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Quantitative EEG Biomarkers for Mild Traumatic Brain Injury

Lewine, Jeffrey D.*,†,‡; Plis, Sergey*; Ulloa, Alvaro*; Williams, Christopher§; Spitz, Mark‖,¶; Foley, John#; Paulson, Kim*; Davis, John*; Bangera, Nitin*; Snyder, Travis**; Weaver, Lindell††,‡‡,§§

Journal of Clinical Neurophysiology: July 2019 - Volume 36 - Issue 4 - p 298–305
doi: 10.1097/WNP.0000000000000588
Original Research

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.

*The Mind Research Network, Albuquerque, New Mexico, U.S.A.;

The Lovelace Family of Companies, Albuquerque, New Mexico, U.S.A.;

Departments of Psychology and Neurology, University of New Mexico, Albuquerque, New Mexico, U.S.A.;

§CHMG Neuroscience, University of Colorado, Colorado Springs, Colorado, U.S.A.;

University of Colorado Health Sciences Center, Aurora, Colorado, U.S.A.;

Denver VAMC, Denver, Colorado, U.S.A.;

#Rocky Mountain Neurological Associates, Intermountain LDS Hospital, Salt Lake City, Utah, U.S.A.;

**Imgen, LLC, Las Vegas, Nevada, U.S.A.;

††Division of Hyperbaric Medicine, Intermountain Medical Center, Murray, Utah, U.S.A.;

‡‡Intermountain LDS Hospital, Salt Lake City, Utah, U.S.A.; and

§§University of Utah School of Medicine, Salt Lake City, Utah, U.S.A.

Address correspondence and reprint requests to Jeffrey D. Lewine, PhD, The Mind Research Network, 1101 Yale Boulevard, NE, Albuquerque, NM 87106, U.S.A.; e-mail:

The authors have no conflicts of interest to disclose.

The views, opinions, and/or findings contained in this report are those of the author(s) and should not be construed as an official Department of the Army position, policy, or decision unless so designated by other documentation. In the conduct of research where humans are the subjects, the investigator(s) adhered to the policies regarding the protection of human subjects as prescribed by Code of Federal Regulations (CFR) Title 45, Volume 1, Part 46; Title 32, Chapter 1, Part 219; and Title 21, Chapter 1, Part 50 (Protection of Human Subjects).

Supported by the US Army Medical Research and Materiel Command under Contract No. W81XWH-15-D-0039-0003.

© 2019 by the American Clinical Neurophysiology Society