Zhou Cong, Cheng Yuqi, Ping Liangliang, Xu Jian, Shen Zonglin, Jiang Linling, Shi Li, Yang Shuran, Lu Yi, Xu Xiufeng
Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China.
Postgraduate College, Kunming Medical University, Kunming, China.
Front Psychiatry. 2018 Oct 23;9:524. doi: 10.3389/fpsyt.2018.00524. eCollection 2018.
Magnetic resonance imaging (MRI) methods have been used to detect cerebral anatomical distinction between obsessive-compulsive disorder (OCD) patients and healthy controls (HC). Machine learning approach allows for the possibility of discriminating patients on the individual level. However, few studies have used this automatic technique based on multiple modalities to identify potential biomarkers of OCD. High-resolution structural MRI and diffusion tensor imaging (DTI) data were acquired from 48 OCD patients and 45 well-matched HC. Gray matter volume (GMV), white matter volume (WMV), fractional anisotropy (FA), and mean diffusivity (MD) were extracted as four features were examined using support vector machine (SVM). Ten brain regions of each feature contributed most to the classification were also estimated. Using different algorithms, the classifier achieved accuracies of 72.08, 61.29, 80.65, and 77.42% for GMV, WMV, FA, and MD, respectively. The most discriminative gray matter regions that contributed to the classification were mainly distributed in the orbitofronto-striatal "affective" circuit, the dorsolateral, prefronto-striatal "executive" circuit and the cerebellum. For WMV feature and the two feature sets of DTI, the shared regions contributed the most to the discrimination mainly included the uncinate fasciculus, the cingulum in the hippocampus, corticospinal tract, as well as cerebellar peduncle. Based on whole-brain volumetry and DTI images, SVM algorithm revealed high accuracies for distinguishing OCD patients from healthy subjects at the individual level. Computer-aided method is capable of providing accurate diagnostic information and might provide a new perspective for clinical diagnosis of OCD.
磁共振成像（MRI）方法已被用于检测强迫症（OCD）患者与健康对照（HC）之间的脑解剖差异。机器学习方法使得在个体水平上区分患者成为可能。然而，很少有研究使用这种基于多种模态的自动技术来识别强迫症的潜在生物标志物。从48名强迫症患者和45名匹配良好的健康对照中获取了高分辨率结构MRI和扩散张量成像（DTI）数据。提取灰质体积（GMV）、白质体积（WMV）、分数各向异性（FA）和平均扩散率（MD）作为四个特征，并使用支持向量机（SVM）进行检查。还估计了每个特征对分类贡献最大的十个脑区。使用不同算法，分类器对GMV、WMV、FA和MD的分类准确率分别达到了72.08%、61.29%、80.65%和77.42%。对分类有贡献的最具区分性的灰质区域主要分布在眶额-纹状体“情感”回路、背外侧前额叶-纹状体“执行”回路和小脑。对于WMV特征和DTI的两个特征集，对区分贡献最大的共享区域主要包括钩束、海马中的扣带、皮质脊髓束以及小脑脚。基于全脑容积测量和DTI图像，SVM算法在个体水平上显示出区分强迫症患者与健康受试者的高准确率。计算机辅助方法能够提供准确的诊断信息，并可能为强迫症的临床诊断提供新的视角。
