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

Niu, Z and Nie, Y and Zhou, Q and Zhu, L and Wei, J (2016) A brain-region-based meta-analysis method utilizing the Apriori algorithm. BMC Neuroscience, 17 (1).

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Abstract

© 2016 The Author(s). 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.


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Details

Item Type: Article
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Niu, Zzhniu@pitt.eduZHNIU
Nie, Y
Zhou, Q
Zhu, L
Wei, J
Date: 18 May 2016
Date Type: Publication
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Journal or Publication Title: BMC Neuroscience
Volume: 17
Number: 1
DOI or Unique Handle: 10.1186/s12868-016-0257-8
Institution: University of Pittsburgh
Schools and Programs: School of Information Sciences > Information Science
Refereed: Yes
Date Deposited: 22 Jul 2016 19:17
Last Modified: 13 Oct 2017 22:58
URI: http://d-scholarship.pitt.edu/id/eprint/28668

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