3/5 Cognitive Neurocomputational Task Reliability & Clinical Applications Consortium

3/5 认知神经计算任务可靠性

• 批准号: 10004738
• 负责人: STEVEN M SILVERSTEIN
• 金额: $ 49.36万
• 依托单位: RUTGERS BIOMEDICAL/HEALTH SCIENCES-RBHS
• 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 2020-07-02
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10004738
• 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; Patient Recruitments; Patients; Pattern; Perception; Performance; Population; Positive Valence; Property; Psychiatry; Psychological reinforcement; Psychometrics; Psychopathology; Psychotic Disorders; Research; Research Domain Criteria; Research Personnel; Resources; Sampling; Short-Term Memory; Site; Specificity; Symptoms; System; Task Performances; Testing; To specify; Translating; Variant; Visual Perception; Work; base; behavior measurement; clinical application; clinical predictors; cognitive function; cognitive neuroscience; cognitive process; cognitive system; cognitive testing; computerized tools; discounting; experience; flexibility; functional outcomes; interest; neuromechanism; neurophysiology; novel; population based; precision medicine; psychologic; recruit; relating to nervous system; 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分离出多种特定的认知机制， 决定人类的行为。该正式建模框架生成定量参数估计， 可以作为病理生理学和精神病理学之间的桥梁。计算精神病学的一个主要目标 是将这些实验室工具转化为临床应用。需要克服两个关键障碍 克服首先，增强的有效性和灵敏度的计算指标需要建立相对 在精神病和非精神病人群中的标准行为表现指标。我们建议 要做到这一点，就必须解决一系列的认知和动机领域，这些领域与 精神病理学，包括工作记忆和情景记忆、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测量学， 指标，以便它们可以用作治疗开发、治疗评估、纵向和 基因研究。这些强有力的指标必须有足够的重测信度，而不受上限的限制 地板效果。我们建议使用一个开放、灵活和可扩展的框架来开发这些方法， 证明它们在实验室和大规模基于互联网的数据收集中提供了有效数据， 促进认知过程的“大数据”研究。为此，本项目将利用 认知神经科学任务的可靠性和严重精神疾病的临床应用（CNTRACS） 联盟，一个多地点的研究小组，具有快速认知工具开发的既定记录， 传播。目的1是建立用于测量认知功能的基于模型的参数， 在评估一系列常见心理缺陷方面， 疾病，并具有增强的能力，以预测临床症状和现实世界的功能，与样本 180例精神病和情感障碍患者（包括药物治疗和未药物治疗）和100例健康人 对照目的2是测量和优化心理测量学特性（测试重测信度，内部效度， 目标1中描述的计算参数的下限和不存在上限以及实践效果），在新的 180名精神病患者和100名健康对照者。目标3是建立可行性和可复制性 在实验室外采用基于模型的分析方法，评估RDoC的相关维度， 评估它们与精神病样经历、抑郁和快感缺乏的变化以及真实的- 在因特网上招募的10 000名社区样本中，目的4是验证关键模型 根据使用EEG记录获得的特征良好的神经生理学测量结果， 在执行任务时。这些目标的成功实现将通过提供 易于管理和可扩展的基于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，并形成认知和情感精神病理学的共同形式的基础。