Differentiation of schizophrenia patients from healthy subjects by mismatch negativity and neuropsychological tests.
Differentiation of schizophrenia patients from healthy subjects by mismatch negativity and neuropsychological tests.
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DOI：
10.1371/journal.pone.0034454
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发表时间：
2012
期刊：
影响因子：
3.7
通讯作者：
Hwu HG
中科院分区：
文献类型：
--
作者：
Lin YT;Liu CM;Chiu MJ;Liu CC;Chien YL;Hwang TJ;Jaw FS;Shan JC;Hsieh MH;Hwu HG
精神分裂症是一种具有多种表现的异质性疾病。目前和拟议的DSM-V诊断系统仍然是现象学的基础上，尽管事实上，已经确定了几个神经生物学和神经心理学标志物。多变量方法比单一标志物方法具有更好的诊断效用。本研究首先在中国汉族人群中复制精神分裂症的错配负波（MMN）缺陷，然后将MMN与几种神经心理学测量相结合来区分精神分裂症患者和健康受试者。120例精神分裂症患者和76名健康对照者被招募。每名受试者接受持续时间MMN、连续操作测验、威斯康星州卡片分类测验和韦氏成人智力量表第三版（WAIS-III）的检查。比较病例组和对照组的MMN，并研究重要的协变量。精神分裂症患者的MMN振幅显着降低，MMN随着年龄的增加，在患者和对照组。神经心理学指标与MMN无相关性。使用MMN和神经心理学测量作为预测因子的预测多变量logistic回归模型被开发。最终预测模型中保留了4个预测因子，包括电极FCz处的MMN和WAIS-III（算术、区组设计和性能IQ）的3个评分。该模型在区分患者和健康受试者方面表现良好（一致对百分比：90.5%）。MMN缺陷被发现在中国汉族精神分裂症患者。多变量方法结合了来自不同模态（如电生理学和神经心理学）的生物标志物，具有更好的诊断效用。
Schizophrenia is a heterogeneous disorder with diverse presentations. The current and the proposed DSM-V diagnostic system remains phenomenologically based, despite the fact that several neurobiological and neuropsychological markers have been identified. A multivariate approach has better diagnostic utility than a single marker method. In this study, the mismatch negativity (MMN) deficit of schizophrenia was first replicated in a Han Chinese population, and then the MMN was combined with several neuropsychological measurements to differentiate schizophrenia patients from healthy subjects. 120 schizophrenia patients and 76 healthy controls were recruited. Each subject received examinations for duration MMN, Continuous Performance Test, Wisconsin Card Sorting Test, and Wechsler Adult Intelligence Scale Third Edition (WAIS-III). The MMN was compared between cases and controls, and important covariates were investigated. Schizophrenia patients had significantly reduced MMN amplitudes, and MMN decreased with increasing age in both patient and control groups. None of the neuropsychological indices correlated with MMN. Predictive multivariate logistic regression models using the MMN and neuropsychological measurements as predictors were developed. Four predictors, including MMN at electrode FCz and three scores from the WAIS-III (Arithmetic, Block Design, and Performance IQ) were retained in the final predictive model. The model performed well in differentiating patients from healthy subjects (percentage of concordant pairs: 90.5%). MMN deficits were found in Han Chinese schizophrenia patients. The multivariate approach combining biomarkers from different modalities such as electrophysiology and neuropsychology had a better diagnostic utility.
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