Neural Mechanisms and Developmental Trajectories of ASD and ADHD

ASD 和 ADHD 的神经机制和发展轨迹

• 批准号: 10426340
• 负责人: Katherine E Lawrence
• 金额: $ 4.71万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2023-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10426340
• 关键词: 11 year old; 13 year old; Address; Adolescent; Age; Attention; Attention deficit hyperactivity disorder; Base of the Brain; Biology; Brain; Categories; Child Behavior Checklist; Childhood; Clinical; Corpus Callosum; Corpus striatum structure; Data; Development; Diagnosis; Diagnostic; Diffusion Magnetic Resonance Imaging; Disease; Dorsal; Exhibits; Frequencies; Hyperactivity; Impairment; Impulsive Behavior; Impulsivity; Individual; Inferior; Lead; Longevity; MRI Scans; Machine Learning; Magnetic Resonance Imaging; Measures; Mental Depression; Methods; Neurobiology; Neurodevelopmental Disorder; Outcome; Performance; Population; Property; Psychopathology; Public Health; Regression Analysis; Research; Rest; Sample Size; Sampling; Scanning; Severities; Social Network; Structure; Subgroup; Symptoms; Testing; Thick; Time; Training; Translational Research; Work; adolescent with autism spectrum disorder; anxious; associated symptom; autism spectrum disorder; autistic; base; cognitive development; comorbidity; course development; depressive symptoms; design; diffusion anisotropy; early adolescence; functional MRI scan; future implementation; improved; inattention; individualized medicine; machine learning algorithm; morphometry; multimodal neuroimaging; neuroimaging; neuromechanism; neuropsychiatric symptom; personalized medicine; relating to nervous system; social; social cognition; symptomatology; therapy development; trait; treatment planning; white matter

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.

项目总结/摘要 自闭症谱系障碍（ASD）和注意力缺陷/多动症（ADHD）是常见的 神经发育障碍，其发育轨迹表现出巨大的变异性。ASD和 ADHD也经常同时发生，例如ASD与ADHD症状升高相关，反之亦然。 值得注意的是，这种共同发生的ASD和ADHD症状与更大的损害有关， 降低治疗反应性。然而，ASD的会聚和发散神经基础， ADHD仍然知之甚少，阻碍了目前治疗的个性化和发展。 更有针对性的。此外，目前还不可能预测一个人的症状将如何变化 发展然而，这样的预测可能有利于治疗计划。目前的项目将 提高我们对ASD和ADHD的共同和不同神经机制的理解，以及 我们预测个体症状如何随时间演变的能力。具体来说，这项研究将使用磁 磁共振成像（MRI）研究ASD和ADHD患者大脑的功能和结构特性 通过比较以下组：ASD、ADHD、ASD+ADHD共病和神经型对照。分析将 在寿命期样本（5-65岁; N> 2，700）和儿科样本（9-10岁; N> 4，900）中完成。 将根据静息状态功能性MRI扫描计算功能连接性，根据静息状态功能性MRI扫描计算结构连接性。 扩散张量成像（DTI）扫描和T1加权结构MRI的结构形态测量 扫描。这种多模态神经影像学数据也将与基线症状严重程度一起用于预测轨迹 ASD，ADHD和内化（例如，焦虑，抑郁）症状之间的儿童晚期（9-11岁）和 青春期早期（11-13岁）纵向样本（N>700）。岭回归分析进行， 每个诊断小组将揭示这些基于大脑的信息是否显著提高了预测能力 与症状严重程度相比。这些分析将在传统的 诊断类别，并在transdiagnosis脑为基础的亚组，以确定这种潜在的效用， 这些子类型将使用相似性网络融合来创建， 受试者的多模态神经成像数据，然后进行谱聚类。总的来说，该项目将使 申请人接受尖端神经成像方法，机器学习方法的广泛培训 （岭回归和谱聚类），并进行转化研究。最重要的是， 这项研究将提高我们对ASD和ADHD的共同和不同机制的理解， 可能最终会带来更有针对性的治疗。此外，拟议的研究可能会显着改善 我们预测个体症状随时间变化的能力。这可能会直接影响 个人治疗计划，以及未来治疗研究的设计和实施。

项目成果

Age and sex effects on advanced white matter microstructure measures in 15,628 older adults: A UK biobank study.

15,628名老年人的年龄和性别对晚期白质微观结构措施的影响：英国生物银行研究。

• DOI: 10.1007/s11682-021-00548-y
• 发表时间: 2021-12
• 期刊: Brain imaging and behavior
• 影响因子: 3.2
• 作者: Lawrence KE;Nabulsi L;Santhalingam V;Abaryan Z;Villalon-Reina JE;Nir TM;Ba Gari I;Zhu AH;Haddad E;Muir AM;Laltoo E;Jahanshad N;Thompson PM
• 通讯作者: Thompson PM

Sex Differences in the Brain's White Matter Microstructure during Development assessed using Advanced Diffusion MRI Models.

使用高级扩散 MRI 模型评估发育过程中大脑白质微观结构的性别差异。

• DOI: 10.1101/2024.02.02.578712
• 发表时间: 2024
• 期刊: bioRxiv : the preprint server for biology
• 作者: Benavidez,SebastianM;Abaryan,Zvart;Kim,GaonS;Laltoo,Emily;McCracken,JamesT;Thompson,PaulM;Lawrence,KatherineE
• 通讯作者: Lawrence,KatherineE

White matter microstructure shows sex differences in late childhood: Evidence from 6797 children.

• DOI: 10.1002/hbm.26079
• 发表时间: 2023-02-01
• 期刊: Human brain mapping
• 影响因子: 4.8