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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Vydáno v:	IEEE transactions on medical imaging Ročník 37; číslo 7; s. 1551 - 1561
Hlavní autoři:	Huang, Heng, Hu, Xintao, Zhao, Yu, Makkie, Milad, Dong, Qinglin, Zhao, Shijie, Guo, Lei, Liu, Tianming
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States IEEE 01.07.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	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
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Shrnutí:	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 structure of tfMRI data. To better understand brain networks based on fMRI data, data-driven approaches have been proposed, for instance, independent component analysis (ICA) and sparse dictionary learning (SDL). However, both ICA and SDL only build shallow models, and they are under the strong assumption that original fMRI signal could be linearly decomposed into time series components with their corresponding spatial maps. As growing evidence shows that human brain function is hierarchically organized, new approaches that can infer and model the hierarchical structure of brain networks are widely called for. Recently, deep convolutional neural network (CNN) has drawn much attention, in that deep CNN has proven to be a powerful method for learning high-level and mid-level abstractions from low-level raw data. Inspired by the power of deep CNN, in this paper, we developed a new neural network structure based on CNN, called deep convolutional auto-encoder (DCAE), in order to take the advantages of both data-driven approach and CNN's hierarchical feature abstraction ability for the purpose of learning mid-level and high-level features from complex, large-scale tfMRI time series in an unsupervised manner. The DCAE has been applied and tested on the publicly available human connectome project tfMRI data sets, and promising results are achieved.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	0278-0062 1558-254X 1558-254X
DOI:	10.1109/TMI.2017.2715285