Modeling Task fMRI Data Via Deep Convolutional Autoencoder

Task-based functional magnetic resonance imaging (tfMRI) has been widely used to study functional brain networks under task performance. Modeling tfMRI data is challenging due to at least two problems: the lack of the ground truth of underlying neural activity and the highly complex intrinsic struct...

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Veröffentlicht in:	IEEE transactions on medical imaging Jg. 37; H. 7; S. 1551 - 1561
Hauptverfasser:	Huang, Heng, Hu, Xintao, Zhao, Yu, Makkie, Milad, Dong, Qinglin, Zhao, Shijie, Guo, Lei, Liu, Tianming
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.07.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Artificial neural networks Brain Brain - diagnostic imaging Brain mapping Brain modeling CNN Connectome Convolution Data models Decoding Deep Learning Functional magnetic resonance imaging Ground truth Hidden Markov models Humans Independent component analysis Learning Levels Machine learning Magnetic resonance imaging Magnetic Resonance Imaging - methods Modelling Neural networks Neuroimaging Structural hierarchy Task fMRI Time series Time series analysis unsupervised Unsupervised Machine Learning
ISSN:	0278-0062, 1558-254X, 1558-254X
Online-Zugang:	Volltext
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