Cargando…
Convolutional Recurrent Neural Network for Dynamic Functional MRI Analysis and Brain Disease Identification
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&...
Autores principales: | Lin, Kai, Jie, Biao, Dong, Peng, Ding, Xintao, Bian, Weixin, Liu, Mingxia |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9298744/ https://www.ncbi.nlm.nih.gov/pubmed/35873806 http://dx.doi.org/10.3389/fnins.2022.933660 |
Ejemplares similares
-
Application of Convolutional Recurrent Neural Network for Individual Recognition Based on Resting State fMRI Data
por: Wang, Lebo, et al.
Publicado: (2019) -
Improving Patch-Based Convolutional Neural Networks for MRI Brain Tumor Segmentation by Leveraging Location Information
por: Kao, Po-Yu, et al.
Publicado: (2020) -
Brain Network Analysis and Classification Based on Convolutional Neural Network
por: Meng, Lu, et al.
Publicado: (2018) -
Using Deep Convolutional Neural Networks for Neonatal Brain Image Segmentation
por: Ding, Yang, et al.
Publicado: (2020) -
Modeling the Dynamics of Human Brain Activity with Recurrent Neural Networks
por: Güçlü, Umut, et al.
Publicado: (2017)