Characterizing mechanistic heterogeneity across ADHD and Autism

描述（由申请人提供）：由于缺乏能够检测和区分精神疾病的客观生物学测量，建立精神疾病病因学的进展受到限制。这个问题在发育障碍中尤为重要，早期识别最终有助于预防终身损害。儿童精神病学中最常见和最昂贵的两种早期发病的发育障碍是注意缺陷多动障碍（ADHD）和自闭症谱系障碍（ASD）。2011年最近的一项研究调查了1997年至2008年，现已证实六分之一的儿童患有发育障碍，在过去十年中增加了17%，主要是由于ASD和ADHD的增加。这些事态发展突出表明，需要采取创新方法来解决这些疾病的根本原因。很可能是临床异质性和其病分学区别的不精确性质从根本上限制了对其病因、预防和治疗的更好理解。有趣的是，一项关于ASD和ADHD的脑成像的新观察表明，他们通常具有相同的非典型功能脑特征。然而，由于这两种综合征几乎完全是单独研究的，因此很难确定非典型脑功能的共同之处，而不是每种疾病的独特之处。如果我们要提高我们对这些疾病的潜在病因的理解，就有必要同时研究这些人群。话虽如此，仅仅根据他们的DSM诊断来比较儿童群体是不够的。每种综合征的行为和生物学异质性进一步使脑成像中发现的任何给定组差异的意义复杂化。因此，我们的理解的进步不仅需要在相同的研究中检查这些疾病，而且还需要确定大脑特征如何与跨越综合症的不同行为成分（即，内表型）相关。在此背景下，并与NIMH的新战略计划，战略1.4（也见RDoC）相一致，目前的提议旨在使用静态功能连接MRI （rs-fcMRI）和结构连接（DTI）来识别与ADHD和/或ASD中发现的基本行为成分（执行，面部识别和情感识别）相对应的大脑特征。我们还旨在利用包括图论和基于支持向量机（SVM）的模式分类在内的计算工具，开发这些疾病的集成，多模态子分类（即神经类型）或“生物特征”。提出的机制分类对ADHD和ASD的未来功能、遗传、治疗和其他转化研究的潜在影响是实质性的。

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.