1/5-Cognitive Neurocomputational Task Reliability & Clinical Applications Consortium

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。这个正式的建模框架生成量化的参数估计 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 就是翻译这些实验室工具，这样它们就可以在临床上使用。有两个关键障碍需要 克服困难。首先，需要建立相对的计算指标的增强的有效性和敏感性 在关键的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，可以解决一系列认知和动机领域的问题，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 基于决策的决策。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。这些强大的指标必须具有足够的重测可靠性，并且不受上限的限制 和地板效果。我们建议使用开放、灵活和可扩展的框架来开发这些方法，并 证明它们在实验室和基于互联网的大规模数据收集中都提供了有效的数据， 促进认知过程的“大数据”研究。为此，目前的项目将利用专业知识 认知神经科学任务信度及在严重精神疾病中的临床应用 联盟，一个多地点研究小组，拥有快速认知工具开发和 传播。目标1是建立基于模型的认知功能测量参数 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，并具有更强的预测临床症状和现实世界功能的能力 在180名精神病和情感障碍患者(包括药物治疗和非药物治疗)和100名健康人中 控制。目标2是测量和优化心理测量学特性(重测信度、内部效度、 目标1中描述的计算参数的下限和无天花板以及实践影响)，在新的 样本包括180名精神疾病患者和100名健康对照。目标3是确定其可行性和可复制性 实验室外评估感兴趣的RDoC维度的基于模型的分析方法，以及 评估它们与类似精神病的经历、抑郁和快感缺乏的变化的关系，以及真实的- 在通过互联网招募的10,000个社区样本中，世界正在发挥作用。目标4是验证关键模型 基于参数与使用脑电记录获得的具有良好特征的神经生理测量的对比 在任务执行期间。这些目标的成功实现将极大地推动该领域的发展 易于管理和可扩展的基于Web的工具，用于评估作为基础的关键神经系统的完整性 正常的认知和动机，并构成认知和情感精神病的常见形式的基础。

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.