Even when concussions are associated with prolonged physical and cognitive sequelae, concussions are typically “invisible” on diagnostic brain imaging, indicating that the neuropathology associated with concussion lies under the detection threshold of routine imaging. However, data from brain structural and functional research imaging studies using diffusion tensor imaging, resting-state functional magnetic resonance imaging, and brain perfusion imaging indicate that these imaging sequences have a role in identifying concussion-related neuropathology. These advanced imaging techniques provide insights into concussion neuropathology and might be useful for differentiating concussed patients from healthy controls. In this review article, we provide an overview of research findings from brain structural and functional imaging studies of concussion, and discuss the accuracy of classification models developed via machine-learning algorithms for identifying individual patients with concussion based on imaging data.

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