University Hospital Münster, Department of Clinical Radiology, Münster, Germany; University Hospital Heidelberg, Department of General Internal Medicine and Psychosomatics, Heidelberg, Germany.
University Hospital Münster, Department of Clinical Radiology, Münster, Germany.
J Affect Disord. 2017 Nov;222:79-87. doi: 10.1016/j.jad.2017.06.055. Epub 2017 Jun 27.
Combinations of resting-state fMRI and machine-learning techniques are increasingly employed to develop diagnostic models for mental disorders. However, little is known about the neurobiological heterogeneity of depression and diagnostic machine learning has mainly been tested in homogeneous samples. Our main objective was to explore the inherent structure of a diverse unipolar depression sample. The secondary objective was to assess, if such information can improve diagnostic classification.
We analyzed data from 360 patients with unipolar depression and 360 non-depressed population controls, who were subdivided into two independent subsets. Cluster analyses (unsupervised learning) of functional connectivity were used to generate hypotheses about potential patient subgroups from the first subset. The relationship of clusters with demographical and clinical measures was assessed. Subsequently, diagnostic classifiers (supervised learning), which incorporated information about these putative depression subgroups, were trained.
Exploratory cluster analyses revealed two weakly separable subgroups of depressed patients. These subgroups differed in the average duration of depression and in the proportion of patients with concurrently severe depression and anxiety symptoms. The diagnostic classification models performed at chance level.
It remains unresolved, if subgroups represent distinct biological subtypes, variability of continuous clinical variables or in part an overfitting of sparsely structured data.
Functional connectivity in unipolar depression is associated with general disease effects. Cluster analyses provide hypotheses about potential depression subtypes. Diagnostic models did not benefit from this additional information regarding heterogeneity.
静息态 fMRI 与机器学习技术的组合正越来越多地被用于开发精神障碍的诊断模型。然而，人们对抑郁症的神经生物学异质性知之甚少，并且诊断机器学习主要在同质样本中进行了测试。我们的主要目的是探索多样化的单相抑郁症样本的内在结构。次要目的是评估这种信息是否可以改善诊断分类。
我们分析了 360 名单相抑郁症患者和 360 名非抑郁人群对照者的数据，这些患者和对照者被分为两个独立的子集。功能连接的聚类分析（无监督学习）用于从第一个子集中生成有关潜在患者亚组的假设。评估了聚类与人口统计学和临床测量之间的关系。随后，使用包含这些假定抑郁症亚组信息的诊断分类器（监督学习）进行训练。
探索性聚类分析显示，抑郁症患者存在两个弱可分离的亚组。这些亚组在抑郁症的平均持续时间以及同时存在严重抑郁和焦虑症状的患者比例方面存在差异。诊断分类模型表现为随机水平。
亚组是否代表不同的生物学亚型、连续临床变量的变异性或部分是稀疏结构数据的过度拟合，仍未解决。
单相抑郁症中的功能连接与一般疾病效应有关。聚类分析提供了有关潜在抑郁症亚型的假设。诊断模型没有从有关异质性的这些额外信息中受益。
