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充分记录，存在于 未受影响的亲属，通常被认为是行为症状的基础，并且与功能相关 结果。因此，认知与精神病理学的病因学和轨迹研究极为相关。 尽管如此，认知减退与特定的精神病理状况之间的关系并不明确。 尚未解决。对于注意力缺陷/多动症，在这方面存在特殊的知识差距 （ADHD），世界上最常见的儿童精神病学疾病之一。虽然 ADHD 模型长期以来 强调执行功能（EF）作为驱动该疾病的行为症状的因素，多动症的认知是 人们越来越认识到其复杂性。双胞胎和家庭研究将认知方面与多动症风险联系起来，但是 调查结果不一致。此外，并非所有受影响的青少年都表现出 EF 缺陷，并且与 EF 分开的领域是 受到不同程度的损害。此外，多动症内外的认知衰退会扰乱学业和 心理社会功能，对有益于该疾病的药物治疗反应有限。 因此，了解 ADHD 及其相关认知的重叠和可分离的遗传神经生物学 系统对精神病理学模型和患者护理具有影响。在本提案中，我们将研究这些 通过 NIMH 的研究领域标准 (RDoC) 框架来解决问题，该框架鼓励 维度方法、特定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，即认知的进步 基因组学、新颖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划分特定认知的可遗传的生物学基础 与 ADHD 结构重叠并延伸的系统。我们的目标与 NIMH PAR 一致 21-263，寻求计算研究来验证 RDoC 矩阵中表示的维度结构 与精神病理学有关。目标 1 将提取 ADHD 相关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，并全面描述它们与 使用尖端统计遗传学方法评估多动症的遗传风险。目标 2 将开发并验证 高效、基于深度学习的纵向神经影像处理流程，可以扩展到青少年 不同的种族和民族背景，并生成大脑区域和神经回路的图谱 认知结构和多动症维度。目标 3 将训练能够集成的机器学习模型 遗传风险评分、神经影像标记以及认知和行为表型来预测未来的多动症 症状、认知功能和功能结果。完成这些目标将推进模型 ADHD 及其相关认知系统的重叠和独特的神经生物学基础，反过来， 将产生风险分层和新疗法的机会。