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Resting-state magnetoencephalography source magnitude imaging with deep-learning neural network for classification of symptomatic combat-related mild traumatic brain injury.

  • Author(s): Huang, Ming-Xiong
  • Huang, Charles W
  • Harrington, Deborah L
  • Robb-Swan, Ashley
  • Angeles-Quinto, Annemarie
  • Nichols, Sharon
  • Huang, Jeffrey W
  • Le, Lu
  • Rimmele, Carl
  • Matthews, Scott
  • Drake, Angela
  • Song, Tao
  • Ji, Zhengwei
  • Cheng, Chung-Kuan
  • Shen, Qian
  • Foote, Ericka
  • Lerman, Imanuel
  • Yurgil, Kate A
  • Hansen, Hayden B
  • Naviaux, Robert K
  • Dynes, Robert
  • Baker, Dewleen G
  • Lee, Roland R
  • et al.

Published Web Location

https://doi.org/10.1002/hbm.25340
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.

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