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基于认知神经科学的计算机化电池,用于高效测量个体差异:标准化和初步结构验证。

A cognitive neuroscience-based computerized battery for efficient measurement of individual differences: standardization and initial construct validation.

机构信息

Brain Behavior Laboratory, Section of Neuropsychiatry, Department of Psychiatry, Philadelphia, PA 19104-4283, United States.

出版信息

J Neurosci Methods. 2010 Mar 30;187(2):254-62. doi: 10.1016/j.jneumeth.2009.11.017. Epub 2009 Nov 27.

Abstract

There is increased need for efficient computerized methods to collect reliable data on a range of cognitive domains that can be linked to specific brain systems. Such need arises in functional neuroimaging studies, where individual differences in cognitive performance are variables of interest or serve as confounds. In genetic studies of complex behavior, which require particularly large samples, such trait measures can serve as endophenotypes. Traditional neuropsychological tests, based on clinical pathological correlations, are protracted, require extensive training in administration and scoring, and leave lengthy paper trails (double-entry for analysis). We present a computerized battery that takes an average of 1h and provides measures of accuracy and speed on 9 neurocognitive domains. They are cognitive neuroscience-based in that they have been linked experimentally to specific brain systems with functional neuroimaging studies. We describe the process of translating tasks used in functional neuroimaging to tests for assessing individual differences. Data are presented on each test with samples ranging from 139 (81 female) to 536 (311 female) of carefully screened healthy individuals ranging in age from 18 to 84. Item consistency was established with acceptable to high Cronbach alpha coefficients. Inter-item correlations were moderate to high within domain and low to nil across domains, indicating construct validity. Initial criterion validity was demonstrated by sensitivity to sex differences and the effects of age, education and parental education. These results encourage the use of this battery in studies needing an efficient assessment of major neurocognitive domains such as multi-site genetic studies and clinical trials.

摘要

人们越来越需要高效的计算机化方法来收集一系列认知领域的可靠数据,这些数据可以与特定的大脑系统相关联。这种需求出现在功能神经影像学研究中,在这些研究中,认知表现的个体差异是感兴趣的变量,或者是混杂因素。在需要特别大样本的复杂行为的遗传研究中,这种特征测量可以作为内表型。基于临床病理相关性的传统神经心理学测试冗长,需要广泛的管理和评分培训,并且留下冗长的纸质记录(分析时的双重录入)。我们提出了一种计算机化的电池,平均需要 1 小时,并提供 9 个神经认知领域的准确性和速度测量。它们是基于认知神经科学的,因为它们已经通过功能神经影像学研究与特定的大脑系统进行了实验联系。我们描述了将功能神经影像学中使用的任务转化为评估个体差异的测试的过程。我们提供了每个测试的数据,样本范围从 139 名(81 名女性)到 536 名(311 名女性)精心筛选的健康个体,年龄从 18 岁到 84 岁不等。项目一致性通过可接受的至高 Cronbach alpha 系数来确定。域内项目之间的相关性是中等至高的,跨域的相关性是低至无的,这表明了结构有效性。通过对性别差异和年龄、教育和父母教育的影响的敏感性,初步证明了标准有效性。这些结果鼓励在需要对主要神经认知领域进行高效评估的研究中使用这种电池,例如多地点遗传研究和临床试验。

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