000840398 001__ 840398 000840398 005__ 20220930130137.0 000840398 0247_ $$2doi$$a10.1016/j.neubiorev.2017.11.012 000840398 0247_ $$2pmid$$apmid:29180258 000840398 0247_ $$2ISSN$$a0149-7634 000840398 0247_ $$2ISSN$$a1873-7528 000840398 0247_ $$2Handle$$a2128/16679 000840398 0247_ $$2WOS$$aWOS:000419419000012 000840398 0247_ $$2altmetric$$aaltmetric:29465723 000840398 037__ $$aFZJ-2017-07931 000840398 041__ $$aEnglish 000840398 082__ $$a150 000840398 1001_ $$0P:(DE-Juel1)131699$$aMüller, Veronika$$b0$$eCorresponding author 000840398 245__ $$aTen simple rules for neuroimaging meta-analysis 000840398 260__ $$aAmsterdam [u.a.]$$bElsevier Science$$c2017 000840398 3367_ $$2DRIVER$$aarticle 000840398 3367_ $$2DataCite$$aOutput Types/Journal article 000840398 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1516714199_21766 000840398 3367_ $$2BibTeX$$aARTICLE 000840398 3367_ $$2ORCID$$aJOURNAL_ARTICLE 000840398 3367_ $$00$$2EndNote$$aJournal Article 000840398 520__ $$aNeuroimaging has evolved into a widely used method to investigate the functional neuroanatomy, brain-behaviour relationships, and pathophysiology of brain disorders, yielding a literature of more than 30,000 papers. With such an explosion of data, it is increasingly difficult to sift through the literature and distinguish spurious from replicable findings. Furthermore, due to the large number of studies, it is challenging to keep track of the wealth of findings. A variety of meta-analytical methods (coordinate-based and image-based) have been developed to help summarise and integrate the vast amount of data arising from neuroimaging studies. However, the field lacks specific guidelines for the conduct of such meta-analyses. Based on our combined experience, we propose best-practice recommendations that researchers from multiple disciplines may find helpful. In addition, we provide specific guidelines and a checklist that will hopefully improve the transparency, traceability, replicability and reporting of meta-analytical results of neuroimaging data. 000840398 536__ $$0G:(DE-HGF)POF3-574$$a574 - Theory, modelling and simulation (POF3-574)$$cPOF3-574$$fPOF III$$x0 000840398 536__ $$0G:(EU-Grant)720270$$aHBP SGA1 - Human Brain Project Specific Grant Agreement 1 (720270)$$c720270$$fH2020-Adhoc-2014-20$$x1 000840398 588__ $$aDataset connected to CrossRef, PubMed, 000840398 7001_ $$0P:(DE-Juel1)131855$$aCieslik, Edna$$b1$$eCorresponding author 000840398 7001_ $$0P:(DE-HGF)0$$aLaird, Angela R$$b2 000840398 7001_ $$0P:(DE-HGF)0$$aFox, Peter T$$b3 000840398 7001_ $$0P:(DE-HGF)0$$aRadua, Joaquim$$b4 000840398 7001_ $$0P:(DE-HGF)0$$aMataix-Cols, David$$b5 000840398 7001_ $$0P:(DE-HGF)0$$aTench, Christopher R$$b6 000840398 7001_ $$0P:(DE-HGF)0$$aYarkoni, Tal$$b7 000840398 7001_ $$0P:(DE-HGF)0$$aNichols, Thomas E$$b8 000840398 7001_ $$0P:(DE-HGF)0$$aTurkeltaub, Peter E$$b9 000840398 7001_ $$0P:(DE-HGF)0$$aWager, Tor D$$b10 000840398 7001_ $$0P:(DE-Juel1)131678$$aEickhoff, Simon$$b11 000840398 773__ $$0PERI:(DE-600)1498433-7$$a10.1016/j.neubiorev.2017.11.012$$gp. 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