Cognition Trajectories in Cognitive Training and Early Intervention Treatment Programs in Schizophrenia

精神分裂症认知训练和早期干预治疗方案中的认知轨迹

基本信息

批准号：
9906914
负责人：
Sophia Vinogradov
金额：
$ 7.7万
依托单位：
UNIVERSITY OF MINNESOTA
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-04-05 至 2021-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9906914
关键词：
Alcohol or Other Drugs use Area Caring Characteristics Clinical Clinical Data Clinical Investigator Cognition Cognitive Cognitive deficits Communities Computer Analysis Data Data Scientist Data Set Disease Disease remission Early Intervention Early treatment Educational Intervention Evidence based intervention Functional disorder Future Goals Graph Growth Heterogeneity Impaired cognition Individual Intervention Lead Link Literature Mental Health Methods Modeling Neurosciences Outcome Patients Pattern Performance Personal Satisfaction Pharmaceutical Preparations Phase Prognostic Factor Psychotic Disorders Quality of life Randomized Controlled Trials Recovery Schizophrenia Schools Symptoms Syndrome Training Work analytical method causal model cognitive training computer game design first episode psychosis follow-up functional outcomes improved outcome insight next generation novel outcome forecast post intervention predictive modeling programs psychosocial response therapy development treatment optimization treatment program treatment response

Alcohol or Other Drugs use Area Caring Characteristics Clinical Clinical Data Clinical Investigator Cognition Cognitive Cognitive deficits Communities Computer Analysis Data Data Scientist Data Set Disease Disease remission Early Intervention Early treatment Educational Intervention Evidence based intervention Functional disorder Future Goals Graph Growth Heterogeneity Impaired cognition Individual Intervention Lead Link Literature Mental Health Methods Modeling Neurosciences Outcome Patients Pattern Performance Personal Satisfaction Pharmaceutical Preparations Phase Prognostic Factor Psychotic Disorders Quality of life Randomized Controlled Trials Recovery Schizophrenia Schools Symptoms Syndrome Training Work analytical method causal model cognitive training computer game design first episode psychosis follow-up functional outcomes improved outcome insight next generation novel outcome forecast post intervention predictive modeling programs psychosocial response therapy development treatment optimization treatment program treatment response

展开

项目摘要

PROJECT SUMMARY The Recovery After an Initial Schizophrenia Episode (RAISE) Early Treatment Program (ETP) is an exciting new evidence-based intervention for schizophrenia spectrum disorders. However, several questions arise from this study. Why are benefits in Quality of Life seen primarily during the first 6 months of treatment, and only for individuals whose duration of untreated psychosis is <74 weeks? Does a deeper understanding of treatment trajectories lead to new insights about prognosis or about novel leverage points to refine and optimize the treatment approach? The purpose of this project is to answer these questions by performing state-of-the-art computational analyses on data from RAISE-ETP and from our own cognitive training trial. Our interdisciplinary team of clinical investigators and data scientists will use data-driven predictive, causal clustering, and causal discovery analyses to examine clinical and cognitive response patterns in the RAISE-ETP data set. We will then validate the baseline causal model(s), describing the mechanistic relationship among patients' baseline characteristics derived from the RAISE-ETP data, against our own early psychosis data set, and explore the effects of cognitive training on key predictive variables identified in the RAISE-ETP analyses. We seek to understand whether data from our experimental neuroscience-informed cognitive training studies in recent-onset schizophrenia can suggest ways of enriching the RAISE-ETP approach in order to optimize outcomes for as many individuals as possible, not just those with a shorter duration of untreated psychosis. One of our main areas of focus will be cognition, since cognitive deficits predict psychosocial dysfunction even when symptoms are in remission and are unresponsive to current medications. In an exploratory Causal Graph Analysis performed on RAISE-ETP data, we find that baseline Cognition Composite performance is causally associated with Quality of Life (QLS) Total Score at 6 months, which in turn predicts the two-year QLS trajectory. Baseline Cognition Composite is also related to Positive and Negative Syndrome Scale (PANSS) Total Symptoms score at 6 months, via an unidentified latent variable linking QLS and PANSS Total Symptoms. Consistent with these causal relationships, exploratory growth curve modeling in our cognitive training data show that individuals assigned to training demonstrate a linear improvement in PANSS Total Symptoms 6 months after the intervention, while those control condition subjects show no such change. Our goal is to examine these findings in depth in order to inform the design of the next generation of “first episode” treatments.

项目摘要初始精神分裂症发作（提升）早期治疗计划（ETP）后的恢复是令人兴奋的新的基于证据的精神分裂谱系疾病。但是，从中出现了几个问题学习。为什么在治疗的头六个月中，主要的生活质量受益，而仅适用于个人谁的未经治疗的精神病持续时间<74周？对治疗轨迹的更深入了解是否会导致有关预后或有关新颖杠杆点的新见解，以完善和优化治疗方法？该项目的目的是通过对数据进行最新的计算分析来回答这些问题来自Rish-ETP和我们自己的认知培训试验。我们的临床研究人员和数据的跨学科团队科学家将使用数据驱动的预测性，因果聚类和因果发现分析来检查临床和 RISE-ETP数据集中的认知响应模式。然后，我们将验证基线因果模型，描述患者的基线特征从升高数据中得出的机械关系，反对我们自己的早期精神病数据集，并探索认知训练对在此中确定的关键预测变量的影响提高ETP分析。我们试图了解我们实验性神经科学知识认知的数据是否在最近发作的精神分裂症中的培训研究可以提出丰富的升级方法的方法，以便为了优化尽可能多的人的结果，而不仅仅是那些未经治疗的精神病持续时间较短的结果。我们的主要重点领域之一是认知，因为认知缺陷即使在症状处于缓解状态，对当前药物无反应。在探索性因果图分析中在Rish-ETP数据上执行，我们发现基线认知复合性能有时与生活质量（QLS）在6个月时总分，这反过来预测了两年QLS轨迹。基线认知复合材料还与6个月的正症状（PANSS）总症状得分有关，连接QLS和PANSS总症状的未识别潜在变量。与这些因果关系一致，在我们的认知培训数据中，探索性生长曲线建模表明，分配给培训的个人证明了干预后6个月，PANSS总症状的线性改善，而这些控制受试者没有显示这样的改变。我们的目标是深入研究这些发现，以告知下一代的设计 “第一集”治疗。