Leveraging computational strategies to disentangle the genetic and neural underpinnings of ADHD and its associated cognitive systems

利用计算策略来解开 ADHD 及其相关认知系统的遗传和神经基础

• 批准号: 10732355
• 负责人: Tian Ge
• 金额: $ 76.88万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-08 至 2028-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10732355
• 关键词: Address; Adolescent; Affect; Atlases; Attention deficit hyperactivity disorder; Automobile Driving; Behavior; Behavioral Symptoms; Biological; Brain; Brain imaging; Brain region; Child; Child Psychiatry; Clinic; Clinical Psychology; Cognition; Cognitive; Collaborations; Complex; Data; Diagnosis; Diagnostic; Dimensions; Disease; Ethnic Origin; Etiology; Executive Dysfunction; Family Study; Future; Genes; Genetic; Genetic Models; Genetic Risk; Genetic Variation; Genomics; Goals; Heritability; Hyperactivity; Image; Impairment; Impulsivity; Investigation; Knowledge; Link; MRI Scans; Machine Learning; Methods; Modeling; National Institute of Mental Health; Neuroanatomy; Neurobiology; Neurocognition; Outcome; Patient Care; Pharmacological Treatment; Phenotype; Psychopathology; Public Health; Research; Research Domain Criteria; Resources; Risk; Sampling; Statistical Models; Symptoms; System; Taxonomy; Therapeutic; Time; Training; Twin Studies; Youth; age group; base; behavioral phenotyping; biobank; cognitive development; cognitive function; cognitive system; computer studies; deep learning; driving behavior; executive function; functional outcomes; genomic variation; improved; inattention; lens; machine learning model; member; neural; neural circuit; neuroimaging; neuroimaging marker; neuropsychiatry; novel; novel therapeutics; psychosocial; racial diversity; response; risk stratification; serial imaging; trait; treatment response; treatment stratification

Impaired higher-order cognition is well-documented in different forms of neuropsychiatric illness, present in unaffected relatives, often presumed to underlie behavioral symptoms, and associated with functional outcome. Cognition is thus exceedingly relevant to studies of the etiology and trajectory of psychopathology. Nonetheless, the relationship between cognitive decrements and specific psychopathological conditions is not yet resolved. There are particular knowledge gaps in this regard for attention deficit/hyperactivity disorder (ADHD), one of the most common child psychiatry conditions worldwide. While models of ADHD have long highlighted executive functions (EF) as driving the behavioral symptoms of the condition, cognition in ADHD is increasingly acknowledged to be complex. Twin and family studies link aspects of cognition to ADHD risk, but findings are inconsistent. Also, not all affected youth show EF deficits, and domains separable from EF are impaired to varying degrees. Moreover, cognitive decrements within and beyond ADHD disrupt academic and psychosocial functioning and show limited response to pharmacologic treatments that benefit the disorder. Thus, understanding the overlapping and separable heritable neurobiology of ADHD and its related cognitive systems has implications for psychopathology models and patient care. In this proposal, we will study these issues through the lens of the NIMH’s Research Domain Criteria (RDoC) framework, which encourages a dimensional approach, interrogation of specific transdiagnostic traits, and multi-level links across genetics, brain systems and behavior. We will also capitalize on new resources in the field, i.e., advances in cognition genomics, novel computational strategies, and the Adolescent Brain Cognitive Development (ABCD) study. Our goal is to validate and demarcate the heritable biological underpinnings of specific cognitive systems that overlap with and extend beyond the ADHD construct. Our aims converge with NIMH PAR- 21-263, which seeks computational studies to validate dimensional constructs represented in the RDoC matrix in relation to psychopathology. Aim 1 will extract the genetic basis of ADHD-related cognitive constructs represented in RDoC using samples in the UK Biobank and comprehensively characterize their overlap with the genetic risk for ADHD using cutting-edge statistical genetics methods. Aim 2 will develop and validate an efficient, deep learning based, longitudinal neuroimaging processing pipeline that can extend to youth with diverse racial and ethnic backgrounds, and produce an atlas of brain regions and neural circuitry that underlie the cognitive constructs and the ADHD dimension. Aim 3 will train machine learning models that can integrate genetic risk scores, neuroimaging markers, and cognitive and behavioral phenotypes to predict future ADHD symptoms, cognitive functioning and functional outcomes. Completing these aims will advance models of the overlapping and distinct neurobiological bases of ADHD and its associated cognitive systems, which, in turn, will yield opportunities for risk stratification and novel therapeutics.

高阶认知受损在不同形式的神经精神疾病中都有很好的记录，在 未受影响的亲属，通常被认为是行为症状的基础，并与功能性 结果。因此，认知与精神病理学的病因学和轨迹研究是极其相关的。 尽管如此，认知能力减退和特定的精神疾病之间的关系并不是 但已经下定决心了。关于注意力缺陷/多动障碍，在这方面有特别的知识空白 (ADHD)，这是全球最常见的儿童精神病学疾病之一。而ADHD的模型已经有很长时间了 强调执行功能(EF)作为驱动ADHD的行为症状的条件，认知是 越来越多的人承认这是复杂的。双胞胎和家庭研究将认知方面与ADHD风险联系起来，但 研究结果并不一致。此外，并不是所有受影响的年轻人都表现出EF缺陷，并且可以从EF分离的域是 不同程度的受损。此外，ADHD内部和外部的认知减退会扰乱学习和学习 心理社会功能障碍，对有益于该疾病的药物治疗反应有限。 因此，了解ADHD的重叠和可分离的遗传神经生物学及其相关认知 系统对精神病理学模型和病人护理具有重要意义。在这项建议中，我们将研究以下内容 通过NIMH的研究领域标准(RDoC)框架的镜头，该框架鼓励 维度方法，对特定的跨诊断特征的询问，以及跨遗传学的多层次联系， 大脑系统和行为。我们还将利用该领域的新资源，即认知方面的进展 基因组学、新的计算策略和青少年大脑认知发展(ABCD)研究。 我们的目标是验证和界定特定认知的可遗传生物学基础 与ADHD结构重叠并延伸到ADHD结构之外的系统。我们的目标与NIMH的标准一致- 21-263，寻求计算研究以验证RDoC矩阵中表示的维度结构 与精神变态有关。目标1将提取与ADHD相关的认知结构的遗传基础 使用英国生物库中的样本在RDoC中表示，并全面表征它们与 用尖端统计遗传学方法研究ADHD的遗传风险。AIM 2将开发和验证 高效、基于深度学习的纵向神经成像处理管道，可扩展到年轻人 不同的种族和民族背景，并产生大脑区域和神经回路的图谱 认知结构和ADHD维度。AIM 3将训练可以集成的机器学习模型 遗传风险评分、神经成像标记物以及认知和行为表型预测未来ADHD 症状、认知功能和功能结果。实现这些目标将推动 ADHD及其相关认知系统的重叠和不同的神经生物学基础，这反过来， 将为风险分层和新的治疗方法带来机会。