ProNET: Psychosis-Risk Outcomes Network

项目摘要 自从精神病临床高危标准首次制定以来，已经有二十年了。 预防精神障碍的目标，这是行为健康中最迫切的未满足的临床需求之一，如果不是所有的 药与大多数精神病患者一样，抑郁症患者从心理治疗中受益，但也经常留下重要的问题。 治疗需要没有得到充分解决。尽管有关键的公共卫生需求，但许多人认为， 四分之一的风险。最令人生畏的障碍可能是航道的异质性。在目标1中，我们将深入地表现出- 在ProNET网络的26个国际站点中，对1040名具有跨越大脑的多模式生物标志物的脑卒中患者进行了分型 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，并将这些生物标志物映射到一组核心的临床结果指标上， 在24个月内的8个时间点，在治疗相关时间窗内确定和轨迹。将收集生物标志物 在两个时间点绘制大脑行为轨迹。健康志愿者（N = 260）将完成一项基线评估， 验证典型变异。我们还将进行探索性研究，评估来自智能手机传感器的实时行为数据 和症状报告的调查;新的重复积极性和α-desertification措施来自标准 EEG范例;并对谷氨酸、谷氨酰胺、 γ-氨基丁酸7特斯拉在目标2中，我们将与NIMH选择的数据处理，分析和协调部门合作 快速数据集成中心和NIMH数据档案（NDA）上传到拟议的信息平台。我们将 实施ProNET范围内的标准化和接近实时的数据集成与DPACC架构，以促进现场 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， 保密协议转让。在目标3中，我们将检验由多变量神经、遗传和基因评估的数据驱动变异的假设。 行为学测量可预测个体化的临床轨迹，扩展了行为学分层， 广泛的临床终点，包括情感，焦虑，认知和APS，目的是确定行为和 生物标志物驱动的模式，可以改善帕金森综合征和促进个性化的治疗决策。这些分析- ses将为CHR综合征提供扩展的结果分层计算器，可以预测可采取行动的心理健康状况 在个别病人身上的轨迹。分层计算器将允许未来的临床试验设计者选择最佳的 用于确定新化合物是否改善了感兴趣的特定药物治疗结果并为 具体阶段和安全的新干预措施，使患者及其家人和社区受益。

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. As with most psychiatric patients, CHR patients benefit from psychotherapies but are also often left with important treatment needs not fully addressed. 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. In Aim 1 we will deeply pheno- type 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 mea- sures and trajectories over a treatment-relevant time window at eight timepoints over 24 months. Biomarkers will be collected at two timepoints to map brain-behavior trajectories. Healthy volunteers (N=260) will complete a baseline assessment to quan- tify typical variation. We will also conduct exploratory studies to assess real-time behavioral data from smartphone sensors and symptom reports from surveys; novel repetition positivity and alpha-desynchronization measures derived from standard EEG paradigms; and pilot an evaluation of excitatory/inhibitory imbalance with MR spectroscopy for glutamate, glutamine, and GABA at 7 Tesla. In Aim 2 we will partner with the NIMH-selected Data Processing, Analysis, and Coordinating Center for rapid data integration and NIMH Data Archive (NDA) uploads with the proposed informatics platform. 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 Aim 3 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 APS with the goal of identifying behavioral and biomarker-driven patterns that can refine the CHR syndrome and promote personalized treatment decisions. These analy- ses 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 new interventions to benefit patients and their families and communities.