Constructing fine-granularity functional brain network atlases via deep convolutional autoencoder

•A new deep 3D convolutional autoencoder to model brain network maps.•Derived fine-granularity functional brain network atlases.•Revealed unique network patterns specific to different brain task states using HCP fMRI data.•Effectively identified abnormal small networks in brain injury patients in co...

Full description

Saved in:

Bibliographic Details
Published in:	Medical image analysis Vol. 42; pp. 200 - 211
Main Authors:	Zhao, Yu, Dong, Qinglin, Chen, Hanbo, Iraji, Armin, Li, Yujie, Makkie, Milad, Kou, Zhifeng, Liu, Tianming
Format:	Journal Article
Language:	English
Published:	Netherlands Elsevier B.V 01.12.2017 Elsevier BV
Subjects:	Artificial neural networks Atlases as Topic Brain Brain mapping Brain Mapping - methods Clustering Computational neuroscience Data analysis Data management Data processing Deep learning Feature extraction fMRI Functional brain networks Functional magnetic resonance imaging Head injuries Humans Image Processing, Computer-Assisted Imaging, Three-Dimensional Independent component analysis Machine learning Magnetic Resonance Imaging - methods Neural coding Neural networks Outliers (statistics) Reproducibility of Results Sensitivity and Specificity Statistical methods Training Traumatic brain injury Deep learning fMRI Functional brain networks
ISSN:	1361-8415, 1361-8423, 1361-8423
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!