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: 10084173
• 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 2023-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10084173
• 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; Psychoses; 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; efficacious treatment; 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 抗精神病药是治疗精神病的中流药物，但它们与大量有关 其治疗效率的异质性。对治疗的无反应会导致生活质量差 患者，以及对医疗保健系统的巨大影响。这些疾病的治疗算法是 缺乏预后测量，临床医生通常必须依靠反复试验。同时，中立 对治疗反应的基础机制尚不清楚，导致缺乏新颖的潜在目标 治疗开发。令人惊讶的是，鉴于紧急的公共卫生和科学需求，工作很少 利用现代神经影像技术来了解抗精神病药反应的机制。 我们最近证明，静止状态功能连接性（RSFC）可能是一个有价值的测定法 生物标志物的发展，都是治疗反应的预处理预测指标，也是动态标记 在治疗过程中的抗精神病药效率。例如，我们的小组开发了纹状体指数 连通性预测对第二代抗精神病药（SGA）的响应高灵敏度和 第一期精神分裂症患者的特异性，并概括为一组慢性患者 精神病。此外，我们发现纹状体与cing的功能相互作用的变化， SGA治疗12周后，海马，丘脑和皮层跟踪了精神病的改善。 迄今为止，这种方法尚未应用于耐治疗的精神分裂症（TRS）人群，也没有 不主要靶向纹状体的治疗策略是广泛研究的。在这个项目中，我们 提议评估两组接受有效干预治疗的TRS患者的RSFC 与传统的D2受体拮抗剂显着不同的策略。在AIM 1中，我们将评估精神病 接受24周治疗试验的氯氮平的患者在抗精神病药中仍然是独一无二的 药物在TRS方面具有出色的效率。在AIM 2中，我们将评估精神病症状仍然存在的患者 甚至对CLZ的难治性，我们称之为超级治疗（UTRS）。我们将扫描UTRS患者 经过为期8周的CLZ治疗试验，结合辅助性电动治疗（CLZ+ECT）， 最近的一种治疗策略在严重病患者的患者中具有出色的效率。两者 目的，我们将使用纵向设计，并在受控治疗前后收集了MRI扫描， 用结构化评分量表评估的症状。 RSFC将使用基于种子的策略进行评估 在我们最近的工作中，但扩展到包括纹状体以外的相关皮层结构。 该项目的结果可能提供：1）用于用于“精密医学”策略的生物标志物，可用于 精神病； 2）纹状体和非介导的抗精神病药效率的生物标志物用于 新型抗精神病药。鉴于缺乏足够的足够，迫切需要这种生物标志物 指导临床实践的证据基础，以及缺乏指导治疗发展的研究基础。

项目成果

Electroconvulsive Therapy and Schizophrenia: A Systematic Review.

• DOI: 10.1159/000497376
• 发表时间: 2019-04-01
• 期刊: Molecular neuropsychiatry
• 作者: Ali, Sana A;Mathur, Nandita;Braga, Raphael J
• 通讯作者: Braga, Raphael J

Assessing the Candidates.

评估候选人。

• DOI: 10.1016/j.biopsych.2019.07.013
• 发表时间: 2019
• 期刊: Biological psychiatry
• 影响因子: 10.6
• 作者: Malhotra,AnilK

The effects of lorazepam on cortico-striatal connectivity in schizophrenia.

劳拉西泮对精神分裂症皮质纹状体连接的影响。

• DOI: 10.1016/j.schres.2020.07.004
• 发表时间: 2020
• 期刊: Schizophrenia research
• 影响因子: 4.5
• 作者: Ali,SanaA;Moyett,Ashley;Argyelan,Miklos;Barber,AnitaD;Homan,Philipp;Rubio,JoseM;Fales,Christina;Gallego,JuanA;Lencz,Todd;Malhotra,AnilK
• 通讯作者: Malhotra,AnilK

ECT-induced cognitive side effects are associated with hippocampal enlargement.

• DOI: 10.1038/s41398-021-01641-y
• 发表时间: 2021-10-08
• 期刊: Translational psychiatry
• 影响因子: 6.8
• 作者: Argyelan M;Lencz T;Kang S;Ali S;Masi PJ;Moyett E;Joanlanne A;Watson P;Sanghani S;Petrides G;Malhotra AK
• 通讯作者: Malhotra AK

Estimation of Dynamic Bivariate Correlation Using a Weighted Graph Algorithm.

• DOI: 10.3390/e22060617
• 发表时间: 2020-06-02
• 期刊: Entropy (Basel, Switzerland)
• 作者: John M;Wu Y;Narayan M;John A;Ikuta T;Ferbinteanu J
• 通讯作者: Ferbinteanu J