ProNET: Psychosis-Risk Outcomes Network

项目总结 自#年首次制定临床精神病高危(CHR)标准以来，至今已有20年。 服务于预防精神病的目标，这是行为领域最紧迫的未得到满足的临床需求之一 健康，如果不是在所有的医学中。尽管存在严重的公共卫生需求，但针对慢性阻塞性肺疾病的药物开发仍被视为 很多季度都是有风险的。最令人望而生畏的障碍可能是CHR过程的异质性。我们将深深地 具有多模式生物标志物的26个国际地点的ProNET网络中的1040名CHR患者的表型 涵盖大脑结构-功能(MRI和EEG)、精神病理学和认知、遗传学、体液分析、 自然语音/语言和被动/生态瞬时数字表型，并绘制这些生物标记物 在14个与治疗相关的时间窗口上的一组核心临床结果测量和轨迹上 超过24个月的时间点。生物标志物将在两个时间点收集，以绘制大脑行为轨迹。 健康志愿者(N=390人)将完成基线评估和后续评估(包括130名 后续生物标志物)来量化典型的变异。我们还将试行兴奋性/抑制性的评估 在7特斯拉，谷氨酸、谷氨酰胺和GABA的磁共振波谱不平衡。我们将协调数据 具有先见之明的网络的收集协议，并与数据处理、分析和 协调中心(DPACC)，用于快速数据集成和NIMH数据归档(NDA)上传 监督NIMH、FNIH和加速药物伙伴关系-精神分裂症。我们将实施 ProNet范围内标准化且接近实时的数据与DPACC架构的集成，以促进现场 跨ProNET实现无缝监控、统一标准操作程序和快速数据聚合 DPACC到NDA的转移。我们将与其他资助机构合作，测试数据驱动的变异 通过多变量神经、遗传和行为测量评估慢性阻塞性肺疾病综合征的预测 个性化的临床轨迹，扩大包括影响在内的广泛临床终点的CHR分层， 焦虑、认知、阳性和阴性症状，目的是确定行为和生物标记物- 驱动模式，可以提炼慢性阻塞性肺疾病综合征，促进个性化治疗决策。这些 分析将为慢性肾衰综合征产生扩展的结果分层计算器，可以预测可操作的 个别患者的心理健康轨迹。分层计算器将允许未来的临床试验 设计师选择最佳样品，以确定一种新化合物是否可以改善特定的CHR 这是一项重要的研究成果，并为针对患者及其家属的分阶段安全干预铺平了道路。

PROJECT SUMMARY It has now been two decades since the clinical high risk for psychosis (CHR) criteria were first formulated in service of the goal of preventing psychotic disorders, one of the most urgent unmet clinical needs in behavioral health if not in all of medicine. Despite the critical public health need, drug development for CHR is viewed in many quarters as risky. The most daunting obstacle may be the heterogeneity of CHR course. We will deeply phenotype 1040 CHR patients across the ProNET network of 26 international sites with multi-modal biomarkers that span brain structure-function (MRI and EEG), psychopathology and cognition, genetics, body fluid analytes, natural speech/language, and passive/ecological momentary digital phenotyping, and map these biomarkers onto a core set of clinical outcome measures and trajectories over a treatment-relevant time window at fourteen timepoints over 24 months. Biomarkers will be collected at two timepoints to map brain-behavior trajectories. Healthy volunteers (N=390) will complete baseline assessment and follow-up assessments (including 130 with follow-up biomarkers) to quantify typical variation. We will also pilot an evaluation of excitatory/inhibitory imbalance with MR spectroscopy for glutamate, glutamine, and GABA at 7 Tesla. We will harmonize data collection protocols with the PRESCIENT network and partner with the Data Processing, Analysis, and Coordinating Center (DPACC) for rapid data integration and NIMH Data Archive (NDA) uploads under the oversight of NIMH, FNIH, and the Accelerating Medicines Partnership - Schizophrenia. We will implement ProNET-wide standardized and near real-time data integration with the DPACC architecture to facilitate on-site monitoring, unification of standard operating procedures, and rapid data aggregation across ProNET for seamless DPACC to NDA transfer. In partnership with the other grants we will test the hypothesis that data-driven variation assessed by multivariate neural, genetic, and behavioral measures within the CHR syndrome predicts individualized clinical trajectories, expanding CHR stratification for broad clinical endpoints encompassing affect, anxiety, cognition, and positive and negative symptoms with the goal of identifying behavioral and biomarker- driven patterns that can refine the CHR syndrome and promote personalized treatment decisions. These analyses will yield expanded outcome stratification calculators for the CHR syndrome that can predict actionable mental health trajectories in individual patients. The stratification calculators will allow future clinical trial designers to select optimal samples for determining whether a novel compound improves the particular CHR outcome of interest and pave the way for phase-specific and safe interventions for patients and their families.