Neurocognitive Graphs of First-Episode Schizophrenia and Major Depression Based on Cognitive Features
Neurocognitive Graphs of First-Episode Schizophrenia and Major Depression Based on Cognitive Features
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基于认知特征的首发精神分裂症和重度抑郁症的神经认知图
DOI：
10.1007/s12264-017-0190-6
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发表时间：
2018-04-01
影响因子：
5.6
通讯作者：
Li, Tao
中科院分区：
文献类型：
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作者：
Liang, Sugai;Vega, Roberto;Li, Tao
神经认知缺陷在精神分裂症和重度抑郁症（MDD）患者中经常被观察到。认知特征之间的关系可以用基于认知特征的神经认知图来表示，建模为高斯马尔可夫随机场。然而，目前尚不清楚是否有可能使用这种神经认知图方法来区分与精神分裂症和抑郁症鉴别诊断相关的表型模式。在这项研究中，我们招募了215名首发精神分裂症患者（FES）， 125名重度抑郁症患者和237名人口统计学匹配的健康对照（hc）。所有参与者的认知表现都是通过一系列神经认知测试来评估的。采用一对一场景训练图形LASSO模型，学习各组神经认知特征的条件独立结构。拒绝数据集中的参与者被划分为可能性最高的不同组。从图形模型转换成偏相关矩阵，进一步探索每组的神经认知图。FESvsHC的平均准确率为73.41%，MDDvsHC的平均准确率为67.07%，FESvsMDD的平均准确率为59.48%。FES和MDD的神经认知图均比HC有更多的连接和更高的节点中心性。与MDD相比，FES的神经认知图更稀疏，连接更多。因此，基于认知特征的神经认知图有望用于描述可能区分精神分裂症和抑郁症的内表型。
Neurocognitive deficits are frequently observed in patients with schizophrenia and major depressive disorder (MDD). The relations between cognitive features may be represented by neurocognitive graphs based on cognitive features, modeled as Gaussian Markov random fields. However, it is unclear whether it is possible to differentiate between phenotypic patterns associated with the differential diagnosis of schizophrenia and depression using this neurocognitive graph approach. In this study, we enrolled 215 first-episode patients with schizophrenia (FES), 125 with MDD, and 237 demographically-matched healthy controls (HCs). The cognitive performance of all participants was evaluated using a battery of neurocognitive tests. The graphical LASSO model was trained with a one-vs-one scenario to learn the conditional independent structure of neurocognitive features of each group. Participants in the holdout dataset were classified into different groups with the highest likelihood. A partial correlation matrix was transformed from the graphical model to further explore the neurocognitive graph for each group. The classification approach identified the diagnostic class for individuals with an average accuracy of 73.41% for FESvsHC, 67.07% for MDDvsHC, and 59.48% for FESvsMDD. Both of the neurocognitive graphs for FES and MDD had more connections and higher node centrality than those for HC. The neurocognitive graph for FES was less sparse and had more connections than that for MDD. Thus, neurocognitive graphs based on cognitive features are promising for describing endophenotypes that may discriminate schizophrenia from depression.