Demonstration and validation of Kernel Density Estimation for spatial meta-analyses in cognitive neuroscience using simulated data

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

The data presented in this article are related to the research article entitled "Convergence of semantics and emotional expression within the IFG pars orbitalis" (Belyk et al., 2017) [1]. The research article reports a spatial meta-analysis of brain imaging experiments on the perception of semantic compared to emotional communicative signals in humans. This Data in Brief article demonstrates and validates the use of Kernel Density Estimation (KDE) as a novel statistical approach to neuroimaging data. First, we performed a side-by-side comparison of KDE with a previously published meta-analysis that applied activation likelihood estimation, which is the predominant approach to meta-analyses in cognitive neuroscience. Second, we analyzed data simulated with known spatial properties to test the sensitivity of KDE to varying degrees of spatial separation. KDE successfully detected true spatial differences in simulated data and displayed few false positives when no true differences were present. R code to simulate and analyze these data is made publicly available to facilitate the further evaluation of KDE for neuroimaging data and its dissemination to cognitive neuroscientists.

Original languageEnglish
Pages (from-to)346-352
Number of pages7
JournalData in brief
Volume13
DOIs
Publication statusPublished - Aug 2017

Keywords

  • Journal Article
  • Activation likelihood estimation
  • Cognitive neuroscience
  • Kernel Density Estimation
  • Inferior frontal gyrus
  • Meta-analysis

Cite this

@article{bedc7033e6024d518b1bb0c49712812a,
title = "Demonstration and validation of Kernel Density Estimation for spatial meta-analyses in cognitive neuroscience using simulated data",
abstract = "The data presented in this article are related to the research article entitled {"}Convergence of semantics and emotional expression within the IFG pars orbitalis{"} (Belyk et al., 2017) [1]. The research article reports a spatial meta-analysis of brain imaging experiments on the perception of semantic compared to emotional communicative signals in humans. This Data in Brief article demonstrates and validates the use of Kernel Density Estimation (KDE) as a novel statistical approach to neuroimaging data. First, we performed a side-by-side comparison of KDE with a previously published meta-analysis that applied activation likelihood estimation, which is the predominant approach to meta-analyses in cognitive neuroscience. Second, we analyzed data simulated with known spatial properties to test the sensitivity of KDE to varying degrees of spatial separation. KDE successfully detected true spatial differences in simulated data and displayed few false positives when no true differences were present. R code to simulate and analyze these data is made publicly available to facilitate the further evaluation of KDE for neuroimaging data and its dissemination to cognitive neuroscientists.",
keywords = "Journal Article, Activation likelihood estimation, Cognitive neuroscience, Kernel Density Estimation, Inferior frontal gyrus, Meta-analysis",
author = "Michel Belyk and Steven Brown and Kotz, {Sonja A.}",
year = "2017",
month = "8",
doi = "10.1016/j.dib.2017.06.003",
language = "English",
volume = "13",
pages = "346--352",
journal = "Data in brief",
issn = "2352-3409",
publisher = "Elsevier Science",

}

Demonstration and validation of Kernel Density Estimation for spatial meta-analyses in cognitive neuroscience using simulated data. / Belyk, Michel; Brown, Steven; Kotz, Sonja A.

In: Data in brief, Vol. 13, 08.2017, p. 346-352.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Demonstration and validation of Kernel Density Estimation for spatial meta-analyses in cognitive neuroscience using simulated data

AU - Belyk, Michel

AU - Brown, Steven

AU - Kotz, Sonja A.

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N2 - The data presented in this article are related to the research article entitled "Convergence of semantics and emotional expression within the IFG pars orbitalis" (Belyk et al., 2017) [1]. The research article reports a spatial meta-analysis of brain imaging experiments on the perception of semantic compared to emotional communicative signals in humans. This Data in Brief article demonstrates and validates the use of Kernel Density Estimation (KDE) as a novel statistical approach to neuroimaging data. First, we performed a side-by-side comparison of KDE with a previously published meta-analysis that applied activation likelihood estimation, which is the predominant approach to meta-analyses in cognitive neuroscience. Second, we analyzed data simulated with known spatial properties to test the sensitivity of KDE to varying degrees of spatial separation. KDE successfully detected true spatial differences in simulated data and displayed few false positives when no true differences were present. R code to simulate and analyze these data is made publicly available to facilitate the further evaluation of KDE for neuroimaging data and its dissemination to cognitive neuroscientists.

AB - The data presented in this article are related to the research article entitled "Convergence of semantics and emotional expression within the IFG pars orbitalis" (Belyk et al., 2017) [1]. The research article reports a spatial meta-analysis of brain imaging experiments on the perception of semantic compared to emotional communicative signals in humans. This Data in Brief article demonstrates and validates the use of Kernel Density Estimation (KDE) as a novel statistical approach to neuroimaging data. First, we performed a side-by-side comparison of KDE with a previously published meta-analysis that applied activation likelihood estimation, which is the predominant approach to meta-analyses in cognitive neuroscience. Second, we analyzed data simulated with known spatial properties to test the sensitivity of KDE to varying degrees of spatial separation. KDE successfully detected true spatial differences in simulated data and displayed few false positives when no true differences were present. R code to simulate and analyze these data is made publicly available to facilitate the further evaluation of KDE for neuroimaging data and its dissemination to cognitive neuroscientists.

KW - Journal Article

KW - Activation likelihood estimation

KW - Cognitive neuroscience

KW - Kernel Density Estimation

KW - Inferior frontal gyrus

KW - Meta-analysis

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