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

Belyk, Michel; Brown, Steven; Kotz, Sonja A.

doi:10.1016/j.dib.2017.06.003

Item

ITEM ACTIONSEXPORT

Add to Basket

Local TagsRelease HistoryDetailsSummary

Released

Journal Article

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

MPS-Authors

/persons/resource/persons19791

Kotz, Sonja A.
Department of Psychology, Neuroscience, and Behaviour, McMaster University, Hamilton, ON, Canada;
Department Neuropsychology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

External Resource

No external resources are shared

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

Fulltext (public)

Belyk_2017.pdf
(Publisher version), 712KB

Supplementary Material (public)

There is no public supplementary material available

Citation

Belyk, M., Brown, S., & Kotz, S. A. (2017). Demonstration and validation of Kernel Density Estimation for spatial meta-analyses in cognitive neuroscience using simulated data. Data in Brief, 13, 346-352. doi:10.1016/j.dib.2017.06.003.

Cite as: https://hdl.handle.net/21.11116/0000-0004-A66C-8

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.