Mental Health Centre, West China Hospital, Sichuan University, Chengdu, 610041, China.
Huaxi Brain Research Centre, West China Hospital, Sichuan University, Chengdu, 610041, China.
Neurosci Bull. 2018 Apr;34(2):312-320. doi: 10.1007/s12264-017-0190-6. Epub 2017 Nov 2.
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 FES vs HC, 67.07% for MDD vs HC, and 59.48% for FES vs MDD. 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.
神经认知缺陷在精神分裂症和重度抑郁症（MDD）患者中经常观察到。认知特征之间的关系可以通过基于认知特征的神经认知图来表示，这些图建模为高斯马尔可夫随机场。然而，目前尚不清楚是否可以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，我们纳入了 215 名首发精神分裂症（FES）患者、125 名 MDD 患者和 237 名人口统计学匹配的健康对照者（HCs）。所有参与者的认知表现均使用一系列神经认知测试进行评估。图形 LASSO 模型使用一对一方案进行训练，以学习每个组的神经认知特征的条件独立结构。使用最高似然度将保留数据集的参与者分类为不同的组。从图形模型转换出偏相关矩阵，以进一步探索每个组的神经认知图。分类方法确定了个体的诊断类别，对于 FES 与 HC、MDD 与 HC 和 FES 与 MDD 的平均准确率分别为 73.41%、67.07%和 59.48%。FES 和 MDD 的神经认知图都比 HC 具有更多的连接和更高的节点中心度。FES 的神经认知图比 MDD 的神经认知图不那么稀疏，并且具有更多的连接。因此，基于认知特征的神经认知图有望描述可能区分精神分裂症和抑郁症的表型。
