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.

高阶认知受损的人是有不同形式的神经精神疾病的有据可查的， 未受影响的亲戚，通常出现在行为症状的基础上，并与功能相关 结果。因此，认知与心理病理学的病因和轨迹的研究至关重要。 但是，认知降低与特定的心理病理条件之间的关系不是 尚未解决。在这方面，注意力不足/多动症障碍存在特定的知识差距 （ADHD），全球最常见的儿童精神病学条件之一。虽然多动症的模型有很长的 强调执行功能（EF）作为驱动疾病的行为症状，多动症的认知是 越来越多地承认很复杂。双胞胎和家庭研究将认知方面与多动症风险联系起来，但 发现不一致。另外，并非所有受影响的青年表演EF定义，并且与EF分开的域是 在不同程度上受损。此外，认知能力下降和超越多动症的学术和 心理社会功能并显示出对有益于该疾病的药物治疗的有限反应。 那样，了解多动症的重叠和分离可遗传的神经生物学及其相关的认知 系统对心理病理学模型和患者护理有影响。在此提案中，我们将研究这些 通过NIMH研究领域标准（RDOC）框架的镜头问题，这鼓励了一个 维度方法，特定转诊特征的审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，新型计算策略和青少年脑认知发展（ABCD）研究。 我们的目标是验证和划定特定认知的遗传生物基础 与ADHD结构重叠并延伸的系统。我们的目标与nimh par- 21-263，它寻求计算研究以验证RDOC矩阵中代表的维构建体 与心理病理学有关。 AIM 1将提取与ADHD相关的认知构建体的遗传基础 在RDOC中使用英国生物库中的样品代表，并全面地表征了它们与 使用尖端统计遗传学方法的ADHD的遗传风险。 AIM 2将发展并验证 高效，基于深度学习的，纵向神经影像学处理管道，可以扩展到年轻人 潜水员种族和种族背景，并产生大脑区域和神经回路的地图集 认知构建体和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，神经影像标记以及认知和行为表型，以预测未来的多动症 症状，认知功能和功能结果。完成这些目标将推动 多动症的重叠和独特的神经生物学基础及其相关的认知系统，而这反过来又 将产生风险分层和新疗法的机会。