Abstract
Introduction:
Chronic traumatic brain injury (cTBI) is associated with long-term cognitive, emotional, and functional impairments. It presents significant diagnostic and therapeutic challenges. Few studies have examined the effectiveness of artificial intelligence (AI) models and advanced diffusion imaging metrics to predict treatment outcomes in cTBI. This study investigated whether hybrid diffusion imaging (HYDI), a technique that employs multiple diffusion analyses, including diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI), can identify imaging biomarkers predictive of treatment response.
Methods:
We prospectively enrolled 41 patients with cTBI who underwent HYDI scans at two timepoints. Neuropsychological outcomes were classified as favorable (clinical improvement) or unfavorable (no improvement or worsening). We assessed 6 diffusion metrics across 20 white matter regions using partial correlation analysis and 5 different AI algorithms. Outcome prediction models were trained and evaluated using k-fold cross-validation to ensure generalizability.
Results:
Results revealed significantly greater post-treatment changes in NODDI indices compared with DTI indices. In particular, regional changes in axonal density showed strong correlations with improvements in cognitive function. AI models incorporating both DTI and NODDI metrics showed promise for the prediction of the Mayo-Portland Adaptability Inventory, an assessment of postinjury adjustment in daily life and community reintegration. Overall, this study highlights the potential of HYDI-derived diffusion metrics, especially NODDI, as promising biomarkers for tracking microstructural changes and predicting therapeutic outcomes in cTBI. These findings support the use of advanced diffusion imaging and AI to inform treatment strategies and monitor recovery in patients with chronic brain injury.
Get full access to this article
View all access options for this article.