Pathophysiology Informed Biomarkers of Treatment Response in Early Psychosis (PIB)

病理生理学为早期精神病 (PIB) 治疗反应提供生物标志物

• 批准号: 10612356
• 负责人: Cameron S. Carter
• 金额: --
• 依托单位: UNIVERSITY OF CALIFORNIA AT DAVIS
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10612356
• 关键词: Adjuvant; Admission activity; Anti-Inflammatory Agents; Artificial Intelligence; Behavioral; Biological Markers; Brain; Caring; Clinical; Clinical assessments; Clozapine; Cognition; Cognitive; Coordinated Specialty Care; Data; Decision Making; Development; Diffusion Magnetic Resonance Imaging; Disease; Dopamine; Drug Prescriptions; Early identification; Effectiveness; Enrollment; Evidence based practice; Functional Magnetic Resonance Imaging; Functional disorder; Future; Goals; Image; Image Analysis; Individual; Intervention; Link; Magnetic Resonance Imaging; Measurement; Measures; Methods; Midbrain structure; Modeling; Noise; Parietal; Parkinson Disease; Participant; Patients; Performance; Pharmaceutical Preparations; Phase; Positron-Emission Tomography; Prediction of Response to Therapy; Prefrontal Cortex; Process; Property; Protocols documentation; Psychoses; Psychotherapy; Psychotic Disorders; Publishing; Recovery; Research; Sampling; Scanning; Schizophrenia; Series; System; Testing; Time; Treatment outcome; candidate marker; clinical decision-making; clinically significant; cognitive control; deep learning; duration of untreated psychosis; early psychosis; experience; gray matter; high risk; imaging biomarker; indexing; ineffective therapies; insight; learning strategy; medication compliance; neural; neuroimaging; neuroinflammation; neuromelanin; neurophysiology; novel; personalized medicine; precision medicine; predicting response; psychosocial; reduce symptoms; responders and non-responders; standard of care; substance use; support tools; tool; transfer learning; treatment responders; treatment response; water diffusion; white matter

The introduction of Coordinated Specialty Care (CSC) has transformed the standard of care and elevated treatment outcome goals for young individuals experiencing the initial stages of a psychotic illness (EP). The response to treatment for EP individuals receiving CSC, however, remains highly variable. A substantial proportion show minimal symptom reduction despite receiving the full range of evidence-based practices comprising this treatment model. Currently, clinicians have no way to predict which EP individuals entering CSC will respond to treatment and published data show that expert clinicians perform no better than chance. Early identification of treatment non-responders has very high clinical significance and would inform and enhance clinical decision making during the first few months of care. Surprisingly, little research has been conducted on baseline predictors of treatment outcomes in EP individuals entering CSC. During the past two decades, considerable progress has been made using neuroimaging to investigate pathophysiological processes during the early phases of illness. Furthermore, limited data suggest that fMRI measures of brain activity and PET measures of increased dopamine synthesis are related to treatment outcomes in EP. We have recently demonstrated in a moderately large sample of EP patients entering CSC that the ability to activate the frontal parietal (FP) cognitive control network (measured using fMRI during the AX- CPT task) is a significant predictor of who will meet responder criterion after one year of CSC. We propose to replicate and extend this result by examining the predictive ability of this and two other promising MRI based measures linked to pathophysiological processes related to psychosis: 1) free water diffusion tensor imaging (FW) - a putative biomarker of neuroinflammation that is increased in EP individuals, and 2) midbrain neuromelanin (NM) scans, which index midbrain dopamine, shown to be decreased in Parkinson's disease and increased in schizophrenia. Each of these measures will be used individually to predict responder status for EP participants entering CSC. In addition to these analyses we will use novel deep learning methods to optimize the prediction of treatment response in EP individuals entering CSC and to obtain new insights into the mechanisms underlying these effects. Our goal is to leverage recent progress in the development of MRI based imaging biomarkers to develop a precision medicine tool that can identify early psychosis patients entering CSC who are at high risk for non-response and thereby inform treatment decision making for all patients in order to optimize the recovery of young individuals following the onset of psychotic illness.

协调专业护理 (CSC) 的引入改变了护理标准 提高经历初始阶段的年轻人的治疗结果目标 精神病（EP）。然而，接受 CSC 的 EP 个体对治疗的反应 仍然高度可变。尽管有很大比例的人表现出轻微的症状减轻 接受构成该治疗模型的全套循证实践。现在， 临床医生无法预测哪些进入 CSC 的 EP 个体会对治疗产生反应 已发表的数据表明，专业临床医生的表现并不比偶然更好。早期的 识别治疗无反应者具有非常高的临床意义，可以告知 并在护理的最初几个月加强临床决策。令人惊讶的是，很少 对 EP 个体治疗结果的基线预测因素进行了研究 进入CSC。在过去的二十年中，使用方面已经取得了相当大的进展 神经影像学研究疾病早期阶段的病理生理过程。 此外，有限的数据表明，fMRI 测量大脑活动，PET 测量 多巴胺合成的增加与 EP 的治疗结果相关。我们最近有 在进入 CSC 的中等大样本 EP 患者中证明， 激活额叶顶叶 (FP) 认知控制网络（在 AX- CPT 任务）是 CSC 一年后谁将达到响应者标准的重要预测因素。 我们建议通过检查这个和两个的预测能力来复制和扩展这个结果 其他有前途的基于 MRI 的措施与相关的病理生理过程相关 精神病：1）自由水扩散张量成像（FW）——一种假定的生物标志物 EP 个体中神经炎症增加，2) 中脑神经黑色素 (NM) 扫描显示中脑多巴胺水平在帕金森病和 精神分裂症时增加。这些措施中的每一个都将单独用于预测 进入 CSC 的 EP 参与者的响应者状态。除了这些分析之外，我们还将使用 新颖的深度学习方法可优化 EP 个体治疗反应的预测 进入 CSC 并获得对这些影响背后的机制的新见解。我们的目标 是利用基于 MRI 的成像生物标志物开发的最新进展来开发 一种精准医疗工具，可以识别进入 CSC 的早期精神病患者 无反应的风险很高，从而为所有患者的治疗决策提供依据 优化年轻人精神病发作后的康复。