Quantification of Mild Traumatic Brain Injury via Cortical Metrics: Analytical Methods

Abstract

Mild traumatic brain injuries are difficult to diagnose or assess with commonly used diagnostic methods. However, the functional state of cerebral cortical networks can be rapidly and effectively probed by measuring tactile-based sensory percepts (called cortical metrics), which are designed to exercise various components of cortical machinery. In this study, such cortical metrics were obtained from 52 college students before and after they experienced sports-related concussions by delivering vibrotactile stimuli to the index and middle fingertips. Performance on four of the sensory test protocols is described: reaction time, amplitude discrimination, temporal order judgment, and duration discrimination. The collected test performance data were analyzed using methods of uni- and multivariate statistics, receiver operated characteristic (ROC) curves, and discriminant analysis. While individual cortical metrics vary extensively in their ability to discriminate between control and concussed subjects, their combined discriminative performance greatly exceeds that of any individual metric, achieving cross-validated 93.0% sensitivity, 92.3% specificity, 93.0% positive predictive value, and 92.3% negative predictive value. The cortical metrics vector can be used to track an individual’s recovery from concussion. The study thus establishes that cortical metrics can be used effectively as a quantitative indicator of central nervous system health status.

Document Details

Document Type
Pub Defense Publication
Publication Date
Mar 01, 2019
Source ID
10.1093/milmed/usy411

Entities

People

  • Eric Francisco
  • Jameson Holden
  • Laila Zai
  • Mark Tommerdahl
  • Olcay Kursun
  • Oleg V. Favorov

Organizations

  • Applied Research Associates (United States)
  • Office of Naval Research
  • University of Central Arkansas
  • University of North Carolina at Chapel Hill

Tags

Readers

  • Computational Modeling and Simulation
  • Neurotrauma and Rehabilitation Medicine.
  • Vision Science/Vision Psychology/Cognitive Neuroscience.

Technology Areas

  • AI & ML