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: 10290878
• 负责人: Katherine E Lawrence
• 金额: $ 6.87万
• 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 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10290878
• 关键词: 11 year old; 13 year old; Address; Adolescent; Age; Attention; Attention deficit hyperactivity disorder; Base of the Brain; Biological; 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; Future; 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; improved; inattention; individualized medicine; machine learning algorithm; morphometry; multimodality; 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/文摘