Hierarchical Organization of Functional Brain Networks Revealed by Hybrid Spatiotemporal Deep Learning
Hierarchical organization of brain function has been an established concept in the neuroscience field for a long time, however, it has been rarely demonstrated how such hierarchical macroscale functional networks are actually organized in the human brain. In this study, to answer this question, we propose a novel methodology to provide an evidence of hierarchical organization of functional brain networks. This article introduces the hybrid spatiotemporal deep learning (HSDL), by jointly using deep belief networks (DBNs) and deep least absolute shrinkage and selection operator (LASSO) to reveal the temporal hierarchical features and spatial hierarchical maps of brain networks based on the Human Connectome Project 900 functional magnetic resonance imaging (fMRI) data sets. Briefly, the key idea of HSDL is to extract the weights between two adjacent layers of DBNs, which are then treated as the hierarchical dictionaries for deep LASSO to identify the corresponding hierarchical spatial maps. Our results demonstrate that both spatial and temporal aspects of dozens of functional networks exhibit multiscale properties that can be well characterized and interpreted based on existing computational tools and neuroscience knowledge. Our proposed novel hybrid deep model is used to provide the first insightful opportunity to reveal the potential hierarchical organization of time series and functional brain networks, using task-based fMRI signals of human brain.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2020 |
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| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:10 |
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| Enthalten in: |
Brain connectivity - 10(2020), 2 vom: 01. März, Seite 72-82 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Zhang, Wei [VerfasserIn] |
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| Links: |
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| Themen: |
Brain networks |
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| Anmerkungen: |
Date Completed 01.10.2020 Date Revised 02.03.2021 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1089/brain.2019.0701 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM306506378 |
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| 520 | |a Hierarchical organization of brain function has been an established concept in the neuroscience field for a long time, however, it has been rarely demonstrated how such hierarchical macroscale functional networks are actually organized in the human brain. In this study, to answer this question, we propose a novel methodology to provide an evidence of hierarchical organization of functional brain networks. This article introduces the hybrid spatiotemporal deep learning (HSDL), by jointly using deep belief networks (DBNs) and deep least absolute shrinkage and selection operator (LASSO) to reveal the temporal hierarchical features and spatial hierarchical maps of brain networks based on the Human Connectome Project 900 functional magnetic resonance imaging (fMRI) data sets. Briefly, the key idea of HSDL is to extract the weights between two adjacent layers of DBNs, which are then treated as the hierarchical dictionaries for deep LASSO to identify the corresponding hierarchical spatial maps. Our results demonstrate that both spatial and temporal aspects of dozens of functional networks exhibit multiscale properties that can be well characterized and interpreted based on existing computational tools and neuroscience knowledge. Our proposed novel hybrid deep model is used to provide the first insightful opportunity to reveal the potential hierarchical organization of time series and functional brain networks, using task-based fMRI signals of human brain | ||
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| 650 | 4 | |a hierarchical organization | |
| 650 | 4 | |a hybrid spatiotemporal representation | |
| 700 | 1 | |a Zhao, Shijie |e verfasserin |4 aut | |
| 700 | 1 | |a Hu, Xintao |e verfasserin |4 aut | |
| 700 | 1 | |a Dong, Qinglin |e verfasserin |4 aut | |
| 700 | 1 | |a Huang, Heng |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Shu |e verfasserin |4 aut | |
| 700 | 1 | |a Zhao, Yu |e verfasserin |4 aut | |
| 700 | 1 | |a Dai, Haixing |e verfasserin |4 aut | |
| 700 | 1 | |a Ge, Fangfei |e verfasserin |4 aut | |
| 700 | 1 | |a Guo, Lei |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Tianming |e verfasserin |4 aut | |
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