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2013 IEEE 13th International Conference on Bioinformatics and Bioengineering (BIBE) (2013)
Chania, Greece
Nov. 10, 2013 to Nov. 13, 2013
ISBN: 978-1-4799-3163-7
pp: 1-4
Nantia D. Iakovidou , Department of Informatics, Data Engineering Laboratory (DELAB), Aristotle University of Thessaloniki, 54124, Greece
Stavros I. Dimitriadis , Department of Informatics, Artificial Intelligence & Information Analysis Laboratory (AIIA), Aristotle University of Thessaloniki, 54124, Greece
Nikos A. Laskaris , Department of Informatics, Artificial Intelligence & Information Analysis Laboratory (AIIA), Aristotle University of Thessaloniki, 54124, Greece
Kostas Tsichlas , Department of Informatics, Data Engineering Laboratory (DELAB), Aristotle University of Thessaloniki, 54124, Greece
ABSTRACT
Dynamic recordings of functional activity maps can naturally and efficiently be represented in the form of functional/effective connectivity networks. New methods for mapping synaptic connections and recording neural signals generate rich and complex data about the structure and dynamics of brain networks. To study the most complex network in nature, the brain, there is need to integrate a huge amount of brain networks collected from laboratories over the world in large databases. Human Brain Project (Europe and USA) aims to explore brain functionality in various ways. Brain networks are central to achieving the goals of this ambitious plan. However, the immense amount of thousands of brain networks, prevent an easy way to utilizable knowledge. In this paper, we demonstrate a data-driven approach that discovers consistent patterns from a collection of brain networks via a querying approach: formulating a query of “finding an increasing or a decreasing consistent subgraph over an amount of subjects” after taking the difference between two sets of graphs referred as two conditions (an active and a baseline). Experiments demonstrated that our data-driven approach allows identifying frequency-dependent selective spatial pattern changes of the EEG functional connectivity network during a mental task. This is the first time that a method fully exploits the connectivity weights of a brain network to discover consistent subgraph patterns.
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CITATION

N. D. Iakovidou, S. I. Dimitriadis, N. A. Laskaris and K. Tsichlas, "Querying functional brain connectomics to discover consistent subgraph patterns," 13th IEEE International Conference on BioInformatics and BioEngineering(BIBE), Chania, Greece Greece, 2014, pp. 1-4.
doi:10.1109/BIBE.2013.6701655
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