AbstractDynamic 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 (FCμstates). Using the so-called chronnectomics (transition rate, occupancy time of FCμstate, and Dwell time) and complexity index over the evolution of the FCμstates, we evaluated the level of discrimination and derived statistical differences between the two groups. In both groups, we detected equal number of FCμstates with statistically significant transitions in the δ, α, β, and γlow frequency bands. The discrimination rate between the two groups was very high in the θ and γlow 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 FCμstates for the θ, α, β, and γlow 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.
Recovery of resting brain connectivity ensuing mild traumatic brain injury
