Striatal Connectivity and Clinical Outcome in Psychosis

纹状体连接性和精神病的临床结果

• 批准号: 10369158
• 负责人: Anil K Malhotra
• 金额: $ 13.74万
• 依托单位: FEINSTEIN INSTITUTE FOR MEDICAL RESEARCH
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2022-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10369158
• 关键词: Antipsychotic Agents; Biological Assay; Biological Markers; Brain imaging; Categories; Clinical; Corpus striatum structure; Data; Delusions; Development; Diagnosis; Disease; Dorsal; Functional Magnetic Resonance Imaging; Functional disorder; Goals; Hallucinations; Individual; Lead; Learning; Linear Models; Measures; Mental disorders; Modeling; Outcome; Patients; Population Heterogeneity; Positive Valence; Prognostic Marker; Psychoses; Research Domain Criteria; Rest; Rewards; Scanning; Standardization; Symptoms; System; Testing; Time; brain circuitry; cohort; design; effective therapy; imaging study; indexing; neural circuit; neuroimaging; patient population; precision medicine; psychotic symptoms; recruit; response; symptomatology; tool; treatment response

ABSTRACT Converging lines of evidence suggest a key role for striatal dysconnectivity in the pathophysiology of psychosis. In the proposed study, we will utilize resting state functional magnetic resonance imaging (rs-fMRI), as well as fMRI tasks derived from the Research Domain Criteria (RDoC) framework, to: 1) develop and validate a prognostic biomarker to predict antipsychotic treatment response; and 2) to model the underlying neural circuitry changes associated with state changes in psychotic symptomatology. As a prognostic biomarker, a neuroimaging assay of striatal connectivity can potentially provide a clinically useful tool to advance the goal of precision medicine. As a longitudinal index of symptom change, our model can serve as an objective index against which to measure potential efficacy of newly developed antipsychotic treatments. A large (n=120), well-characterized cohort of patients presenting with a first episode active psychosis (regardless of DSM diagnosis) will be recruited, along with matched controls (n=50). We will utilize two well- validated fMRI tasks capturing two portions of the positive valence system: probabilistic category learning and reward responsiveness; these tasks are designed to interrogate dorsal and ventral corticostriatal circuits, respectively. Our design will be longitudinal, with two scanning sessions performed for each patient: at baseline, and after 12 weeks of treatment. Treatment will be standardized across all patients to reduce potential confounds, and healthy controls will also be scanned at baseline and 12 weeks in order to control for effects of time and practice. Level of psychotic symptomatology (hallucinations, delusions, and thought disorder) will be measured at regular intervals using a comprehensive battery of rating scales. We will utilize Kaplan-Meier estimators and hierarchical linear modeling to examine the association of baseline striatal connectivity, and changes in connectivity over time, with clinical response of psychotic symptoms to antipsychotic treatment. Deliverables will include both baseline and longitudinal biomarkers that can subsequently be tested in broader, more heterogeneous populations of patients with psychosis.

抽象的 多种证据表明纹状体连接失调在病理生理学中发挥着关键作用 精神病。在拟议的研究中，我们将利用静息态功能磁共振成像（rs-fMRI）， 以及源自研究领域标准 (RDoC) 框架的功能磁共振成像任务，以：1) 开发和 验证预后生物标志物以预测抗精神病药物治疗反应； 2）对底层进行建模 神经回路的变化与精神病症状的状态变化相关。作为预测 生物标志物，一种纹状体连接性的神经影像分析，可能会提供一种临床上有用的工具 推进精准医疗目标。作为症状变化的纵向指标，我们的模型可以作为 衡量新开发的抗精神病药物潜在疗效的客观指标。 一大群 (n=120)、特征明确的首次发作活动性精神病患者队列 （无论 DSM 诊断如何）将被招募，以及匹配的对照（n = 50）。我们将充分利用两个 经过验证的功能磁共振成像任务捕获正价系统的两个部分：概率类别学习和 奖励反应能力；这些任务旨在询问背侧和腹侧皮质纹状体回路， 分别。我们的设计将是纵向的，为每位患者进行两次扫描： 基线和治疗 12 周后。所有患者的治疗都将标准化，以减少 潜在的混淆，健康对照也将在基线和 12 周进行扫描，以控制 时间和练习的影响。精神病症状水平（幻觉、妄想和思维 障碍）将使用一套全面的评级量表定期进行测量。我们将利用 Kaplan-Meier 估计器和分层线性模型来检查基线纹状体的关联 连接性，以及连接性随时间的变化，以及精神病症状的临床反应 抗精神病治疗。可交付成果将包括基线和纵向生物标志物 随后在更广泛、更异质的精神病患者群体中进行测试。