Brain Network Changes Accompanying and Predicting Responses to Pharmacotherapy in OCD

摘要 强迫症和强迫症影响约30%的人口;当它们变得严重时，它们会导致诊断为 强迫症（OCD），每40人中就有一人受到影响。现有的治疗方法，包括选择性5-羟色胺再摄取抑制剂（SSRI）抗抑郁药的药物治疗和专门的心理治疗，对许多人有益，但个体化的反应是异质的和不可预测的。迫切需要了解治疗变化的大脑机制，并可能指导新干预措施的开发。最终，预测谁将对特定治疗作出反应的能力将是一个重大的理论和临床进步，将加速有效治疗的部署，从而大大降低发病率。早期使用灌注成像的研究已经暗示，基线神经标记物可以预测对药物治疗的反应。然而，这些研究并没有利用现代网络为中心的分析方法，并没有产生机制的见解或临床效用。 神经精神障碍被假设为来自改变的功能性大脑网络。静息态功能 连接性MRI（rs-fcMRI）已经成为表征人类功能网络结构的有力工具。 我们建议使用rs-fcMRI，采用人类连接组计划开创的最先进的方法，映射功能神经网络和治疗反应之间的关系，在强迫症。具体而言，我们的目标是 描述rs-fcMRI连接特征，映射到治疗相关的变化和预测反应。的 这个项目的可行性得到了我们的试验数据的支持。我们专注于一线SSRI药物治疗，氟西汀作为易于处理的第一步;未来的研究将纳入其他治疗方式，包括心理治疗。 我们提出了一种创新的临床设计，将治疗与时间效应分离，这是一个重大挑战， 治疗机制的研究。80例无药物强迫症受试者将以1：1的比例随机接受氟西汀治疗，立即开始或在6周安慰剂导入期后开始。OCD受试者将在基线和第6、12和18周时接受成像。将汇总所有受试者，以确定症状改善的相关性。将对立即和延迟给药组进行对比，以将给药诱导的神经变化与治疗接触（即安慰剂）的非特异性效应分离。将对40名匹配的对照进行一次扫描，并与基线时的OCD受试者进行比较，在药物治疗前，以表征未用药状态下的连接性改变。将使用全脑一般线性模型（GLM）分析神经影像学数据，包括组间和纵向效应，以分离时间效应、药物暴露本身效应和临床改善相关性。将使用基于GLM的回归和最近优化的个体分类器检查基线成像数据以预测治疗反应，对75%的样本进行训练，然后对剩余的25%进行测试。 这项研究将产生一个丰富的多模态神经影像数据集阐明神经相关的强迫症神经病学 和治疗反应。如果成功，我们将确定新型治疗的网络目标，并迈出重要一步。 致力于在精神病学的精准医学服务中开发预测措施的目标。

ABSTRACT Obsessions and compulsions affect ~30% of the population; when they become severe they lead to a diagnosis of obsessive-compulsive disorder (OCD), which affects one person in 40. Available treatments, including pharmacotherapy with the selective serotonin reuptake inhibitor (SSRI) antidepressants and specialized psychotherapy, are of benefit to many, but individualized response is heterogeneous and unpredictable. Understanding the brain mechanisms of therapeutic change is urgently needed and may guide the development of new interventions. Ultimately, the ability to predict who will respond to a particular treatment would be a major theoretical and clinical advance, would accelerate deployment of effective treatment, and would thereby greatly reduce morbidity. Early studies using perfusion imaging have hinted that baseline neural markers can predict response to pharmacotherapy. However, these studies have not harnessed modern network-focused analytic methods and have not yielded mechanistic insight or clinical utility. Neuropsychiatric disorders are hypothesized to derive from altered functional brain networks. Resting-state functional connectivity MRI (rs-fcMRI) has emerged as a powerful tool to characterize functional network architecture in humans. We propose to use rs-fcMRI, employing state-of-the-art methodologies pioneered by the Human Connectome Project, to map the relationship between functional neural networks and treatment response in OCD. Specifically, we aim to characterize rs-fcMRI connectivity profiles that map onto treatment-associated changes and that predict response. The feasibility of this project is supported by our pilot data. We focus on first-line SSRI pharmacotherapy with fluoxetine as a tractable first step; future studies will incorporate other treatment modalities, including psychotherapy. We propose an innovative clinical design that dissociates treatment from time effects, which is a major challenge in studies of treatment mechanism. 80 medication-free OCD subjects will be randomized 1:1 to receive fluoxetine treatment starting either immediately or after a 6-week placebo lead-in phase. OCD subjects will undergo imaging at baseline and at 6, 12 and 18 weeks. All subjects will be pooled to identify correlates of symptom improvement. The immediate and delayed treatment groups will be contrasted to dissociate treatment-induced neural changes from the non-specific effects of therapeutic contact (i.e. placebo). 40 matched controls will be scanned once and compared with OCD subjects at baseline, prior to pharmacotherapy, to characterize connectivity alterations in the unmedicated state. Neuroimaging data will be analyzed using whole-brain general linear models (GLMs), including between-group and longitudinal effects to isolate effects of time, effects of drug exposure per se, and correlates of clinical improvement. Baseline imaging data will be examined for treatment response prediction, using both a GLM-based regression and via a recently optimized individual classifier, trained on 75% of the sample and then tested on the remaining 25%. This study will yield a rich multi-modal neuroimaging dataset elucidating the neural correlates of OCD symptomatology and of treatment response. If successful, we will identify network targets for novel treatments and take a major step towards the goal of developing predictive measures in the service of precision medicine in psychiatry.