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Technical University of Crete
Technical University of Crete
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| URI | http://purl.tuc.gr/dl/dias/FDBA6415-F8AF-4EB5-94DD-EC9611AEA28F | - |
| Identifier | https://doi.org/10.3389/fncom.2019.00090 | - |
| Identifier | https://www.frontiersin.org/articles/10.3389/fncom.2019.00090/full | - |
| Language | en | - |
| Extent | 19 pages | en |
| Extent | 6,85 megabytes | en |
| Title | Aberrant whole-brain transitions and dynamics of spontaneous network microstates in mild traumatic brain injury | en |
| Creator | Antonakakis Marios | en |
| Creator | Αντωνακακης Μαριος | el |
| Creator | Dimitriadis Stavros I. | en |
| Creator | Zervakis Michail | en |
| Creator | Ζερβακης Μιχαηλ | el |
| Creator | Papanicolaou, Andrew C | en |
| Creator | Zouridakis, George | en |
| Publisher | Frontiers Media | en |
| Content Summary | Dynamic Functional Connectivity (DFC) analysis is a promising approach for the characterization of brain electrophysiological activity. In this study, we investigated abnormal alterations due to mild Traumatic Brain Injury (mTBI) using DFC of the source reconstructed magnetoencephalographic (MEG) resting-state recordings. Brain activity in several well-known frequency bands was first reconstructed using beamforming of the MEG data to determine ninety anatomical brain regions of interest. A DFC graph was formulated using the imaginary part of phase-locking values, which were obtained from 30 mTBI patients and 50 healthy controls (HC). Subsequently, we estimated normalized Laplacian transformations of individual, statistically and topologically filtered quasi-static graphs. The corresponding eigenvalues of each node synchronization were then computed and through the neural-gas algorithm, we quantized the evolution of the eigenvalues resulting in distinct network microstates (NMstates). The discrimination level between the two groups was assessed using an iterative cross-validation classification scheme with features either the NMstates in each frequency band, or the combination of the so-called chronnectomics (flexibility index, occupancy time of NMstate, and Dwell time) with the complexity index over the evolution of the NMstates across all frequency bands. Classification performance based on chronnectomics showed 80% accuracy, 99% sensitivity, and 49% specificity. However, performance was much higher (accuracy: 91–97%, sensitivity: 100%, and specificity: 77–93%) when focusing on the microstates. Exploring the mean node degree within and between brain anatomical networks (default mode network, frontoparietal, occipital, cingulo-opercular, and sensorimotor), a reduced pattern occurred from lower to higher frequency bands, with statistically significant stronger degrees for the HC than the mTBI group. A higher entropic profile on the temporal evolution of the modularity index was observed for both NMstates for the mTBI group across frequencies. A significant difference in the flexibility index was observed between the two groups for the β frequency band. The latter finding may support a central role of the thalamus impairment in mTBI. The current study considers a complete set of frequency-dependent connectomic markers of mTBI-caused alterations in brain connectivity that potentially could serve as markers to assess the return of an injured subject back to normality. | en |
| Type of Item | Peer-Reviewed Journal Publication | en |
| Type of Item | Δημοσίευση σε Περιοδικό με Κριτές | el |
| License | http://creativecommons.org/licenses/by/4.0/ | en |
| Date of Item | 2021-09-21 | - |
| Date of Publication | 2020 | - |
| Subject | MEG | en |
| Subject | mTBI | en |
| Subject | Beamforming | en |
| Subject | Dynamic functional connectivity analysis | en |
| Subject | Network microstates | en |
| Subject | Symbolic dynamics | en |
| Subject | Chronnectomics | en |
| Subject | Connectomic biomarkers | en |
| Bibliographic Citation | M. Antonakakis, S. I. Dimitriadis, M. Zervakis, A. C. Papanicolaou and G. Zouridakis, “Aberrant whole-brain transitions and dynamics of spontaneous network microstates in mild traumatic brain injury,” Front. Comput. Neurosci., vol. 13, Jan. 2020. doi: 10.3389/fncom.2019.00090 | en |
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