Predicting treatment outcomes in major depressive disorder using brain magnetic resonance imaging: a meta-analysis

被引:13
|
作者
Long, Fenghua [1 ,2 ,3 ]
Chen, Yufei [1 ,2 ,3 ]
Zhang, Qian [1 ,2 ,3 ]
Li, Qian [1 ,2 ,3 ]
Wang, Yaxuan [1 ,2 ,3 ]
Wang, Yitian [1 ,2 ,3 ]
Li, Haoran [1 ,2 ,3 ]
Zhao, Youjin [1 ,2 ,3 ]
McNamara, Robert K. [4 ]
DelBello, Melissa P. [4 ]
Sweeney, John A. [1 ,2 ,4 ]
Gong, Qiyong [1 ,2 ,3 ]
Li, Fei [1 ,2 ,3 ]
机构
[1] Sichuan Univ, West China Hosp, Dept Radiol, Chengdu 610041, Sichuan, Peoples R China
[2] Sichuan Univ, West China Hosp, Huaxi MR Res Ctr HMRRC, Funct & Mol Imaging Key Lab Sichuan Prov, Chengdu 610041, Sichuan, Peoples R China
[3] Chinese Acad Med Sci, Res Unit Psychoradiol, Chengdu 610041, Sichuan, Peoples R China
[4] Univ Cincinnati, Dept Psychiat & Behav Neurosci, Cincinnati, OH 45219 USA
关键词
DEFAULT MODE NETWORK; ELECTROCONVULSIVE-THERAPY; FUNCTIONAL CONNECTIVITY; TREATMENT RESPONSE; ANTIDEPRESSANT TREATMENT; NEUROIMAGING BIOMARKERS; LEARNING APPROACH; EMOTIONAL FACES; REMISSION; RTMS;
D O I
10.1038/s41380-024-02710-6
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
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.
引用
收藏
页码:825 / 837
页数:13
相关论文
共 50 条
  • [21] Meta-analysis of magnetic resonance imaging brain morphometry studies in bipolar disorder
    McDonald, C
    Zanelli, J
    Rabe-Hesketh, S
    Ellison-Wright, I
    Sham, P
    Kalidindi, S
    Murray, RM
    Kennedy, N
    BIOLOGICAL PSYCHIATRY, 2004, 56 (06) : 411 - 417
  • [22] The effect of treatment as usual on major depressive disorder: A meta-analysis
    Kolovos, Spyros
    van Tulder, Maurits W.
    Cuijpers, Pim
    Prigent, Amelie
    Chevreul, Karine
    Riper, Heleen
    Bosmans, Judith E.
    JOURNAL OF AFFECTIVE DISORDERS, 2017, 210 : 72 - 81
  • [23] Magnetic Resonance Imaging Measures of Brain Structure to Predict Antidepressant Treatment Outcome in Major Depressive Disorder
    Korgaonkar, Mayuresh S.
    Rekshan, William
    Gordon, Evian
    Rush, A. John
    Williams, Leanne M.
    Blasey, Christine
    Grieve, Stuart M.
    EBIOMEDICINE, 2015, 2 (01): : 37 - 45
  • [24] Vulnerable brain regions in adolescent major depressive disorder: A resting-state functional magnetic resonance imaging activation likelihood estimation meta-analysis
    Ding, Hui
    Zhang, Qin
    Shu, Yan-Ping
    Tian, Bin
    Peng, Ji
    Hou, Yong-Zhe
    Wu, Gang
    Lin, Li-Yun
    Li, Jia-Lin
    WORLD JOURNAL OF PSYCHIATRY, 2024, 14 (03):
  • [25] The predicting value of post neoadjuvant treatment magnetic resonance imaging: a meta-analysis
    Zager, Yaniv
    Horesh, Nir
    Abdelmasseh, Michael
    Aquina, Christopher T.
    Alfonso, Bustamante Lopez Leonardo
    Soliman, Mark K.
    Albert, Matthew R.
    Monson, John R. T.
    SURGICAL ENDOSCOPY AND OTHER INTERVENTIONAL TECHNIQUES, 2024, 38 (11): : 6846 - 6853
  • [26] The neural correlates of reward-related processing in major depressive disorder: A meta-analysis of functional magnetic resonance imaging studies
    Zhang, Wei-Na
    Chang, Su-Hua
    Guo, Li-Yuan
    Zhang, Kun-Lin
    Wang, Jing
    JOURNAL OF AFFECTIVE DISORDERS, 2013, 151 (02) : 531 - 539
  • [27] Review of 1H magnetic resonance spectroscopy findings in major depressive disorder:: A meta-analysis
    Yildiz-Yesiloglu, Aysegul
    Ankerst, Donna Pauler
    PSYCHIATRY RESEARCH-NEUROIMAGING, 2006, 147 (01) : 1 - 25
  • [28] Myelination of the brain in Major Depressive Disorder: An in vivo quantitative magnetic resonance imaging study
    Matthew D. Sacchet
    Ian H. Gotlib
    Scientific Reports, 7
  • [29] Myelination of the brain in Major Depressive Disorder: An in vivo quantitative magnetic resonance imaging study
    Sacchet, Matthew D.
    Gotlib, Ian H.
    SCIENTIFIC REPORTS, 2017, 7
  • [30] Discrimination between schizophrenia and major depressive disorder by magnetic resonance imaging of the female brain
    Ota, Miho
    Ishikawa, Masanori
    Sato, Noriko
    Hori, Hiroaki
    Sasayama, Daimei
    Hattori, Kotaro
    Teraishi, Toshiya
    Noda, Takamasa
    Obu, Satoko
    Nakata, Yasuhiro
    Higuchi, Teruhiko
    Kunugi, Hiroshi
    JOURNAL OF PSYCHIATRIC RESEARCH, 2013, 47 (10) : 1383 - 1388