Towards Understanding Comprehensive Morphometric Changes and Its Correlation with Cognition and Exposure to Fighting in Active Professional Boxers
Professional athletes exposed to repetitive head impacts are at increased risk for developing a progressive neurological syndrome known as traumatic encephalopathy syndrome and neuropathology seen on autopsy called chronic traumatic encephalopathy (CTE). The early identification of individuals at increased risk for CTE is important and the search for biomarkers is underway. In this study, we utilized data from a large cohort study to compare differences in regional brain volumes, cortical thickness, voxel-based morphometric (VBM)-derived measures, and graph-theoretical measures derived from large-scale topographical maps in active professional boxers. We compared the above morphometric measures between active professional boxers with low cognitive scores (impaired boxers) and active professional boxers with intact cognitive scores (nonimpaired boxers). The cognitive scores were evaluated through neuropsychological evaluation. As an exploratory analysis, we also examined the power of various machine-learning algorithms to identify impaired and nonimpaired boxers using both group-level regression-driven analysis and previously identified hypothesis-driven cortical thickness and volumetric measures. We found significant group-level differences between impaired and nonimpaired boxers in cortical thickness in a single brain region (right precuneus), differences in VBM-derived gray matter density encompassing the caudate, putamen, and thalamus; and white matter density encompassing the right paracentral lobule, but no differences in any graph-theoretical network properties. Additionally, we found that a priori hypothesis-driven T1-derived cortical thickness and volumetric analysis performed better than traditional regression-based analysis. Overall, this study suggests that neuroanatomical differences exist between impaired and nonimpaired active professional boxers, and that hypothesis-driven techniques are likely necessary to become reliable biomarkers.