ProNET: Psychosis-Risk Outcomes Network

ProNET：精神病风险结果网络

基本信息

批准号：
10256743
负责人：
CARRIE E BEARDEN
金额：
$ 1262.37万
依托单位：
YALE UNIVERSITY
依托单位国家：
美国
项目类别：
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/10256743
关键词：
Address Affect Affective Symptoms Anxiety Architecture Attenuated Behavioral Biological Markers Body Fluids Brain Cellular Phone Clinical Clinical Drug Development Clinical Trials Cognition Cognitive deficits Collaborations Communities Data Data Aggregation Databases Development Disease Ecological momentary assessment Electrophysiology (science)Evaluation Family Future Genetic Glutamates Glutamine Goals Heterogeneity Image Individual Informatics International Intervention Intervention Studies Knowledge Language Left 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 Psychopathology Psychoses Psychotherapy Psychotic Disorders Public Health Reporting Research Risk Sampling Schizophrenia Secure Services Site Speech Standardization Stratification Structure Surveys 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 integration design digital effective therapy falls 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 sensor severe mental illness success therapy development tool working group

项目摘要

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.

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是行为健康领域最紧迫的未满足的临床需求之一药品。与大多数精神病患者一样，CHR 患者从心理治疗中受益，但也常常留下重要的问题。治疗需求未得到充分解决。尽管公共卫生需求至关重要，但 CHR 的药物开发仍受到许多方面的关注宿舍同样有风险。最令人畏惧的障碍可能是CHR课程的异质性。在目标 1 中，我们将深刻认识到 26 个国际站点的 ProNET 网络中的 1040 名 CHR 患者具有跨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，并将这些生物标志物映射到一组核心的临床结果测量上 24 个月内八个时间点的治疗相关时间窗口的确定和轨迹。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) 将完成基线评估以定量 tify 典型变化。我们还将进行探索性研究，以评估来自智能手机传感器的实时行为数据调查的症状报告；来自标准的新颖的重复积极性和阿尔法去同步措施脑电图范式；并利用 MR 光谱对谷氨酸、谷氨酰胺、 GABA 为 7 特斯拉。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通过拟议的信息学平台进行快速数据集成和 NIMH 数据存档 (NDA) 上传的中心。我们将与 DPACC 架构实现 ProNET 范围内的标准化和近实时数据集成，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 保密协议转让。在目标 3 中，我们将检验以下假设：通过多元神经、遗传和神经元评估数据驱动的变异。 CHR 综合征中的行为测量可预测个体化的临床轨迹，扩大 CHR 分层用于涵盖情感、焦虑、认知和 APS 的广泛临床终点，目的是识别行为和生物标志物驱动的模式可以改善 CHR 综合征并促进个性化治疗决策。这些分析—— ses 将为 CHR 综合征提供扩展的结果分层计算器，可以预测可行的心理健康状况个别患者的轨迹。分层计算器将使未来的临床试验设计者能够选择最佳的用于确定新型化合物是否改善特定 CHR 结果的样品，并为针对特定阶段的、安全的新干预措施，使患者及其家人和社区受益。