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: 10394304
• 负责人: Cameron S. Carter
• 金额: $ 70.02万
• 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 2025-04-23
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10394304
• 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; Process; Property; Protocols documentation; Psychoses; Psychotherapy; Psychotic Disorders; Publishing; Recovery; Research; Sampling; Scanning; Schizophrenia; Series; System; Testing; Time; Treatment outcome; base; 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; neuroimaging; neuroinflammation; neuromelanin; neurophysiology; novel; personalized medicine; precision medicine; predicting response; psychosocial; reduce symptoms; relating to nervous system; 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.

引入协调专业护理（CSC）已改变了护理标准， 升高的治疗结果目标是年轻人经历初始阶段 精神病（EP）。但是 仍然是高度可变的。大部分的比例显示，尽管症状减少了 接受包括此治疗模型的全部基于证据的实践。现在， 临床医生无法预测哪些EP进入CSC的EP将对治疗做出反应 并发布的数据表明，专家临床医生的表现不比机会更好。早期的 鉴定非反应者的治疗具有很高的临床意义，并将告知 并在护理的前几个月增强临床决策。令人惊讶的是，很少 已经对EP个体治疗结果的基线预测指标进行了研究 输入CSC。在过去的二十年中，使用 神经影像学以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的大脑活动和PET度量 多巴胺合成的增加与EP中的治疗结果有关。我们最近有 在进入CSC的EP患者的中等大型样本中证明的能力 激活额叶顶壁（FP）认知控制网络（在AX-期间使用fMRI测量 CPT任务）是CSC一年后谁将符合响应者标准的重要预测指标。 我们建议通过检查该结果和两个的预测能力来复制和扩展此结果 与与病理生理过程有关的其他有希望的基于MRI的措施 精神病：1）游离水扩散张量成像（FW） - 推定的生物标志物 EP个体中增加的神经炎症和2）中脑神经苯胺（NM） 扫描，指数中脑多巴胺，显示在帕金森氏病和 精神分裂症的增加。这些措施中的每一个都将单独使用以预测 EP参与者进入CSC的响应者状态。除了这些分析，我们还将使用 新的深度学习方法，以优化EP个体的治疗反应预测 输入CSC并获得对这些效果基础机制的新见解。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 可以识别出进入CSC的早期精神病患者的精确药物工具 无反应的高风险，从而为所有患者提供了治疗决策的信息 为了优化精神病发作后的年轻人的恢复。