Traumatic brain injury (TBI) is an important public health issue. TBI includes a broad spectrum of injury severities and abnormalities. Functional MR imaging (fMR imaging), both resting state (rs) and task, has been used often in research to study the effects of TBI. Although rs-fMR imaging is not currently applicable in clinical diagnosis of TBI, computer-aided tools are making this a possibility for the future. Specifically, graph theory is being used to study the change in networks after TBI. Machine learning methods allow researchers to build models capable of predicting injury severity and recovery trajectories.
Keywords: BOLD; Graph theory; Machine learning; Magnetoencephalography; Resting state; TBI; fMR imaging.
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