Li Qinghe, Dong Fanghui, Gai Qun, Che Kaili, Ma Heng, Zhao Feng, Chu Tongpeng, Mao Ning, Wang Peiyuan
Department of Radiology, Yantai Affiliated Hospital of Binzhou Medical University, Yantai, Shandong, People's Republic of China.
School of Medical Imaging, Binzhou Medical University, Yantai, Shandong, People's Republic of China.
J Magn Reson Imaging. 2023 Nov;58(5):1420-1430. doi: 10.1002/jmri.28650. Epub 2023 Feb 16.
Previous studies have found qualitative structural and functional brain changes in major depressive disorder (MDD) patients. However, most studies ignored the complementarity of multisequence MRI neuroimaging features and cannot determine accurate biomarkers.
To evaluate machine-learning models combined with multisequence MRI neuroimaging features to diagnose patients with MDD.
Prospective.
A training cohort including 111 patients and 90 healthy controls (HCs) and a test cohort including 28 patients and 22 HCs.
FIELD STRENGTH/SEQUENCE: A 3.0 T/T1-weighted imaging, resting-state functional MRI with echo-planar sequence, and single-shot echo-planar diffusion tensor imaging.
Recruitment and integration were used to reflect the dynamic changes of functional networks, while gray matter volume and fractional anisotropy were used to reflect the changes in the morphological and anatomical network. We then fused features with significant differences in functional, morphological, and anatomical networks to evaluate a random forest (RF) classifier to diagnose patients with MDD. Furthermore, a support vector machine (SVM) classifier was used to verify the stability of neuroimaging features. Linear regression analyses were conducted to investigate the relationships among multisequence neuroimaging features and the suicide risk of patients.
The comparison of functional network attributes between patients and controls by two-sample t-test. Network-based statistical analysis was used to identify structural and anatomical connectivity changes between MDD and HCs. The performance of the model was evaluated by receiver operating characteristic (ROC) curves.
The performance of the RF model integrating multisequence neuroimaging features in the diagnosis of depression was significantly improved, with an AUC of 93.6%. In addition, we found that multisequence neuroimaging features could accurately predict suicide risk in patients with MDD (r = 0.691).
The RF model fusing functional, morphological, and anatomical network features performed well in diagnosing patients with MDD and provided important insights into the pathological mechanisms of MDD.
Stage 2.
先前的研究发现重度抑郁症（MDD）患者存在定性的大脑结构和功能变化。然而，大多数研究忽略了多序列MRI神经影像特征的互补性，无法确定准确的生物标志物。
评估结合多序列MRI神经影像特征的机器学习模型，以诊断MDD患者。
前瞻性研究。
一个训练队列，包括111例患者和90名健康对照（HCs）；一个测试队列，包括28例患者和22名HCs。
场强/序列：3.0 T/T1加权成像、回波平面序列静息态功能MRI和单次激发回波平面扩散张量成像。
采用募集和整合来反映功能网络的动态变化，而灰质体积和各向异性分数则用于反映形态和解剖网络的变化。然后，我们融合功能、形态和解剖网络中具有显著差异的特征，以评估随机森林（RF）分类器诊断MDD患者的能力。此外，使用支持向量机（SVM）分类器验证神经影像特征的稳定性。进行线性回归分析，以研究多序列神经影像特征与患者自杀风险之间的关系。
采用两样本t检验比较患者和对照之间的功能网络属性。基于网络的统计分析用于识别MDD和HCs之间的结构和解剖连接变化。通过受试者工作特征（ROC）曲线评估模型的性能。
整合多序列神经影像特征的RF模型在抑郁症诊断中的性能显著提高，曲线下面积（AUC）为93.6%。此外，我们发现多序列神经影像特征可以准确预测MDD患者的自杀风险（r = 0.691）。
融合功能、形态和解剖网络特征的RF模型在诊断MDD患者方面表现良好，并为MDD的病理机制提供了重要见解。
1级。
2级。
