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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Published in:	Medical image analysis Vol. 42; pp. 200 - 211
Main Authors:	Zhao, Yu, Dong, Qinglin, Chen, Hanbo, Iraji, Armin, Li, Yujie, Makkie, Milad, Kou, Zhifeng, Liu, Tianming
Format:	Journal Article
Language:	English
Published:	Netherlands Elsevier B.V 01.12.2017 Elsevier BV
Subjects:	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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Cites_doi	10.1007/s11222-007-9033-z 10.1016/j.neuroimage.2013.05.039 10.1002/brb3.454 10.1007/s11682-015-9359-7 10.1073/pnas.1315235110 10.1016/j.neuroimage.2013.05.033 10.1002/hbm.460020402 10.1109/ICCV.2015.123 10.1016/0042-6989(82)90112-2 10.1109/TMI.2015.2418734 10.1162/neco.2006.18.7.1527 10.1002/hbm.23013 10.1017/S0140525X11001567 10.1098/rstb.2005.1634 10.1038/sj.npp.1300543 10.1016/j.tics.2010.01.004 10.1109/TMI.2013.2259248 10.1089/neu.2014.3610 10.1044/hhd16.1.3 10.1007/s11682-012-9157-4 10.1109/TAMD.2015.2409835 10.1016/j.pscychresns.2015.07.012 10.1016/j.nicl.2016.06.004 10.1002/(SICI)1097-0193(1997)5:4<254::AID-HBM9>3.0.CO;2-2 10.1073/pnas.0905267106 10.1038/nrn2575 10.1001/archinte.158.15.1617 10.1109/TBME.2014.2369495 10.1016/j.neubiorev.2016.08.035 10.31887/DCNS.2013.15.3/osporns 10.1016/j.media.2014.10.011 10.1016/j.neuron.2006.04.031 10.1038/nrn730 10.1073/pnas.0504136102 10.1007/s11682-015-9487-0 10.1044/hhd16.1.18
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References	Iraji, Benson, Welch, O'Neil, Woodard, Ayaz, Kulek, Mika, Medado, Soltanian-Zadeh, Liu, Haacke, Kou (bib0019) 2015; 32 Lecun, Eon Bottou, Bengio, Haaner (bib0024) 1998 Friston, Holmes, Worsley, Poline, Frith, Frackowiak (bib0012) 1994; 2 Bullmore, Sporns (bib0004) 2009; 10 Pessoa, Amaral, Behniea, Kelly, Averbeck, Seo, Bressler, Menon, Damaraju, Huang, Barrett, Pessoa, Honey, Kotter, Breakspear, Sporns, Modha, Singh, Newman, Padmala, Pessoa, Pessoa, Pessoa, Pessoa, Adolphs, Pessoa, Petrovich, Canteras, Swanson, Swanson, Thompson, Varela (bib0035) 2012; 35 Vander Werff (bib0044) 2012; 16 Simonyan, K., Zisserman, A., 2014. Very Deep Convolutional Networks for Large-Scale Image Recognition. De Valois, William Yund, Hepler (bib0007) 1982; 22 Lv, Jiang, Li, Zhu, Zhang, Zhao, Chen, Zhang, Hu, Han, Ye, Guo, Liu (bib0029) 2015; 62 Zhang, Li, Lv, Jiang, Guo, Liu (bib0049) 2016; 10 Schairer (bib0036) 2012; 16 Li, Makkie, Lin, Sedigh Fazli, Davidson, Ye, Liu, Quinn (bib0025) 2016 Kushner (bib0023) 1998; 158 Bengio, Bengio, Lamblin, Popovici, Larochelle (bib0003) 2007 Calhoun, Pekar, Pearlson (bib0005) 2004; 29 Zhao, Chen, Li, Lv, Jiang, Ge, Zhang, Zhang, Ge, Lyu, Zhao, Han, Guo, Liu (bib0051) 2016; 12 Stevens, Lovejoy, Kim, Oakes, Kureshi, Witt (bib0043) 2012; 6 Jiang, Li, Lv, Zhang, Zhang, Guo, Liu (bib0020) 2015; 36 Hinton, Osindero, Teh (bib0017) 2006; 18 Zhao, Han, Lv, Jiang, Hu, Zhao, Ge, Guo, Liu (bib0050) 2015; 34 He, K., Zhang, X., Ren, S., Sun, J., 2015. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. Ge, Makkie, Wang, Zhao, Jiang, Li, Lv, Zhang, Zhang, Han, Guo, Liu (bib0013) 2016; 10 Hu, Lv, Cheng, Lv, Guo, Han, Liu (bib0046) 2015; 7 Stevens, Lovejoy, Kim, Oakes, Kureshi, Witt (bib0042) 2012; 6 Gilmore, Camchong, Davenport, Nelson, Kardon, Lim, Sponheim (bib0014) 2016; 6 Luxburg (bib0027) 2007; 17 Worsley (bib0045) 1997; 5 Smith, Beckmann, Andersson, Auerbach, Bijsterbosch, Douaud, Duff, Feinberg, Griffanti, Harms, Kelly, Laumann, Miller, Moeller, Petersen, Power, Salimi-Khorshidi, Snyder, Vu, Woolrich, Xu, Yacoub, Uğurbil, Van Essen, Glasser (bib0039) 2013; 80 Masci, Meier, Cireşan, Schmidhuber (bib0033) 2011 Sporns (bib0041) 2013; 15 Krizhevsky, Sutskever, Hinton (bib0022) 2012 Lv, Jiang, Li, Zhu, Chen, Zhang, Zhang, Hu, Han, Huang, Zhang, Guo, Liu (bib0028) 2015; 20 Zeiler, M.D., 2012. ADADELTA: An Adaptive Learning Rate Method. Lv, Lin, Zhang, Jiang, Hu, Han, Guo, Ye, Liu (bib0031) 2015 Smith, Fox, Miller, Glahn, Fox, Mackay, Filippini, Watkins, Toro, Laird, Beckmann (bib0040) 2009; 106 Chen, Li, Zhu, Jiang, Yuan, Lv, Zhang, Guo, Shen, Liu (bib0006) 2013; 32 Beckmann, DeLuca, Devlin, Smith (bib0002) 2005; 360 Lowe (bib0026) 1999; 2 Duncan (bib0009) 2010; 14 Karpathy, Toderici, Shetty, Leung, Sukthankar, Fei-Fei (bib0021) 2014 Mairal, Bach, Ponce, Sapiro (bib0032) 2010; 11 Serre, Wolf, Poggio (bib0037) 2005 Xu, Potenza, Calhoun, Zhang, Yip, Wall, Pearlson, Worhunsky, Garrison, Moran (bib0047) 2016; 71 Barch, Burgess, Harms, Petersen, Schlaggar, Corbetta, Glasser, Curtiss, Dixit, Feldt, Nolan, Bryant, Hartley, Footer, Bjork, Poldrack, Smith, Johansen-Berg, Snyder, Van Essen, WU-Minn HCP Consortium (bib0001) 2013; 80 Fedorenko, Duncan, Kanwisher (bib0010) 2013; 110 Lv, Jiang, Li, Zhu, Zhao, Zhang, Hu, Han, Guo, Li, Coles, Hu, Liu (bib0030) 2015; 233 Zhao, Han, Hu, Jiang, Lv, Zhang, Zhang, Guo, Liu (bib0052) 2017 Dosenbach, Visscher, Palmer, Miezin, Wenger, Kang, Burgund, Grimes, Schlaggar, Petersen (bib0008) 2006; 50 Heeger, Ress (bib0016) 2002; 3 Fox, Snyder, Vincent, Corbetta, Van Essen, Raichle (bib0011) 2005; 102 Huettel, Song, McCarthy (bib0018) 2004 Liu, Han, Zhang, Shifeng, Liu (bib0034) 2015 Serre (10.1016/j.media.2017.08.005_bib0037) 2005 Ge (10.1016/j.media.2017.08.005_bib0013) 2016; 10 Sporns (10.1016/j.media.2017.08.005_bib0041) 2013; 15 10.1016/j.media.2017.08.005_bib0048 Bullmore (10.1016/j.media.2017.08.005_bib0004) 2009; 10 Zhao (10.1016/j.media.2017.08.005_bib0050) 2015; 34 Vander Werff (10.1016/j.media.2017.08.005_bib0044) 2012; 16 De Valois (10.1016/j.media.2017.08.005_bib0007) 1982; 22 Stevens (10.1016/j.media.2017.08.005_bib0043) 2012; 6 Calhoun (10.1016/j.media.2017.08.005_bib0005) 2004; 29 Xu (10.1016/j.media.2017.08.005_bib0047) 2016; 71 Liu (10.1016/j.media.2017.08.005_bib0034) 2015 Beckmann (10.1016/j.media.2017.08.005_bib0002) 2005; 360 Karpathy (10.1016/j.media.2017.08.005_bib0021) 2014 Lv (10.1016/j.media.2017.08.005_bib0030) 2015; 233 Zhang (10.1016/j.media.2017.08.005_bib0049) 2016; 10 Worsley (10.1016/j.media.2017.08.005_sbref0043) 1997; 5 Lv (10.1016/j.media.2017.08.005_bib0029) 2015; 62 Fedorenko (10.1016/j.media.2017.08.005_bib0010) 2013; 110 Hu (10.1016/j.media.2017.08.005_bib0046) 2015; 7 Lowe (10.1016/j.media.2017.08.005_bib0026) 1999; 2 Mairal (10.1016/j.media.2017.08.005_bib0032) 2010; 11 Kushner (10.1016/j.media.2017.08.005_bib0023) 1998; 158 Zhao (10.1016/j.media.2017.08.005_bib0051) 2016; 12 Zhao (10.1016/j.media.2017.08.005_bib0052) 2017 Iraji (10.1016/j.media.2017.08.005_bib0019) 2015; 32 10.1016/j.media.2017.08.005_bib0038 Lecun (10.1016/j.media.2017.08.005_bib0024) 1998 Barch (10.1016/j.media.2017.08.005_bib0001) 2013; 80 Bengio (10.1016/j.media.2017.08.005_bib0003) 2007 Stevens (10.1016/j.media.2017.08.005_bib0042) 2012; 6 Smith (10.1016/j.media.2017.08.005_bib0040) 2009; 106 Heeger (10.1016/j.media.2017.08.005_bib0016) 2002; 3 Lv (10.1016/j.media.2017.08.005_bib0031) 2015 Pessoa (10.1016/j.media.2017.08.005_bib0035) 2012; 35 Luxburg (10.1016/j.media.2017.08.005_bib0027) 2007; 17 Chen (10.1016/j.media.2017.08.005_bib0006) 2013; 32 Duncan (10.1016/j.media.2017.08.005_bib0009) 2010; 14 Jiang (10.1016/j.media.2017.08.005_bib0020) 2015; 36 Lv (10.1016/j.media.2017.08.005_bib0028) 2015; 20 Schairer (10.1016/j.media.2017.08.005_bib0036) 2012; 16 Friston (10.1016/j.media.2017.08.005_bib0012) 1994; 2 10.1016/j.media.2017.08.005_bib0015 Hinton (10.1016/j.media.2017.08.005_bib0017) 2006; 18 Fox (10.1016/j.media.2017.08.005_bib0011) 2005; 102 Smith (10.1016/j.media.2017.08.005_bib0039) 2013; 80 Dosenbach (10.1016/j.media.2017.08.005_bib0008) 2006; 50 Krizhevsky (10.1016/j.media.2017.08.005_bib0022) 2012 Huettel (10.1016/j.media.2017.08.005_bib0018) 2004 Gilmore (10.1016/j.media.2017.08.005_bib0014) 2016; 6 Li (10.1016/j.media.2017.08.005_bib0025) 2016 Masci (10.1016/j.media.2017.08.005_bib0033) 2011
References_xml	– reference: He, K., Zhang, X., Ren, S., Sun, J., 2015. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. – volume: 360 start-page: 1001 year: 2005 end-page: 1013 ident: bib0002 article-title: Investigations into resting-state connectivity using independent component analysis publication-title: Philos. Trans. R. Soc. Lond. B. Biol. Sci. – volume: 5 start-page: 254 year: 1997 end-page: 258 ident: bib0045 article-title: An overview and some new developments in the statistical analysis of PET and fMRI data publication-title: Hum. Brain Mapp. – volume: 20 start-page: 112 year: 2015 end-page: 134 ident: bib0028 article-title: Sparse representation of whole-brain fMRI signals for identification of functional networks publication-title: Med. Image Anal. – volume: 2 start-page: 189 year: 1994 end-page: 210 ident: bib0012 article-title: Statistical parametric maps in functional imaging: a general linear approach publication-title: Hum. Brain Mapp. – volume: 29 start-page: 2017 year: 2004 end-page: 2097 ident: bib0005 article-title: Alcohol intoxication effects on simulated driving: exploring alcohol-dose effects on brain activation using functional MRI publication-title: Neuropsychopharmacology – volume: 32 start-page: 1576 year: 2013 end-page: 1586 ident: bib0006 article-title: Inferring group-wise consistent multimodal brain networks via multi-view spectral clustering publication-title: IEEE Trans. Med. Imaging – year: 2017 ident: bib0052 article-title: Extendable Supervised Dictionary Learning for Exploring Diverse and Concurrent Brain Activities in Task-based fMRI, publication-title: Brain Imaging and Behavior. – year: 1998 ident: bib0024 article-title: Gradient-based learning applied to document recognition RS-SVM reduced-set support vector method. SDNN space displacement neural network. SVM support vector method publication-title: Proceedings of V-SVM Virtual Support Vector Method. Time Delay Neural Network (TDNN) – volume: 62 start-page: 1120 year: 2015 end-page: 1131 ident: bib0029 article-title: Holistic atlases of functional networks and interactions reveal reciprocal organizational architecture of cortical function publication-title: IEEE Trans. Biomed. Eng. – volume: 6 start-page: 293 year: 2012 end-page: 318 ident: bib0042 article-title: Multiple resting state network functional connectivity abnormalities in mild traumatic brain injury publication-title: Brain Imaging Behav. – volume: 16 start-page: 18 year: 2012 ident: bib0036 article-title: Mild traumatic brain injury and associated effects on the auditory system publication-title: Perspect. Hear. Hear. Disord. Res. Diagn. – start-page: 1097 year: 2012 end-page: 1105 ident: bib0022 article-title: ImageNet Classification with Deep Convolutional Neural Networks – volume: 110 start-page: 16616 year: 2013 end-page: 16621 ident: bib0010 article-title: Broad domain generality in focal regions of frontal and parietal cortex publication-title: Proc. Natl. Acad. Sci. USA – volume: 106 start-page: 13040 year: 2009 end-page: 13045 ident: bib0040 article-title: Correspondence of the brain's functional architecture during activation and rest publication-title: Proc. Natl. Acad. Sci. USA – volume: 18 start-page: 1527 year: 2006 end-page: 1554 ident: bib0017 article-title: A fast learning algorithm for deep belief nets publication-title: Neural Comput. – volume: 15 start-page: 247 year: 2013 end-page: 262 ident: bib0041 article-title: Structure and function of complex brain networks publication-title: Dialogues Clin. Neurosci. – volume: 50 start-page: 799 year: 2006 end-page: 812 ident: bib0008 article-title: A core system for the implementation of task sets publication-title: Neuron – start-page: 52 year: 2011 end-page: 59 ident: bib0033 article-title: Stacked Convolutional Auto-Encoders For Hierarchical Feature Extraction – year: 2004 ident: bib0018 article-title: Functional Magnetic Resonance Imaging – volume: 3 start-page: 142 year: 2002 end-page: 151 ident: bib0016 article-title: What does fMRI tell us about neuronal activity? publication-title: Nat. Rev. Neurosci. – volume: 16 start-page: 3 year: 2012 ident: bib0044 article-title: Auditory dysfunction among long-term consequences of mild traumatic brain injury (mTBI) publication-title: Perspect. Hear. Hear. Disord. Res. Diagn. – volume: 158 start-page: 1617 year: 1998 ident: bib0023 article-title: Mild traumatic brain injury publication-title: Arch. Intern. Med. – volume: 34 start-page: 2036 year: 2015 end-page: 2045 ident: bib0050 article-title: Supervised dictionary learning for inferring concurrent brain networks publication-title: IEEE Trans. Med. Imaging – volume: 10 start-page: 21 year: 2016 end-page: 32 ident: bib0049 article-title: Characterizing and differentiating task-based and resting state fMRI signals via two-stage sparse representations publication-title: Brain Imaging Behav. – start-page: 511 year: 2016 end-page: 519 ident: bib0025 article-title: Scalable fast rank-1 dictionary learning for fMRI big data analysis publication-title: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) – volume: 11 start-page: 19 year: 2010 end-page: 60 ident: bib0032 article-title: Online learning for matrix factorization and sparse coding publication-title: J. Mach. Learn. Res. – start-page: 1725 year: 2014 end-page: 1732 ident: bib0021 article-title: Large-scale video classification with convolutional neural networks publication-title: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) – volume: 6 start-page: e00454 year: 2016 ident: bib0014 article-title: Deficits in visual system functional connectivity after blast-related mild TBI are associated with injury severity and executive dysfunction publication-title: Brain Behav. – volume: 233 start-page: 254 year: 2015 end-page: 268 ident: bib0030 article-title: Assessing effects of prenatal alcohol exposure using group-wise sparse representation of fMRI data publication-title: Psychiatry Res. – volume: 32 start-page: 1031 year: 2015 end-page: 1045 ident: bib0019 article-title: Resting state functional connectivity in mild traumatic brain injury at the acute stage: independent component and seed-based analyses publication-title: J. Neurotrauma – volume: 12 start-page: 23 year: 2016 end-page: 33 ident: bib0051 article-title: Connectome-scale group-wise consistent resting-state network analysis in autism spectrum disorder publication-title: NeuroImage Clin. – year: 2007 ident: bib0003 article-title: Greedy Layer-Wise Training of Deep Networks – start-page: 239 year: 2015 end-page: 246 ident: bib0031 article-title: Modeling Task FMRI Data via Supervised Stochastic Coordinate Coding – volume: 2 start-page: 1150 year: 1999 end-page: 1157 ident: bib0026 article-title: Object recognition from local scale-invariant features publication-title: Proceedings of the Seventh IEEE International Conference on Computer Vision – volume: 10 start-page: 186 year: 2009 end-page: 198 ident: bib0004 article-title: Complex brain networks: graph theoretical analysis of structural and functional systems publication-title: Nat. Rev. Neurosci. – volume: 22 start-page: 531 year: 1982 end-page: 544 ident: bib0007 article-title: The orientation and direction selectivity of cells in macaque visual cortex publication-title: Vis. Res. – volume: 17 start-page: 395 year: 2007 end-page: 416 ident: bib0027 article-title: A tutorial on spectral clustering publication-title: Stat. Comput. – volume: 7 start-page: 65 year: 2015 end-page: 75 ident: bib0046 article-title: Sparsity-constrained fMRI decoding of visual saliency in naturalistic video streams publication-title: IEEE Trans. Auton. Ment. Dev. – start-page: 994 year: 2005 end-page: 1000 ident: bib0037 article-title: Object recognition with features inspired by visual cortex publication-title: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05) – volume: 71 start-page: 83 year: 2016 end-page: 100 ident: bib0047 article-title: Large-scale functional network overlap is a general property of brain functional organization: reconciling inconsistent fMRI findings from general-linear-model-based analyses publication-title: Neurosci. Biobehav. Rev. – volume: 14 start-page: 172 year: 2010 end-page: 179 ident: bib0009 article-title: The multiple-demand (MD) system of the primate brain: mental programs for intelligent behaviour publication-title: Trends Cogn. Sci. – reference: Simonyan, K., Zisserman, A., 2014. Very Deep Convolutional Networks for Large-Scale Image Recognition. – volume: 102 start-page: 9673 year: 2005 end-page: 9678 ident: bib0011 article-title: The human brain is intrinsically organized into dynamic, anticorrelated functional networks publication-title: Proc. Natl. Acad. Sci. USA – volume: 35 start-page: 158 year: 2012 end-page: 159 ident: bib0035 article-title: Beyond brain regions: network perspective of cognition–emotion interactions publication-title: Behav. Brain Sci. – start-page: 362 year: 2015 end-page: 370 ident: bib0034 article-title: Predicting eye fixations using convolutional neural networks publication-title: Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) – volume: 36 start-page: 5301 year: 2015 end-page: 5319 ident: bib0020 article-title: Sparse representation of HC publication-title: Hum. Brain Mapp. – volume: 80 start-page: 144 year: 2013 end-page: 168 ident: bib0039 article-title: Resting-state fMRI in the human connectome project publication-title: Neuroimage – reference: Zeiler, M.D., 2012. ADADELTA: An Adaptive Learning Rate Method. – volume: 10 start-page: 1206 year: 2016 end-page: 1222 ident: bib0013 article-title: Signal sampling for efficient sparse representation of resting state FMRI data publication-title: Brain Imaging Behav. – volume: 6 start-page: 293 year: 2012 end-page: 318 ident: bib0043 article-title: Multiple resting state network functional connectivity abnormalities in mild traumatic brain injury publication-title: Brain Imaging Behav. – volume: 80 start-page: 169 year: 2013 end-page: 189 ident: bib0001 article-title: Function in the human connectome: task-fMRI and individual differences in behavior publication-title: Neuroimage – volume: 17 start-page: 395 year: 2007 ident: 10.1016/j.media.2017.08.005_bib0027 article-title: A tutorial on spectral clustering publication-title: Stat. Comput. doi: 10.1007/s11222-007-9033-z – volume: 80 start-page: 144 year: 2013 ident: 10.1016/j.media.2017.08.005_bib0039 article-title: Resting-state fMRI in the human connectome project publication-title: Neuroimage doi: 10.1016/j.neuroimage.2013.05.039 – volume: 6 start-page: e00454 year: 2016 ident: 10.1016/j.media.2017.08.005_bib0014 article-title: Deficits in visual system functional connectivity after blast-related mild TBI are associated with injury severity and executive dysfunction publication-title: Brain Behav. doi: 10.1002/brb3.454 – year: 2017 ident: 10.1016/j.media.2017.08.005_bib0052 article-title: Extendable Supervised Dictionary Learning for Exploring Diverse and Concurrent Brain Activities in Task-based fMRI, publication-title: Brain Imaging and Behavior. – volume: 10 start-page: 21 year: 2016 ident: 10.1016/j.media.2017.08.005_bib0049 article-title: Characterizing and differentiating task-based and resting state fMRI signals via two-stage sparse representations publication-title: Brain Imaging Behav. doi: 10.1007/s11682-015-9359-7 – volume: 110 start-page: 16616 year: 2013 ident: 10.1016/j.media.2017.08.005_bib0010 article-title: Broad domain generality in focal regions of frontal and parietal cortex publication-title: Proc. Natl. Acad. Sci. USA doi: 10.1073/pnas.1315235110 – year: 2004 ident: 10.1016/j.media.2017.08.005_bib0018 – year: 1998 ident: 10.1016/j.media.2017.08.005_bib0024 article-title: Gradient-based learning applied to document recognition RS-SVM reduced-set support vector method. SDNN space displacement neural network. SVM support vector method – volume: 80 start-page: 169 year: 2013 ident: 10.1016/j.media.2017.08.005_bib0001 article-title: Function in the human connectome: task-fMRI and individual differences in behavior publication-title: Neuroimage doi: 10.1016/j.neuroimage.2013.05.033 – volume: 2 start-page: 189 year: 1994 ident: 10.1016/j.media.2017.08.005_bib0012 article-title: Statistical parametric maps in functional imaging: a general linear approach publication-title: Hum. Brain Mapp. doi: 10.1002/hbm.460020402 – start-page: 52 year: 2011 ident: 10.1016/j.media.2017.08.005_bib0033 – ident: 10.1016/j.media.2017.08.005_bib0015 doi: 10.1109/ICCV.2015.123 – volume: 22 start-page: 531 year: 1982 ident: 10.1016/j.media.2017.08.005_bib0007 article-title: The orientation and direction selectivity of cells in macaque visual cortex publication-title: Vis. Res. doi: 10.1016/0042-6989(82)90112-2 – year: 2007 ident: 10.1016/j.media.2017.08.005_bib0003 – volume: 34 start-page: 2036 year: 2015 ident: 10.1016/j.media.2017.08.005_bib0050 article-title: Supervised dictionary learning for inferring concurrent brain networks publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2015.2418734 – volume: 18 start-page: 1527 year: 2006 ident: 10.1016/j.media.2017.08.005_bib0017 article-title: A fast learning algorithm for deep belief nets publication-title: Neural Comput. doi: 10.1162/neco.2006.18.7.1527 – volume: 36 start-page: 5301 year: 2015 ident: 10.1016/j.media.2017.08.005_bib0020 article-title: Sparse representation of HC P grayordinate data reveals novel functional architecture of cerebral cortex publication-title: Hum. Brain Mapp. doi: 10.1002/hbm.23013 – volume: 35 start-page: 158 year: 2012 ident: 10.1016/j.media.2017.08.005_bib0035 article-title: Beyond brain regions: network perspective of cognition–emotion interactions publication-title: Behav. Brain Sci. doi: 10.1017/S0140525X11001567 – volume: 360 start-page: 1001 year: 2005 ident: 10.1016/j.media.2017.08.005_bib0002 article-title: Investigations into resting-state connectivity using independent component analysis publication-title: Philos. Trans. R. Soc. Lond. B. Biol. Sci. doi: 10.1098/rstb.2005.1634 – volume: 29 start-page: 2017 year: 2004 ident: 10.1016/j.media.2017.08.005_bib0005 article-title: Alcohol intoxication effects on simulated driving: exploring alcohol-dose effects on brain activation using functional MRI publication-title: Neuropsychopharmacology doi: 10.1038/sj.npp.1300543 – start-page: 1725 year: 2014 ident: 10.1016/j.media.2017.08.005_bib0021 article-title: Large-scale video classification with convolutional neural networks – start-page: 239 year: 2015 ident: 10.1016/j.media.2017.08.005_bib0031 – start-page: 511 year: 2016 ident: 10.1016/j.media.2017.08.005_bib0025 article-title: Scalable fast rank-1 dictionary learning for fMRI big data analysis – volume: 14 start-page: 172 year: 2010 ident: 10.1016/j.media.2017.08.005_bib0009 article-title: The multiple-demand (MD) system of the primate brain: mental programs for intelligent behaviour publication-title: Trends Cogn. Sci. doi: 10.1016/j.tics.2010.01.004 – volume: 32 start-page: 1576 year: 2013 ident: 10.1016/j.media.2017.08.005_bib0006 article-title: Inferring group-wise consistent multimodal brain networks via multi-view spectral clustering publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2013.2259248 – volume: 32 start-page: 1031 year: 2015 ident: 10.1016/j.media.2017.08.005_bib0019 article-title: Resting state functional connectivity in mild traumatic brain injury at the acute stage: independent component and seed-based analyses publication-title: J. Neurotrauma doi: 10.1089/neu.2014.3610 – volume: 16 start-page: 3 year: 2012 ident: 10.1016/j.media.2017.08.005_bib0044 article-title: Auditory dysfunction among long-term consequences of mild traumatic brain injury (mTBI) publication-title: Perspect. Hear. Hear. Disord. Res. Diagn. doi: 10.1044/hhd16.1.3 – start-page: 994 year: 2005 ident: 10.1016/j.media.2017.08.005_bib0037 article-title: Object recognition with features inspired by visual cortex – volume: 6 start-page: 293 year: 2012 ident: 10.1016/j.media.2017.08.005_bib0042 article-title: Multiple resting state network functional connectivity abnormalities in mild traumatic brain injury publication-title: Brain Imaging Behav. doi: 10.1007/s11682-012-9157-4 – start-page: 362 year: 2015 ident: 10.1016/j.media.2017.08.005_bib0034 article-title: Predicting eye fixations using convolutional neural networks – volume: 7 start-page: 65 year: 2015 ident: 10.1016/j.media.2017.08.005_bib0046 article-title: Sparsity-constrained fMRI decoding of visual saliency in naturalistic video streams publication-title: IEEE Trans. Auton. Ment. Dev. doi: 10.1109/TAMD.2015.2409835 – volume: 6 start-page: 293 year: 2012 ident: 10.1016/j.media.2017.08.005_bib0043 article-title: Multiple resting state network functional connectivity abnormalities in mild traumatic brain injury publication-title: Brain Imaging Behav. doi: 10.1007/s11682-012-9157-4 – ident: 10.1016/j.media.2017.08.005_bib0038 – volume: 233 start-page: 254 year: 2015 ident: 10.1016/j.media.2017.08.005_bib0030 article-title: Assessing effects of prenatal alcohol exposure using group-wise sparse representation of fMRI data publication-title: Psychiatry Res. doi: 10.1016/j.pscychresns.2015.07.012 – volume: 12 start-page: 23 year: 2016 ident: 10.1016/j.media.2017.08.005_bib0051 article-title: Connectome-scale group-wise consistent resting-state network analysis in autism spectrum disorder publication-title: NeuroImage Clin. doi: 10.1016/j.nicl.2016.06.004 – volume: 11 start-page: 19 year: 2010 ident: 10.1016/j.media.2017.08.005_bib0032 article-title: Online learning for matrix factorization and sparse coding publication-title: J. Mach. Learn. Res. – volume: 5 start-page: 254 year: 1997 ident: 10.1016/j.media.2017.08.005_sbref0043 article-title: An overview and some new developments in the statistical analysis of PET and fMRI data publication-title: Hum. Brain Mapp. doi: 10.1002/(SICI)1097-0193(1997)5:4<254::AID-HBM9>3.0.CO;2-2 – volume: 106 start-page: 13040 year: 2009 ident: 10.1016/j.media.2017.08.005_bib0040 article-title: Correspondence of the brain's functional architecture during activation and rest publication-title: Proc. Natl. Acad. Sci. USA doi: 10.1073/pnas.0905267106 – ident: 10.1016/j.media.2017.08.005_bib0048 – volume: 10 start-page: 186 year: 2009 ident: 10.1016/j.media.2017.08.005_bib0004 article-title: Complex brain networks: graph theoretical analysis of structural and functional systems publication-title: Nat. Rev. Neurosci. doi: 10.1038/nrn2575 – start-page: 1097 year: 2012 ident: 10.1016/j.media.2017.08.005_bib0022 – volume: 158 start-page: 1617 year: 1998 ident: 10.1016/j.media.2017.08.005_bib0023 article-title: Mild traumatic brain injury publication-title: Arch. Intern. Med. doi: 10.1001/archinte.158.15.1617 – volume: 62 start-page: 1120 year: 2015 ident: 10.1016/j.media.2017.08.005_bib0029 article-title: Holistic atlases of functional networks and interactions reveal reciprocal organizational architecture of cortical function publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2014.2369495 – volume: 71 start-page: 83 year: 2016 ident: 10.1016/j.media.2017.08.005_bib0047 article-title: Large-scale functional network overlap is a general property of brain functional organization: reconciling inconsistent fMRI findings from general-linear-model-based analyses publication-title: Neurosci. Biobehav. Rev. doi: 10.1016/j.neubiorev.2016.08.035 – volume: 15 start-page: 247 year: 2013 ident: 10.1016/j.media.2017.08.005_bib0041 article-title: Structure and function of complex brain networks publication-title: Dialogues Clin. Neurosci. doi: 10.31887/DCNS.2013.15.3/osporns – volume: 2 start-page: 1150 year: 1999 ident: 10.1016/j.media.2017.08.005_bib0026 article-title: Object recognition from local scale-invariant features – volume: 20 start-page: 112 year: 2015 ident: 10.1016/j.media.2017.08.005_bib0028 article-title: Sparse representation of whole-brain fMRI signals for identification of functional networks publication-title: Med. Image Anal. doi: 10.1016/j.media.2014.10.011 – volume: 50 start-page: 799 year: 2006 ident: 10.1016/j.media.2017.08.005_bib0008 article-title: A core system for the implementation of task sets publication-title: Neuron doi: 10.1016/j.neuron.2006.04.031 – volume: 3 start-page: 142 year: 2002 ident: 10.1016/j.media.2017.08.005_bib0016 article-title: What does fMRI tell us about neuronal activity? publication-title: Nat. Rev. Neurosci. doi: 10.1038/nrn730 – volume: 102 start-page: 9673 year: 2005 ident: 10.1016/j.media.2017.08.005_bib0011 article-title: The human brain is intrinsically organized into dynamic, anticorrelated functional networks publication-title: Proc. Natl. Acad. Sci. USA doi: 10.1073/pnas.0504136102 – volume: 10 start-page: 1206 year: 2016 ident: 10.1016/j.media.2017.08.005_bib0013 article-title: Signal sampling for efficient sparse representation of resting state FMRI data publication-title: Brain Imaging Behav. doi: 10.1007/s11682-015-9487-0 – volume: 16 start-page: 18 year: 2012 ident: 10.1016/j.media.2017.08.005_bib0036 article-title: Mild traumatic brain injury and associated effects on the auditory system publication-title: Perspect. Hear. Hear. Disord. Res. Diagn. doi: 10.1044/hhd16.1.18
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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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