Search bioRxiv⌕ Search

Biology subjects

Zervakis, M.

Publications and source records attributed to Zervakis, M..

2 recordsLinked to original sources
bioRxiv2019

Alterations in Dynamic Spontaneous Network Microstates in Mild Traumatic Brain Injury: A MEG Beamformed Dynamic Connectivity Analysis

Dynamic functional connectivity (DFC) analysis has attracted interest in the last years for the characterization of brain electrophysiological activity at rest. In this work, we investigated changes in mild Traumatic Brain Injury (mTBI) patients using magnetoencephalographic (MEG) resting-state recordings and a DFC approach. The activity of several well-known brain rhythms was first beamformed using linearly constrained minimum norm variance of the MEG data to determine ninety anatomical brain regions of interest. A DFC graph was formulated using the imaginary part of phase lag value which were obtained from 30 mTBI patients and 50 normal controls. Filtering each quasi-static graph statistically and topologically, we estimated a normalized Laplacian transformation of every single quasistatic graph based on the degree of each node. Then, the related eigenvalues of the synchronization of each node were computed by incorporating the complete topology. Using the neural-gas algorithm, we modelled the evolution of the eigenvalues for each group, resulting in distinct FC microstates (FCstates). Using the so-called chronnectomics (transition rate, occupancy time of FCstate, and Dwell time) and complexity index over the evolution of the FCstates, we evaluated the level of discrimination and derived statistical differences between the two groups. In both groups, we detected equal number of FCstates with statistically significant transitions in the {delta}, , {beta}, and {gamma}low frequency bands. The discrimination rate between the two groups was very high in the {theta} and {gamma}low bands, followed by a statistically significant difference between the two groups in all the chronnectomics and the complexity index. Statistically significant differences in the degree of several anatomical subnetworks (BAN - brain anatomical networks: default mode network; frontoparietal; occipital; cingulo-opercular; and sensorimotor) were revealed in most FCstates for the {theta}, , {beta}, and {gamma}low brain rhythms, indicating a higher level of communication within and between the BAN in the mTBI group. In our previous studies, we focused on intra- and inter-frequency couplings of static FC. Our current study summarizes a complete set of frequency-dependent connectomic markers of mTBI-caused alterations in brain connectivity that potentially could also serve as markers to assess the return of an injured subject back to normality.

neuroscience↗
bioRxiv2017

Reconfiguration Of Dominant Coupling Modes In Mild Traumatic Brain Injury Mediated By δ-Band Activity: A Resting State MEG Study

During the last few years, rich-club (RC) organization has been studied as a possible brain-connectivity organization model for large-scale brain networks. At the same time, empirical and simulated data of neurophysiological models have demonstrated the significant role of intra-frequency and inter-frequency coupling among distinct brain areas. The current study investigates further the importance of these couplings using recordings of resting-state magnetoencephalographic activity obtained from 30 mild traumatic brain injury (mTBI) subjects and 50 healthy controls. Intra-frequency and inter-frequency coupling modes are incorporated in a single graph to detect group differences within individual rich-club subnetworks (type I networks) and networks connecting RC nodes with the rest of the nodes (type II networks). Our results show a higher probability of inter-frequency coupling for ({delta}-{gamma}1), ({delta}-{gamma}2), ({theta}-{beta}), ({theta}-{gamma}2), (-{gamma}2), ({gamma}1-{gamma}2) and intra-frequency coupling for ({gamma}1-{gamma}1) and ({delta}-{delta}) for both type I and type II networks in the mTBI group. Additionally, mTBI and control subjects can be correctly classified with high accuracy (98.6%), whereas a general linear regression model can effectively predict the subject group using the ratio of type I and type II coupling in the ({delta}, {theta}), ({delta}, {beta}), ({delta}, {gamma}1), and ({delta}, {gamma}2) frequency pairs. These findings support the presence of an RC organization simultaneously with dominant frequency interactions within a single functional graph. Our results demonstrate a hyperactivation of intrinsic RC networks in mTBI subjects compared to controls, which can be seen as a plausible compensatory mechanism for alternative frequency-dependent routes of information flow in mTBI subjects.

neuroscience↗