<efrbr:recordSet xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:efrbr="http://vfrbr.info/efrbr/1.1" xmlns:efrbr-work="http://vfrbr.info/efrbr/1.1/work" xmlns:efrbr-expression="http://vfrbr.info/efrbr/1.1/expression" xmlns:efrbr-manifestation="http://vfrbr.info/efrbr/1.1/manifestation" xmlns:efrbr-person="http://vfrbr.info/efrbr/1.1/person" xmlns:efrbr-corporateBody="http://vfrbr.info/efrbr/1.1/corporateBody" xmlns:efrbr-concept="http://vfrbr.info/efrbr/1.1/concept" xmlns:efrbr-structure="http://vfrbr.info/efrbr/1.1/structure" xmlns:efrbr-responsible="http://vfrbr.info/efrbr/1.1/responsible" xmlns:efrbr-subject="http://vfrbr.info/efrbr/1.1/subject" xmlns:efrbr-other="http://vfrbr.info/efrbr/1.1/other" xsi:schemaLocation="http://vfrbr.info/efrbr/1.1 http://vfrbr.info/schemas/1.1/efrbr.xsd"><efrbr:entities><efrbr-work:work identifier="http://purl.tuc.gr/dl/dias/CC30C001-EE33-498B-A39D-A0C4D1C047C9"><efrbr-work:titleOfTheWork>Comparison of brain network models using cross-frequency coupling and attack strategies</efrbr-work:titleOfTheWork></efrbr-work:work><efrbr-expression:expression identifier="http://purl.tuc.gr/dl/dias/CC30C001-EE33-498B-A39D-A0C4D1C047C9"><efrbr-expression:titleOfTheExpression>Comparison of brain network models using cross-frequency coupling and attack strategies</efrbr-expression:titleOfTheExpression><efrbr-expression:formOfExpression vocabulary="DIAS:TYPES">
            Πλήρης Δημοσίευση σε Συνέδριο
            Conference Full Paper
         </efrbr-expression:formOfExpression><efrbr-expression:dateOfExpression type="issued">2015-10-26</efrbr-expression:dateOfExpression><efrbr-expression:dateOfExpression type="published">2015</efrbr-expression:dateOfExpression><efrbr-expression:languageOfExpression vocabulary="iso639-1">en</efrbr-expression:languageOfExpression><efrbr-expression:summarizationOfContent>Several neuroimaging studies have suggested that functional brain connectivity networks exhibit “small-world” characteristics, whereas recent studies based on structural data have proposed a “rich-club” organization of whereby hubs of high connection density tend to connect among themselves compared to nodes of lower density brain networks.
In this study, we adopted an “attack strategy” to compare the rich-club and small-world organizations and identify the model that describes best the topology of brain connectivity. We hypothesized that the highest reduction in global efficiency caused by a targeted attack on each model’s hubs would reveal the organization that better describes the topology of the underlying brain	. We applied this approach to magnetoencephalographic data obtained at rest from neurologically intact controls and mild traumatic brain injury patients. Functional connectivity networks were computed using phase-to-amplitude cross- frequency coupling between the δ and β frequency bands. Our results suggest that resting state MEG connectivity networks follow a rich-club organization.</efrbr-expression:summarizationOfContent><efrbr-expression:useRestrictionsOnTheExpression type="creative-commons">http://creativecommons.org/licenses/by/4.0/</efrbr-expression:useRestrictionsOnTheExpression><efrbr-expression:note type="page range">7426-7429</efrbr-expression:note><efrbr-expression:note type="conference name">37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society</efrbr-expression:note></efrbr-expression:expression><efrbr-person:person identifier="http://users.isc.tuc.gr/~mzervakis"><efrbr-person:nameOfPerson vocabulary="TUC:LDAP">
            Zervakis Michail
            Ζερβακης Μιχαηλ
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            Rezaie Roozbeh
         </efrbr-person:nameOfPerson></efrbr-person:person><efrbr-person:person identifier="http://users.isc.tuc.gr/~mantonakakis"><efrbr-person:nameOfPerson vocabulary="TUC:LDAP">
            Antonakakis Marios
            Αντωνακακης Μαριος
         </efrbr-person:nameOfPerson></efrbr-person:person><efrbr-person:person identifier="26207EA1-7CC6-4410-8887-E521B7563349"><efrbr-person:nameOfPerson vocabulary="">
            Dimitriadis Stavros I.
         </efrbr-person:nameOfPerson></efrbr-person:person><efrbr-person:person identifier="E82DB25C-3280-48EB-BFB6-159436C7ED51"><efrbr-person:nameOfPerson vocabulary="">
            Babajani-Feremi Abbas
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            Μιχελογιάννης Σήφης
            Micheloyannis Sifis
            Michelogiannis Sifis
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            Zouridakis, George
         </efrbr-person:nameOfPerson></efrbr-person:person><efrbr-person:person identifier="http://viaf.org/viaf/71516599"><efrbr-person:nameOfPerson vocabulary="VIAF">
            Papanicolaou, Andrew C
         </efrbr-person:nameOfPerson></efrbr-person:person><efrbr-corporateBody:corporateBody identifier="http://www.ieee.org/index.html"><efrbr-corporateBody:nameOfTheCorporateBody vocabulary="S/R:PUBLISHERS">
            Institute of Electrical and Electronics Engineers
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            EEG
            Electroencephalography
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