Long Fenghua, Chen Yufei, Zhang Qian, Li Qian, Wang Yaxuan, Wang Yitian, Li Haoran, Zhao Youjin, McNamara Robert K, DelBello Melissa P, Sweeney John A, Gong Qiyong, Li Fei
Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, PR China.
Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, Sichuan, PR China.
Mol Psychiatry. 2025 Mar;30(3):825-837. doi: 10.1038/s41380-024-02710-6. Epub 2024 Aug 26.
Recent studies have provided promising evidence that neuroimaging data can predict treatment outcomes for patients with major depressive disorder (MDD). As most of these studies had small sample sizes, a meta-analysis is warranted to identify the most robust findings and imaging modalities, and to compare predictive outcomes obtained in magnetic resonance imaging (MRI) and studies using clinical and demographic features. We conducted a literature search from database inception to July 22, 2023, to identify studies using pretreatment clinical or brain MRI features to predict treatment outcomes in patients with MDD. Two meta-analyses were conducted on clinical and MRI studies, respectively. The meta-regression was employed to explore the effects of covariates and compare the predictive performance between clinical and MRI groups, as well as across MRI modalities and intervention subgroups. Meta-analysis of 13 clinical studies yielded an area under the curve (AUC) of 0.73, while in 44 MRI studies, the AUC was 0.89. MRI studies showed a higher sensitivity than clinical studies (0.78 vs. 0.62, Z = 3.42, P = 0.001). In MRI studies, resting-state functional MRI (rsfMRI) exhibited a higher specificity than task-based fMRI (tbfMRI) (0.79 vs. 0.69, Z = -2.86, P = 0.004). No significant differences in predictive performance were found between structural and functional MRI, nor between different interventions. Of note, predictive MRI features for treatment outcomes in studies using antidepressants were predominantly located in the limbic and default mode networks, while studies of electroconvulsive therapy (ECT) were restricted mainly to the limbic network. Our findings suggest a promise for pretreatment brain MRI features to predict MDD treatment outcomes, outperforming clinical features. While tasks in tbfMRI studies differed, those studies overall had less predictive utility than rsfMRI data. Overlapping but distinct network-level measures predicted antidepressants and ECT outcomes. Future studies are needed to predict outcomes using multiple MRI features, and to clarify whether imaging features predict outcomes generally or differ depending on treatments.
近期研究提供了有前景的证据，表明神经影像学数据可预测重度抑郁症（MDD）患者的治疗结果。由于这些研究大多样本量较小，因此有必要进行一项荟萃分析，以确定最可靠的研究结果和成像方式，并比较磁共振成像（MRI）以及使用临床和人口统计学特征的研究所获得的预测结果。我们从数据库建立至2023年7月22日进行了文献检索，以确定使用治疗前临床或脑部MRI特征来预测MDD患者治疗结果的研究。分别对临床研究和MRI研究进行了两项荟萃分析。采用荟萃回归来探讨协变量的影响，并比较临床组和MRI组之间以及不同MRI模式和干预亚组之间的预测性能。对13项临床研究的荟萃分析得出曲线下面积（AUC）为0.73，而在44项MRI研究中，AUC为0.89。MRI研究显示出比临床研究更高的敏感性（0.78对0.62，Z = 3.42，P = 0.001）。在MRI研究中，静息态功能MRI（rsfMRI）表现出比基于任务的功能MRI（tbfMRI）更高的特异性（0.79对0.69，Z = -2.86，P = 0.004）。在结构MRI和功能MRI之间，以及不同干预措施之间，未发现预测性能有显著差异。值得注意的是，在使用抗抑郁药的研究中，治疗结果的预测性MRI特征主要位于边缘系统和默认模式网络，而电休克治疗（ECT）的研究主要局限于边缘系统网络。我们的研究结果表明，治疗前脑部MRI特征有望预测MDD的治疗结果，优于临床特征。虽然tbfMRI研究中的任务各不相同，但这些研究总体上比rsfMRI数据的预测效用更低。重叠但不同的网络水平测量指标可预测抗抑郁药和ECT的治疗结果。未来需要开展研究，利用多种MRI特征来预测治疗结果，并阐明成像特征是普遍预测治疗结果还是因治疗方法而异。
