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Optimization and Data Analysis in Biomedical Informatics pp 171–189Cite as

EEG Based Biomarker Identification Using Graph-Theoretic Concepts: Case Study in Alcoholism

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Part of the book series: Fields Institute Communications ((FIC,volume 63))

Abstract

Over the past few years there has been an increased interest in studying the under-lying neural mechanism of cognitive brain activity in order to identify features capable of discriminating brain engagement tasks in terms of cognitive load. Rather recently there is a growing suspicion that the noninvasive technique of high-resolution quantitative electroencephalography may provide features able to identify and quantify functional interdependencies among synchronized brain lobes based on graph-theoretic algorithms. In the emerging view of translational medicine, graph-theoretic measures and tools, currently used to describe large scale networks, can be potential candidates for future inclusion in a clinical trial setting. This paper discusses different families of graph theoretical measures able to capture the topology of brain networks as potential EEG-based biomarkers. As a case study, statistically significant graph-theoretic indices, capable of capturing and quantifying collective motifs in an alcoholism paradigm are identified and presented.

Mathematics Subject Classification (2010): Primary 54C40, 14E20; Secondary 46E25, 20C20

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Acknowledgements

The authors would like to thank Henri Begleiter at the Neurodynamics Laboratory, State University of NY Health Center at Brooklyn for kindly providing the EEG dataset.

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Authors and Affiliations

  1. Institute of Computer Science, Foundation for Research and Technology, Heraklion, Greece

    Vangelis Sakkalis & Konstantinos Marias

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  1. Vangelis Sakkalis
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  2. Konstantinos Marias
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Correspondence to Vangelis Sakkalis .

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Editors and Affiliations

  1. , Department of Industrial & Systems Engin, University of Florida, Weil Hall 401, Gainesville, 32611, Florida, USA

    Panos M. Pardalos

  2. , Department of Mathematics, University of Waterloo, University Avenue West 200, Waterloo, N2L 3G1, Ontario, Canada

    Thomas F. Coleman

  3. , Department of Industrial Engineering, University of Central Florida, Central Florida Blvd 4000, Orlando, 32816, Florida, USA

    Petros Xanthopoulos

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Sakkalis, V., Marias, K. (2013). EEG Based Biomarker Identification Using Graph-Theoretic Concepts: Case Study in Alcoholism. In: Pardalos, P., Coleman, T., Xanthopoulos, P. (eds) Optimization and Data Analysis in Biomedical Informatics. Fields Institute Communications, vol 63. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-4133-5_9

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