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Non-linear synchronization methods on magnetoencephalographic (MEG) recordings

Antonakakis Marios

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TitleNon-linear synchronization methods on magnetoencephalographic (MEG) recordings en
CreatorAntonakakis Mariosen
CreatorΑντωνακακης Μαριοςel
Contributor [Thesis Supervisor]Zervakis Michalisen
Contributor [Thesis Supervisor]Ζερβακης Μιχαληςel
Contributor [Committee Member]Lagoudakis Michaelen
Contributor [Committee Member]Λαγουδακης Μιχαηλel
Contributor [Committee Member]Mania Aikaterinien
Contributor [Committee Member]Μανια Αικατερινηel
PublisherTechnical University of Creteen
PublisherΠολυτεχνείο Κρήτηςel
Academic UnitTechnical University of Crete::School of Electronic and Computer Engineeringen
Academic UnitΠολυτεχνείο Κρήτης::Σχολή Ηλεκτρονικών Μηχανικών και Μηχανικών Υπολογιστώνel
Content SummaryCross-frequency coupling (CFC) is thought to represent a basic mechanism of functional integration of neural networks across distant brain regions. Furthermore, 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 brain networks, whereby nodes of high connection density tend to connect among themselves compared to nodes of lower density. In this study, CFC profiles are analyzed from resting state Magnetoencephalographic (MEG) recordings obtained from 30 mild traumatic brain injury (mTBI) patients and 50 controls. The non-linear synchronization metric, mutual information (MI) is used 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 (FCGs), a tensor representation and tensor subspace analysis is employed to identify an set of features with low dimensions for subject classification as mTBI or control. Keeping FCGs from the optimal set of features, an “attack strategy” to is developed to compare the rich-club and small-world organizations and identify the model that describes best the topology of brain connectivity. Results show that the controls form 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. Finally, the results suggest that resting state MEG connectivity networks follow a rich-club organization. These findings indicate that the 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.en
Type of ItemΜεταπτυχιακή Διατριβήel
Type of ItemMaster Thesisen
Date of Item2015-09-08-
Date of Publication2015-
SubjectBiomedical signal processingen
SubjectBiological neural networksen
SubjectNets, Neural (Neurobiology)en
SubjectNetworks, Neural (Neurobiology)en
SubjectNeural nets (Neurobiology)en
Subjectneural networks neurobiologyen
Subjectbiological neural networksen
Subjectnets neural neurobiologyen
Subjectnetworks neural neurobiologyen
Subjectneural nets neurobiologyen
SubjectGraph theory--Extremal problemsen
SubjectGraphs, Theory ofen
SubjectTheory of graphsen
Subjectgraph theoryen
Subjectgraph theory extremal problemsen
Subjectgraphs theory ofen
Subjecttheory of graphsen
SubjectLearning, Machineen
Subjectmachine learningen
Subjectlearning machineen
Bibliographic CitationMarios Antonakakis, "Non-linear synchronization methods on magnetoencephalographic (MEG) recordings ", Master Thesis, School of Electronic and Computer Engineering, Technical University of Crete, Chania, Greece, 2015en

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