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Full-Text Articles in Medical Specialties
Deep Learning-Based Multimodality Classification Of Chronic Mild Traumatic Brain Injury Using Resting-State Functional Mri And Pet Imaging, Faezeh Vedaei, Najmeh Mashhadi, Mahdi Alizadeh, George Zabrecky, Daniel A. Monti, Md, Nancy Wintering, Emily Navarreto, Chloe Hriso, Andrew B. Newberg, Feroze B. Mohamed
Deep Learning-Based Multimodality Classification Of Chronic Mild Traumatic Brain Injury Using Resting-State Functional Mri And Pet Imaging, Faezeh Vedaei, Najmeh Mashhadi, Mahdi Alizadeh, George Zabrecky, Daniel A. Monti, Md, Nancy Wintering, Emily Navarreto, Chloe Hriso, Andrew B. Newberg, Feroze B. Mohamed
Department of Radiology Faculty Papers
Mild traumatic brain injury (mTBI) is a public health concern. The present study aimed to develop an automatic classifier to distinguish between patients with chronic mTBI (n = 83) and healthy controls (HCs) (n = 40). Resting-state functional MRI (rs-fMRI) and positron emission tomography (PET) imaging were acquired from the subjects. We proposed a novel deep-learning-based framework, including an autoencoder (AE), to extract high-level latent and rectified linear unit (ReLU) and sigmoid activation functions. Single and multimodality algorithms integrating multiple rs-fMRI metrics and PET data were developed. We hypothesized that combining different imaging modalities provides complementary information and …