Quantitative EEG Biomarkers for Mild Traumatic Brain Injury. Issue 4 (July 2019)

• Record Type: Journal Article
• Authors: Lewine, Jeffrey D.; Plis, Sergey; Ulloa, Alvaro; Williams, Christopher; Spitz, Mark; Foley, John; Paulson, Kim; Davis, John; Bangera, Nitin; Snyder, Travis; Weaver, Lindell
• Is Part Of: Journal of clinical neurophysiology. Volume 36:Issue 4(2019)
• Publication Date: 2019-07
• Subjects: mTBI -- EEG -- Absolute power -- Relative power -- Interhemispheric coherence; Electroencephalography -- Periodicals; Neurophysiology -- Periodicals; Electroencephalography -- Periodicals; Electroencephalography; Periodicals; 612.805
• DOI: 10.1097/WNP.0000000000000588
• Languages: English
• ISSNs: 0736-0258

Abstract

Abstract : Purpose: The development of objective biomarkers for mild traumatic brain injury (mTBI) in the chronic period is an important clinical and research goal. Head trauma is known to affect the mechanisms that support the electrophysiological processing of information within and between brain regions, so methods like quantitative EEG may provide viable indices of brain dysfunction associated with even mTBI. Methods: Resting-state, eyes-closed EEG data were obtained from 71 individuals with military-related mTBI and 82 normal comparison subjects without traumatic brain injury. All mTBI subjects were in the chronic period of injury (>5 months since the time of injury). Quantitative metrics included absolute and relative power in delta, theta, alpha, beta, high beta, and gamma bands, plus a measure of interhemispheric coherence in each band. Data were analyzed using univariate and multivariate methods, the latter coupled to machine learning strategies. Results: Analyses revealed significant ( P < 0.05) group level differences in global relative theta power (increased for mTBI patients), global relative alpha power (decreased for mTBI patients), and global beta-band interhemispheric coherence (decreased for mTBI patients). Single variables were limited in their ability to predict group membership (e.g., mTBI vs. control) for individual subjects, each with a predictive accuracy that was below 60%. In contrast, the combination of a multivariate approach with machine learning methods yielded a composite metric that provided an overall predictive accuracy of 75% for correct classification of individual subjects as coming from control versus mTBI groups. Conclusions: This study indicates that quantitative EEG methods may be useful in the identification, classification, and tracking of individual subjects with mTBI.