Resting state connectivity signatures of obsessive compulsive symptoms

项目总结 强迫症(OCD)是一种致残性疾病，发病时间呈双峰型，发病比例高达一半 从孩提时代开始的病例。亚临床强迫症(OCS)通常先于 临床上有意义的强迫症的发展，尽管一些儿童的症状会随着时间的推移自然缓解。 然而，人们对OCS的神经基础及其在发育过程中的变化知之甚少。利用 大型公开可用的数据集和复杂的计算方法，我建议检查函数式 儿童和青少年OCS的MRI征象及其纵向轨迹。具体地说，我将申请 从青少年大脑认知发展(ABCD)研究中获得公开数据的机器学习 在美国21个不同地点纵向跟踪的预期社区样本显示 与OCS严重程度相对应的全脑功能MRI模式。基准数据已在以下位置提供 约有12,000名9-10岁儿童的家庭承诺继续采取后续行动。然后我将使用 来自独立的健康大脑网络(HBN)研究的数据，其中包括来自大约2500人的数据 来自纽约市地区的儿童，以统计和临床验证这些模式(目标1)。那我会的 将基线神经影像和临床数据与ABCD研究的纵向随访临床数据结合起来 检查预测后续OCS轨迹的神经特征(目标2)。最后，我将利用数据 从临床严重的强迫症(即强迫症)儿童收集金标认知前后 纽约州精神病学研究所(NYSPI)的行为疗法，以确定治疗前的预测因素 缓解和缓解(探索性目标)。总的来说，这些目标将确定大脑连接功能， 对应于OCS共同变化的可靠模式，这可能暗示潜在的共同机制 多个症状和牵连的特定电路，可以在未来的研究中针对旨在开发 并测试新的治疗方法和预防策略。此外，这项研究方案整合了一个 详细的培训计划，将使我更接近成为一名专注于临床的内科科学家的目标 和计算精神病学。由哥伦比亚大学欧文医学中心 在NYSPI，我将加深我在MRI图像处理、神经成像分析和机器方面的技术技能 学习的同时，也提高了我的科学写作、口头演示和临床技能。

PROJECT SUMMARY Obsessive-compulsive disorder (OCD) is a disabling illness that exhibits bimodal timing of onset, with up to half of cases beginning in childhood. Subclinical obsessive-compulsive symptoms (OCS) often precede the development of clinically significant OCD, though symptoms in some children remit naturally over time. However, the neural bases of OCS and their changes over development are poorly understood. Capitalizing on large, publicly available datasets and sophisticated computational methods, I propose to examine functional MRI signatures of OCS and their longitudinal trajectories in children and adolescents. Specifically, I will apply machine learning to publicly available data from the Adolescent Brain Cognitive Development (ABCD) study, a prospective community sample tracked longitudinally at 21 diverse sites across the United States, to reveal whole-brain functional MRI patterns that correspond to OCS severity. Baseline data is already available from approximately 12,000 children aged 9-10 whose families have committed to ongoing follow-up. I will then use data from the independent Healthy Brain Network (HBN) study, which includes data from approximately 2,500 children from the New York City area, to statistically and clinically validate these patterns (Aim 1). I will then combine baseline neuroimaging and clinical data with longitudinal follow-up clinical data from the ABCD study to examine neural signatures that predict subsequent OCS trajectories (Aim 2). Finally, I will leverage data collected from children with clinical-severity OCS (i.e., OCD) before and after gold-standard cognitive behavioral therapy at the New York State Psychiatric Institute (NYSPI) to identify pre-treatment predictors of response and remission (Exploratory Aim). Collectively, these aims will identify brain connectivity features that correspond to reliable patterns in which OCS co-vary, which could hint at common mechanisms underlying multiple symptoms and implicate specific circuits that can be targeted in future studies aimed at developing and testing novel treatments and prevention strategies. Furthermore, this research proposal integrates a detailed training plan that will bring me closer to my goal of becoming a physician-scientist focused on clinical and computational psychiatry. Supported by the resources of both Columbia University Irving Medical Center and the NYSPI, I will deepen my technical skills in MRI image processing, neuroimaging analysis, and machine learning, while also improving my scientific writing, oral presentation, and clinical skills.