利用脑磁共振成像预测重度抑郁障碍的治疗效果:一项荟萃分析。

IF 9.6 1区 医学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY Molecular Psychiatry Pub Date : 2024-08-26 DOI:10.1038/s41380-024-02710-6
Fenghua Long, Yufei Chen, Qian Zhang, Qian Li, Yaxuan Wang, Yitian Wang, Haoran Li, Youjin Zhao, Robert K McNamara, Melissa P DelBello, John A Sweeney, Qiyong Gong, Fei Li
{"title":"利用脑磁共振成像预测重度抑郁障碍的治疗效果:一项荟萃分析。","authors":"Fenghua Long, Yufei Chen, Qian Zhang, Qian Li, Yaxuan Wang, Yitian Wang, Haoran Li, Youjin Zhao, Robert K McNamara, Melissa P DelBello, John A Sweeney, Qiyong Gong, Fei Li","doi":"10.1038/s41380-024-02710-6","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":19008,"journal":{"name":"Molecular Psychiatry","volume":null,"pages":null},"PeriodicalIF":9.6000,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Predicting treatment outcomes in major depressive disorder using brain magnetic resonance imaging: a meta-analysis.\",\"authors\":\"Fenghua Long, Yufei Chen, Qian Zhang, Qian Li, Yaxuan Wang, Yitian Wang, Haoran Li, Youjin Zhao, Robert K McNamara, Melissa P DelBello, John A Sweeney, Qiyong Gong, Fei Li\",\"doi\":\"10.1038/s41380-024-02710-6\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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.</p>\",\"PeriodicalId\":19008,\"journal\":{\"name\":\"Molecular Psychiatry\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":9.6000,\"publicationDate\":\"2024-08-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Molecular Psychiatry\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1038/s41380-024-02710-6\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BIOCHEMISTRY & MOLECULAR BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Molecular Psychiatry","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1038/s41380-024-02710-6","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

最近的研究提供了令人鼓舞的证据,表明神经影像学数据可以预测重度抑郁障碍(MDD)患者的治疗效果。由于这些研究的样本量大多较小,因此有必要进行荟萃分析,以确定最可靠的研究结果和成像模式,并比较磁共振成像(MRI)与使用临床和人口学特征的研究得出的预测结果。我们进行了从数据库开始到 2023 年 7 月 22 日的文献检索,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。我们分别对临床研究和 MRI 研究进行了两项荟萃分析。元回归用于探索协变量的影响,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 项临床研究进行的元分析得出的曲线下面积(AUC)为 0.73,而对 44 项核磁共振成像研究得出的曲线下面积(AUC)为 0.89。磁共振成像研究的灵敏度高于临床研究(0.78 对 0.62,Z = 3.42,P = 0.001)。在核磁共振成像研究中,静息态功能核磁共振成像(rsfMRI)的特异性高于基于任务的核磁共振成像(tbfMRI)(0.79 vs. 0.69,Z = -2.86,P = 0.004)。结构性磁共振成像和功能性磁共振成像之间以及不同干预措施之间的预测性能没有明显差异。值得注意的是,在使用抗抑郁药物的研究中,对治疗结果具有预测作用的磁共振成像特征主要位于边缘和默认模式网络,而对电休克疗法(ECT)的研究则主要局限于边缘网络。我们的研究结果表明,治疗前脑磁共振成像特征有望预测 MDD 的治疗结果,其效果优于临床特征。虽然tbfMRI研究的任务各不相同,但这些研究的预测效用总体上不如rsfMRI数据。重叠但不同的网络水平测量可预测抗抑郁药物和电痉挛疗法的疗效。未来的研究需要使用多种磁共振成像特征来预测结果,并明确成像特征是普遍预测结果还是因治疗方法而异。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Predicting treatment outcomes in major depressive disorder using brain magnetic resonance imaging: a meta-analysis.

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.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Molecular Psychiatry
Molecular Psychiatry 医学-精神病学
CiteScore
20.50
自引率
4.50%
发文量
459
审稿时长
4-8 weeks
期刊介绍: Molecular Psychiatry focuses on publishing research that aims to uncover the biological mechanisms behind psychiatric disorders and their treatment. The journal emphasizes studies that bridge pre-clinical and clinical research, covering cellular, molecular, integrative, clinical, imaging, and psychopharmacology levels.
期刊最新文献
Genetic and functional analyses of CTBP2 in anorexia nervosa and body weight regulation Peripartum allopregnanolone blood concentrations and depressive symptoms: a systematic review and individual participant data meta-analysis Cortico-limbic volume abnormalities in late life depression are distinct from β amyloid and white matter pathologies Neuroimaging-based variability in subtyping biomarkers for psychiatric heterogeneity White matter microstructure in obesity and bipolar disorders: an ENIGMA bipolar disorder working group study in 2186 individuals
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1