PittsburghV1, PascalFrancis, Curation, bibRecord, 001B58

A new meta-analytic method for neuroimaging studies that combines reported peak coordinates and statistical parametric maps

Identifieur interne : 001B58 ( PascalFrancis/Curation ); précédent : 001B57; suivant : 001B59

A new meta-analytic method for neuroimaging studies that combines reported peak coordinates and statistical parametric maps

Auteurs : J. Radua [Royaume-Uni, Espagne] ; D. Mataix-Cols [Royaume-Uni] ; M. L. Phillips [États-Unis, Royaume-Uni] ; W. El-Hage [France] ; D. M. Kronhaus [Royaume-Uni] ; N. Cardoner [Espagne] ; S. Surguladze [Royaume-Uni]

Source :

European psychiatry [ 0924-9338 ] ; 2012.

RBID : Pascal:12-0442295

Descripteurs français

Pascal (Inist)
- Métaanalyse, Article synthèse, Méthode paramétrique, Cartographie, Imagerie cérébrale.
Wicri :
- topic : Cartographie.

English descriptors

KwdEn :
- Cartography, Metaanalysis, Neuroimaging, Parametric method, Review.

Abstract

Meta-analyses are essential to summarize the results of the growing number of neuroimaging studies in psychiatry, neurology and allied disciplines. Image-based meta-analyses use full image information (i.e. the statistical parametric maps) and well-established statistics, but images are rarely available making them highly unfeasible. Peak-probability meta-analyses such as activation likelihood estimation (ALE) or multilevel kernel density analysis (MKDA) are more feasible as they only need reported peak coordinates. Signed-differences methods, such as signed differential mapping (SDM) build upon the positive features of existing peak-probability methods and enable meta-analyses of studies comparing patients with controls. In this paper we present a new version of SDM, named Effect Size SDM (ES-SDM), which enables the combination of statistical parametric maps and peak coordinates and uses well-established statistics. We validated the new method by comparing the results of an ES-SDM meta-analysis of studies on the brain response to fearful faces with the results of a pooled analysis of the original individual data. The results showed that ES-SDM is a valid and reliable coordinate-based method, whose performance might be additionally increased by including statistical parametric maps. We anticipate that ES-SDM will be a helpful tool for researchers in the fields of psychiatry, neurology and allied disciplines.

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A11	`05`	`1`		`@1 KRONHAUS (D. M.)`
A11	`06`	`1`		`@1 CARDONER (N.)`
A11	`07`	`1`		`@1 SURGULADZE (S.)`
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A14	`03`			`@1 Department of psychiatry, western psychiatric institute and clinic, university of Pittsburgh school of medicine @2 Pittsburgh @3 USA @Z 3 aut.`
A14	`04`			`@1 Department of psychological medicine, Cardiff university school of medicine @2 Cardiff @3 GBR @Z 3 aut.`
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C01	`01`		`ENG`	@0 Meta-analyses are essential to summarize the results of the growing number of neuroimaging studies in psychiatry, neurology and allied disciplines. Image-based meta-analyses use full image information (i.e. the statistical parametric maps) and well-established statistics, but images are rarely available making them highly unfeasible. Peak-probability meta-analyses such as activation likelihood estimation (ALE) or multilevel kernel density analysis (MKDA) are more feasible as they only need reported peak coordinates. Signed-differences methods, such as signed differential mapping (SDM) build upon the positive features of existing peak-probability methods and enable meta-analyses of studies comparing patients with controls. In this paper we present a new version of SDM, named Effect Size SDM (ES-SDM), which enables the combination of statistical parametric maps and peak coordinates and uses well-established statistics. We validated the new method by comparing the results of an ES-SDM meta-analysis of studies on the brain response to fearful faces with the results of a pooled analysis of the original individual data. The results showed that ES-SDM is a valid and reliable coordinate-based method, whose performance might be additionally increased by including statistical parametric maps. We anticipate that ES-SDM will be a helpful tool for researchers in the fields of psychiatry, neurology and allied disciplines.
C02	`01`	`X`		`@0 002B18`
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C03	`04`	`X`	`FRE`	`@0 Cartographie @5 04`
C03	`04`	`X`	`ENG`	`@0 Cartography @5 04`
C03	`04`	`X`	`SPA`	`@0 Cartografía @5 04`
C03	`05`	`X`	`FRE`	`@0 Imagerie cérébrale @4 CD @5 96`
C03	`05`	`X`	`ENG`	`@0 Neuroimaging @4 CD @5 96`
C03	`05`	`X`	`SPA`	`@0 Imágenes del cerebro @4 CD @5 96`
N21				`@1 345`
N44	`01`			`@1 OTO`
N82				`@1 OTO`

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<front><div type="abstract" xml:lang="en">Meta-analyses are essential to summarize the results of the growing number of neuroimaging studies in psychiatry, neurology and allied disciplines. Image-based meta-analyses use full image information (i.e. the statistical parametric maps) and well-established statistics, but images are rarely available making them highly unfeasible. Peak-probability meta-analyses such as activation likelihood estimation (ALE) or multilevel kernel density analysis (MKDA) are more feasible as they only need reported peak coordinates. Signed-differences methods, such as signed differential mapping (SDM) build upon the positive features of existing peak-probability methods and enable meta-analyses of studies comparing patients with controls. In this paper we present a new version of SDM, named Effect Size SDM (ES-SDM), which enables the combination of statistical parametric maps and peak coordinates and uses well-established statistics. We validated the new method by comparing the results of an ES-SDM meta-analysis of studies on the brain response to fearful faces with the results of a pooled analysis of the original individual data. The results showed that ES-SDM is a valid and reliable coordinate-based method, whose performance might be additionally increased by including statistical parametric maps. We anticipate that ES-SDM will be a helpful tool for researchers in the fields of psychiatry, neurology and allied disciplines.</div>
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<fC01 i1="01" l="ENG"><s0>Meta-analyses are essential to summarize the results of the growing number of neuroimaging studies in psychiatry, neurology and allied disciplines. Image-based meta-analyses use full image information (i.e. the statistical parametric maps) and well-established statistics, but images are rarely available making them highly unfeasible. Peak-probability meta-analyses such as activation likelihood estimation (ALE) or multilevel kernel density analysis (MKDA) are more feasible as they only need reported peak coordinates. Signed-differences methods, such as signed differential mapping (SDM) build upon the positive features of existing peak-probability methods and enable meta-analyses of studies comparing patients with controls. In this paper we present a new version of SDM, named Effect Size SDM (ES-SDM), which enables the combination of statistical parametric maps and peak coordinates and uses well-established statistics. We validated the new method by comparing the results of an ES-SDM meta-analysis of studies on the brain response to fearful faces with the results of a pooled analysis of the original individual data. The results showed that ES-SDM is a valid and reliable coordinate-based method, whose performance might be additionally increased by including statistical parametric maps. We anticipate that ES-SDM will be a helpful tool for researchers in the fields of psychiatry, neurology and allied disciplines.</s0>
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   |texte=   A new meta-analytic method for neuroimaging studies that combines reported peak coordinates and statistical parametric maps
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A new meta-analytic method for neuroimaging studies that combines reported peak coordinates and statistical parametric maps

A new meta-analytic method for neuroimaging studies that combines reported peak coordinates and statistical parametric maps

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