Convolutional Recurrent Neural Network for Dynamic Functional MRI Analysis and Brain Disease Identification.

ABSTRACT: Dynamic functional connectivity (dFC) networks derived from resting-state functional magnetic resonance imaging (rs-fMRI) help us understand fundamental dynamic characteristics of human brains, thereby providing an efficient solution for automated identification of brain diseases, such as Alzheimer's disease (AD) and its prodromal stage. Existing studies have applied deep learning methods to dFC network analysis and achieved good performance compared with traditional machine learning methods. However, they seldom take advantage of sequential information conveyed in dFC networks that could be informative to improve the diagnosis performance. In this paper, we propose a convolutional recurrent neural network (CRNN) for automated brain disease classification with rs-fMRI data. Specifically, w

SUBMITTER: Lin K 

PROVIDER: S-EPMC9298744 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature