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

• 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
• 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

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.

项目概要 精神分裂症初始发作后的恢复 (RAISE) 早期治疗计划 (ETP) 是一个令人兴奋的新项目 针对精神分裂症谱系障碍的循证干预。然而，由此产生了几个问题 学习。为什么生活质量的改善主要在治疗的前 6 个月内显现出来，而且仅针对个人 谁的精神病未经治疗的持续时间<74周？对治疗轨迹的更深入了解是否会导致 关于预后的新见解或关于改进和优化治疗方法的新杠杆点？ 该项目的目的是通过对数据进行最先进的计算分析来回答这些问题 来自 RAISE-ETP 和我们自己的认知训练试验。我们的跨学科临床研究人员和数据团队 科学家将使用数据驱动的预测、因果聚类和因果发现分析来检查临床和 RAISE-ETP 数据集中的认知反应模式。然后我们将验证基线因果模型，描述 从 RAISE-ETP 数据得出的患者基线特征之间的机械关系，与我们的 自己的早期精神病数据集，并探索认知训练对在数据中确定的关键预测变量的影响 RAISE-ETP 分析。我们试图了解来自我们实验神经科学的认知数据是否 新发精神分裂症的培训研究可以提出丰富 RAISE-ETP 方法的方法，以便 为尽可能多的人优化结果，而不仅仅是那些未经治疗的精神病持续时间较短的人。 我们关注的主要领域之一是认知，因为认知缺陷可以预测心理社会功能障碍，即使 症状已缓解，对当前药物无反应。在探索性因果图分析中 对 RAISE-ETP 数据执行，我们发现基线认知综合表现与 6 个月时的生活质量 (QLS) 总分，进而预测两年的 QLS 轨迹。基线认知 综合还与 6 个月时的阳性和阴性症状量表 (PANSS) 总症状评分相关，通过 连接 QLS 和 PANSS 总症状的未识别潜在变量。与这些因果关系相一致， 我们的认知训练数据中的探索性增长曲线模型表明，分配到训练的个人表现出 干预后 6 个月，PANSS 总症状呈线性改善，而那些控制病情的受试者 没有显示出这样的变化。我们的目标是深入研究这些发现，以便为下一代的设计提供信息 “第一集”治疗。