dc.contributor.author | Antonakakis, Marios | |
dc.contributor.author | Dimitriadis, Stavros I. | |
dc.contributor.author | Zervakis, Michalis | |
dc.contributor.author | Micheloyannis, Sifis | |
dc.contributor.author | Rezaie, Roozbeh | |
dc.contributor.author | Babajani-Ferem, Abbas | |
dc.contributor.author | Zouridakis, George | |
dc.contributor.author | Papanicolaou, Andrew C. | |
dc.date.accessioned | 2017-09-28T14:59:59Z | |
dc.date.available | 2017-09-28T14:59:59Z | |
dc.date.issued | 2016 | |
dc.identifier.citation | Marios Antonakakis, Stavros I. Dimitriadis, Michalis Zervakis, Sifis Micheloyannis, Roozbeh Rezaie, Abbas Babajani-Feremi, George Zouridakis, Andrew C. Papanicolaou, Altered cross-frequency coupling in resting-state MEG after mild traumatic brain injury, International Journal of Psychophysiology, Volume 102, April 2016, Pages 1-11, ISSN 0167-8760, https://doi.org/10.1016/j.ijpsycho.2016.02.002. | es_ES |
dc.identifier.issn | 0167-8760 | |
dc.identifier.uri | http://hdl.handle.net/10810/22738 | |
dc.description | Available online 22 February 2016 | es_ES |
dc.description.abstract | Cross-frequency coupling (CFC) is thought to represent a basic mechanism of functional integration of neural networks across distant brain regions. In this study, we analyzed CFC profiles from resting state Magnetoencephalographic (MEG) recordings obtained from 30 mild traumatic brain injury (mTBI) patients and 50 controls. We used mutual information (MI) to quantify the phase-to-amplitude coupling (PAC) of activity among the recording sensors in six nonoverlapping frequency bands. After forming the CFC-based functional connectivity graphs, we employed a tensor representation and tensor subspace analysis to identify the optimal set of features for subject classification as mTBI or control. Our results showed that controls formed a dense network of stronger local and global connections indicating higher functional integration compared to mTBI patients. Furthermore, mTBI patients could be separated from controls with more than 90% classification accuracy. These findings indicate that analysis of brain networks computed from resting-state MEG with PAC and tensorial representation of connectivity profiles may provide a valuable biomarker for the diagnosis of mTBI. | es_ES |
dc.description.sponsorship | The project reported here is part of a larger study, the Integrated
Clinical Protocol, conducted by the investigators and staff of theMission
ConnectMild Traumatic Brain Injury Translational Research Consortium
and supported by the Department of Defense Congressionally Directed
Medical Research Program. The graph representation and analysis is
supported by a THALES project (CYBERSENSORS — High Frequency
Monitoring Systemfor IntegratedWater ResourcesManagement of Rivers)
funded by the NSRF2007-13 of the GreekMinistry of Development.
The analysis methodology was partly supported by the “YPERTHEN”
project under the Interreg framework of Greek-Cyprus Co-operation
2007–2013. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | International Journal of Psychophysiology | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.subject | Magnetoencephalography (MEG) | es_ES |
dc.subject | Mild traumatic brain injury | es_ES |
dc.subject | Cross-frequency coupling | es_ES |
dc.subject | Tensors | es_ES |
dc.subject | Biomarkers | es_ES |
dc.title | Altered cross-frequency coupling in resting-state MEG after mild traumatic brain injury | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.holder | © 2016 Elsevier B.V. All rights reserved. | es_ES |
dc.relation.publisherversion | www.elsevier.com/locate/ijpsycho | es_ES |
dc.identifier.doi | 10.1016/j.ijpsycho.2016.02.002 | |