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Pooling fMRI Data: Meta-Analysis, Mega-Analysis and Multi-Center Studies

The quantitative analysis of pooled data from related functional magnetic resonance imaging (fMRI) experiments has the potential to significantly accelerate progress in brain mapping. Such data-pooling can be achieved through meta-analysis (the pooled analysis of published results), mega-analysis (t...

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Detalles Bibliográficos
Autor principal: Costafreda, Sergi G.
Formato: Texto
Lenguaje:English
Publicado: Frontiers Research Foundation 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2759345/
https://www.ncbi.nlm.nih.gov/pubmed/19826498
http://dx.doi.org/10.3389/neuro.11.033.2009
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author Costafreda, Sergi G.
author_facet Costafreda, Sergi G.
author_sort Costafreda, Sergi G.
collection PubMed
description The quantitative analysis of pooled data from related functional magnetic resonance imaging (fMRI) experiments has the potential to significantly accelerate progress in brain mapping. Such data-pooling can be achieved through meta-analysis (the pooled analysis of published results), mega-analysis (the pooled analysis of raw data) or multi-site studies, which can be seen as designed mega-analyses. Current limitations in function-location brain mapping and how data-pooling can be used to remediate them are reviewed, with particular attention to power aggregation and mitigation of false positive results. Some recently developed analysis tools for meta- and mega-analysis are also presented, and recommendations for the conduct of valid fMRI data pooling are formulated.
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spelling pubmed-27593452009-10-13 Pooling fMRI Data: Meta-Analysis, Mega-Analysis and Multi-Center Studies Costafreda, Sergi G. Front Neuroinformatics Neuroscience The quantitative analysis of pooled data from related functional magnetic resonance imaging (fMRI) experiments has the potential to significantly accelerate progress in brain mapping. Such data-pooling can be achieved through meta-analysis (the pooled analysis of published results), mega-analysis (the pooled analysis of raw data) or multi-site studies, which can be seen as designed mega-analyses. Current limitations in function-location brain mapping and how data-pooling can be used to remediate them are reviewed, with particular attention to power aggregation and mitigation of false positive results. Some recently developed analysis tools for meta- and mega-analysis are also presented, and recommendations for the conduct of valid fMRI data pooling are formulated. Frontiers Research Foundation 2009-09-30 /pmc/articles/PMC2759345/ /pubmed/19826498 http://dx.doi.org/10.3389/neuro.11.033.2009 Text en Copyright © 2009 Costafreda. http://www.frontiersin.org/licenseagreement This is an open-access article subject to an exclusive license agreement between the authors and the Frontiers Research Foundation, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are credited.
spellingShingle Neuroscience
Costafreda, Sergi G.
Pooling fMRI Data: Meta-Analysis, Mega-Analysis and Multi-Center Studies
title Pooling fMRI Data: Meta-Analysis, Mega-Analysis and Multi-Center Studies
title_full Pooling fMRI Data: Meta-Analysis, Mega-Analysis and Multi-Center Studies
title_fullStr Pooling fMRI Data: Meta-Analysis, Mega-Analysis and Multi-Center Studies
title_full_unstemmed Pooling fMRI Data: Meta-Analysis, Mega-Analysis and Multi-Center Studies
title_short Pooling fMRI Data: Meta-Analysis, Mega-Analysis and Multi-Center Studies
title_sort pooling fmri data: meta-analysis, mega-analysis and multi-center studies
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2759345/
https://www.ncbi.nlm.nih.gov/pubmed/19826498
http://dx.doi.org/10.3389/neuro.11.033.2009
work_keys_str_mv AT costafredasergig poolingfmridatametaanalysismegaanalysisandmulticenterstudies