A diffusion-tensor MRI (DTI-MRI) scanning algorithm may help evaluate mild traumatic brain injury (mTBI), according to a study published in Radiology.
Researchers from the University of Pittsburgh Medical Center in Pennsylvania performed a retrospective study to determine the performance of Shannon entropy (SE) as a diagnostic tool in patients with mTBI, who experienced posttraumatic migraines (PTMs) and those without PTMs on the basis of analysis of fractional anisotropy (FA) maps.
The study included data from 74 subjects with mTBI (57 with PTM and 17 without); 52 were male, with a mean age of 18. Of the 74 subjects, 30 (40%) had sustained prior concussion. Median time from initial trauma to clinical presentation was 20 days. The most common cause of concussive injury was from sports injuries, followed by motor vehicle accidents. The study also included data from 22 healthy control subjects (10 males, mean age 18.8) and 20 subjects who had a history of migraines.
Fractional anisotropy maps were obtained for all subjects. Mean FA and SE were extracted from total brain FA histograms and were compared between patients with mTBI and control subjects, and between patients with and those without PTM.
The results showed that the subjects with mTBI had significantly lower SE and trended toward lower mean FA compared with control subjects. “SE inversely correlated with time to recovery (TTR),” the authors wrote. “Patients with mTBI with PTM had significantly lower SE but not mean FA than did other patients with mTBI. SE provided better discrimination between patients with mTBI and control subjects than mean FA, as well as better discrimination between patients with mTBI with PTM and those without PTM. SE of less than 0.751 resulted in a 16.1 increased likelihood of having experienced mTBI and a 3.2 increased likelihood of developing PTM.”
The authors concluded that SE more accurately reveals mTBI than mean FA, and those patients with mTBI who develop PTM, and inversely correlates with TTR. This may allow for SE to be used as an effective data-mining tool for physicians to identify patients with true concussive injury and those with more severe injuries. It may also allow for a reproducible biomarker to help predict recovery times among patients with mTBI, as well as determining which patients may have a higher chance of developing PTM.