Mental Health Centre and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, China; Huaxi Brain Research Centre, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Department of Psychiatry, University of Alberta, Edmonton, AB, Canada.
Schizophr Res. 2018 Feb;192:327-334. doi: 10.1016/j.schres.2017.06.004. Epub 2017 Jun 23.
Neurocognitive 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.
The 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.
The 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.
Neurocognitive 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.
精神分裂症和重度抑郁症（MDD）常伴有神经认知损伤。然而，目前尚不清楚所报道的神经认知异常是否能客观地区分出个体患有精神分裂症或 MDD。
本研究纳入了 220 例首发精神分裂症患者、110 例 MDD 患者和 240 名年龄、性别相匹配的健康对照者（HC）。所有参与者均在中国完成韦氏成人智力量表修订版的简短版、中国韦氏记忆量表修订版的即时和延迟逻辑记忆测试以及剑桥神经认知测试自动电池的七个测试，以评估神经认知表现。采用基于 AdaBoost 树的集成算法识别可能区分精神分裂症、抑郁和 HC 三个类别受试者的神经认知内表型。采用层次聚类分析进一步探索每组的神经认知模式。
AdaBoost 算法识别个体诊断类别的平均准确率为 77.73%（精神分裂症为 80.81%，抑郁症为 53.49%，HC 为 86.21%）。ROC 曲线下面积的平均值为 0.92（精神分裂症为 0.96，抑郁症为 0.86，HC 为 0.92）。层次聚类分析显示，对于 MDD 和精神分裂症，与转换、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存在汇聚性改变。MDD 和精神分裂症的发散性神经认知模式与运动速度、一般智力、知觉敏感性和反转学习相关。
神经认知异常可以以较高的准确率预测个体是否患有精神分裂症、抑郁症或两者都没有。此外，神经认知特征有望成为区分精神分裂症和抑郁症的内表型。
