Deep learning-based multimodality classification of chronic mild traumatic brain injury using resting-state functional MRI and PET imaging.

ABSTRACT: 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 improves classification performance. Additionally, a novel data interp

SUBMITTER: Vedaei F 

PROVIDER: S-EPMC10837852 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature