Resting state connectivity signatures of obsessive compulsive symptoms

强迫症状的静息状态连接特征

• 批准号: 10478918
• 负责人: Tracey Chen Shi
• 金额: $ 5.03万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10478918
• 关键词: Adolescent; Affect; Area; Big Data; Brain; Child; Childhood; Clinical; Clinical Data; Clinical Skills; Cognitive Therapy; Communities; Complex; Computing Methodologies; Data; Data Set; Development; Diagnosis; Diffuse; Disease remission; Early Intervention; Exhibits; Family; Functional Magnetic Resonance Imaging; Future; Generations; Goals; Gold; Individual; Institutes; Life; Machine Learning; Magnetic Resonance Imaging; Major Depressive Disorder; Medical center; Mental disorders; National Institute of Mental Health; New York; New York City; Obsessive compulsive behavior; Obsessive-Compulsive Disorder; Oral; Outcome; Pattern; Physicians; Prediction of Response to Therapy; Prevalence; Prevention strategy; Probability; Psychiatric therapeutic procedure; Psychiatry; Reproducibility; Research; Research Proposals; Resources; Rest; Risk; Sampling; Scientist; Severities; Site; Symptoms; Technical Expertise; Techniques; Testing; Thinking; Time; Training; United States; Universities; Writing; Youth; aged; attenuation; base; case control; clinical development; clinically relevant; clinically significant; cognitive development; computerized tools; design; follow-up; image processing; improved; machine learning algorithm; machine learning method; neuroimaging; neuromechanism; novel; predicting response; predictive signature; prevent; prospective; psychiatric symptom; relating to nervous system; repetitive behavior; response; supervised learning; symptomatic improvement; theories; treatment strategy; unsupervised learning

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.

项目摘要 强迫症（OCD）是一种残疾疾病，表现出双峰的发作时间，最高一半 从童年开始的病例。亚临床强迫症症状（OC）通常在 临床上显着的强迫症的发展，尽管某些儿童的症状随着时间的流逝而自然发出。 但是，OC的神经底座及其对发展的变化知之甚少。大写 我建议大型，公开可用的数据集和复杂的计算方法，以检查功能 OCS的MRI签名及其在儿童和青少年中的纵向轨迹。具体来说，我会申请 机器学习是从青春期大脑认知发展（ABCD）研究中公开可用的数据，这是一项 潜在的社区样本在美国的21个不同地点进行了纵向跟踪，以揭示 与OCS严重程度相对应的全脑功能性MRI模式。基线数据已经可以从 大约有12,000名9-10岁的儿童致力于进行持续的随访。然后我会使用 来自独立健康大脑网络（HBN）研究的数据，其中包括大约2,500的数据 从纽约市地区到统计和临床验证这些模式的孩子（AIM 1）。然后我会 将基线神经影像学和临床数据与ABCD研究的纵向随访临床数据相结合 检查预测随后的OC轨迹的神经特征（AIM 2）。最后，我将利用数据 从具有临床严重性OCS（即OCD）的儿童收集到金标准认知之前和之后 纽约州精神病学研究所（NYSPI）的行为疗法，以确定 响应和缓解（探索目的）。总的来说，这些目标将确定大脑连接性功能 对应于OCS共同变化的可靠模式，这些模式可能暗示了基础的常见机制 多种症状，并暗示可以在未来的研究中针对的特定电路 并测试新的治疗方法和预防策略。此外，该研究提案将 详细的培训计划将使我更接近成为专注于临床的医师科学家的目标 和计算精神病学。由哥伦比亚大学欧文医学中心的资源支持 和NYSPI，我将加深我在MRI图像处理，神经影像分析和机器方面的技术技能 学习，同时还提高了我的科学写作，口头表现和临床技能。