Local and large-scale beta oscillatory dysfunction in males with mild traumatic brain injury

Mild traumatic brain injury (mTBI) is impossible to detect with standard neuroradiological assessment such as structural magnetic resonance imaging (MRI). Injury does, however, disrupt the dynamic repertoire of neural activity indexed by neural oscillations. In particular, beta oscillations are reliable predictors of cognitive, perceptual, and motor system functioning, as well as correlating highly with underlying myelin architecture and brain connectivity-all factors particularly susceptible to dysregulation after mTBI. We measured local and large-scale neural circuit function by magnetoencephalography (MEG) with a data-driven model fit approach using the fitting oscillations and one-over f algorithm in a group of young adult men with mTBI and a matched healthy control group. We quantified band-limited regional power and functional connectivity between brain regions. We found reduced regional power and deficits in functional connectivity across brain areas, which pointed to the well-characterized thalamocortical dysconnectivity associated with mTBI. Furthermore, our results suggested that beta functional connectivity data reached the best mTBI classification performance compared with regional power and symptom severity [measured with Sport Concussion Assessment Tool 2 (SCAT2)]. The present study reveals the relevance of beta oscillations as a window into neurophysiological dysfunction in mTBI and also highlights the reliability of neural synchrony biomarkers in disorder classification. Mild traumatic brain injury (mTBI) disrupts the dynamic repertoire of neural oscillations, but so far beta activity has not been studied. In mTBI, we found reductions in frontal beta and large-scale beta networks, indicative of thalamocortical dysconnectivity and disrupted information flow through cortico-basal ganglia-thalamic circuits. Relatively, connectivity more accurately classifies individual mTBI cases compared with regional power. We show the relevance of beta oscillations in mTBI and the reliability of these markers in classification.