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BMC Neurosci. 2016 May 18;17(1):23. doi: 10.1186/s12868-016-0257-8.

A brain-region-based meta-analysis method utilizing the Apriori algorithm.

Niu Z1,2,3, Nie Y4, Zhou Q4, Zhu L4, Wei J4.

Author information

1
School of Computer Science, Beijing Institute of Technology, Beijing, China. zniu@bit.edu.cn.
2
Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications, School of Computer Science, Beijing Institute of Technology, Beijing, China. zniu@bit.edu.cn.
3
The Information School, University of Pittsburgh, Pittsburgh, PA, 15260, USA. zniu@bit.edu.cn.
4
School of Computer Science, Beijing Institute of Technology, Beijing, China.

Abstract

BACKGROUND:

Brain network connectivity modeling is a crucial method for studying the brain's cognitive functions. Meta-analyses can unearth reliable results from individual studies. Meta-analytic connectivity modeling is a connectivity analysis method based on regions of interest (ROIs) which showed that meta-analyses could be used to discover brain network connectivity.

RESULTS:

In this paper, we propose a new meta-analysis method that can be used to find network connectivity models based on the Apriori algorithm, which has the potential to derive brain network connectivity models from activation information in the literature, without requiring ROIs. This method first extracts activation information from experimental studies that use cognitive tasks of the same category, and then maps the activation information to corresponding brain areas by using the automatic anatomical label atlas, after which the activation rate of these brain areas is calculated. Finally, using these brain areas, a potential brain network connectivity model is calculated based on the Apriori algorithm. The present study used this method to conduct a mining analysis on the citations in a language review article by Price (Neuroimage 62(2):816-847, 2012). The results showed that the obtained network connectivity model was consistent with that reported by Price.

CONCLUSIONS:

The proposed method is helpful to find brain network connectivity by mining the co-activation relationships among brain regions. Furthermore, results of the co-activation relationship analysis can be used as a priori knowledge for the corresponding dynamic causal modeling analysis, possibly achieving a significant dimension-reducing effect, thus increasing the efficiency of the dynamic causal modeling analysis.

KEYWORDS:

Apriori algorithm; Brain network connectivity; Co-activation relationship; Meta-analysis; Word reading; fMRI

PMID:
27194281
PMCID:
PMC4872339
DOI:
10.1186/s12868-016-0257-8
[Indexed for MEDLINE]
Free PMC Article

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