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10.1002-hbm.25340 |
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220427s2021 CNT 000 0 und d |
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|a 10659471 (ISSN)
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|a Resting-state magnetoencephalography source magnitude imaging with deep-learning neural network for classification of symptomatic combat-related mild traumatic brain injury
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|b John Wiley and Sons Inc
|c 2021
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|z View Fulltext in Publisher
|u https://doi.org/10.1002/hbm.25340
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|a 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. © 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.
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|a adult
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|a Adult
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|a Article
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|a battle injury
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|a brain concussion
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|a Brain Concussion
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|a clinical feature
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|a Combat Disorders
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|a connectome
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|a Connectome
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|a controlled study
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|a deep learning
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|a Deep Learning
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|a Delis-Kaplan executive function system
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|a delta rhythm
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|a delta rhythm
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|a diagnostic accuracy
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|a diagnostic imaging
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|a diagnostic test accuracy study
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|a female
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|a gamma rhythm
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|a gamma rhythm
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|a human
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|a Humans
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|a machine learning
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|a magnetoencephalography
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|a magnetoencephalography
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|a Magnetoencephalography
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|a major clinical study
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|a male
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|a Male
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|a military service members
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|a neuroimaging
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|a neuropsychological test
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|a neuropsychology
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|a pathophysiology
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|a posttraumatic stress disorder
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|a priority journal
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|a procedures
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|a resting state magnetoencephalography
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|a resting-state MEG
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|a sensitivity and specificity
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|a Sensitivity and Specificity
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|a task performance
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|a theta rhythm
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|a three-dimensional imaging
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|a traumatic brain injury
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|a traumatic brain injury
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|a Veterans
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|a Wechsler adult intelligence scale
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|a young adult
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|a Young Adult
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|a Angeles-Quinto, A.
|e author
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|a Baker, D.G.
|e author
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|a Cheng, C.-K.
|e author
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|a Drake, A.
|e author
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|a Dynes, R.
|e author
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|a Foote, E.
|e author
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|a Hansen, H.B.
|e author
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|a Harrington, D.L.
|e author
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|a Huang, C.W.
|e author
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|a Huang, J.W.
|e author
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|a Huang, M.-X.
|e author
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|a Ji, Z.
|e author
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|a Le, L.
|e author
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|a Lee, R.R.
|e author
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|a Lerman, I.
|e author
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|a Matthews, S.
|e author
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|a Naviaux, R.K.
|e author
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|a Nichols, S.
|e author
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|a Rimmele, C.
|e author
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|a Robb-Swan, A.
|e author
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|a Shen, Q.
|e author
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|a Song, T.
|e author
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|a Yurgil, K.A.
|e author
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|t Human Brain Mapping
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