The Capabilities of MR Tractography in Predicting Motor and Speech Disorders in Patients with Diffuse Axonal Injury

The relevance of this study is driven by the need to develop an objective neuroimaging criterion for the early diagnosis and prediction of long-term neurological outcomes in patients with diffuse axonal injury (DAI). A comprehensive assessment of the condition of major neural pathways using modern imaging techniques such as diffusion tensor MRI (DTI) and HARDI-CSD, followed by follow-up observation, represents a new and promising direction in neurotrauma. Aim . To analyze the relationship between fractional anisotropy (FA) parameters in the corticospinal and arcuate tracts of the cerebral white matter and key neurological status indicators and functional outcomes in patients with acute diffuse axonal injury (DAI). Materials and Methods . The study included 74 patients with DAI and 12 healthy volunteers. In the acute phase (within 1 month) of traumatic brain injury (TBI), all DAI patients underwent diffusion tensor MRI (DTI), and an HARDI-CSD MRI sequence was also performed. The study involved analyzing the mean fractional anisotropy (FA) parameter along the entire length of the corticospinal and arcuate tracts, with subsequent correlation to long-term functional outcomes (assessed no earlier than 3 months post-injury). Results . Significant differences in FA parameters (p < 0.001) were found between healthy volunteers and DAI patients, as well as between patients with and without motor/speech impairments. Patients with unfavorable outcomes showed significant decrease in FA parameters in both corticospinal (p < 0.001) and arcuate tracts (p = 0.003–0.007) compared to the favorable outcome group. Reduction of FA in corticospinal tracts to 0.47–0.54 during the acute injury phase demonstrated high predictive value for motor impairments (AUC = 0.98–0.99), while decrease in arcuate tract FA to 0.39—0.45 was associated with speech disorders (AUC = 0.90–0.95). Conclusion . The FA parameter is informative for differentiating between healthy and impaired cerebral pathways and predicting outcomes in DAI.