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Institutional Repository [SANDBOX]
Technical University of Crete
Technical University of Crete
EN
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| URI: | http://purl.tuc.gr/dl/dias/4589523D-31DD-4D2C-8983-E40AE8249C1E | ||
| Year | 2015 | ||
| Type of Item | Master Thesis | ||
| License |
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| Bibliographic Citation | Marios Antonakakis, "Non-linear synchronization methods on magnetoencephalographic (MEG) recordings ", Master Thesis, School of Electronic and Computer Engineering, Technical University of Crete, Chania, Greece, 2015 https://doi.org/10.26233/heallink.tuc.29031 | ||
| Appears in Collections |
Cross-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.
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