i.e., endophenotypes) that span the syndromes. Under this context, and consistent with NIMH's new strategic plan, Strategy 1.4 (also see RDoC), the current proposal aims to use resting state functional connectivity MRI (rs-fcMRI) and structural connectivity (DTI) to identify brain signatures that correspond to fundamental behavioral components (executive, facial recognition, and affect recognition) found in ADHD and/or ASD. We also aim to develop integrated, multimodal sub-classifications (i.e. neurotypes) or "biosignatures" of these disorders with computational tools that include Graph Theory and support vector machine (SVM) based pattern classification. The potential impact of the proposed mechanistic categorization on future functional, genetic, treatment, and other translational studies of ADHD and ASD are substantial.