Resting-state magnetoencephalography source magnitude imaging with deep-learning neural network for classification of symptomatic combat-related mild traumatic brain injury
| Autor: | Deborah L. Harrington, Imanuel Lerman, Dewleen G. Baker, Carl Rimmele, Ericka Foote, Qian Shen, Chung-Kuan Cheng, Robert C. Dynes, Kate A. Yurgil, Hayden B Hansen, Angela Drake, Zhengwei Ji, Jeffrey W Huang, Annemarie Angeles-Quinto, Scott C. Matthews, Ashley Robb-Swan, Mingxiong Huang, Charles Huang, Roland R. Lee, Sharon Nichols, Tao Song, Lu Le, Robert K. Naviaux |
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| Rok vydání: | 2021 |
| Předmět: |
Male
neuropsychology gamma rhythm Audiology Neurodegenerative 0302 clinical medicine Discriminative model Medicine Research Articles Veterans Combat Disorders screening and diagnosis Radiological and Ultrasound Technology medicine.diagnostic_test traumatic brain injury 05 social sciences Neuropsychology Magnetoencephalography Cognition Experimental Psychology Neuropsychological test Detection machine learning Mental Health Neurology Neurological Biomedical Imaging Cognitive Sciences Anatomy resting-state MEG Research Article 4.2 Evaluation of markers and technologies Adult medicine.medical_specialty Physical Injury - Accidents and Adverse Effects Traumatic brain injury delta rhythm Sensitivity and Specificity 050105 experimental psychology 03 medical and health sciences Young Adult Deep Learning Neuroimaging Behavioral and Social Science Connectome Humans 0501 psychology and cognitive sciences Radiology Nuclear Medicine and imaging Brain Concussion Resting state fMRI resting‐state MEG business.industry military service members Neurosciences medicine.disease Brain Disorders Neurology (clinical) business 030217 neurology & neurosurgery |
| Zdroj: | Human brain mapping, vol 42, iss 7 Human Brain Mapping |
| Popis: | 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. This study developed a novel resting‐state magnetoencephalography (rs‐MEG) source‐magnitude imaging method, 3D‐MEGNET, using deep learning. The optimized 3D‐MEGNET method combining rs‐MEG data from all frequency bands distinguished individuals with combat‐related mild traumatic brain injury (cmTBI) from combat‐deployed healthy controls with high sensitivity, specificity, and diagnostic accuracy. 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. |
| Databáze: | OpenAIRE |
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