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Altered cross-frequency coupling in resting-state MEG after mild traumatic brain injury

Antonakakis Marios, Dimitriadis Stavros I., Zervakis Michail, Micheloyannis Sifis, Rezaie Roozbeh, Babajani-Feremi Abbas, Zouridakis, George, Papanicolaou, Andrew C

Πλήρης Εγγραφή


URI: http://purl.tuc.gr/dl/dias/340DD917-8765-4E11-8391-96066A8CD6F1
Έτος 2016
Τύπος Δημοσίευση σε Περιοδικό με Κριτές
Άδεια Χρήσης
Λεπτομέρειες
Βιβλιογραφική Αναφορά M. Antonakakis, S. I. Dimitriadis, M. Zervakis, S. Micheloyannis, R. Rezaie, A. Babajani-Feremi, G. Zouridakis and A. C. Papanicolaou, "Altered cross-frequency coupling in resting-state MEG after mild traumatic brain injury," Int. J. Psychophysiol., vol. 102, pp. 1-11, Apr. 2016. doi: 10.1016/j.ijpsycho.2016.02.002 https://doi.org/10.1016/j.ijpsycho.2016.02.002
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Περίληψη

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.

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