Automatic Recognition of fMRI-Derived Functional Networks Using 3-D Convolutional Neural Networks

• Published in: IEEE transactions on biomedical engineering Vol. 65; no. 9; pp. 1975 - 1984
• Main Authors: Zhao, Yu, Dong, Qinglin, Zhang, Shu, Zhang, Wei, Chen, Hanbo, Jiang, Xi, Guo, Lei, Hu, Xintao, Han, Junwei, Liu, Tianming
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.09.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Brain; Brain - diagnostic imaging; Brain - physiology; Brain mapping; Brain Mapping - methods; Classification; Cognitive ability; convolutional neural networks; deep learning; Dictionaries; fMRI; functional brain networks; Functional magnetic resonance imaging; Game theory; Humans; Image Processing, Computer-Assisted; Image reconstruction; Independent component analysis; Language disorders; Machine learning; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Modelling; Nerve Net - diagnostic imaging; Nerve Net - physiology; Nervous system; Neural coding; Neural networks; Neural Networks (Computer); Neuroimaging; Pattern Recognition, Automated - methods; Recognition; Robustness; Signal Processing, Computer-Assisted; Sociology; Statistics; Three-dimensional displays; Training
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2017.2715281

Current functional magnetic resonance imaging (fMRI) data modeling techniques, such as independent component analysis and sparse coding methods, can effectively reconstruct dozens or hundreds of concurrent interacting functional brain networks simultaneously from the whole brain fMRI signals. However, such reconstructed networks have no correspondences across different subjects. Thus, automatic, effective, and accurate classification and recognition of these large numbers of fMRI-derived functional brain networks are very important for subsequent steps of functional brain analysis in cognitive and clinical neuroscience applications. However, this task is still a challenging and open problem due to the tremendous variability of various types of functional brain networks and the presence of various sources of noises. In recognition of the fact that convolutional neural networks (CNN) has superior capability of representing spatial patterns with huge variability and dealing with large noises, in this paper, we design, apply, and evaluate a deep 3-D CNN framework for automatic, effective, and accurate classification and recognition of large number of functional brain networks reconstructed by sparse representation of whole-brain fMRI signals. Our extensive experimental results based on the Human Connectome Project fMRI data showed that the proposed deep 3-D CNN can effectively and robustly perform functional networks classification and recognition tasks, while maintaining a high tolerance for mistakenly labeled training instances. This study provides a new deep learning approach for modeling functional connectomes based on fMRI data.