Convergence and divergence of neurocognitive patterns in schizophrenia and depression.
Convergence and divergence of neurocognitive patterns in schizophrenia and depression.
复制标题
精神分裂症和抑郁症神经认知模式的趋同和分歧
DOI：
10.1016/j.schres.2017.06.004
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发表时间：
2018
影响因子：
4.5
通讯作者：
Li Tao
中科院分区：
文献类型：
--
作者：
Liang Sugai;Brown Matthew R G;Deng Wei;Wang Qiang;Ma Xiaohong;Li Mingli;Hu Xun;Juhas Michal;Li Xinmin;Greiner Russell;Greenshaw Andrew J;Li Tao
神经认知功能障碍常见于精神分裂症和重度抑郁障碍(MDD)。然而，目前尚不清楚已报道的神经认知异常是否能客观地识别一个人患有精神分裂症或MDD。方法目前的研究包括220名首发精神分裂症患者、110名MDD患者和240名人口统计学匹配的健康对照(HC)。所有参与者都进行了韦克斯勒成人智力量表的简短版本-中国修订；韦克斯勒记忆量表的即时和延迟逻辑记忆-中国修订；以及计算机化剑桥神经认知测试自动化电池的7项测试，以评估神经认知能力。三类AdaBoost基于树的集成算法被用来识别可能区分精神分裂症、抑郁症和HC类别的受试者的神经认知内表型。结果AdaBoost算法识别个体诊断类别的平均准确率为77.73%(精神分裂症80.81%，抑郁症53.49%，HC 86.21%)。平均ROC曲线下面积为0.92，其中精神分裂症为0.96，抑郁症为0.86，精神分裂症为0.92。等级聚类分析显示，对于MDD和精神分裂症，汇聚性改变了与转移、持续注意力、计划、工作记忆和视觉记忆相关的神经认知模式。MDD和精神分裂症的神经认知模式与运动速度、一般智力、知觉敏感度和反向学习有关。结论神经认知异常对个体是否患有精神分裂症、抑郁症或两者都不存在具有较高的预测准确率。此外，神经认知特征显示出作为区分精神分裂症和抑郁症的内在表型的希望。
BackgroundNeurocognitive impairments are frequently observed in schizophrenia and major depressive disorder (MDD). However, it remains unclear whether reported neurocognitive abnormalities could objectively identify an individual as having schizophrenia or MDD.MethodsThe current study included 220 first-episode patients with schizophrenia, 110 patients with MDD and 240 demographically matched healthy controls (HC). All participants performed the short version of the Wechsler Adult Intelligence Scale-Revised in China; the immediate and delayed logical memory of the Wechsler Memory Scale-Revised in China; and seven tests from the computerized Cambridge Neurocognitive Test Automated Battery to evaluate neurocognitive performance. The three-class AdaBoost tree-based ensemble algorithm was employed to identify neurocognitive endophenotypes that may distinguish between subjects in the categories of schizophrenia, depression and HC. Hierarchical cluster analysis was applied to further explore the neurocognitive patterns in each group.ResultsThe AdaBoost algorithm identified individual's diagnostic class with an average accuracy of 77.73% (80.81% for schizophrenia, 53.49% for depression and 86.21% for HC). The average area under ROC curve was 0.92 (0.96 in schizophrenia, 0.86 in depression and 0.92 in HC). Hierarchical cluster analysis revealed for MDD and schizophrenia, convergent altered neurocognition patterns related to shifting, sustained attention, planning, working memory and visual memory. Divergent neurocognition patterns for MDD and schizophrenia related to motor speed, general intelligence, perceptual sensitivity and reversal learning were identified.ConclusionsNeurocognitive abnormalities could predict whether the individual has schizophrenia, depression or neither with relatively high accuracy. Additionally, the neurocognitive features showed promise as endophenotypes for discriminating between schizophrenia and depression.