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: 10194614
• 负责人: Cameron S. Carter
• 金额: $ 71.06万
• 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/10194614
• 关键词: Adjuvant; Admission activity; Anti-Inflammatory Agents; Artificial Intelligence; Behavioral; Biological Markers; Brain; Caring; Clinical; Clinical assessments; Clozapine; Cognition; Cognitive; Data; Decision Making; Development; Diffusion Magnetic Resonance Imaging; Disease; Dopamine; Drug Prescriptions; Early identification; Early treatment; 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; Psychological Transfer; 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; experience; gray matter; high risk; imaging biomarker; indexing; ineffective therapies; insight; learning strategy; medical specialties; 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; 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.

协调专科护理(CSC)的引入改变了护理标准和 提高治疗结果目标的年轻个人经历的初始阶段 精神病(EP)。然而，接受CSC的EP患者对治疗的反应， 仍然是高度多变的。相当大比例的人表现出轻微的症状缓解，尽管 接受构成这一治疗模式的各种循证做法。目前， 临床医生无法预测哪些进入CSC的EP患者会对治疗有反应 公布的数据显示，专业临床医生的表现并不比Chance好。早些时候 识别治疗无效者具有非常高的临床意义，并将通知 并在护理的头几个月加强临床决策。令人惊讶的是，几乎没有 已经进行了关于EP患者治疗结果的基线预测因素的研究 进入CSC。在过去的二十年里，使用 神经成像研究疾病早期的病理生理过程。 此外，有限的数据表明，脑部活动的fMRI测量和PET测量 多巴胺合成增加与EP的治疗结果有关。我们最近做了 在进入CSC的中等大样本EP患者中证明了 激活额叶顶叶(FP)认知控制网络(在AX-2期间使用fMRI测量) CPT任务)是CSC一年后谁将达到应答者标准的显著预测因子。 我们建议通过检验这个和两个模型的预测能力来复制和推广这一结果 其他有希望的基于MRI的措施与与以下相关的病理生理过程有关 精神病：1)自由水扩散张量成像(FW)--一种可能的 EP患者的神经炎症增加，以及2)中脑神经黑素(NM) 扫描显示中脑多巴胺在帕金森氏症和 在精神分裂症患者中增加。这些衡量标准中的每一项都将单独用于预测 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 优化EP患者治疗反应预测的新型深度学习方法 进入CSC，并对这些效应背后的机制获得新的见解。我们的目标 是利用基于MRI的成像生物标记物开发的最新进展来开发 一种精准医学工具，可以识别进入CSC的早期精神病患者 无反应的高风险，从而为所有患者的治疗决策提供信息 以优化年轻人在精神病发作后的康复。