Behavioral and Neural Heterogeneity in OCD and Depression

项目摘要 该项目调查强迫症(OCD)和少年儿童的行为和神经异质性。 使用假设和数据驱动的方法的抑郁障碍(MDD)。强迫症和MDD是 世界各地的残疾患者中，只有不到一半的患者对一线治疗有足够的反应。治疗 可能是因为强迫症和MDD都是高度不同的，可能会出现一连串的症状 源自不同的心理和神经生物学病因。研究领域标准(RDoC) 框架试图通过调查跨越的行为的维度组件来部分地解决这个问题 与多方面的诊断表型相比，多方面的诊断表型更容易与大脑回路保持一致。在……里面 在本项目中，我们遵循RDoC方法通过调查关系来研究异质性 使用基于任务的fMRI和静息状态fMRI在行为、临床症状和脑功能之间的关系 75例强迫症患者和75例MDD患者。我们对强迫症患者和抑郁症患者的初步数据 症状指向内感敏感性(IS)和持之以恒的消极思维(PT)为维度 行为的组成部分，不仅是跨诊断的，而且还解释了重要的内在障碍 异质性；本提案的第一个目标是通过调查IS和PT的关联来建立这些发现 与强迫症和MDD的临床症状、行为和脑功能有关。补充这一假设驱动的 该项目的第二个目标是使用无监督机器学习(ML)进行数据驱动的调查 揭示强迫症和MDD患者大脑功能变化的神经生物学特征。建房 根据我们的初步分析，在强迫症中识别出三个不同的亚群 静息状态功能连接，我们将研究强迫症和MDD之间的神经生物学异质性，以及 使用全面的“深度表型”将临床和行为数据与神经生物学亚群联系起来 数据采集和分析的方法。这两个目标的首要目标都是确定促成因素 到异质性，可以开发为基于个体的个性化治疗的新目标 病人的神经行为特征。

Project Summary This project investigates behavioral and neural heterogeneity in obsessive-compulsive disorder (OCD) and major depressive disorder (MDD) using hypothesis- and data-driven approaches. OCD and MDD are major sources of disability worldwide, with fewer than half of patients responding adequately to first-line treatments. Treatment may be impeded by the fact that OCD and MDD are both highly heterogeneous, with clusters of symptoms likely derived from differing psychological and neurobiological etiologies. The Research Domain Criteria (RDoC) framework seeks to address this problem in part by investigating dimensional components of behavior that span across disorders and more closely align with brain circuitry than does the multifaceted diagnostic phenotype. In the present project, we follow an RDoC approach to the study of heterogeneity by investigating relationships between behavior, clinical symptoms, and brain function using task-based and resting-state fMRI in a sample of 75 OCD patients and 75 MDD patients. Our preliminary data in OCD patients and individuals with depressive symptoms point to interoceptive sensitivity (IS) and perseverative negative thinking (PT) as dimensional components of behavior that are not only transdiagnostic but also explain important within-disorder heterogeneity; the first aim of this proposal builds on these findings by investigating associations of IS and PT with clinical symptoms, behavior, and brain function in OCD and MDD. Complementing this hypothesis-driven approach, the second aim of the project is a data-driven investigation using unsupervised machine learning (ML) to uncover neurobiological profiles that characterize variability in brain function across OCD and MDD. Building on our preliminary analysis identifying three subgroups within OCD distinguished by differential patterns of resting-state functional connectivity, we will investigate neurobiological heterogeneity across OCD and MDD and link clinical and behavioral data to neurobiological subgroups using a comprehensive ‘deep phenotyping’ approach to data acquisition and analysis. The overarching goals of both aims are to identify factors contributing to heterogeneity that can be developed as novel targets for personalized treatments based on an individual patient’s neurobehavioral profile.