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...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Medical image analysis Ročník 42; s. 200 - 211
Hlavní autoři:	Zhao, Yu, Dong, Qinglin, Chen, Hanbo, Iraji, Armin, Li, Yujie, Makkie, Milad, Kou, Zhifeng, Liu, Tianming
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Netherlands Elsevier B.V 01.12.2017 Elsevier BV
Témata:	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
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Buďte první, kdo okomentuje tento záznam!