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Institutional Repository [SANDBOX]
Technical University of Crete
Technical University of Crete
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| URI: | http://purl.tuc.gr/dl/dias/CC30C001-EE33-498B-A39D-A0C4D1C047C9 | ||
| Year | 2015 | ||
| Type of Item | Conference Full Paper | ||
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| Bibliographic Citation | M. Antonakakis, S. I. Dimitriadis, M. Zervakis, R. Rezaie, A. B Feremi, S. Micheloyannis, G. Zouridakis and A. C. Papanicolaou, "Comparison of brain network models using cross-frequency coupling and attack strategies," in 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2015, pp. 7426-7429. doi: 10.1109/EMBC.2015.7320108 https://doi.org/10.1109/EMBC.2015.7320108 | ||
| Appears in Collections |
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.
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