Gong Liang, Xu Ronghua, Yang Dan, Wang Jian, Ding Xin, Zhang Bei, Zhang Xingping, Hu Zhengjun, Xi Chunhua
Department of Neurology, Chengdu Second People's Hospital, Chengdu, China.
Department of General Practice, Chengdu Second People's Hospital, Chengdu, China.
Front Psychiatry. 2022 Jul 6;13:907978. doi: 10.3389/fpsyt.2022.907978. eCollection 2022.
Depression is a common comorbid symptom in patients with chronic insomnia disorder (CID). Previous neuroimaging studies found that the orbital frontal cortex (OFC) might be the core brain region linking insomnia and depression. Here, we used a machine learning approach to differentiate CID patients with depressive symptoms from CID patients without depressive symptoms based on OFC functional connectivity. Seventy patients with CID were recruited and subdivided into CID with high depressive symptom (CID-HD) and low depressive symptom (CID-LD) groups. The OFC functional connectivity (FC) network was constructed using the altered structure of the OFC region as a seed. A linear kernel SVM-based machine learning approach was carried out to classify the CID-HD and CID-LD groups based on OFC FC features. The predict model was further verified in a new cohort of CID group ( = 68). The classification model based on the OFC FC pattern showed a total accuracy of 76.92% ( = 0.0009). The area under the receiver operating characteristic curve of the classification model was 0.84. The OFC functional connectivity with reward network, salience network and default mode network contributed the highest weights to the prediction model. These results were further validated in an independent CID group with high and low depressive symptom (accuracy = 67.9%). These findings provide a potential biomarker for early diagnosis and intervention in CID patients comorbid with depression based on an OFC FC-based machine learning approach.
抑郁症是慢性失眠障碍（CID）患者常见的共病症状。以往的神经影像学研究发现，眶额皮质（OFC）可能是连接失眠和抑郁症的核心脑区。在此，我们使用机器学习方法，基于OFC功能连接性，将有抑郁症状的CID患者与无抑郁症状的CID患者区分开来。招募了70例CID患者，并将其细分为高抑郁症状CID（CID-HD）组和低抑郁症状CID（CID-LD）组。以OFC区域的改变结构为种子构建OFC功能连接（FC）网络。采用基于线性核支持向量机的机器学习方法，根据OFC FC特征对CID-HD组和CID-LD组进行分类。在一个新的CID组队列（n = 68）中进一步验证了预测模型。基于OFC FC模式的分类模型总准确率为76.92%（p = 0.0009）。分类模型的受试者工作特征曲线下面积为0.84。OFC与奖赏网络、突显网络和默认模式网络的功能连接对预测模型的权重贡献最大。这些结果在一个独立的高、低抑郁症状CID组中得到进一步验证（准确率 = 67.9%）。这些发现基于基于OFC FC的机器学习方法，为合并抑郁症的CID患者的早期诊断和干预提供了一种潜在的生物标志物。
