Citation

Huang MX, Huang CW, Harrington DL, Robb-Swan A, Angeles-Quinto A, Nichols S, Huang JW, Le L, Rimmele C, Matthews S, Drake A, Song T, Ji Z, Cheng CK, Shen Q, Foote E, Lerman I, Yurgil KA, Hansen HB, Naviaux RK, Dynes R, Baker DG, Lee RR. Hum. Brain Mapp. 2021; ePub(ePub): ePub.

Copyright

(Copyright © 2021, John Wiley and Sons)

DOI

10.1002/hbm.25340

PMID

unavailable

Abstract

Combat-related mild traumatic brain injury (cmTBI) is a leading cause of sustained physical, cognitive, emotional, and behavioral disabilities in Veterans and active-duty military personnel. Accurate diagnosis of cmTBI is challenging since the symptom spectrum is broad and conventional neuroimaging techniques are insensitive to the underlying neuropathology. The present study developed a novel deep-learning neural network method, 3D-MEGNET, and applied it to resting-state magnetoencephalography (rs-MEG) source-magnitude imaging data from 59 symptomatic cmTBI individuals and 42 combat-deployed healthy controls (HCs). Analytic models of individual frequency bands and all bands together were tested. The All-frequency model, which combined delta-theta (1-7 Hz), alpha (8-12 Hz), beta (15-30 Hz), and gamma (30-80 Hz) frequency bands, outperformed models based on individual bands. The optimized 3D-MEGNET method distinguished cmTBI individuals from HCs with excellent sensitivity (99.9 ± 0.38%) and specificity (98.9 ± 1.54%). Receiver-operator-characteristic curve analysis showed that diagnostic accuracy was 0.99. The gamma and delta-theta band models outperformed alpha and beta band models. Among cmTBI individuals, but not controls, hyper delta-theta and gamma-band activity correlated with lower performance on neuropsychological tests, whereas hypo alpha and beta-band activity also correlated with lower neuropsychological test performance. This study provides an integrated framework for condensing large source-imaging variable sets into optimal combinations of regions and frequencies with high diagnostic accuracy and cognitive relevance in cmTBI. The all-frequency model offered more discriminative power than each frequency-band model alone. This approach offers an effective path for optimal characterization of behaviorally relevant neuroimaging features in neurological and psychiatric disorders.

Language: en

Keywords

traumatic brain injury; machine learning; Veterans; delta rhythm; gamma rhythm; military service members; neuropsychology; resting-state MEG