提高基于任务的功能磁共振成像研究用于荟萃分析的资格:综述和报告建议。

IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Neuroinformatics Pub Date : 2024-01-01 Epub Date: 2023-11-04 DOI:10.1007/s12021-023-09643-5
Freya Acar, Camille Maumet, Talia Heuten, Maya Vervoort, Han Bossier, Ruth Seurinck, Beatrijs Moerkerke
{"title":"提高基于任务的功能磁共振成像研究用于荟萃分析的资格:综述和报告建议。","authors":"Freya Acar, Camille Maumet, Talia Heuten, Maya Vervoort, Han Bossier, Ruth Seurinck, Beatrijs Moerkerke","doi":"10.1007/s12021-023-09643-5","DOIUrl":null,"url":null,"abstract":"<p><p>Decisions made during the analysis or reporting of an fMRI study influence the eligibility of that study to be entered into a meta-analysis. In a meta-analysis, results of different studies on the same topic are combined. To combine the results, it is necessary that all studies provide equivalent pieces of information. However, in task-based fMRI studies we see a large variety in reporting styles. Several specific meta-analysis methods have been developed to deal with the reporting practices occurring in task-based fMRI studies, therefore each requiring a specific type of input. In this manuscript we provide an overview of the meta-analysis methods and the specific input they require. Subsequently we discuss how decisions made during the study influence the eligibility of a study for a meta-analysis and finally we formulate some recommendations about how to report an fMRI study so that it complies with as many meta-analysis methods as possible.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":" ","pages":"5-22"},"PeriodicalIF":2.7000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Improving the Eligibility of Task-Based fMRI Studies for Meta-Analysis: A Review and Reporting Recommendations.\",\"authors\":\"Freya Acar, Camille Maumet, Talia Heuten, Maya Vervoort, Han Bossier, Ruth Seurinck, Beatrijs Moerkerke\",\"doi\":\"10.1007/s12021-023-09643-5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Decisions made during the analysis or reporting of an fMRI study influence the eligibility of that study to be entered into a meta-analysis. In a meta-analysis, results of different studies on the same topic are combined. To combine the results, it is necessary that all studies provide equivalent pieces of information. However, in task-based fMRI studies we see a large variety in reporting styles. Several specific meta-analysis methods have been developed to deal with the reporting practices occurring in task-based fMRI studies, therefore each requiring a specific type of input. In this manuscript we provide an overview of the meta-analysis methods and the specific input they require. Subsequently we discuss how decisions made during the study influence the eligibility of a study for a meta-analysis and finally we formulate some recommendations about how to report an fMRI study so that it complies with as many meta-analysis methods as possible.</p>\",\"PeriodicalId\":49761,\"journal\":{\"name\":\"Neuroinformatics\",\"volume\":\" \",\"pages\":\"5-22\"},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2024-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Neuroinformatics\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s12021-023-09643-5\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2023/11/4 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neuroinformatics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s12021-023-09643-5","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/11/4 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0

摘要

fMRI研究分析或报告过程中做出的决定会影响该研究进入荟萃分析的资格。在荟萃分析中,对同一主题的不同研究结果进行了组合。为了综合这些结果,所有研究都必须提供同等的信息。然而,在基于任务的功能磁共振成像研究中,我们看到了各种各样的报告风格。已经开发了几种特定的荟萃分析方法来处理基于任务的功能磁共振成像研究中出现的报告实践,因此每种方法都需要特定类型的输入。在这份手稿中,我们对荟萃分析方法及其所需的具体投入进行了概述。随后,我们讨论了研究过程中做出的决定如何影响研究的荟萃分析资格,最后,我们就如何报告功能磁共振成像研究提出了一些建议,使其符合尽可能多的荟萃分析方法。
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
Improving the Eligibility of Task-Based fMRI Studies for Meta-Analysis: A Review and Reporting Recommendations.

Decisions made during the analysis or reporting of an fMRI study influence the eligibility of that study to be entered into a meta-analysis. In a meta-analysis, results of different studies on the same topic are combined. To combine the results, it is necessary that all studies provide equivalent pieces of information. However, in task-based fMRI studies we see a large variety in reporting styles. Several specific meta-analysis methods have been developed to deal with the reporting practices occurring in task-based fMRI studies, therefore each requiring a specific type of input. In this manuscript we provide an overview of the meta-analysis methods and the specific input they require. Subsequently we discuss how decisions made during the study influence the eligibility of a study for a meta-analysis and finally we formulate some recommendations about how to report an fMRI study so that it complies with as many meta-analysis methods as possible.

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
Neuroinformatics
Neuroinformatics 医学-计算机:跨学科应用
CiteScore
6.00
自引率
6.70%
发文量
54
审稿时长
3 months
期刊介绍: Neuroinformatics publishes original articles and reviews with an emphasis on data structure and software tools related to analysis, modeling, integration, and sharing in all areas of neuroscience research. The editors particularly invite contributions on: (1) Theory and methodology, including discussions on ontologies, modeling approaches, database design, and meta-analyses; (2) Descriptions of developed databases and software tools, and of the methods for their distribution; (3) Relevant experimental results, such as reports accompanie by the release of massive data sets; (4) Computational simulations of models integrating and organizing complex data; and (5) Neuroengineering approaches, including hardware, robotics, and information theory studies.
期刊最新文献
Teaching Research Data Management with DataLad: A Multi-year, Multi-domain Effort. Hands-On Neuroinformatics Education at the Crossroads of Online and In-Person: Lessons Learned from NeuroHackademy. Utilizing fMRI to Guide TMS Targets: the Reliability and Sensitivity of fMRI Metrics at 3 T and 1.5 T. Bayesian Tensor Modeling for Image-based Classification of Alzheimer's Disease. A Bayesian Multiplex Graph Classifier of Functional Brain Connectivity Across Diverse Tasks of Cognitive Control.
×
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