Recognizing Brain States Using Deep Sparse Recurrent Neural Network
Brain activity is a dynamic combination of different sensory responses and thus brain activity/state is continuously changing over time. However, the brain's dynamical functional states recognition at fast time-scales in task fMRI data have been rarely explored. In this paper, we propose a novel 5-layer deep sparse recurrent neural network (DSRNN) model to accurately recognize the brain states across the whole scan session. Specifically, the DSRNN model includes an input layer, one fully-connected layer, two recurrent layers, and a softmax output layer. The proposed framework has been tested on seven task fMRI data sets of Human Connectome Project. Extensive experiment results demonstrate that the proposed DSRNN model can accurately identify the brain's state in different task fMRI data sets and significantly outperforms other auto-correlation methods or non-temporal approaches in the dynamic brain state recognition accuracy. In general, the proposed DSRNN offers a new methodology for basic neuroscience and clinical research.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2019 |
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| Erschienen: |
2019 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:38 |
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| Enthalten in: |
IEEE transactions on medical imaging - 38(2019), 4 vom: 24. Apr., Seite 1058-1068 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Wang, Han [VerfasserIn] |
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| Links: |
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| Themen: |
Journal Article |
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| Anmerkungen: |
Date Completed 10.02.2020 Date Revised 01.04.2020 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1109/TMI.2018.2877576 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM290034310 |
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| 520 | |a Brain activity is a dynamic combination of different sensory responses and thus brain activity/state is continuously changing over time. However, the brain's dynamical functional states recognition at fast time-scales in task fMRI data have been rarely explored. In this paper, we propose a novel 5-layer deep sparse recurrent neural network (DSRNN) model to accurately recognize the brain states across the whole scan session. Specifically, the DSRNN model includes an input layer, one fully-connected layer, two recurrent layers, and a softmax output layer. The proposed framework has been tested on seven task fMRI data sets of Human Connectome Project. Extensive experiment results demonstrate that the proposed DSRNN model can accurately identify the brain's state in different task fMRI data sets and significantly outperforms other auto-correlation methods or non-temporal approaches in the dynamic brain state recognition accuracy. In general, the proposed DSRNN offers a new methodology for basic neuroscience and clinical research | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, N.I.H., Extramural | |
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| 650 | 4 | |a Research Support, U.S. Gov't, Non-P.H.S. | |
| 700 | 1 | |a Zhao, Shijie |e verfasserin |4 aut | |
| 700 | 1 | |a Dong, Qinglin |e verfasserin |4 aut | |
| 700 | 1 | |a Cui, Yan |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Yaowu |e verfasserin |4 aut | |
| 700 | 1 | |a Han, Junwei |e verfasserin |4 aut | |
| 700 | 1 | |a Xie, Li |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Tianming |e verfasserin |4 aut | |
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