Multimodal imaging biomarkers of cognitive control network deficits in youths with disruptive behavior

具有破坏性行为的青少年认知控制网络缺陷的多模态成像生物标志物

基本信息

批准号：
10525037
负责人：
Karim Ibrahim
金额：
$ 19.31万
依托单位：
YALE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-16 至 2027-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10525037
关键词：
12 year old Achievement Adolescence Adolescent Adult Advanced Development Affect Aggressive behavior Amygdaloid structure Anger Area Base of the Brain Behavior Big Data Methods Biological Markers Biometry Brain Buffers Child Childhood Clinical Computer Models DSM-V Data Data Set Development Diagnosis Dimensions Disease Disruptive Behavior Disorder Dorsal Emotions Ensure Environment Exhibits Functional Magnetic Resonance Imaging Functional disorder Generations Goals Image Impairment Individual Differences Intervention Link Machine Learning Magnetic Resonance Imaging Mental disorders Mentorship Modality Modeling Multimodal Imaging National Institute of Mental Health Neurobiology Neurosciences Outcome Prefrontal Cortex Problem behavior Psychology Psychopathology Public Health Research Research Design Research Domain Criteria Research Personnel Research Priority Research Training Rest Risk Sampling Services Severities Sex Differences Structure Subgroup Substance abuse problem Surface Symptoms Testing Thick Training Work Youth adolescent with autism spectrum disorder antisocial behavior autism spectrum disorder autistic children base biomarker development boys callous unemotional trait cognitive control cognitive development cognitive reappraisal cognitive task connectome data modeling disorder control disorder subtype emotion regulation girls gray matter imaging biomarker interest intervention refinement machine learning prediction multidisciplinary neuroimaging non-compliance novel open data predictive modeling recruit sex translational neuroscience translational potential

项目摘要

Project Summary The goal of the proposed project is to investigate functional and structural brain networks that predict disruptive behavior in children. Disruptive behavior disorders (DBD) affect over 113 million youths worldwide and are characterized by irritability/anger, aggression, noncompliance, and/or antisocial behavior. These disorders are of great interest because they are highly predictive of delinquency, criminality, and substance abuse in later adolescence and adulthood. DBD also co-occur in over 50% of children with autism spectrum disorder (ASD). A large body of evidence links DBD with perturbations in frontoparietal circuitry that support the cognitive control of emotion (i.e., emotion regulation), particularly connections between regions of the dorsal and ventral prefrontal cortex (d/vPFC) and amygdala. However, it is unclear if dysregulation in emotion regulation circuitry can contribute to a biomarker of DBD in children with and without ASD. With a focus on the amygdala-d/vPFC circuit, this study will be the first to examine disruptions in brain-wide connectivity and structure in emotion regulation networks as a transdiagnostic biomarker of DBD and disruptive behavior problems more broadly in children. First, we will develop and test a multimodal imaging biomarker of DBD in the Adolescent Brain Cognitive Development study dataset, which contains clinical and fMRI data for 11,878 9-12 year olds in the first and second releases. Next, we will test the hypothesized disruptions in emotion regulation circuitry using a fMRI task of cognitive reappraisal in a new, transdiagnostic sample of children with disruptive behavior with and without ASD. This study will leverage cutting-edge neuroimaging analytics that resonate with several NIMH research priorities: network neuroscience or connectomics, multimodal imaging, computational modeling (machine learning), big data analytics, and the RDoC domain of cognitive control. The proposed research will push forward the development of brain-based biomarkers of disruptive behavior that could guide development of targeted interventions, refinement of existing treatments, or identify children likely to respond to a particular treatment. The proposed project will prepare Dr. Karim Ibrahim to become an independent clinical researcher with a unique niche and expertise in transdiagnostic brain biomarkers of emotion regulation in childhood-onset psychiatric disorders using connectomics, multimodal imaging, and predictive modeling approaches. To accomplish this, the proposed training will provide Dr. Karim Ibrahim with multidisciplinary training in network neuroscience/connectomics, machine learning/predictive modeling, biostatistical approaches for the analysis of large imaging datasets, and emotion regulation circuitry. The training and research are enhanced by the intellectually rigorous environment at the Yale Child Study Center and Department of Psychology. The mentorship of a multidisciplinary team with complementary expertise in fMRI, connectomics, and child psychopathology ensures achievement of the research and training aims.

项目概要该项目的目标是研究预测破坏性的功能和结构大脑网络儿童的行为。破坏性行为障碍 (DBD) 影响着全球超过 1.13 亿青少年，并且以烦躁/愤怒、攻击性、不服从和/或反社会行为为特征。这些疾病是引起人们极大的兴趣，因为它们可以高度预测以后的不良行为、犯罪和药物滥用青春期和成年期。 DBD 还同时出现在超过 50% 的自闭症谱系障碍 (ASD) 儿童中。一个大量证据表明 DBD 与支持认知控制的额顶叶回路扰动有关情绪（即情绪调节），特别是背侧和腹侧前额叶区域之间的连接皮质 (d/vPFC) 和杏仁核。然而，目前尚不清楚情绪调节回路的失调是否会导致有助于患有和不患有 ASD 儿童的 DBD 生物标志物。重点关注杏仁核-d/vPFC 电路，这项研究将是第一个检查情绪调节中全脑连接和结构中断的研究网络作为 DBD 和更广泛的儿童破坏性行为问题的跨诊断生物标志物。首先，我们将开发并测试青少年大脑认知中 DBD 的多模态成像生物标志物发育研究数据集，包含第一年和第二年 11,878 名 9-12 岁儿童的临床和功能磁共振成像数据第二次发布。接下来，我们将使用功能磁共振成像任务来测试假设的情绪调节电路中断对有或没有破坏性行为的儿童进行新的跨诊断样本的认知重新评估自闭症谱系障碍。这项研究将利用与 NIMH 多项研究产生共鸣的尖端神经影像分析优先事项：网络神经科学或连接组学、多模态成像、计算建模（机器学习）、大数据分析和认知控制的 RDoC 领域。拟议的研究将推动开发基于大脑的破坏性行为生物标志物，可以指导针对性的开发干预措施、改进现有治疗方法或确定可能对特定治疗产生反应的儿童。拟议的项目将使 Karim Ibrahim 博士成为一名具有独特能力的独立临床研究员。儿童期精神病患者情绪调节的跨诊断大脑生物标志物的利基和专业知识使用连接组学、多模态成像和预测建模方法来治疗疾病。为了实现这一目标，拟议的培训将为 Karim Ibrahim 博士提供网络方面的多学科培训神经科学/连接组学、机器学习/预测模型、生物统计方法分析大型成像数据集和情绪调节电路。培训和研究得到加强耶鲁儿童研究中心和心理学系拥有严谨的智力环境。这多学科团队的指导，在功能磁共振成像、连接组学和儿童方面具有互补的专业知识精神病理学确保研究和培训目标的实现。