Connectivity Biomarkers of Clinical Response in Treatment Resistant Schizophrenia

难治性精神分裂症临床反应的连通性生物标志物

• 批准号: 9891084
• 负责人: Anil K Malhotra
• 金额: $ 69.24万
• 依托单位: FEINSTEIN INSTITUTE FOR MEDICAL RESEARCH
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-01-13 至 2021-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9891084
• 关键词: Address; Aftercare; Algorithms; Antipsychotic Agents; Biological Assay; Biological Markers; Brain region; Brief Psychiatric Rating Scale; Chronic; Chronically Ill; Clinical; Clozapine; Corpus striatum structure; Data; Dopamine D2 Receptor; Electroconvulsive Therapy; Generations; Health Resources; Healthcare Systems; Heterogeneity; Hippocampus (Brain); Intervention; MRI Scans; Maintenance; Measures; Mediating; Modernization; Neurobiology; Patients; Pattern; Population; Prediction of Response to Therapy; Prognostic Marker; Psychotic Disorders; Public Health; Quality of life; Refractory; Research; Resistance; Rest; Scanning; Schizophrenia; Seeds; Sensitivity and Specificity; Structure; Symptoms; Techniques; Thalamic structure; Time; Treatment Efficacy; Work; base; biomarker development; clinical practice; cohort; drug development; economic impact; effective intervention; evidence base; first episode schizophrenia; impression; indexing; longitudinal dataset; longitudinal design; neuroimaging; neuromechanism; novel; potential biomarker; precision medicine; predicting response; prognostic; psychotic symptoms; response; therapy development; treatment group; treatment response; treatment strategy; treatment trial

Project Summary Antipsychotic drugs are the mainstay for treatment of psychosis, yet they are associated with substantial heterogeneity in their therapeutic efficacy. Non-response to treatment contributes to poor quality of life for patients, and a large economic impact on healthcare systems. Treatment algorithms for these illnesses are devoid of prognostic measures, and clinicians generally must rely on trial-and-error. At the same time, neural mechanisms underlying response to treatment remain unclear, resulting in a lack of potential targets for novel treatment development. Surprisingly, given the urgent public health and scientific needs, very little work has utilized modern neuroimaging techniques to understand the mechanisms of antipsychotic response. We have recently demonstrated that resting state functional connectivity (RSFC) may be a valuable assay for biomarker development, both as pre-treatment predictors of treatment response, as well as dynamic markers of antipsychotic efficacy over the course of treatment. For example, our group developed an index of striatal connectivity that predicted response to second-generation antipsychotics (SGAs) with high sensitivity and specificity in first-episode schizophrenia patients, and generalized to a cohort of chronic patients with psychosis. Moreover, we found that changes in the functional interactions of the striatum with the cingulate, hippocampus, thalamus, and cortex tracked improvements in psychosis after 12 weeks of SGA treatment. To date, this approach has not been applied to treatment-resistant schizophrenia (TRS) populations, nor have treatment strategies that do not primarily target the striatum been extensively studied. In this project, we propose to assess RSFC in two groups of TRS patients undergoing treatment with effective intervention strategies that significantly differ from traditional D2 receptor antagonists. In Aim 1, we will assess psychotic patients undergoing a 24-week treatment trial with clozapine, which remains unique amongst antipsychotic drugs for its superior efficacy in TRS. In Aim 2, we will assess patients whose psychotic symptoms remain refractory even to CLZ, whom we refer to as ultra-treatment-resistant (uTRS). We will scan uTRS patients undergoing an 8-week treatment trial of CLZ combined with adjunctive electro-convulsive therapy (CLZ+ECT), a treatment strategy recently demonstrated to have remarkable efficacy in severely ill uTRS patients. For both aims, we will use a longitudinal design with MRI scans collected before and after controlled treatment, with symptoms assessed with structured rating scales. RSFC will be assessed using a seed-based strategy based upon our recent work, but expanded to include relevant subcortical structures beyond the striatum. Results from this project may provide: 1) biomarkers for use in “precision medicine” strategies for patients with psychotic illnesses; and 2) biomarkers of striatal- and nonstriatally-mediated antipsychotic efficacy for use in novel antipsychotic drug development. Such biomarkers are urgently needed, given the lack of a sufficient evidence base to guide clinical practice, and the lack of a research base to guide treatment development.

项目摘要 抗精神病药物是治疗精神病的主要药物，但它们与 治疗效果的异质性。对治疗无反应会导致患者的生活质量下降 患者，以及对医疗系统的巨大经济影响。这些疾病的治疗算法是 缺乏预后指标，临床医生通常必须依靠反复试验。同时，神经性 对治疗的潜在反应机制仍不清楚，导致缺乏新的潜在靶点 治疗的发展。令人惊讶的是，考虑到迫切的公共卫生和科学需求，几乎没有工作 利用现代神经成像技术了解抗精神病药物反应的机制。 我们最近证明，静息状态功能连通性(RSFC)可能是一种有价值的检测方法 生物标记物的发展，既是治疗前预测治疗反应的指标，也是动态标记物 在治疗过程中抗精神病药物的疗效。例如，我们小组开发了一个纹状体指数 预测对第二代抗精神病药物(SGA)的反应的连接具有高敏感度和 在首发精神分裂症患者中的特异性，并推广到一组慢性精神分裂症患者 精神错乱。此外，我们还发现，纹状体与扣带回功能相互作用的变化， 在SGA治疗12周后，海马体、丘脑和皮质追踪到精神病的改善。 到目前为止，这种方法还没有应用于难治性精神分裂症(TRS)人群，也没有 不主要针对纹状体的治疗策略已被广泛研究。在这个项目中，我们 建议评估两组接受有效干预治疗的TRS患者的RSFC 与传统的D2受体拮抗剂有显著不同的策略。在目标1中，我们将评估精神病患者 接受氯氮平24周治疗试验的患者，氯氮平在抗精神病药物中仍是独一无二的 药物在TRS中具有优越的疗效。在目标2中，我们将评估仍有精神病症状的患者。 甚至对我们称为超耐药(UTRs)的CLZ也是难治性的。我们将扫描UTRS患者 进行为期8周的CLZ联合电惊厥治疗试验(CLZ+ECT)， 一种治疗策略最近被证明对病情严重的输尿管结石患者有显著疗效。对两个人都是 AIMS，我们将使用纵向设计，在对照治疗前后收集MRI扫描， 用结构化评定量表评估症状。RSFC将使用基于种子的策略进行评估 根据我们最近的工作，但扩大到包括纹状体以外的相关皮质下结构。 该项目的结果可能提供：1)用于精准医学策略的生物标记物 精神疾病；以及2)纹状体和非纹状体介导的抗精神病疗效的生物标记物 新型抗精神病药物开发。鉴于缺乏足够的生物标记物，这种生物标志物是迫切需要的 指导临床实践的证据基础不足，缺乏指导治疗发展的研究基地。