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，但它们与大量的精神疾病有关。 其治疗功效的异质性。对治疗无反应导致生活质量差， 患者，以及对医疗保健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赖试错法。同时，神经 治疗反应的潜在机制尚不清楚，导致缺乏新的治疗反应的潜在靶点。 治疗发展。令人惊讶的是，考虑到紧迫的公共卫生和科学需求， 利用现代神经影像学技术来了解抗精神病药物反应的机制。 我们最近已经证明，静息状态功能连接（RSFC）可能是一个有价值的分析， 生物标志物开发，既作为治疗反应的治疗前预测因子，也作为动态标志物 抗精神病药物的疗效例如，我们的团队开发了一个纹状体指数， 预测对第二代抗精神病药（SGAs）反应的连接性具有高敏感性， 特异性在首发精神分裂症患者，并推广到一个队列的慢性患者 精神病此外，我们发现纹状体与扣带回的功能相互作用的变化， 海马体、丘脑和皮质追踪SGA治疗12周后精神病的改善。 迄今为止，这种方法尚未应用于难治性精神分裂症（TRS）人群， 不主要针对纹状体的治疗策略已被广泛研究。本课题 建议评估两组接受有效干预治疗的TRS患者的RSFC 这些策略与传统的D2受体拮抗剂显著不同。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然是独一无二的 其上级疗效在TRS药物。在目标2中，我们将评估精神病症状仍然存在的患者 甚至对CLZ也是难治的，我们称之为超治疗耐药（uTRS）。我们将扫描uTRS患者 接受为期8周的CLZ联合连续性电休克治疗（CLZ+ECT）治疗试验， 最近证实的一种治疗策略在重症uTRS患者中具有显著的疗效。为 目的，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， 我们最近的工作，但扩大到包括相关的皮层下结构以外的纹状体。 该项目的结果可以提供：1）用于“精确医学”策略的生物标志物，用于患有 精神病;和2）纹状体和非纹状体介导的抗精神病功效的生物标志物，用于 新型抗精神病药物的开发。由于缺乏足够的生物标志物， 缺乏证据基础来指导临床实践，缺乏研究基础来指导治疗开发。