An Adaptive Testing Platform for Optimizing RDoC Experimental Cognitive Measures

用于优化 RDoC 实验认知测量的自适应测试平台

• 批准号: 10412095
• 负责人: Michael L Thomas
• 金额: $ 51.14万
• 依托单位: COLORADO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-14 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10412095
• 关键词: Address; Back; Biological Markers; Brain; Brain imaging; Brain region; Clinical; Clinical Research; Cognitive; Communities; Companions; Control Groups; Data; Delayed Memory; Development; Floor; Individual; Intervention Studies; Lead; Learning; Measurement; Measures; Mediator of activation protein; Memory; Mental Health; Mentored Patient-Oriented Research Career Development Award; Methodology; Methods; Modernization; National Institute of Mental Health; Neurocognitive; Neurons; Neurosciences Research; Outcome; Outcome Measure; Participant; Patients; Performance; Population; Process; Property; Psychometrics; Psychoses; Pythons; Research; Research Domain Criteria; Rewards; Sampling; Schizophrenia; Short-Term Memory; Standardization; Stimulus; System; Task Performances; Technology; Testing; Time; Translational Research; Ursidae Family; Variant; Work; brain dysfunction; cognitive ability; cognitive control; cognitive function; cognitive neuroscience; cognitive process; cognitive task; cognitive testing; computerized; design; experimental study; improved; neuroimaging; neuronal patterning; neurophysiology; novel; open source; performance tests; psychologic; relating to nervous system; response; theories; tool; translational study

Project Summary As part of the Research Domain Criteria (RDoC) initiative, the NIMH seeks to improve measures of neuronal and psychological targets for use in intervention research. RDoC tools must precisely measure cognitive and neuronal systems to produce reliable findings. Unfortunately, many RDoC tasks have not been psychometrically evaluated, nor refined, and are liable to confounds that can lead to inaccurate claims of differential deficit, weakened effect size, and contradictory brain imaging findings. As highlighted by FOA PAR- 18-930, there is a critical need for modern psychometric methods and tools designed to support cognitive and clinical neuroscience research. This project responds to this FOA by evaluating and refining a methodology designed to administer statistically robust variants of RDoC tasks, especially in the context of brain imaging. We have created a quantitative methodology designed to administer computerized adaptive tests (CATs) in the context of cognitive and clinical neuroscience research. CATs manipulate stimulus properties in real time in order to improve measurement precision, avoid ceiling and floor effects, and maximize effect size, even for individuals and groups with highly discrepant levels of cognitive functioning. CATs also perform psychometric adjustments to cognitive tasks so that brain functioning abnormalities can be interpreted independent of performance deficits. In pilot work, we have used this approach with an RDoC working memory task, the N- back, to show that the methodology improves the reliability of both cognitive and brain imaging data. We will evaluate the generalizability and impact of adaptive testing, beyond the N-back task, for use in translational and experimental testing. Patients with schizophrenia and controls will be administered both adaptive and non- adaptive versions of four RDoC paradigms used to assess working memory: delayed match-to-sample, Sternberg, self-ordered pointing, and N-back. Additionally, participants will be administered the 5-Choice Continuous Performance Task and Probabilistic Learning Task, translational measures of control and learning respectively. Both groups will undergo functional neuroimaging and respond to adaptive versions of these tasks. The specific aims are to: (1) Determine whether adaptive testing improves the precision and effect size estimates of performance differences produced by RDoC tasks; and (2) Determine whether adaptive testing improves the reliability and effect size estimates of brain activation differences produced by RDoC tasks. While these aims are designed to evaluate a methodology, and to address critical concerns related to the use of RDoC working memory tasks as neural probes, mediators, and outcomes in psychosis research, results also have broad implications across populations, brain regions and networks, and cognitive domains. By addressing concerns of poor reliability, weak effect size, and brain activation confounds, this project will show that adaptive testing broadly improves cognitive neuroscience tasks. To facilitate rapid deployment of adaptive RDoC tasks, we will develop freely available versions of the paradigms used and a companion R package ‘catCog’.

项目摘要 作为研究领域标准（RDOC）倡议的一部分，NIMH试图改善神经元的度量 和用于干预研究的心理目标。 RDOC工具必须精确衡量认知和 神经元系统产生可靠的发现。不幸的是，许多RDOC任务没有 经过心理评估，或精制的评估，并有可能混淆，可能导致不准确的主张 差异防御，效应大小减弱以及矛盾的大脑成像发现。正如foa par的强调 18-930，对现代心理测量方法和工具的迫切需要，旨在支持认知和 临床神经科学研究。该项目通过评估和完善方法来回应该FOA 旨在管理RDOC任务的统计稳定变体，尤其是在大脑成像的背景下。 我们创建了一种定量方法，旨在管理计算机化自适应测试（CAT） 认知和临床神经科学研究的背景。猫实时操纵刺激特性 为了提高测量精度，避免天花板和地板效应，并最大化效果大小，即使 具有高度差异认知功能水平的个人和群体。猫还执行心理测量 对认知任务的调整，以便可以独立于解释大脑功能异常 性能定义。在飞行员工作中，我们已经使用了RDOC工作记忆任务，N-使用了这种方法 后面，表明该方法可以提高认知和脑成像数据的可靠性。我们将 评估自适应测试的普遍性和影响，超出N-BACK任务，用于翻译 和实验测试。精神分裂症和对照组的患者将受到适应性和非适应性的治疗 用于评估工作记忆的四个RDOC范式的自适应版本：延迟匹配到样本， Sternberg，自我排序的指向和n-back。此外，将对参与者进行5个挑战 持续的绩效任务和概率学习任务，翻译的控制和学习度量 两组都将经历功能性神经影像学并响应其中的自适应版本 任务。具体目的是：（1）确定自适应测试是否提高了精度和效果大小 RDOC任务产生的性能差异的估计； （2）确定是否进行自适应测试 改善RDOC任务产生的大脑激活差异的可靠性和效果大小的估计值。尽管 这些目标旨在评估一种方法，并解决与使用有关的关键问题 RDOC工作记忆任务是神经问题，介体和精神病研究结果，结果也 在人群，大脑区域和网络以及认知领域之间具有广泛的影响。通过解决 对可靠性差，效果较弱和大脑激活混淆的关注，该项目将表明适应性 测试广泛改善了认知神经科学任务。为了促进自适应RDOC任务的快速部署， 我们将开发免费的使用范式的免费版本和伴随的R软件包“ catcog”。