ProNET: Psychosis-Risk Outcomes Network

ProNET：精神病风险结果网络

• 批准号: 10464673
• 负责人: CARRIE E BEARDEN
• 金额: $ 211.59万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-08 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10464673
• 关键词: Address; Affect; Affective Symptoms; Anxiety; Architecture; Attenuated; Behavioral; Biological Markers; Body Fluids; Brain; Clinical; Clinical Drug Development; Clinical Trials; Cognition; Cognitive deficits; Collaborations; Communities; Data; Data Aggregation; Data Collection; Databases; Development; Disease; Ecological momentary assessment; Electrophysiology (science); Evaluation; Family; Future; Genetic; Glutamates; Glutamine; Goals; Grant; Heterogeneity; Image; Individual; Informatics; International; Intervention; Intervention Studies; Knowledge; Language; Link; Liquid substance; Magnetic Resonance Imaging; Magnetic Resonance Spectroscopy; Maps; Measures; Medicine; Mental Health; Methods; Monitor; National Institute of Mental Health; Neurobiology; Onset of illness; Outcome; Participant; Pathogenicity; Patients; Pattern; Phase; Phenotype; Procedures; Prognostic Marker; Protocols documentation; Psychopathology; Psychoses; Psychotic Disorders; Public Health; Research; Risk; Sampling; Schizophrenia; Secure; Services; Sister; Site; Speech; Standardization; Stratification; Structure; Symptoms; Syndrome; Testing; Therapeutic; Time; Validation; Variant; Youth; attenuated psychosis syndrome; base; behavior measurement; behavioral health; behavioral outcome; biomarker-driven; brain behavior; clinical biomarkers; clinical heterogeneity; clinical outcome measures; clinically actionable; clinically relevant; computerized data processing; data archive; data harmonization; data integration; design; digital; effective therapy; falls; follow up assessment; follow-up; functional disability; gamma-Aminobutyric Acid; healthy volunteer; high risk; improved; individual patient; insight; interest; member; multimodality; novel; patient stratification; personalized medicine; personalized predictions; predictive marker; prevent; programs; prospective; psychotic symptoms; recruit; relating to nervous system; severe mental illness; success; therapy development; tool

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.

项目概要 自从首次制定临床精神病高风险（CHR）标准以来，已经过去了二十年。 预防精神障碍的目标服务，精神障碍是行为学领域最紧迫的未满足的临床需求之一 健康，即使不是所有的医学。尽管公共卫生需求至关重要，但 CHR 的药物开发仍被视为 许多季度同样存在风险。最令人畏惧的障碍可能是CHR课程的异质性。我们将深深地 ProNET 网络中 26 个国际站点的表型 1040 名 CHR 患者具有多模式生物标志物 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，并绘制这些生物标志物 在十四岁的治疗相关时间窗口内进行一组核心的临床结果测量和轨迹 时间点超过24个月。将在两个时间点收集生物标记物以绘制大脑行为轨迹。 健康志愿者（N=390）将完成基线评估和后续评估（其中130名有 后续生物标志物）来量化典型变异。我们还将试行兴奋/抑制的评估 7 特斯拉时谷氨酸、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、分析和合作伙伴合作 协调中心 (DPACC)，负责快速数据集成和 NIMH 数据存档 (NDA) 上传 监督 NIMH、FNIH 和加速药物合作伙伴关系 - 精神分裂症。我们将实施 ProNET 范围内的标准化和近乎实时的数据与 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，以实现无缝 DPACC 至 NDA 转移。与其他赠款合作，我们将测试数据驱动变异的假设 通过 CHR 综合征预测中的多变量神经、遗传和行为测量进行评估 个性化的临床轨迹，扩大 CHR 分层以实现广泛的临床终点，包括情感、 焦虑、认知以及阳性和阴性症状，目的是识别行为和生物标志物 驱动模式可以改善 CHR 综合征并促进个性化治疗决策。这些 分析将产生 CHR 综合征的扩展结果分层计算器，可以预测可采取的行动 个体患者的心理健康轨迹。分层计算器将允许未来的临床试验 设计人员选择最佳样品来确定新型化合物是否可以改善特定的 CHR 感兴趣的结果，并为患者及其家人进行特定阶段的安全干预措施铺平道路。