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Traumatic brain injury (TBI) is a major cause of death and disability in the United States, contributing to about 30% of all injury-related deaths. TBI survivors often develop clinical impairments and long-term disabilities. These include impaired thinking or memory, effects on movement and sensations such as vision, hearing, or emotional functioning including personality changes, depression, burst of anger, abnormal social behavior, and insomnia. These issues not only affect individuals but can have a deleterious impact on families and communities. The advances in computer software applied to a non-invasive acquisition of images containing digital data, provides us with objective examination of brain structure and function. Magnetic resonance (MR) imaging of the brain makes it possible to investigate morphological and functional connectivity without exposing the patient to ionizing radiations. In patients with TBI, computed tomography and conventional MR scans seldom show limited or no abnormalities to explain clinical symptomatology. For these reasons, we propose an "ad hoc" protocol that exploits advances in MR sequences to predict long-term outcomes including evaluation of cortical thickness, detecting hemosiderin deposits via magnetic susceptibility weighted images, to explore indemnity of fiber tracts using diffusion tensor with fractional anisotropy measurement, to assess metabolic changes in the frontal lobe and cingulate cortex by utilizing the properties of magnetic resonance spectroscopy, and lastly to detect abnormal connectivity in the brain networks via resting-state functional magnetic resonance imaging. Meticulous application of our protocol can potentially detect subtle abnormalities in patients with mild TBI such as detection of iron or mineral deposits, abnormal cortical thickness, abnormal metabolites, disruption of white matter tracts, and decreased or loss connectivity in brain networks. Application of special MR sequences as described in our protocol can optimize clinical outcomes, offer predictive capabilities of short and long-term prognosis, and aid in risk-stratification tailored upon individual comorbidities.

DOI: 10.1007/978-3-319-53126-7_31

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