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...

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Vydané v:	Medical image analysis Ročník 42; s. 200 - 211
Hlavní autori:	Zhao, Yu, Dong, Qinglin, Chen, Hanbo, Iraji, Armin, Li, Yujie, Makkie, Milad, Kou, Zhifeng, Liu, Tianming
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Netherlands Elsevier B.V 01.12.2017 Elsevier BV
Predmet:	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
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