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

如精准医学所设想的那样，在大规模研究中包括行为测量的努力是 受到时间和专业知识的限制纸笔测试目前主导临床评估 和神经心理学测试显然是不可行的。美国国立卫生研究院的测试系统包含许多计算机化的测试， 临床评估工具的可行性不同。宾夕法尼亚大学计算机神经认知 电池（CNB），其中包含14个测试，需要一个小时的管理。CNB已经过验证， 功能性神经影像学和全球多个规范和临床人群的寿命，以及 可以免费用于研究。临床评估工具通常用于特定的疾病， 不同的是他们对特定疾病症状的关注程度。我们已经制定了一个广泛的评估 工具（GOASSESS），目前需要大约一个小时的管理。这些仪器是由 优化和验证与经典的心理测试理论（CTT），是有效的，因为CTT允许。 然而，基因组研究需要更省时的工具，可以大规模应用。 基于项目反应理论（IRT）的新方法可以大大提高测试效率， 临床评估。项目反应理论通过对项目的估计，将重点从测试转移到组成测试的项目上 参数，如“难度”和“歧视”范围内的一般特质水平。IRT有助于缩短 在不影响数据质量的情况下，管理的长度，并且对于许多领域， 测试（CAT），进一步优化测试，以个人能力。我们建议开发和验证自适应 CNB和GOASSESS的版本，导致神经认知和临床筛选，使用机器 学习工具，将不断优化，变得更短，更精确，因为它是部署。该工具将 在公共领域中的可获得性。我们有项目级的信息来执行IRT分析 现有的数据，并使用这些信息来开发CAT实现，并生成自适应的项目池 试验.我们的具体目标是：1。使用Penn CNB和GOASSESS上的可用逐项数据，并添加 新的测试和项目，以生成项目池，用于在使用IRT-CAT验证测试时扩展范围， 其他方法。目前的项目池将扩大，以允许在CAT期间选择大量项目 并将临床项目添加到GOASSESS。新项目将通过众包进行校准。2. 制作涵盖主要RDoC的神经认知和临床评估组合的模块化CAT版本 领域和一系列精神症状。我们已经在一些CNB测试中实施了此程序 和临床规模，并将适用于剩余和新的测试类似的程序。3.验证 100例精神病谱系障碍（PS）和100例抑郁/焦虑障碍患者的CAT版本 (DA)健康对照组（HC）100例。我们将使用这个数据集来实现和测试数据挖掘算法 优化特定结果的预测。所有测试、算法和规范数据都将在工具箱中。

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.