Creating an adaptive screening tool for detecting neurocognitive deficits and psychopathology across the lifespan

创建自适应筛查工具来检测整个生命周期的神经认知缺陷和精神病理学

• 批准号: 9920211
• 负责人: Ruben C. Gur
• 金额: $ 70.95万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-05-01 至 2023-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9920211
• 关键词: Algorithms; Anxiety; Anxiety Disorders; Assessment tool; Behavior; Biological Markers; Calibration; Characteristics; Classification; Clinical; Clinical Assessment Tool; Clinical assessments; Cognitive; Collection; Complex; Computers; Data; Data Compromising; Data Set; Databases; Diagnosis; Diagnostic; Dimensions; Discrimination; Disease; Environmental Risk Factor; Feedback; Female; Genomics; Hour; Individual; Internet; Internet of Things; Intervention Studies; Length; Link; Longevity; Machine Learning; Measures; Medicine; Mental Depression; Mental disorders; Methods; Molecular Genetics; Moods; Neurocognitive; Neurocognitive Deficit; Neuropsychological Tests; Neurosciences; Outcome; Paper; Pathway interactions; Performance; Phenotype; Population; Precision Medicine Initiative; Preparation; Preventive Intervention; Procedures; Psychiatry; Psychometrics; Psychopathology; Psychotic Disorders; Public Domains; Research; Research Domain Criteria; Sampling; Screening procedure; Sensitivity and Specificity; Severities; Speed; Structure; Symptoms; Tablets; Testing; Time; Translational Research; United States National Institutes of Health; Validation; base; behavior measurement; cognitive performance; computerized; crowdsourcing; data mining; data quality; digital; genomic variation; improved; individualized prevention; instrument; male; mobile computing; neuroimaging; neuropsychiatric disorder; novel; novel strategies; open source; precision medicine; protective factors; psychiatric symptom; response; symptom cluster; theories; tool; trait; validation studies

Efforts to include behavioral measures in large-scale studies as envisioned by precision medicine are hampered by the time and expertise required. Paper-and-pencil tests currently dominating clinical assessment and neuropsychological testing are plainly unfeasible. The NIH Toolbox contains many computerized tests and clinical assessment tools varying in feasibility. Unique in the Toolbox is the Penn Computerized Neurocognitive Battery (CNB), which contains 14 tests that take one hour to administer. CNB has been validated with functional neuroimaging and in multiple normative and clinical populations across the lifespan worldwide, and is freely available for research. Clinical assessment tools are usually devoted to specific disorders, and scales vary in their concentration on symptoms that are disorder specific. We have developed a broad assessment tool (GOASSESS), which currently takes about one hour to administer. These instruments were constructed, optimized and validated with classical psychometric test theory (CTT), and are efficient as CTT allows. However, genomic studies require even more time-efficient tools that can be applied massively. Novel approaches, based on item response theory (IRT) can vastly enhance efficiency of testing and clinical assessment. IRT shifts the emphasis from the test to the items composing it by estimating item parameters such as “difficulty” and “discrimination” within ranges of general trait levels. IRT helps shorten the length of administration without compromising data quality, and for many domains leads to computer adaptive testing (CAT) that further optimizes tests to individual abilities. We propose to develop and validate adaptive versions of the CNB and GOASSESS, resulting in a neurocognitive and clinical screener that, using machine learning tools, will be continually optimized, becoming shorter and more precise as it is deployed. The tool will be in the Toolbox available in the public domain. We have item-level information to perform IRT analyses on existing data and use this information to develop CAT implementations and generate item pools for adaptive testing. Our Specific Aims are: 1. Use available itemwise data on the Penn CNB and the GOASSESS and add new tests and items to generate item pools for extending scope while abbreviating tests using IRT-CAT and other methods. The current item pool will be augmented to allow large selection of items during CAT administration and add clinical items to GOASSESS. New items will be calibrated through crowdsourcing. 2. Produce a modular CAT version of a neurocognitive and clinical assessment battery that covers major RDoC domains and a full range of psychiatric symptoms. We have implemented this procedure on some CNB tests and clinical scales and will apply similar procedures to remaining and new tests as appropriate. 3. Validate the CAT version in 100 individuals with psychosis spectrum disorders (PS), 100 with depression/anxiety disorders (DA), and 100 healthy controls (HC). We will use this dataset to implement and test data mining algorithms that optimize prediction of specific outcomes. All tests, algorithms and normative data will be in the toolbox.

正如精准医学所设想的那样，将行为测量纳入大规模研究的努力是 目前主导临床评估的纸笔测试阻碍了所需的时间和专业知识。 NIH 工具箱包含许多计算机化测试和神经心理学测试显然是不可行的。 工具箱中独特的临床评估工具的可行性各不相同。 电池 (CNB) 包含 14 项测试，需要 1 小时才能完成 CNB 的验证。 功能性神经影像学以及全球多个正常和临床人群的生命周期，以及 可免费用于研究的临床评估工具通常专门用于特定的疾病和量表。 他们对特定疾病症状的关注程度各不相同，我们已经制定了广泛的评估。 工具（GOASSESS），目前构建这些工具需要大约一小时， 使用经典心理测试理论 (CTT) 进行优化和验证，并且在 CTT 允许的范围内有效。 然而，基因组研究需要更省时、可以大规模应用的工具。 基于项目反应理论（IRT）的新方法可以极大地提高测试和测试的效率 IRT 将重点从测试转移到通过估计项目组成测试的项目。 一般特质水平范围内的“难度”和“歧视”等参数有助于缩短 IRT。 管理时间长而不影响数据质量，并且对于许多领域来说导致计算机自适应 我们建议开发和验证适应性测试（CAT），进一步优化个人能力测试。 CNB 和 GOASSESS 的版本，产生了一种神经认知和临床筛查仪，使用机器 学习工具将随着部署不断优化，变得更短、更精确。 位于公共领域可用的工具箱中，我们拥有可用于执行 IRT 分析的项目级信息。 现有数据并使用此信息来开发 CAT 实施并生成用于自适应的项目池 我们的具体目标是： 1. 使用 Penn CNB 和 GOASSESS 上可用的逐项数据并添加 新的测试和项目，用于生成项目池以扩展范围，同时使用 IRT-CAT 和缩写测试 其他方法将扩大当前的项目池，以允许在 CAT 期间进行大量项目选择。 管理并将临床项目添加到 GOASSESS 新项目将通过众包进行校准 2。 生成涵盖主要 RDoC 的神经认知和临床评估电池的模块化 CAT 版本 我们已在一些 CNB 测试中实施了此程序。 和临床量表，并将酌情对剩余的和新的测试应用类似的程序。 100 名精神病谱系障碍 (PS) 患者和 100 名抑郁/焦虑症患者的 CAT 版本 (DA) 和 100 个健康对照 (HC) 我们将使用该数据集来实现和测试数据挖掘算法。 所有测试、算法和规范数据都将位于工具箱中。