4/5-Cognitive Neurocomputational Task Reliability & Clinical Applications Consortium

4/5-认知神经计算任务可靠性

• 批准号: 10661589
• 负责人: James M. Gold
• 金额: $ 50.88万
• 依托单位: UNIVERSITY OF MARYLAND BALTIMORE
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-30 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10661589
• 关键词: Address; Affective; Anhedonia; Assessment tool; Attention; Behavior; Behavioral; Big Data; Brain; Clinic; Clinical; Cognition; Cognitive; Communities; Complement; Data; Data Collection; Decision Making; Dimensions; Electroencephalography; Episodic memory; Evaluation Studies; Floor; Functional disorder; Genetic; Genetic study; Goals; Gold; Human; Impaired cognition; Impairment; Individual; Internet; Intervention; Laboratories; Learning; Life; Longitudinal Studies; Mathematics; Measurement; Measures; Mental Depression; Mental disorders; Methods; Modeling; Mood Disorders; Motivation; Parameter Estimation; Patient Recruitments; Patients; Pattern; Perception; Performance; Population; Positive Valence; Productivity; Property; Psychiatry; Psychological reinforcement; Psychometrics; Psychopathology; Psychotic Disorders; Research; Research Domain Criteria; Research Personnel; Resources; Sampling; Short-Term Memory; Site; Specific qualifier value; Specificity; Symptoms; System; Task Performances; Testing; Translating; Variant; Visual Perception; Work; behavior measurement; clinical application; clinical predictors; cognitive function; cognitive neuroscience; cognitive process; cognitive system; cognitive testing; computerized tools; discounting; flexibility; functional outcomes; interest; neural; neuromechanism; neurophysiology; novel; population based; precision medicine; psychologic; psychotic-like experiences; recruit; severe mental illness; spatiotemporal; therapy development; tool; tool development; web-based tool

Advancements in computational psychiatry allow us to isolate multiple, specific cognitive mechanisms that determine human behavior. This formal modeling framework generates quantitative parameter estimates that can serve as bridges between pathophysiology and psychopathology. A major goal of computational psychiatry is to translate these laboratory tools so that they can be used in the clinic. Two critical hurdles need to be overcome. First, the enhanced validity and sensitivity of computational metrics needs to be established relative to standard behavioral performance metrics in key psychiatric and nonpsychiatric populations. We propose to do that by addressing a range of cognitive and motivational domains that have been strongly implicated in psychopathology, including working and episodic memory, visual perception, reinforcement learning, and effort based decision making. Second, we need to establish and optimize the psychometrics of these computational metrics so that they can be used as tools in treatment development, treatment evaluation, longitudinal, and genetic studies. These powerful metrics must have adequate test-retest reliability, and not be limited by ceiling and floor effects. We propose to develop these methods using an open, flexible, and scalable framework and demonstrate that they provide valid data both in the laboratory and in large-scale Internet-based data collection, facilitating “big data” studies of cognitive processes. To this end, the current project will leverage the expertise of Cognitive Neuroscience Task Reliability and Clinical applications in Serious mental illness (CNTRACS) consortium, a multi-site research group with an established record of rapid cognitive tool development and dissemination. Aim 1 is to establish that model based parameters for the measurement of cognitive function are more sensitive than standard behavioral methods in assessing deficits across a range of common mental disorders, and have an enhanced capacity to predict clinical symptoms and real-world functioning, with a sample of 180 patients with psychotic and affective disorders (both medicated and unmedicated) and 100 healthy controls. Aim 2 is to measure and optimize the psychometric properties (test re-test reliability, internal validity, floor and absence of ceiling and practice effects) of computational parameters described in Aim 1, in a new sample of 180 psychiatric patients and 100 healthy controls. Aim 3 is to establish the feasibility and replicability of model-based analytic approaches outside the laboratory for assessing RDoC dimensions of interest, and to assess their relationships to variation in psychotic-like experience, depression and anhedonia, as well as real- world functioning in a community sample of 10,000 recruited over the Internet. Aim 4 is to validate key model based parameters against well-characterized neurophysiological measures acquired using EEG recordings during task performance. Successful completion of these Aims will significantly advance the field by providing easily administered and scalable web-based tools for estimating the integrity of key neural systems that underlie normal cognition and motivation and form the basis of common forms of cognitive and affective psychopathology.

计算精神病学的进步使我们能够隔离多种特定的认知机制 确定人类行为。这个正式的建模框架生成了定量参数估计， 可以用作病理生理学和心理病理学之间的桥梁。计算精神病学的主要目标 是要翻译这些实验室工具，以便可以在诊所中使用。需要两个关键障碍 克服。首先，需要建立相对的计算指标的有效性和灵敏度 关键精神病和非精神病学人群中的标准行为绩效指标。我们建议 通过解决一系列认知和激励领域的范围来做到这一点 心理病理学，包括工作和情节记忆，视觉感知，强化学习和努力 基于决策。其次，我们需要建立和优化这些计算的心理计量学 指标可以用作治疗开发，治疗评估，纵向和 遗传研究。这些功能强大的指标必须具有足够的重测可靠性，并且不受天花板的限制 和地板效应。我们建议使用开放，灵活和可扩展的框架开发这些方法，并且 证明它们在实验室和基于大规模的Internet数据收集中提供有效数据， 促进认知过程的“大数据”研究。为此，当前项目将利用专业知识 认知神经科学任务可靠性和严重精神疾病（CNTRACS）的临床应用 财团，一个多站点的研究小组，具有快速认知工具开发和 传播。目标1是确定用于测量认知功能的基于模型的参数是 比标准行为方法更敏感的行为方法在评估一系列常见的精神范围内的定义方面更敏感 疾病，并具有增强的预测临床症状和现实世界功能的能力。 在180例精神病和情感障碍（均药物和未药物）和100例健康的患者中 控件。目标2是测量和优化心理测量特性（测试重新测试可靠性，内部有效性， 在新的 样本的180名精神病患者和100个健康对照。目标3是建立可行性和可复制性 实验室以外的基于模型的分析方法，用于评估RDOC感兴趣的维度和 评估他们与精神病经验，抑郁症和Anhedonia的变化的关系，以及 通过互联网招募的10,000个社区样本的世界运作。目标4是验证关键模型 针对使用脑电图记录获得的良好特征神经生理学措施的基于良好特征的参数 在任务执行期间。这些目标的成功完成将通过提供大大推动整个领域 易于管理且可扩展的基于Web的工具，用于估计关键神经系统的完整性 正常的认知和动机，并形成了认知和情感心理病理学的共同形式的基础。