The objectives of this machine-learning (ML) resting-state magnetoencephalography (rs-MEG) study involving children with mild traumatic brain injury (mTBI) and orthopedic injury (OI) controls were to define a neural injury signature of mTBI and to delineate the pattern(s) of neural injury that determine behavioral recovery. Children ages 8-15 years with mTBI (n = 59) and OI (n = 39) from consecutive admissions to an emergency department were studied prospectively for parent-rated post-concussion symptoms (PCS) at: 1) baseline (average of 3 weeks post-injury) to measure pre-injury symptoms and also concurrent symptoms; and 2) at 3-months post-injury. rs-MEG was conducted at the baseline assessment. The ML algorithm predicted cases of mTBI versus OI with sensitivity of 95.5 ± 1.6% and specificity of 90.2 ± 2.7% at 3-weeks post-injury for the combined delta-gamma frequencies. The sensitivity and specificity were significantly better (p < 0.0001) for the combined delta-gamma frequencies compared with the delta-only and gamma-only frequencies. There were also spatial differences in rs-MEG activity between mTBI and OI groups in both delta and gamma bands in frontal and temporal lobe, as well as more widespread differences in the brain. The ML algorithm accounted for 84.5% of the variance in predicting recovery measured by PCS changes between 3 weeks and 3 months post-injury in the mTBI group, and this was significantly lower (p < 10-4) in the OI group (65.6%). Frontal lobe pole (higher) gamma activity was significantly (p < 0.001) associated with (worse) PCS recovery exclusively in the mTBI group. These findings demonstrate a neural injury signature of pediatric mTBI and patterns of mTBI-induced neural injury related to behavioral recovery.
Keywords: delta rhythm; gamma rhythm; machine learning; pediatric traumatic brain injury; resting-state MEG.