Practical recommendations to conduct a neuroimaging meta-analysis for neuropsychiatric disorders.
guideline
meta-analysis
neuroimaging
neuropsychiatric disorders
systematic review
Journal
Human brain mapping
ISSN: 1097-0193
Titre abrégé: Hum Brain Mapp
Pays: United States
ID NLM: 9419065
Informations de publication
Date de publication:
01 12 2019
01 12 2019
Historique:
received:
06
02
2019
revised:
09
07
2019
accepted:
16
07
2019
pubmed:
6
8
2019
medline:
17
4
2020
entrez:
6
8
2019
Statut:
ppublish
Résumé
Over the past decades, neuroimaging has become widely used to investigate structural and functional brain abnormality in neuropsychiatric disorders. The results of individual neuroimaging studies, however, are frequently inconsistent due to small and heterogeneous samples, analytical flexibility, and publication bias toward positive findings. To consolidate the emergent findings toward clinically useful insight, meta-analyses have been developed to integrate the results of studies and identify areas that are consistently involved in pathophysiology of particular neuropsychiatric disorders. However, it should be considered that the results of meta-analyses could also be divergent due to heterogeneity in search strategy, selection criteria, imaging modalities, behavioral tasks, number of experiments, data organization methods, and statistical analysis with different multiple comparison thresholds. Following an introduction to the problem and the concepts of quantitative summaries of neuroimaging findings, we propose practical recommendations for clinicians and researchers for conducting transparent and methodologically sound neuroimaging meta-analyses. This should help to consolidate the search for convergent regional brain abnormality in neuropsychiatric disorders.
Identifiants
pubmed: 31379049
doi: 10.1002/hbm.24746
pmc: PMC6865620
mid: NIHMS1588691
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
5142-5154Subventions
Organisme : European Union's Horizon 2020 Research and Innovation Programme
ID : 785907
Pays : International
Organisme : NIMH NIH HHS
ID : R01 MH074457
Pays : United States
Organisme : Deutsche Forschungsgemeinschaft
ID : EI 816/11-1
Pays : International
Organisme : NIMH NIH HHS
ID : R56 MH074457
Pays : United States
Organisme : NIMH NIH HHS
ID : R01-MH074457
Pays : United States
Organisme : European Union's Horizon 2020 Research and Innovation Programme
ID : 7202070
Pays : International
Organisme : Helmholtz Portfolio Theme "Supercomputing and Modeling for the Human Brain"
Pays : International
Informations de copyright
© 2019 Wiley Periodicals, Inc.
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