A novel meta-analytic approach: mining frequent co-activation patterns in neuroimaging databases.

ABSTRACT: In recent years, coordinate-based meta-analyses have become a powerful and widely used tool to study co-activity across neuroimaging experiments, a development that was supported by the emergence of large-scale neuroimaging databases like BrainMap. However, the evaluation of co-activation patterns is constrained by the fact that previous coordinate-based meta-analysis techniques like Activation Likelihood Estimation (ALE) and Multilevel Kernel Density Analysis (MKDA) reveal all brain regions that show convergent activity within a dataset without taking into account actual within-experiment co-occurrence patterns. To overcome this issue we here propose a novel meta-analytic approach named PaMiNI that utilizes a combination of two well-established data-mining techniques, Gaussian mixture mod

SUBMITTER: Caspers J 

PROVIDER: S-EPMC4981640 | biostudies-literature | 2014 Apr

REPOSITORIES: biostudies-literature