Neural Mechanisms and Developmental Trajectories of ASD and ADHD

项目摘要/摘要 自闭症谱系障碍(ASD)和注意力缺陷/多动障碍(ADHD)是常见的 神经发育障碍在其发展轨迹上表现出巨大的变异性。ASD和 ADHD也经常并存，因此ASD与ADHD症状增加相关，反之亦然。 值得注意的是，这种共同出现的ASD和ADHD症状与更大的损害有关，以及 治疗反应性降低。然而，ASD和ASD的收敛和发散神经基础 对ADHD的了解仍然很少，阻碍了目前治疗的个性化和 更有针对性的。此外，还不可能预测一个人的症状将如何变化 发展。然而，这样的预测可能对治疗计划有利。当前项目将 提高我们对ASD和ADHD共同的和不同的神经机制的理解，以及 我们预测一个人的症状如何随着时间的推移而演变的能力。具体地说，这项研究将使用磁性 磁共振成像(MRI)研究ASD和ADHD患者的脑功能和结构特征 通过比较以下几组：ASD、ADHD、合并ASD ADHD和神经典型对照组。分析将 在寿命样本(5-65岁；N&>2,700岁)和儿科样本(9-10岁；N&>4,900岁)中完成。 功能连通性将通过静息状态功能磁共振扫描计算，结构连通性来自 T1加权结构MRI的扩散张量成像(DTI)扫描和结构形态测量 扫描。这种多模式神经成像数据也将与基线症状严重程度一起用于预测轨迹 儿童晚期(9-11岁)的自闭症、多动症和内在化(如焦虑、抑郁)症状 青春期早期(11-13岁)的纵向样本(N&GT；700)。进行的岭回归分析 每个诊断小组将揭示这种基于大脑的信息是否显著提高预测能力 与症状严重程度相比。这些分析将在传统定义的组内进行 诊断类别和基于跨诊断脑的亚组内，以确定这种 提高预测精度中的子组；这些子组将使用相似性网络融合在 受试者的多模式神经成像数据，然后进行光谱聚类。作为一个整体，这个项目将允许 申请者将接受尖端神经成像方法、机器学习方法方面的广泛培训 (岭回归和谱聚类)，并进行翻译研究。最重要的是，来自 这项研究将加深我们对ASD和ADHD共同而不同的机制的理解 可能最终导致更多量身定做的治疗方法。此外，拟议的研究可能会显著改善 我们预测一个人的症状会随着时间的推移发生变化的能力。这可能会对 个体化治疗计划，以及未来治疗研究的设计和实施。

Project Summary/Abstract Autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) are common neurodevelopmental disorders which exhibit enormous variability in their developmental trajectories. ASD and ADHD also frequently co-occur, such that ASD is associated with elevated ADHD symptoms and vice versa. Notably, such co-occurring ASD and ADHD symptoms are associated with greater impairment, as well as reduced treatment responsiveness. However, the convergent and divergent neural underpinnings of ASD and ADHD remain poorly understood, impeding the personalization of current treatments and the development of more targeted ones. Furthermore, it is not yet possible to predict how an individual’s symptoms will change over development. Yet, such predictions could be advantageous for treatment planning. The current project will improve our understanding of the shared and distinct neural mechanisms underlying ASD and ADHD, as well as our ability to predict how an individual’s symptoms may evolve over time. Specifically, this study will use magnetic resonance imaging (MRI) to investigate the functional and structural properties of the brain in ASD and ADHD by comparing the following groups: ASD, ADHD, comorbid ASD+ADHD, and neurotypical controls. Analyses will be completed in both a lifespan sample (ages 5-65; N>2,700) and a pediatric sample (ages 9-10; N>4,900). Functional connectivity will be calculated from resting-state functional MRI scans, structural connectivity from diffusion tensor imaging (DTI) scans, and structural morphometry measures from T1-weighted structural MRI scans. This multimodal neuroimaging data will also be used with baseline symptom severity to predict trajectories of ASD, ADHD, and internalizing (e.g., anxious, depressive) symptoms between late childhood (ages 9-11) and early adolescence (ages 11-13) in a longitudinal sample (N>700). Ridge regression analyses conducted within each diagnostic group will reveal whether such brain-based information significantly improves predictive ability compared to symptom severity alone. These analyses will be conducted both within groups defined by traditional diagnostic categories and within transdiagnostic brain-based subgroups to determine the potential utility of such subgroups in increasing predictive accuracy; these subtypes will be created using similarity network fusion on subjects’ multimodal neuroimaging data, followed by spectral clustering. As a whole, this project will allow the applicant to receive extensive training in cutting-edge neuroimaging methods, machine learning approaches (ridge regression and spectral clustering), and conducting translational research. Most importantly, findings from this research will improve our understanding of the shared and distinct mechanisms of ASD and ADHD, which may ultimately lead to more tailored treatments. Furthermore, the proposed research may significantly improve our ability to predict how an individual’s symptoms will change over time. This could have a direct impact on individual treatment planning, as well as the design and implementation of future treatment studies.