001015277 001__ 1015277
001015277 005__ 20231116095326.0
001015277 0247_ $$2doi$$a10.1016/j.neuroimage.2023.120383
001015277 0247_ $$2ISSN$$a1053-8119
001015277 0247_ $$2ISSN$$a1095-9572
001015277 0247_ $$2datacite_doi$$a10.34734/FZJ-2023-03632
001015277 0247_ $$2pmid$$a37734477
001015277 0247_ $$2WOS$$aWOS:001083784800001
001015277 037__ $$aFZJ-2023-03632
001015277 082__ $$a610
001015277 1001_ $$0P:(DE-Juel1)185961$$aFrahm, Lennart$$b0$$eCorresponding author$$ufzj
001015277 245__ $$aALE meta-analyses of voxel-based morphometry studies: Parameter validation via large-scale simulations
001015277 260__ $$aOrlando, Fla.$$bAcademic Press$$c2023
001015277 3367_ $$2DRIVER$$aarticle
001015277 3367_ $$2DataCite$$aOutput Types/Journal article
001015277 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1695972102_21433
001015277 3367_ $$2BibTeX$$aARTICLE
001015277 3367_ $$2ORCID$$aJOURNAL_ARTICLE
001015277 3367_ $$00$$2EndNote$$aJournal Article
001015277 500__ $$aThis study was supported by the Deutsche Forschungsgemeinschaft (DFG, EI 816/11-1 and International Research Training Group 2150, 269953372/GRK2150), the National Institute of Mental Health (R01-MH074457), the National Institute of Aging (P30-AG066546), and the Jülich-Aachen Research Alliance (JARA) granting computation time on the supercomputer JURECA (Jülich Supercomputing Centre, 2018) at Forschungszentrum Jülich. Open access funding enabled and organized by Projekt DEAL.
001015277 520__ $$aActivation likelihood estimation (ALE) meta-analysis has been applied to structural neuroimaging data since long, but up to now, any systematic assessment of the algorithm's behavior, power and sensitivity has been based on simulations using functional neuroimaging databases as their foundation. Here, we aimed to determine whether the guidelines offered by previous evaluations can be generalized to ALE meta-analyses of voxel-based morphometry (VBM) studies. We ran 365000 distinct ALE analyses filled with simulated experiments, randomly sampling parameters from BrainMap's VBM experiment database. We then examined the algorithm's sensitivity, its susceptibility to spurious convergence, and its susceptibility to excessive contributions by individual experiments. In general, the performance of the ALE algorithm was highly comparable between imaging modalities, with the algorithm's sensitivity and specificity reaching similar levels with structural data as previously observed with functional data. Because of the lower number of foci reported and the higher number of participants usually included in structural experiments, individual studies had, on average, a higher impact towards significant clusters. To prevent significant clusters from being driven by single experiments, we recommend that researchers include at least 23 experiments in a VBM ALE dataset, instead of the previously recommended minimum of n = 17. While these recommendations do not constitute hard borders, running ALE analyses on smaller datasets would require special diligence in assessing and reporting the contributions of experiments to individual clusters.
001015277 536__ $$0G:(DE-HGF)POF4-5251$$a5251 - Multilevel Brain Organization and Variability (POF4-525)$$cPOF4-525$$fPOF IV$$x0
001015277 588__ $$aDataset connected to CrossRef, Journals: juser.fz-juelich.de
001015277 7001_ $$0P:(DE-HGF)0$$aSatterthwaite, Theodore D.$$b1
001015277 7001_ $$0P:(DE-HGF)0$$aFox, Peter T.$$b2
001015277 7001_ $$0P:(DE-Juel1)131693$$aLangner, Robert$$b3$$ufzj
001015277 7001_ $$0P:(DE-Juel1)131678$$aEickhoff, Simon B.$$b4$$ufzj
001015277 773__ $$0PERI:(DE-600)1471418-8$$a10.1016/j.neuroimage.2023.120383$$gVol. 281, p. 120383 -$$p120383 -$$tNeuroImage$$v281$$x1053-8119$$y2023
001015277 8564_ $$uhttps://juser.fz-juelich.de/record/1015277/files/1-s2.0-S1053811923005347-main.pdf$$yOpenAccess
001015277 909CO $$ooai:juser.fz-juelich.de:1015277$$pdnbdelivery$$pdriver$$pVDB$$popen_access$$popenaire
001015277 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)185961$$aForschungszentrum Jülich$$b0$$kFZJ
001015277 9101_ $$0I:(DE-588b)36225-6$$6P:(DE-Juel1)185961$$aRWTH Aachen$$b0$$kRWTH
001015277 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131693$$aForschungszentrum Jülich$$b3$$kFZJ
001015277 9101_ $$0I:(DE-HGF)0$$6P:(DE-Juel1)131693$$a HHU Düsseldorf$$b3
001015277 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131678$$aForschungszentrum Jülich$$b4$$kFZJ
001015277 9101_ $$0I:(DE-HGF)0$$6P:(DE-Juel1)131678$$a HHU Düsseldorf$$b4
001015277 9131_ $$0G:(DE-HGF)POF4-525$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5251$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vDecoding Brain Organization and Dysfunction$$x0
001015277 9141_ $$y2023
001015277 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2022-11-12
001015277 915__ $$0StatID:(DE-HGF)1190$$2StatID$$aDBCoverage$$bBiological Abstracts$$d2022-11-12
001015277 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2022-11-12
001015277 915__ $$0StatID:(DE-HGF)0700$$2StatID$$aFees$$d2022-11-12
001015277 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
001015277 915__ $$0StatID:(DE-HGF)0561$$2StatID$$aArticle Processing Charges$$d2022-11-12
001015277 915__ $$0LIC:(DE-HGF)CCBY4$$2HGFVOC$$aCreative Commons Attribution CC BY 4.0
001015277 915__ $$0StatID:(DE-HGF)0501$$2StatID$$aDBCoverage$$bDOAJ Seal$$d2023-05-02T08:47:40Z
001015277 915__ $$0StatID:(DE-HGF)0500$$2StatID$$aDBCoverage$$bDOAJ$$d2023-05-02T08:47:40Z
001015277 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bDOAJ : Anonymous peer review$$d2023-05-02T08:47:40Z
001015277 915__ $$0StatID:(DE-HGF)0420$$2StatID$$aNationallizenz$$d2023-10-21$$wger
001015277 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bNEUROIMAGE : 2022$$d2023-10-21
001015277 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2023-10-21
001015277 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2023-10-21
001015277 915__ $$0StatID:(DE-HGF)0600$$2StatID$$aDBCoverage$$bEbsco Academic Search$$d2023-10-21
001015277 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bASC$$d2023-10-21
001015277 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2023-10-21
001015277 915__ $$0StatID:(DE-HGF)1050$$2StatID$$aDBCoverage$$bBIOSIS Previews$$d2023-10-21
001015277 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2023-10-21
001015277 915__ $$0StatID:(DE-HGF)1030$$2StatID$$aDBCoverage$$bCurrent Contents - Life Sciences$$d2023-10-21
001015277 915__ $$0StatID:(DE-HGF)9905$$2StatID$$aIF >= 5$$bNEUROIMAGE : 2022$$d2023-10-21
001015277 920__ $$lyes
001015277 9201_ $$0I:(DE-Juel1)INM-7-20090406$$kINM-7$$lGehirn & Verhalten$$x0
001015277 980__ $$ajournal
001015277 980__ $$aVDB
001015277 980__ $$aUNRESTRICTED
001015277 980__ $$aI:(DE-Juel1)INM-7-20090406
001015277 9801_ $$aFullTexts