Speech analysis has shown potential for identifying neurological impairment. With brain trauma, changes in brain structure or connectivity may result in changes in source, prosodic, or articulatory aspects of voice. In this work, we examine the articulatory components of speech reflected in formant tracks, and how changes in track dynamics and coordination map to cognitive decline. We address a population of athletes regularly receiving impacts to the head and showing signs of preclinical mild traumatic brain injury (mTBI), a state indicated by impaired cognitive performance occurring prior to concussion. We hypothesize that this preclinical damage results in 1) changes in average vocal tract dynamics measured by formant frequencies, their velocities, and acceleration, and 2) changes in articulatory coordination measured by a novel formant-frequency cross-correlation characterization. These features allow machine learning algorithms to detect preclinical mTBI identified by a battery of cognitive tests. A comparison is performed of the effectiveness of vocal tract dynamics features versus articulatory coordination features. This evaluation is done using receiver operating characteristic (ROC) curves along with confidence bounds. The articulatory dynamics features achieve area under the ROC curve (AUC) values between 0.72 and 0.98, whereas the articulatory coordination features achieve AUC values between 0.94 and 0.97.