Amsterdam UMC, University of Amsterdam, Department of Psychiatry, Amsterdam Neuroscience, Amsterdam, Netherlands.
Department of Physiology, Anatomy and Genetics, Oxford, UK.
Transl Psychiatry. 2020 Oct 8;10(1):342. doi: 10.1038/s41398-020-01013-y.
No diagnostic biomarkers are available for obsessive-compulsive disorder (OCD). Here, we aimed to identify magnetic resonance imaging (MRI) biomarkers for OCD, using 46 data sets with 2304 OCD patients and 2068 healthy controls from the ENIGMA consortium. We performed machine learning analysis of regional measures of cortical thickness, surface area and subcortical volume and tested classification performance using cross-validation. Classification performance for OCD vs. controls using the complete sample with different classifiers and cross-validation strategies was poor. When models were validated on data from other sites, model performance did not exceed chance-level. In contrast, fair classification performance was achieved when patients were grouped according to their medication status. These results indicate that medication use is associated with substantial differences in brain anatomy that are widely distributed, and indicate that clinical heterogeneity contributes to the poor performance of structural MRI as a disease marker.
目前还没有用于强迫症（OCD）的诊断生物标志物。在这里，我们旨在使用 ENIGMA 联盟的 46 个数据集（包含 2304 名 OCD 患者和 2068 名健康对照者），确定强迫症的 MRI 生物标志物。我们对皮质厚度、表面积和皮质下体积的区域测量值进行了机器学习分析，并使用交叉验证测试了分类性能。使用不同的分类器和交叉验证策略对完整样本进行 OCD 与对照组的分类，其性能较差。当在其他站点的数据上验证模型时，模型性能未超过随机水平。相比之下，当根据患者的用药情况对患者进行分组时，可实现较好的分类性能。这些结果表明，药物使用与大脑解剖结构的广泛分布存在显著差异，表明临床异质性导致结构 MRI 作为疾病标志物的性能不佳。
