ProNET: Psychosis-Risk Outcomes Network

项目摘要 自年首次制定精神病临床高风险标准以来， 服务的目标是预防精神障碍，其中一个最迫切的未满足的临床需求，在行为 如果不是所有的医学。尽管有关键的公共卫生需求，但药物开发仍被视为 很多地方都有风险。最令人生畏的障碍可能是航道的异质性。我们将深入 ProNET网络中26个国际研究中心的1040例具有多模式生物标志物的COPD患者表型 它涵盖了大脑结构-功能（MRI和EEG）、精神病理学和认知、遗传学、体液分析物， 自然语音/语言和被动/生态瞬时数字表型，并绘制这些生物标志物 在14岁时的治疗相关时间窗内， 24个月内的时间点。将在两个时间点收集生物标志物，以绘制大脑行为轨迹。 健康志愿者（N=390）将完成基线评估和随访评估（包括130名 随访生物标志物）以量化典型变化。我们还将试点评估兴奋/抑制 在7特斯拉下，谷氨酸、谷氨酰胺和GABA的MR光谱不平衡。我们将协调数据 与PRESCIENT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数据存档（NDA） NIMH，FNIH和加速药物伙伴关系的监督-精神分裂症。落实 ProNET范围内的标准化和近实时数据与DPACC架构集成，以方便现场 监控、统一标准操作程序，以及跨ProNET的快速数据聚合， DPACC到NDA转移。在与其他赠款合作，我们将测试的假设，数据驱动的变化 通过多变量神经、遗传和行为测量评估， 个性化的临床轨迹，扩大包括影响在内的广泛临床终点的分层， 焦虑，认知，阳性和阴性症状，目的是确定行为和生物标志物- 驱动的模式，可以改善神经系统综合征和促进个性化的治疗决策。这些 分析将产生扩展的结果分层计算器，用于预测可采取行动的 心理健康轨迹的研究分层计算器将允许未来的临床试验 设计人员选择最佳样品，以确定新化合物是否改善了特定的生物相容性。 这是一个重要的结果，并为患者及其家属的阶段特异性和安全的干预措施铺平道路。

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.