Identification of Dynamic Functional Connectivity Pattern in Restingstate after Acute Mild Traumatic Brain Injury

As a major health problem, traumatic brain injury has received increasing attention in recent years. Mild traumatic brain injury (mTBI) is the representative of the vast majority of traumatic brain injury. However, most studies have focused on moderate to severe traumatic brain injury. According to reports, mTBI patients during the acute period have the most prominent cognitive disorders. Hence, we collected the functional magnetic resonance imaging data in resting-state (rsfMRI) of the acute-mTBI patients and the healthy controls to explore the differences of brain functional connectivity between them. We chose dynamic functional connectivity in resting-state as features and performed feature selection/extraction. Finally, the classifier based on machine learning methods achieved a good classification accuracy of 85.48%. Most of the extracted brain regions of interest (ROIs) with high identification power belong to the sensorimotor and functional connectivities extracted within the sensorimotor has the highest proportion which suggested the sensorimotor may be the most severely damaged part of mTBI patients in the acute stage.