TY  - JOUR
PY  - 2021//
TI  - Resting-state magnetoencephalography source magnitude imaging with deep-learning neural network for classification of symptomatic combat-related mild traumatic brain injury
JO  - Human brain mapping
A1  - Nichols, Sharon
A1  - Dynes, Robert
A1  - Naviaux, Robert K.
A1  - Hansen, Hayden B.
A1  - Yurgil, Kate A.
A1  - Lerman, Imanuel
A1  - Foote, Ericka
A1  - Shen, Qian
A1  - Cheng, Chung-Kuan
A1  - Ji, Zhengwei
A1  - Baker, Dewleen G.
A1  - Lee, Roland R.
A1  - Harrington, Deborah L.
A1  - Huang, Charles W.
A1  - Huang, Ming-Xiong
A1  - Angeles-Quinto, Annemarie
A1  - Song, Tao
A1  - Drake, Angela
A1  - Matthews, Scott
A1  - Rimmele, Carl
A1  - Le, Lu
A1  - Huang, Jeffrey W.
A1  - Robb-Swan, Ashley
SP  - ePub
EP  - ePub
VL  - ePub
IS  - ePub
N2  - 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.<p />  <p>Language: en</p>
LA  - en
SN  - 1065-9471
UR  - http://dx.doi.org/10.1002/hbm.25340
ID  - ref1
ER  -