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- W7524991 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." @default.
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- W7524991 date "2012-07-20" @default.
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- W7524991 title "EEG Based Biomarker Identification Using Graph-Theoretic Concepts: Case Study in Alcoholism" @default.
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- W7524991 doi "https://doi.org/10.1007/978-1-4614-4133-5_9" @default.
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