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.

项目概要 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相关。 治疗效果的异质性导致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。 缺乏预后措施，并且通常必须依赖于神经试验。 治疗反应的潜在机制仍不清楚，导致缺乏新的潜在靶点 令人惊讶的是，鉴于紧迫的公共卫生和科学需求，相关工作却很少。 利用现代神经影像技术来了解抗精神病反应的机制。 我们最近证明，静息态功能连接（RSFC）可能是一种有价值的检测方法 生物标记物的开发，既作为治疗反应的治疗前预测因子，又作为动态标记物 例如，我们小组开发了纹状体指数。 连接性预测对第二代抗精神病药物（SGA）的反应具有高灵敏度和 首发精神分裂症患者的特异性，并推广到一组慢性患者 此外，我们发现纹状体与扣带回的功能相互作用发生变化， 经过 12 周的 SGA 治疗后，海马体、丘脑和皮质的精神病状况得到改善。 迄今为止，这种方法尚未应用于难治性精神分裂症（TRS）人群，也没有应用于 在这个项目中，我们对不主要针对纹状体的治疗策略进行了广泛的研究。 建议评估两组接受有效干预治疗的 TRS 患者的 RSFC 与传统 D2 受体拮抗剂显着不同的策略 在目标 1 中，我们将评估精神病。 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 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 也难以治疗，我们将其称为超级耐药 (uTRS) 我们将对 uTRS 患者进行扫描。 接受为期 8 周的 CLZ 联合辅助电休克治疗（CLZ+ECT）治疗试验， 最近证明，一种治疗策略对严重的 uTRS 患者具有显着疗效。 目标，我们将使用纵向设计，在对照治疗之前和之后收集 MRI 扫描， 使用结构化评级量表评估的症状将使用基于种子的策略进行评估。 基于我们最近的工作，但扩展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）用于针对患有以下疾病的患者的“精准医疗”策略的生物标志物 精神疾病；和 2) 纹状体和非纹状体介导的抗精神病药功效的生物标志物 鉴于缺乏足够的生物标志物，迫切需要开发新型抗精神病药物。 指导临床实践的证据基础，缺乏指导治疗开发的研究基础。

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

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

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

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