Department of Psychiatry, Ludwig-Maximilians University Munich, Munich.
Department of Psychiatry, Ludwig-Maximilians University Munich, Munich.
Biol Psychiatry. 2017 Sep 1;82(5):330-338. doi: 10.1016/j.biopsych.2016.10.028. Epub 2016 Nov 9.
Multiple studies have examined functional and structural brain alteration in patients diagnosed with major depressive disorder (MDD). The introduction of multivariate statistical methods allows investigators to utilize data concerning these brain alterations to generate diagnostic models that accurately differentiate patients with MDD from healthy control subjects (HCs). However, there is substantial heterogeneity in the reported results, the methodological approaches, and the clinical characteristics of participants in these studies.
We conducted a meta-analysis of all studies using neuroimaging (volumetric measures derived from T1-weighted images, task-based functional magnetic resonance imaging [MRI], resting-state MRI, or diffusion tensor imaging) in combination with multivariate statistical methods to differentiate patients diagnosed with MDD from HCs.
Thirty-three (k = 33) samples including 912 patients with MDD and 894 HCs were included in the meta-analysis. Across all studies, patients with MDD were separated from HCs with 77% sensitivity and 78% specificity. Classification based on resting-state MRI (85% sensitivity, 83% specificity) and on diffusion tensor imaging data (88% sensitivity, 92% specificity) outperformed classifications based on structural MRI (70% sensitivity, 71% specificity) and task-based functional MRI (74% sensitivity, 77% specificity).
Our results demonstrate the high representational capacity of multivariate statistical methods to identify neuroimaging-based biomarkers of depression. Future studies are needed to elucidate whether multivariate neuroimaging analysis has the potential to generate clinically useful tools for the differential diagnosis of affective disorders and the prediction of both treatment response and functional outcome.
多项研究已经检查了被诊断患有重度抑郁症（MDD）的患者的大脑功能和结构变化。多元统计方法的引入使研究人员能够利用有关这些大脑改变的数据来生成能够准确地区分 MDD 患者与健康对照（HC）的诊断模型。然而，这些研究报告的结果、方法学方法以及参与者的临床特征存在很大的异质性。
我们对所有使用神经影像学（来自 T1 加权图像的容积测量值、基于任务的功能磁共振成像[MRI]、静息状态 MRI 或扩散张量成像）与多元统计方法相结合以区分 MDD 患者与 HCs 的研究进行了荟萃分析。
荟萃分析共纳入 33 项研究（k = 33），共纳入 912 例 MDD 患者和 894 例 HCs。在所有研究中，MDD 患者的敏感性为 77%，特异性为 78%，可与 HCs 区分开来。基于静息状态 MRI 的分类（85%敏感性，83%特异性）和基于扩散张量成像数据的分类（88%敏感性，92%特异性）优于基于结构 MRI（70%敏感性，71%特异性）和基于任务的功能 MRI（74%敏感性，77%特异性）的分类。
我们的结果表明，多元统计方法具有很高的代表性，可以识别基于神经影像学的抑郁生物标志物。需要进一步的研究来阐明多元神经影像学分析是否有可能生成用于情感障碍的鉴别诊断以及治疗反应和功能结果预测的临床有用工具。
