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: 10915211
• 负责人: Cameron S. Carter
• 金额: $ 49.37万
• 依托单位: UNIVERSITY OF CALIFORNIA-IRVINE
• 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 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10915211
• 关键词: Pathophysiology; Informed; Biomarkers; Treatment; Response

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， 提高治疗结果的目标，为年轻人经历的初始阶段， 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。相当大比例的患者表现出轻微的症状减轻， 接受包括这种治疗模式在内的全方位循证实践。目前， 临床医生无法预测进入CSC的EP个体将对治疗产生反应 已发表的数据显示，专家临床医生的表现并不比偶然更好。早期 识别治疗无应答者具有非常高的临床意义， 并在最初几个月的护理中加强临床决策。令人惊讶的是，小 已经对EP个体治疗结果的基线预测因子进行了研究 进入CSC。在过去的二十年里， 神经影像学研究疾病早期的病理生理过程。 此外，有限的数据表明，大脑活动的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 在进入CSC的EP患者的中等规模样本中证明， 激活额顶叶（FP）认知控制网络（在AX- CPT任务）是一个显着的预测谁将满足反应标准后，一年的CSC。 我们建议复制和扩展这一结果，通过检查的预测能力，这两个 其他基于MRI的有前景的措施与病理生理过程相关， 精神病：1）游离水扩散张量成像（FW）-一种假定的生物标志物， 在EP个体中增加的神经炎症，和2）中脑神经黑色素（NM） 扫描显示，中脑多巴胺的指数在帕金森病中减少， 精神分裂症的发病率上升。每一项指标都将单独用于预测 进入CSC的EP受试者的应答者状态。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 进入CSC，并获得这些影响的机制的新见解。我们的目标 是利用基于MRI的成像生物标志物的最新进展， 一种精确的医学工具，可以识别进入CSC的早期精神病患者， 无应答的高风险，从而为所有患者制定治疗决策提供信息， 以优化年轻人在精神病发作后的康复。

项目成果

Data augmentation with Mixup: Enhancing performance of a functional neuroimaging-based prognostic deep learning classifier in recent onset psychosis.

• DOI: 10.1016/j.nicl.2022.103214
• 发表时间: 2022
• 期刊: NEUROIMAGE-CLINICAL
• 影响因子: 4.2
• 作者: Smucny, Jason;Shi, Ge;Lesh, Tyler A.;Carter, Cameron S.;Davidson, Ian
• 通讯作者: Davidson, Ian

Are We There Yet? Predicting Conversion to Psychosis Using Machine Learning.

我们到了吗？

• DOI: 10.1176/appi.ajp.20220973
• 发表时间: 2023
• 期刊: The American journal of psychiatry
• 作者: Smucny,Jason;Davidson,Ian;Carter,CameronS
• 通讯作者: Carter,CameronS