ProNET: Psychosis-Risk Outcomes Network

项目总结 自临床精神病高危(CHR)标准首次制定以来，已经过去了20年。 预防精神病的目标，这是行为健康领域最紧迫的未得到满足的临床需求之一，如果不是全部的话 医药。与大多数精神病患者一样，慢性阻塞性肺疾病患者受益于心理治疗，但也经常留下重要的 治疗不需要完全解决。尽管存在严重的公共卫生需求，但许多人认为用于慢性阻塞性肺疾病的药物开发 同样危险的是25美分。最令人望而生畏的障碍可能是CHR过程的异质性。在目标1中，我们将深入研究-- 跨26个国际站点的ProNET网络中的1040名CHR患者，具有跨越大脑的多模式生物标志物 结构-功能(MRI和EEG)，精神病理学和认知，遗传学，体液分析，自然言语/语言， 和被动/生态瞬时数字表型，并将这些生物标记物映射到一套核心的临床结果指标上。 在24个月的8个时间点上，在与治疗相关的时间窗口内的Sure和轨迹。将收集生物标记物 在两个时间点绘制大脑行为轨迹图。健康志愿者(N=260)将完成对全能的基线评估- 标明典型变种。我们还将进行探索性研究，以评估智能手机传感器的实时行为数据 来自调查的症状报告；来自标准的新的重复阳性和阿尔法去同步测量 脑电范例；并利用磁共振波谱对谷氨酸、谷氨酰胺、 和7特斯拉的GABA。在目标2中，我们将与NIMH选定的数据处理、分析和协调部门合作 快速数据集成和NIMH数据档案中心(NDA)与拟议的信息学平台一起上传。我们会 利用DPACC架构实施ProNET范围内的标准化和近乎实时的数据集成，以促进现场 跨ProNET进行监控、统一标准操作程序和快速数据聚合，以实现无缝的DPACC 保密协议转账。在目标3中，我们将测试这样一个假设，即由多变量神经、遗传和 慢性阻塞性肺疾病综合征中的行为测量预测个性化的临床轨迹，扩大慢性阻塞性肺疾病分层 广泛的临床终端，包括情感、焦虑、认知和AP，目标是识别行为和 生物标记物驱动的模式，可以提炼慢性阻塞性肺疾病综合征并促进个性化治疗决策。这些分析- SES将为慢性阻塞性肺疾病综合征产生扩展的结果分层计算器，可以预测可操作的心理健康 个别病人的轨迹。分层计算器将允许未来的临床试验设计者选择最佳 用于确定一种新化合物是否改善了感兴趣的特定CHR结果的样本，并为 分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.