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Neuroimage. 2014 Apr 15;90:390-402. doi: 10.1016/j.neuroimage.2013.12.024. Epub 2013 Dec 21.

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

Author information

1
Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, 52425 Jülich, Germany; Department of Diagnostic and Interventional Radiology, University Dusseldorf, Medical Faculty, D-40225 Dusseldorf, Germany. Electronic address: j.caspers@fz-juelich.de.
2
Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, 52425 Jülich, Germany; JARA-BRAIN, Jülich-Aachen Research Alliance, 52425 Jülich, Germany; Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, 52074 Aachen, Germany.
3
Department of Computer Science, FernUniversität in Hagen, 58084 Hagen, Germany.
4
Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, 52425 Jülich, Germany; Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, 52074 Aachen, Germany.
5
Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, 52425 Jülich, Germany; Institute of Clinical Neuroscience and Medical Psychology, University Hospital Düsseldorf, Düsseldorf, Germany.

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 modeling and the Apriori algorithm. By this, PaMiNI enables a data-driven detection of frequent co-activation patterns within neuroimaging datasets. The feasibility of the method is demonstrated by means of several analyses on simulated data as well as a real application. The analyses of the simulated data show that PaMiNI identifies the brain regions underlying the simulated activation foci and perfectly separates the co-activation patterns of the experiments in the simulations. Furthermore, PaMiNI still yields good results when activation foci of distinct brain regions become closer together or if they are non-Gaussian distributed. For the further evaluation, a real dataset on working memory experiments is used, which was previously examined in an ALE meta-analysis and hence allows a cross-validation of both methods. In this latter analysis, PaMiNI revealed a fronto-parietal "core" network of working memory and furthermore indicates a left-lateralization in this network. Finally, to encourage a widespread usage of this new method, the PaMiNI approach was implemented into a publicly available software system.

KEYWORDS:

Association analysis; BrainMap database; Coordinate-based meta-analysis; Gaussian mixture modeling; PaMiNI

PMID:
24365675
PMCID:
PMC4981640
DOI:
10.1016/j.neuroimage.2013.12.024
[Indexed for MEDLINE]
Free PMC Article

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