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.

摘要 越来越多的证据表明，纹状体连接障碍在帕金森病的病理生理学中起着关键作用。 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）， 以及来自研究领域标准（RDoC）框架的fMRI任务，以：1）开发和 验证预测抗精神病药物治疗反应的预后生物标志物;和2）模拟潜在的 神经回路变化与精神病精神病学的状态变化有关。作为预后 作为一种生物标志物，纹状体连接的神经影像学测定可能提供一种临床有用的工具， 推进精准医疗的目标。作为症状变化的纵向指标，我们的模型可以作为 一个客观的指标来衡量新开发的抗精神病药物治疗的潜在疗效。 一个大型（n=120），特征良好的队列患者首次发作活动性精神病 （不考虑DSM诊断）与匹配的对照（n=50）一起沿着招募。我们会好好利用两个- 有效的功能磁共振成像任务捕捉两个部分的积极效价系统：概率类别学习和 奖赏反应;这些任务被设计为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，对每位患者进行两次扫描： 基线和治疗12周后。将对所有患者进行标准化治疗， 还将在基线和12周时对潜在混淆和健康对照进行扫描，以控制 时间和实践的影响。精神病性幻觉水平（幻觉、妄想和思维） 将使用一组全面的评定量表定期测量。我们将利用 Kaplan-Meier估计值和分层线性建模，以检查基线纹状体 连接性，以及连接性随时间的变化，以及精神病症状对 抗精神病药物治疗样本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。