Constructing fine-granularity functional brain network atlases via deep convolutional autoencoder
Copyright © 2017 Elsevier B.V. All rights reserved..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2017 |
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| Erschienen: |
2017 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:42 |
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| Enthalten in: |
Medical image analysis - 42(2017) vom: 15. Dez., Seite 200-211 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Zhao, Yu [VerfasserIn] |
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| Anmerkungen: |
Date Completed 09.07.2018 Date Revised 04.11.2023 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.media.2017.08.005 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM275159566 |
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| 520 | |a Copyright © 2017 Elsevier B.V. All rights reserved. | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Deep learning | |
| 650 | 4 | |a Functional brain networks | |
| 650 | 4 | |a fMRI | |
| 700 | 1 | |a Dong, Qinglin |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Hanbo |e verfasserin |4 aut | |
| 700 | 1 | |a Iraji, Armin |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Yujie |e verfasserin |4 aut | |
| 700 | 1 | |a Makkie, Milad |e verfasserin |4 aut | |
| 700 | 1 | |a Kou, Zhifeng |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Tianming |e verfasserin |4 aut | |
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