LCGNet: Local Sequential Feature Coupling Global Representation Learning for Functional Connectivity Network Analysis With fMRI

• Published in: IEEE transactions on medical imaging Vol. 43; no. 12; pp. 4319 - 4330
• Main Authors: Zhou, Jie, Jie, Biao, Wang, Zhengdong, Zhang, Zhixiang, Du, Tongchun, Bian, Weixin, Yang, Yang, Jia, Jun
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.12.2024
• Subjects: Adult; Alzheimer Disease - diagnostic imaging; Attention Deficit Disorder with Hyperactivity - diagnostic imaging; Attention Deficit Disorder with Hyperactivity - physiopathology; Brain - diagnostic imaging; Brain - physiology; convolutional neural network; Couplings; disease classification; Diseases; Feature extraction; Female; Functional connectivity network; Functional magnetic resonance imaging; Humans; Image Processing, Computer-Assisted - methods; Long short term memory; Machine Learning; Magnetic Resonance Imaging - methods; Male; Nerve Net - diagnostic imaging; Neural Networks, Computer; Representation learning; transformer; Transformers

Analysis of functional connectivity networks (FCNs) derived from resting-state functional magnetic resonance imaging (rs-fMRI) has greatly advanced our understanding of brain diseases, including Alzheimer's disease (AD) and attention deficit hyperactivity disorder (ADHD). Advanced machine learn...