3/5 Cognitive Neurocomputational Task Reliability & Clinical Applications Consortium

3/5 认知神经计算任务可靠性

基本信息

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
项目类别：
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

项目摘要

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，这些机制该正式建模框架生成定量参数估计，从而确定人类行为。可以充当病理生理学和精神病理学之间的桥梁，这是计算精神病学的主要目标。就是要转化这些实验室工具，以便它们可以在临床中使用，需要克服两个关键障碍。首先，需要相对确定计算指标的增强有效性和敏感性。我们建议制定关键精神病和非精神病人群的标准行为表现指标。通过解决与以下问题密切相关的一系列认知和动机领域来做到这一点精神病理学，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、纵向和治疗的工具这些强大的指标必须具有足够的测试可靠性，并且不受上限的限制。我们建议使用开放、灵活和可扩展的框架来开发这些方法。证明他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的数据，为此，当前项目将利用专业知识，促进认知过程的“大数据”研究。认知神经科学任务可靠性和严重精神疾病的临床应用（CNTRACS）联盟，一个多地点研究小组，在快速认知工具开发方面拥有良好的记录目标 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比标准行为方法更敏感疾病，并且具有增强的预测临床症状和现实世界功能的能力，通过样本 180 名精神和情感障碍患者（药物治疗和未药物治疗）和 100 名健康患者目标 2 是测量和优化心理测量特性（测试重测可靠性、内部有效性、目标 1 中描述的计算参数的下限和缺乏上限和实践效果），在一个新的目标 3 是建立 180 名精神病患者和 100 名健康对照者的样本，以建立可行性和可复制性。实验室外基于模型的分析方法，用于评估感兴趣的 RDoC 维度，并评估他们与精神病样经历、抑郁和快感缺失以及真实体验变化的关系目标 4 是通过互联网招募 10,000 名社区样本来验证关键模型。基于使用脑电图记录获得的充分表征的神经生理学测量的参数成功完成这些目标将通过提供显着推进该领域。易于管理且可扩展的基于网络的工具，用于评估基础关键神经系统的完整性正常的认知和动机，构成认知和情感精神病理学常见形式的基础。