Citation

Chong CD, Schwedt TJ. Headache 2018; 58(6): 827-835.

Affiliation

Department of Neurology, Mayo Clinic, AZ, USA.

Copyright

(Copyright © 2018, American Headache Society; American Association for the Study of Headache, Publisher John Wiley and Sons)

DOI

10.1111/head.13269

PMID

29476532

Abstract

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.

© 2018 American Headache Society.

Language: en

Keywords

arterial spin labeling; concussion; diffusion tensor imaging; functional magnetic resonance imaging; magnetic resonance imaging; migraine; mild traumatic brain injury; post-traumatic headache; resting-state; single-photon emission tomography