TY - JOUR
T1 - Large-scale circuitry interactions upon earthquake experiences revealed by recurrent neural networks
AU - Wang, Han
AU - Xie, Kun
AU - Lian, Zhichao
AU - Cui, Yan
AU - Chen, Yaowu
AU - Zhang, Jing
AU - Xie, Leo
AU - Tsien, Joseph Zhuo
AU - Liu, Tianming
N1 - Funding Information:
Manuscript received November 27, 2017; revised March 7, 2018 and August 2, 2018; accepted September 9, 2018. Date of publication October 5, 2018; date of current version November 20, 2018. The work of H. Wang was supported by the Fundamental Research Funds for the Central Universities. (Li Xie and Joe Tsien contributed equally to this work.) (Corresponding author: Tianming Liu.) H. Wang and Y. Cui are with the College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou 310027, China (e-mail: sdzbwh@zju.edu.cn; cuiy@zju.edu.cn).
Publisher Copyright:
© 2018 IEEE.
PY - 2018/11
Y1 - 2018/11
N2 - Brain dynamics has recently received increasing interest due to its significant importance in basic and clinical neurosciences. However, due to inherent difficulties in both data acquisition and data analysis methods, studies on large-scale brain dynamics of mouse with local field potential (LFP) recording are very rare. In this paper, we did a series of works on modeling large-scale mouse brain dynamic activities responding to fearful earthquake. Based on LFP recording data from 13 brain regions that are closely related to fear learning and memory and the effective Bayesian connectivity change point model, we divided the response time series into four stages: 'Before,' 'Earthquake,' 'Recovery,' and 'After.' We first reported the changes in power and theta-gamma coupling during stage transitions. Then, a recurrent neural network model was designed to model the functional dynamics in these thirteen brain regions and six frequency bands in response to the fear stimulus. Interestingly, our results showed that the functional brain connectivities in theta and gamma bands exhibited distinct response processes: In theta band, there is a separated-united-separated alternation in whole-brain connectivity and a low-high-low change in connectivity strength; however, gamma bands have a united-separated-united transition and a high-low-high alternation in connectivity pattern and strength. In general, our results offer a novel perspective in studying functional brain dynamics under fearful stimulus and reveal its relationship to the brain's structural connectivity substrates.
AB - Brain dynamics has recently received increasing interest due to its significant importance in basic and clinical neurosciences. However, due to inherent difficulties in both data acquisition and data analysis methods, studies on large-scale brain dynamics of mouse with local field potential (LFP) recording are very rare. In this paper, we did a series of works on modeling large-scale mouse brain dynamic activities responding to fearful earthquake. Based on LFP recording data from 13 brain regions that are closely related to fear learning and memory and the effective Bayesian connectivity change point model, we divided the response time series into four stages: 'Before,' 'Earthquake,' 'Recovery,' and 'After.' We first reported the changes in power and theta-gamma coupling during stage transitions. Then, a recurrent neural network model was designed to model the functional dynamics in these thirteen brain regions and six frequency bands in response to the fear stimulus. Interestingly, our results showed that the functional brain connectivities in theta and gamma bands exhibited distinct response processes: In theta band, there is a separated-united-separated alternation in whole-brain connectivity and a low-high-low change in connectivity strength; however, gamma bands have a united-separated-united transition and a high-low-high alternation in connectivity pattern and strength. In general, our results offer a novel perspective in studying functional brain dynamics under fearful stimulus and reveal its relationship to the brain's structural connectivity substrates.
KW - Brain dynamics
KW - fear conditioning
KW - large-scale LFP recordings
KW - recurrent neural network
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U2 - 10.1109/TNSRE.2018.2872919
DO - 10.1109/TNSRE.2018.2872919
M3 - Article
C2 - 30296236
AN - SCOPUS:85054550545
SN - 1534-4320
VL - 26
SP - 2115
EP - 2125
JO - IEEE Transactions on Neural Systems and Rehabilitation Engineering
JF - IEEE Transactions on Neural Systems and Rehabilitation Engineering
IS - 11
M1 - 8482281
ER -