Moore Tyler M, Reise Steven P, Gur Raquel E, Hakonarson Hakon, Gur Ruben C
Brain Behavior Laboratory, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania.
Department of Psychology, University of California-Los Angeles.
Neuropsychology. 2015 Mar;29(2):235-46. doi: 10.1037/neu0000093. Epub 2014 Sep 1.
The Penn Computerized Neurocognitive Battery (CNB) was designed to measure performance accuracy and speed on specific neurobehavioral domains using tests that were previously validated with functional neuroimaging. The goal of the present study was to evaluate the neuropsychological theory used to construct the CNB by confirming the factor structure of the tests composing it.
In a large community sample (N = 9,138; age range 8-21), we performed a correlated-traits confirmatory factor analysis (CFA) and multiple exploratory factor analyses (EFAs) on the 12 CNB measures of Efficiency (which combine Accuracy and Speed). We then performed EFAs of the Accuracy and Speed measures separately. Finally, we performed a confirmatory bifactor analysis of the Efficiency scores. All analyses were performed with Mplus using maximum likelihood estimation.
RESULTS strongly support the a priori theory used to construct the CNB, showing that tests designed to measure executive, episodic memory, complex cognition, and social cognition aggregate their loadings within these domains. When Accuracy and Speed were analyzed separately, Accuracy produced 3 reliable factors: executive and complex cognition, episodic memory, and social cognition, while speed produced 2 factors: tests that require fast responses and those where each item requires deliberation. The statistical "Fit" of almost all models described above was acceptable (usually excellent).
Based on the analysis from these large-scale data, the CNB offers an effective means for measuring the integrity of intended neurocognitive domains in about 1 hour of testing and is thus suitable for large-scale clinical and genomic studies.
宾夕法尼亚计算机化神经认知测试组合(CNB)旨在通过使用先前经功能神经影像学验证的测试,来测量特定神经行为领域的表现准确性和速度。本研究的目的是通过确认构成CNB的测试的因子结构,来评估用于构建CNB的神经心理学理论。
在一个大型社区样本(N = 9138;年龄范围8 - 21岁)中,我们对CNB的12项效率测量指标(结合了准确性和速度)进行了相关特质验证性因子分析(CFA)和多次探索性因子分析(EFA)。然后我们分别对准确性和速度测量指标进行了EFA。最后,我们对效率得分进行了验证性双因子分析。所有分析均使用Mplus通过最大似然估计进行。
结果有力地支持了用于构建CNB的先验理论,表明旨在测量执行功能、情景记忆、复杂认知和社会认知的测试在这些领域内聚集了它们的载荷。当分别分析准确性和速度时,准确性产生了3个可靠因子:执行功能和复杂认知、情景记忆以及社会认知,而速度产生了2个因子:需要快速反应的测试和每个项目都需要深思熟虑的测试。上述几乎所有模型的统计“拟合度”都是可接受的(通常非常好)。
基于这些大规模数据的分析,CNB提供了一种在约1小时的测试中测量预期神经认知领域完整性的有效方法,因此适用于大规模临床和基因组研究。