Genderson Margo R, Dickinson Dwight, Diaz-Asper Catherine M, Egan Michael F, Weinberger Daniel R, Goldberg Terry E
Clinical Brain Disorders Branch, Genes, Cognition and Psychosis Program, National Institute of Mental Health, NIH, 10 Center Drive, CRC 7-5342, Bethesda, MD 20892, United States.
Schizophr Res. 2007 Aug;94(1-3):231-9. doi: 10.1016/j.schres.2006.12.031. Epub 2007 Jun 13.
Large batteries of neuropsychological tests are typically necessary to identify cognitive deficits in schizophrenia and routinely examine multiple cognitive processes, with many tests often yielding more than one measure of interest. This study investigates the feasibility of a partial solution to the problem of multiple comparisons: the use of factor analysis to reduce the number of phenotypic variables and to better understand the underlying cognitive architecture in schizophrenia. Using a principle components analysis followed by a varimax rotation, we identified factor structures for schizophrenic patients (n=99), their unaffected siblings (n=167), and control subjects (n=131), both separately and as a composite group. Exploratory factor analysis of the full sample yielded a 7-factor model that included verbal memory, working memory, visual memory, IQ/speed/fluency, executive function, attention and digit span. A confirmatory factor analysis (CFA) with maximum likelihood estimation revealed that the 7-factor model fit observed data from the three groups adequately. Since we identified a factor structure representative of all groups that reduced 24 original variables to 7 variables of interest, factor analysis was useful in reducing the complexity of large batteries of cognitive measures to more manageable numbers of phenotypic variables. Furthermore, these findings provide the first confirmation that cognitive structure is comparable in family members of schizophrenia patients, as well as in patients themselves and controls.
通常需要大量的神经心理学测试来识别精神分裂症患者的认知缺陷，并常规检查多种认知过程，许多测试往往会产生不止一种感兴趣的测量指标。本研究探讨了部分解决多重比较问题的可行性：使用因子分析来减少表型变量的数量，并更好地理解精神分裂症潜在的认知结构。通过主成分分析，随后进行方差最大化旋转，我们分别为精神分裂症患者（n = 99）、其未受影响的兄弟姐妹（n = 167）和对照组（n = 131）以及作为一个综合组确定了因子结构。对全样本进行探索性因子分析得到了一个7因子模型，包括言语记忆、工作记忆、视觉记忆、智商/速度/流畅性、执行功能、注意力和数字广度。采用最大似然估计的验证性因子分析（CFA）表明，7因子模型能充分拟合三组的观测数据。由于我们确定了一个代表所有组的因子结构，将24个原始变量减少到7个感兴趣的变量，因子分析有助于将大量认知测量的复杂性降低到更易于管理的表型变量数量。此外，这些发现首次证实，精神分裂症患者的家庭成员以及患者自身和对照组的认知结构具有可比性。
