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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Abstract	•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 comparison with controls. State-of-the-art functional brain network reconstruction methods such as independent component analysis (ICA) or sparse coding of whole-brain fMRI data can effectively infer many thousands of volumetric brain network maps from a large number of human brains. However, due to the variability of individual brain networks and the large scale of such networks needed for statistically meaningful group-level analysis, it is still a challenging and open problem to derive group-wise common networks as network atlases. Inspired by the superior spatial pattern description ability of the deep convolutional neural networks (CNNs), a novel deep 3D convolutional autoencoder (CAE) network is designed here to extract spatial brain network features effectively, based on which an Apache Spark enabled computational framework is developed for fast clustering of larger number of network maps into fine-granularity atlases. To evaluate this framework, 10 resting state networks (RSNs) were manually labeled from the sparsely decomposed networks of Human Connectome Project (HCP) fMRI data and 5275 network training samples were obtained, in total. Then the deep CAE models are trained by these functional networks’ spatial maps, and the learned features are used to refine the original 10 RSNs into 17 network atlases that possess fine-granularity functional network patterns. Interestingly, it turned out that some manually mislabeled outliers in training networks can be corrected by the deep CAE derived features. More importantly, fine granularities of networks can be identified and they reveal unique network patterns specific to different brain task states. By further applying this method to a dataset of mild traumatic brain injury study, it shows that the technique can effectively identify abnormal small networks in brain injury patients in comparison with controls. In general, our work presents a promising deep learning and big data analysis solution for modeling functional connectomes, with fine granularities, based on fMRI data. The proposed computational framework. (a) Sparse representation of fMRI data for generation of functional network maps in each individual. Then 10 RSNs (resting state networks) were manually labeled for these networks via sparse coding methods for each task/resting state fMRI scan of each subject. (b) Training 3D CAE (convolutional autoencoder) using all the labeled networks, and extracting features using the trained encoder. (c) Network clustering using the extracted features and generation of fine-granularity ICNs (intrinsic connectivity networks). [Display omitted]
AbstractList	State-of-the-art functional brain network reconstruction methods such as independent component analysis (ICA) or sparse coding of whole-brain fMRI data can effectively infer many thousands of volumetric brain network maps from a large number of human brains. However, due to the variability of individual brain networks and the large scale of such networks needed for statistically meaningful group-level analysis, it is still a challenging and open problem to derive group-wise common networks as network atlases. Inspired by the superior spatial pattern description ability of the deep convolutional neural networks (CNNs), a novel deep 3D convolutional autoencoder (CAE) network is designed here to extract spatial brain network features effectively, based on which an Apache Spark enabled computational framework is developed for fast clustering of larger number of network maps into fine-granularity atlases. To evaluate this framework, 10 resting state networks (RSNs) were manually labeled from the sparsely decomposed networks of Human Connectome Project (HCP) fMRI data and 5275 network training samples were obtained, in total. Then the deep CAE models are trained by these functional networks' spatial maps, and the learned features are used to refine the original 10 RSNs into 17 network atlases that possess fine-granularity functional network patterns. Interestingly, it turned out that some manually mislabeled outliers in training networks can be corrected by the deep CAE derived features. More importantly, fine granularities of networks can be identified and they reveal unique network patterns specific to different brain task states. By further applying this method to a dataset of mild traumatic brain injury study, it shows that the technique can effectively identify abnormal small networks in brain injury patients in comparison with controls. In general, our work presents a promising deep learning and big data analysis solution for modeling functional connectomes, with fine granularities, based on fMRI data. State-of-the-art functional brain network reconstruction methods such as independent component analysis (ICA) or sparse coding of whole-brain fMRI data can effectively infer many thousands of volumetric brain network maps from a large number of human brains. However, due to the variability of individual brain networks and the large scale of such networks needed for statistically meaningful group-level analysis, it is still a challenging and open problem to derive group-wise common networks as network atlases. Inspired by the superior spatial pattern description ability of the deep convolutional neural networks (CNNs), a novel deep 3D convolutional autoencoder (CAE) network is designed here to extract spatial brain network features effectively, based on which an Apache Spark enabled computational framework is developed for fast clustering of larger number of network maps into fine-granularity atlases. To evaluate this framework, 10 resting state networks (RSNs) were manually labeled from the sparsely decomposed networks of Human Connectome Project (HCP) fMRI data and 5275 network training samples were obtained, in total. Then the deep CAE models are trained by these functional networks' spatial maps, and the learned features are used to refine the original 10 RSNs into 17 network atlases that possess fine-granularity functional network patterns. Interestingly, it turned out that some manually mislabeled outliers in training networks can be corrected by the deep CAE derived features. More importantly, fine granularities of networks can be identified and they reveal unique network patterns specific to different brain task states. By further applying this method to a dataset of mild traumatic brain injury study, it shows that the technique can effectively identify abnormal small networks in brain injury patients in comparison with controls. In general, our work presents a promising deep learning and big data analysis solution for modeling functional connectomes, with fine granularities, based on fMRI data.State-of-the-art functional brain network reconstruction methods such as independent component analysis (ICA) or sparse coding of whole-brain fMRI data can effectively infer many thousands of volumetric brain network maps from a large number of human brains. However, due to the variability of individual brain networks and the large scale of such networks needed for statistically meaningful group-level analysis, it is still a challenging and open problem to derive group-wise common networks as network atlases. Inspired by the superior spatial pattern description ability of the deep convolutional neural networks (CNNs), a novel deep 3D convolutional autoencoder (CAE) network is designed here to extract spatial brain network features effectively, based on which an Apache Spark enabled computational framework is developed for fast clustering of larger number of network maps into fine-granularity atlases. To evaluate this framework, 10 resting state networks (RSNs) were manually labeled from the sparsely decomposed networks of Human Connectome Project (HCP) fMRI data and 5275 network training samples were obtained, in total. Then the deep CAE models are trained by these functional networks' spatial maps, and the learned features are used to refine the original 10 RSNs into 17 network atlases that possess fine-granularity functional network patterns. Interestingly, it turned out that some manually mislabeled outliers in training networks can be corrected by the deep CAE derived features. More importantly, fine granularities of networks can be identified and they reveal unique network patterns specific to different brain task states. By further applying this method to a dataset of mild traumatic brain injury study, it shows that the technique can effectively identify abnormal small networks in brain injury patients in comparison with controls. In general, our work presents a promising deep learning and big data analysis solution for modeling functional connectomes, with fine granularities, based on fMRI data. •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 comparison with controls. State-of-the-art functional brain network reconstruction methods such as independent component analysis (ICA) or sparse coding of whole-brain fMRI data can effectively infer many thousands of volumetric brain network maps from a large number of human brains. However, due to the variability of individual brain networks and the large scale of such networks needed for statistically meaningful group-level analysis, it is still a challenging and open problem to derive group-wise common networks as network atlases. Inspired by the superior spatial pattern description ability of the deep convolutional neural networks (CNNs), a novel deep 3D convolutional autoencoder (CAE) network is designed here to extract spatial brain network features effectively, based on which an Apache Spark enabled computational framework is developed for fast clustering of larger number of network maps into fine-granularity atlases. To evaluate this framework, 10 resting state networks (RSNs) were manually labeled from the sparsely decomposed networks of Human Connectome Project (HCP) fMRI data and 5275 network training samples were obtained, in total. Then the deep CAE models are trained by these functional networks’ spatial maps, and the learned features are used to refine the original 10 RSNs into 17 network atlases that possess fine-granularity functional network patterns. Interestingly, it turned out that some manually mislabeled outliers in training networks can be corrected by the deep CAE derived features. More importantly, fine granularities of networks can be identified and they reveal unique network patterns specific to different brain task states. By further applying this method to a dataset of mild traumatic brain injury study, it shows that the technique can effectively identify abnormal small networks in brain injury patients in comparison with controls. In general, our work presents a promising deep learning and big data analysis solution for modeling functional connectomes, with fine granularities, based on fMRI data. The proposed computational framework. (a) Sparse representation of fMRI data for generation of functional network maps in each individual. Then 10 RSNs (resting state networks) were manually labeled for these networks via sparse coding methods for each task/resting state fMRI scan of each subject. (b) Training 3D CAE (convolutional autoencoder) using all the labeled networks, and extracting features using the trained encoder. (c) Network clustering using the extracted features and generation of fine-granularity ICNs (intrinsic connectivity networks). [Display omitted] State-of-the-art functional brain network reconstruction methods such as independent component analysis (ICA) or sparse coding of whole-brain fMRI data can effectively infer many thousands of volumetric brain network maps from a large number of human brains. However, due to the variability of individual brain networks and the large scale of such networks needed for statistically meaningful group-level analysis, it is still a challenging and open problem to derive group-wise common networks as network atlases. Inspired by the superior spatial pattern description ability of the deep convolutional neural networks (CNNs), a novel deep 3D convolutional autoencoder (CAE) network is designed here to extract spatial brain network features effectively, based on which an Apache Spark enabled computational framework is developed for fast clustering of larger number of network maps into fine-granularity atlases. To evaluate this framework, 10 resting state networks (RSNs) were manually labeled from the sparsely decomposed networks of Human Connectome Project (HCP) fMRI data and 5275 network training samples were obtained, in total. Then the deep CAE models are trained by these functional networks’ spatial maps, and the learned features are used to refine the original 10 RSNs into 17 network atlases that possess fine-granularity functional network patterns. Interestingly, it turned out that some manually mislabeled outliers in training networks can be corrected by the deep CAE derived features. More importantly, fine granularities of networks can be identified and they reveal unique network patterns specific to different brain task states. By further applying this method to a dataset of mild traumatic brain injury study, it shows that the technique can effectively identify abnormal small networks in brain injury patients in comparison with controls. In general, our work presents a promising deep learning and big data analysis solution for modeling functional connectomes, with fine granularities, based on fMRI data. The proposed computational framework. (a) Sparse representation of fMRI data for generation of functional network maps in each individual. Then 10 RSNs (resting state networks) were manually labeled for these networks via sparse coding methods for each task/resting state fMRI scan of each subject. (b) Training 3D CAE (convolutional autoencoder) using all the labeled networks, and extracting features using the trained encoder. (c) Network clustering using the extracted features and generation of fine-granularity ICNs (intrinsic connectivity networks).
Author	Kou, Zhifeng Chen, Hanbo Zhao, Yu Dong, Qinglin Makkie, Milad Iraji, Armin Liu, Tianming Li, Yujie
AuthorAffiliation	1 Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA 2 Department of Biomedical Engineering, Wayne State University, Detroit, MI
AuthorAffiliation_xml	– name: 2 Department of Biomedical Engineering, Wayne State University, Detroit, MI – name: 1 Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA
Author_xml	– sequence: 1 givenname: Yu surname: Zhao fullname: Zhao, Yu organization: Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, United States – sequence: 2 givenname: Qinglin surname: Dong fullname: Dong, Qinglin organization: Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, United States – sequence: 3 givenname: Hanbo surname: Chen fullname: Chen, Hanbo organization: Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, United States – sequence: 4 givenname: Armin surname: Iraji fullname: Iraji, Armin organization: Department of Biomedical Engineering, Wayne State University, Detroit, MI, United States – sequence: 5 givenname: Yujie surname: Li fullname: Li, Yujie organization: Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, United States – sequence: 6 givenname: Milad surname: Makkie fullname: Makkie, Milad organization: Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, United States – sequence: 7 givenname: Zhifeng surname: Kou fullname: Kou, Zhifeng organization: Department of Biomedical Engineering, Wayne State University, Detroit, MI, United States – sequence: 8 givenname: Tianming surname: Liu fullname: Liu, Tianming email: tliu@uga.edu, tianming.liu@gmail.com organization: Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, United States
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Snippet	•A new deep 3D convolutional autoencoder to model brain network maps.•Derived fine-granularity functional brain network atlases.•Revealed unique network... State-of-the-art functional brain network reconstruction methods such as independent component analysis (ICA) or sparse coding of whole-brain fMRI data can...
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SubjectTerms	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
Title	Constructing fine-granularity functional brain network atlases via deep convolutional autoencoder
URI	https://dx.doi.org/10.1016/j.media.2017.08.005 https://www.ncbi.nlm.nih.gov/pubmed/28843214 https://www.proquest.com/docview/1981302800 https://www.proquest.com/docview/1932846579 https://pubmed.ncbi.nlm.nih.gov/PMC5654647
Volume	42
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