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Lv X, Zhang X, Zhao Q, Li C, Zhang T, Yang X. Acute stress promotes brain oscillations and hippocampal-cortical dialog in emotional processing. Biochem Biophys Res Commun 2022; 598:55-61. [PMID: 35151204 DOI: 10.1016/j.bbrc.2022.01.116] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 01/28/2022] [Indexed: 12/30/2022]
Abstract
Hippocampal-cortical circuit oscillations in local field potential (LFP) represent network-level signals which promotes behavior. Investigating these signals promote our understanding on how the brain process cognition and emotion, and provide further perspectives into electroencephalogram endophenotypes, especially under the pathological state. The physiological adaptive stress responses to threatening stimuli are critical for individuals. The disturbance of stress response may lead to psychiatric disorders such as major depressive disorder (MDD). To quantitatively examine the effects of acute stress on hippocampal-cortical circuit, we recorded LFPs in the hippocampus (HC) and the medial prefrontal cortex (mPFC). We analyzed three major LFP oscillations with their temporal coupling. Consistent with our hypothesis that strengthened communication of hippocampal-cortical circuit may occur in stress adaption, we found that intensive acute stress induced enhanced ripple-delta-spindle coupling. The LFP coupling may facilitate the recruitment of relevant structures in hippocampal-cortical circuit, in response to acute stress, and play a role in emotional encoding migration.
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Affiliation(s)
- Xin Lv
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China
| | - Xiaolin Zhang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China
| | - Qian Zhao
- Institute of Psychology and Behavioral Sciences, Shanghai Jiao Tong University, Shanghai, 200030, China; Laboratory of Molecular Neurodegeneration, Graduate School of Biomedical Systems and Technologies, Institute of Biomedical Systems and Biotechnology, Peter the Great St. Petersburg Polytechnic University, Saint Petersburg, 195251, Russia
| | - Chunbo Li
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China.
| | - Tianhong Zhang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China.
| | - Xiangyu Yang
- Institute of Psychology and Behavioral Sciences, Shanghai Jiao Tong University, Shanghai, 200030, China; Laboratory of Molecular Neurodegeneration, Graduate School of Biomedical Systems and Technologies, Institute of Biomedical Systems and Biotechnology, Peter the Great St. Petersburg Polytechnic University, Saint Petersburg, 195251, Russia.
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Qiang N, Dong Q, Liang H, Ge B, Zhang S, Sun Y, Zhang C, Zhang W, Gao J, Liu T. Modeling and augmenting of fMRI data using deep recurrent variational auto-encoder. J Neural Eng 2021; 18. [PMID: 34229310 DOI: 10.1088/1741-2552/ac1179] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Accepted: 07/06/2021] [Indexed: 11/11/2022]
Abstract
Objective. Recently, deep learning models have been successfully applied in functional magnetic resonance imaging (fMRI) modeling and associated applications. However, there still exist at least two challenges. Firstly, due to the lack of sufficient data, deep learning models tend to suffer from overfitting in the training process. Secondly, it is still challenging to model the temporal dynamics from fMRI, due to that the brain state is continuously changing over scan time. In addition, existing methods rarely studied and applied fMRI data augmentation.Approach. In this work, we construct a deep recurrent variational auto-encoder (DRVAE) that combined variational auto-encoder and recurrent neural network, aiming to address all of the above mentioned challenges. The encoder of DRVAE can extract more generalized temporal features from assumed Gaussian distribution of input data, and the decoder of DRVAE can generate new data to increase training samples and thus partially relieve the overfitting issue. The recurrent layers in DRVAE are designed to effectively model the temporal dynamics of functional brain activities. LASSO (least absolute shrinkage and selection operator) regression is applied on the temporal features and input fMRI data to estimate the corresponding spatial networks.Main results. Extensive experimental results on seven tasks from HCP dataset showed that the DRVAE and LASSO framework can learn meaningful temporal patterns and spatial networks from both real data and generated data. The results on group-wise data and single subject suggest that the brain activities may follow certain distribution. Moreover, we applied DRVAE on four resting state fMRI datasets from ADHD-200 for data augmentation, and the results showed that the classification performances on augmented datasets have been considerably improved.Significance. The proposed method can not only derive meaningful temporal features and spatial networks from fMRI, but also generate high-quality new data for fMRI data augmentation and associated applications.
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Affiliation(s)
- Ning Qiang
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an, People's Republic of China.,Center for Brain and Brain-Inspired Computing Research, Department of Computer Science, Northwestern Polytechnical University, Xi'an, People's Republic of China
| | - Qinglin Dong
- Advanced Medical Computing and Analysis, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States of America
| | - Hongtao Liang
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an, People's Republic of China
| | - Bao Ge
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an, People's Republic of China.,Center for Brain and Brain-Inspired Computing Research, Department of Computer Science, Northwestern Polytechnical University, Xi'an, People's Republic of China
| | - Shu Zhang
- Center for Brain and Brain-Inspired Computing Research, Department of Computer Science, Northwestern Polytechnical University, Xi'an, People's Republic of China
| | - Yifei Sun
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an, People's Republic of China
| | - Cheng Zhang
- School of Electronics Engineering and Computer Science, Peking University, Beijing, People's Republic of China
| | - Wei Zhang
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, United States of America
| | - Jie Gao
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an, People's Republic of China
| | - Tianming Liu
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, United States of America
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Cui Y, Zhao S, Chen Y, Han J, Guo L, Xie L, Liu T. Modeling Brain Diverse and Complex Hemodynamic Response Patterns via Deep Recurrent Autoencoder. IEEE Trans Cogn Dev Syst 2020. [DOI: 10.1109/tcds.2019.2949195] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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Qiang N, Dong Q, Zhang W, Ge B, Ge F, Liang H, Sun Y, Gao J, Liu T. Modeling task-based fMRI data via deep belief network with neural architecture search. Comput Med Imaging Graph 2020; 83:101747. [PMID: 32593949 DOI: 10.1016/j.compmedimag.2020.101747] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Revised: 04/20/2020] [Accepted: 06/04/2020] [Indexed: 01/13/2023]
Abstract
It has been shown that deep neural networks are powerful and flexible models that can be applied on fMRI data with superb representation ability over traditional methods. However, a challenge of neural network architecture design has also attracted attention: due to the high dimension of fMRI volume images, the manual process of network model design is very time-consuming and not optimal. To tackle this problem, we proposed an unsupervised neural architecture search (NAS) framework on a deep belief network (DBN) that models volumetric fMRI data, named NAS-DBN. The NAS-DBN framework is based on Particle Swarm Optimization (PSO) where the swarms of neural architectures can evolve and converge to a feasible optimal solution. The experiments showed that the proposed NAS-DBN framework can quickly find a robust architecture of DBN, yielding a hierarchy organization of functional brain networks (FBNs) and temporal responses. Compared with 3 manually designed DBNs, the proposed NAS-DBN has the lowest testing loss of 0.0197, suggesting an overall performance improvement of up to 47.9 %. For each task, the NAS-DBN identified 260 FBNs, including task-specific FBNs and resting state networks (RSN), which have high overlap rates to general linear model (GLM) derived templates and independent component analysis (ICA) derived RSN templates. The average overlap rate of NAS-DBN to GLM on 20 task-specific FBNs is as high as 0.536, indicating a performance improvement of up to 63.9 % in respect of network modeling. Besides, we showed that the NAS-DBN can simultaneously generate temporal responses that resemble the task designs very well, and it was observed that widespread overlaps between FBNs from different layers of NAS-DBN model form a hierarchical organization of FBNs. Our NAS-DBN framework contributes an effective, unsupervised NAS method for modeling volumetric task fMRI data.
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Affiliation(s)
- Ning Qiang
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China
| | - Qinglin Dong
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, Georgia, United States
| | - Wei Zhang
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, United States
| | - Bao Ge
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China
| | - Fangfei Ge
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, Georgia, United States
| | - Hongtao Liang
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China
| | - Yifei Sun
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China
| | - Jie Gao
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China.
| | - Tianming Liu
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, Georgia, United States.
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Dong Q, Ge F, Ning Q, Zhao Y, Lv J, Huang H, Yuan J, Jiang X, Shen D, Liu T. Modeling Hierarchical Brain Networks via Volumetric Sparse Deep Belief Network. IEEE Trans Biomed Eng 2020; 67:1739-1748. [DOI: 10.1109/tbme.2019.2945231] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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