Meta-analytic connectivity modeling revisited: controlling for activation base rates.

ABSTRACT: Co-activation of distinct brain regions is a measure of functional interaction, or connectivity, between those regions. The co-activation pattern of a given region can be investigated using seed-based activation likelihood estimation meta-analysis of functional neuroimaging data stored in databases such as BrainMap. This method reveals inter-regional functional connectivity by determining brain regions that are consistently co-activated with a given region of interest (the "seed") across a broad range of experiments. In current implementations of this meta-analytic connectivity modeling (MACM), significant spatial convergence (i.e. consistent co-activation) is distinguished from noise by comparing it against an unbiased null-distribution of random spatial associations between experiments a

SUBMITTER: Langner R 

PROVIDER: S-EPMC4112007 | biostudies-literature | 2014 Oct

REPOSITORIES: biostudies-literature