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Funder: Stephen Erskine Fellowship, Traumatic Brain Injury (TBI) is a frequently occurring condition and approximately 90% of TBI cases are classified as mild (mTBI). However, conventional MRI has limited diagnostic and prognostic value, thus warranting the utilization of additional imaging modalities and analysis procedures. The functional connectomic approach using resting-state functional MRI (rs-fMRI) has shown great potential and promising diagnostic capabilities across multiple clinical scenarios, including mTBI. Additionally, there is increasing recognition of a fundamental role of brain dynamics in healthy and pathological cognition. Here, we undertake an in-depth investigation of mTBI-related connectomic disturbances and their emotional and cognitive correlates. We leveraged machine learning and graph theory to combine static and dynamic functional connectivity (FC) with regional entropy values, achieving classification accuracy up to 75% (77, 74 and 76% precision, sensitivity and specificity, respectively). As compared to healthy controls, the mTBI group displayed hypoconnectivity in the temporal poles, which correlated positively with semantic (r = 0.43, p < 0.008) and phonemic verbal fluency (r = 0.46, p < 0.004), while hypoconnectivity in the right dorsal posterior cingulate correlated positively with depression symptom severity (r = 0.54, p < 0.0006). These results highlight the importance of residual FC in these regions for preserved cognitive and emotional function in mTBI. Conversely, hyperconnectivity was observed in the right precentral and supramarginal gyri, which correlated negatively with semantic verbal fluency (r=-0.47, p < 0.003), indicating a potential ineffective compensatory mechanism. These novel results are promising toward understanding the pathophysiology of mTBI and explaining some of its most lingering emotional and cognitive symptoms.

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 (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.