Sundermann Benedikt, Feder Stephan, Wersching Heike, Teuber Anja, Schwindt Wolfram, Kugel Harald, Heindel Walter, Arolt Volker, Berger Klaus, Pfleiderer Bettina
Department of Clinical Radiology, University Hospital Münster, Albert-Schweitzer-Campus 1, Gebäude A1, 48149, Münster, Germany.
Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany.
J Neural Transm (Vienna). 2017 May;124(5):589-605. doi: 10.1007/s00702-016-1673-8. Epub 2016 Dec 31.
In small, selected samples, an approach combining resting-state functional connectivity MRI and multivariate pattern analysis has been able to successfully classify patients diagnosed with unipolar depression. Purposes of this investigation were to assess the generalizability of this approach to a large clinically more realistic sample and secondarily to assess the replicability of previously reported methodological feasibility in a more homogeneous subgroup with pronounced depressive symptoms. Two independent subsets were drawn from the depression and control cohorts of the BiDirect study, each with 180 patients with and 180 controls without depression. Functional connectivity either among regions covering the gray matter or selected regions with known alterations in depression was assessed by resting-state fMRI. Support vector machines with and without automated feature selection were used to train classifiers differentiating between individual patients and controls in the entire first subset as well as in the subgroup. Model parameters were explored systematically. The second independent subset was used for validation of successful models. Classification accuracies in the large, heterogeneous sample ranged from 45.0 to 56.1% (chance level 50.0%). In the subgroup with higher depression severity, three out of 90 models performed significantly above chance (60.8-61.7% at independent validation). In conclusion, common classification methods previously successful in small homogenous depression samples do not immediately translate to a more realistic population. Future research to develop diagnostic classification approaches in depression should focus on more specific clinical questions and consider heterogeneity, including symptom severity as an important factor.
在小型的特定样本中，一种将静息态功能连接磁共振成像（MRI）与多变量模式分析相结合的方法已成功地对被诊断为单相抑郁症的患者进行了分类。本研究的目的是评估该方法在更大的、临床上更具现实意义的样本中的可推广性，其次是评估在一个具有明显抑郁症状的更同质亚组中先前报道的方法可行性的可重复性。从BiDirect研究的抑郁症和对照组队列中抽取了两个独立的子集，每个子集有180名抑郁症患者和180名无抑郁症的对照者。通过静息态功能磁共振成像（fMRI）评估覆盖灰质区域之间或抑郁症中已知有改变的选定区域之间的功能连接。使用有和没有自动特征选择的支持向量机在整个第一个子集中以及在亚组中训练区分个体患者和对照者的分类器，并系统地探索模型参数。第二个独立子集用于验证成功的模型。在这个大型异质样本中的分类准确率范围为45.0%至56.1%（机遇水平为50.0%）。在抑郁症严重程度较高的亚组中，90个模型中有3个在独立验证时的表现显著高于机遇水平（60.8% - 61.7%）。总之，先前在小型同质抑郁症样本中成功的常见分类方法并不能直接应用于更具现实意义的人群。未来开发抑郁症诊断分类方法的研究应关注更具体的临床问题，并考虑异质性因素包括症状严重程度作为一个重要因素。
