Major depressive disorder (MDD) is a debilitating condition that is associated with changes in the default-mode network (DMN). Commonly reported features include alterations in gray matter volume (GMV), cortical thickness (CoT), and gyrification. A comprehensive examination of these variables using ultra-high field strength MRI and machine learning methods may lead to novel insights into the pathophysiology of depression and help develop a more personalized therapy. Cerebral images were obtained from 41 patients with confirmed MDD and 41 healthy controls, matched for age and gender, using a 7-T-MRI. DMN parcellation followed the Schaefer 600 Atlas. Based on the results of a mixed-model repeated measures analysis, a support vector machine (SVM) calculation followed by leave-one-out cross-validation determined the predictive ability of structural features for the presence of MDD. A consecutive permutation procedure identified which areas contributed to the classification results. Correlating changes in those areas with BDI-II and AMDP scores added an explanatory aspect to this study. CoT did not delineate relevant changes in the mixed model and was excluded from further analysis. The SVM achieved a good prediction accuracy of 0.76 using gyrification data. GMV was not a viable predictor for disease presence, however, it correlated in the left parahippocampal gyrus with disease severity as measured by the BDI-II. Structural data of the DMN may therefore contain the necessary information to predict the presence of MDD. However, there may be inherent challenges with predicting disease course or treatment response due to high GMV variance and the static character of gyrification. Further improvements in data acquisition and analysis may help to overcome these difficulties.

重度抑郁症 （MDD） 是一种与默认模式网络 （DMN） 变化相关的使人衰弱的疾病。常见报告的特征包括灰质体积 （GMV） 、皮质厚度 （CoT） 和回旋的改变。使用超高场强 MRI 和机器学习方法对这些变量进行全面检查可能会对抑郁症的病理生理学产生新的见解，并有助于开发更加个性化的疗法。使用 7-T-MRI 从 41 名确诊的 MDD 患者和 41 名健康对照者那里获得脑图像，年龄和性别匹配。DMN 包裹遵循 Schaefer 600 Atlas。基于混合模型重复测量分析的结果，支持向量机 （SVM） 计算，然后留一法交叉验证确定了结构特征对 MDD 存在的预测能力。连续的排列过程确定了哪些区域对分类结果有贡献。将这些区域的变化与 BDI-II 和 AMDP 评分相关联，为本研究增加了解释性。CoT 没有描述混合模型中的相关变化，因此被排除在进一步分析之外。SVM 使用回转数据实现了 0.76 的良好预测精度。GMV 不是疾病存在的可行预测因子，但是，它在左侧海马旁回中与 BDI-II 测量的疾病严重程度相关。因此，DMN 的结构数据可能包含预测 MDD 存在的必要信息。然而，由于 GMV 方差高和旋转的静态特性，预测病程或治疗反应可能存在固有的挑战。数据采集和分析的进一步改进可能有助于克服这些困难。