Large-scale circuitry interactions upon earthquake experiences revealed by recurrent neural networks

Han Wang, Kun Xie, Zhichao Lian, Yan Cui, Yaowu Chen, Jing Zhang, Leo Xie, Joseph Zhuo Tsien, Tianming Liu

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
Article number8482281
Pages (from-to)2115-2125
Number of pages11
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Volume26
Issue number11
DOIs
StatePublished - Nov 2018

Fingerprint

Earthquakes
Recurrent neural networks
Brain
Fear
Data recording
Neural Networks (Computer)
Neurosciences
Frequency bands
Reaction Time
Time series
Data acquisition
Learning
Data storage equipment
Recovery

Keywords

  • Brain dynamics
  • fear conditioning
  • large-scale LFP recordings
  • recurrent neural network

ASJC Scopus subject areas

  • Internal Medicine
  • Neuroscience(all)
  • Biomedical Engineering

Cite this

Large-scale circuitry interactions upon earthquake experiences revealed by recurrent neural networks. / Wang, Han; Xie, Kun; Lian, Zhichao; Cui, Yan; Chen, Yaowu; Zhang, Jing; Xie, Leo; Tsien, Joseph Zhuo; Liu, Tianming.

In: IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 26, No. 11, 8482281, 11.2018, p. 2115-2125.

Research output: Contribution to journalArticle

Wang, Han ; Xie, Kun ; Lian, Zhichao ; Cui, Yan ; Chen, Yaowu ; Zhang, Jing ; Xie, Leo ; Tsien, Joseph Zhuo ; Liu, Tianming. / Large-scale circuitry interactions upon earthquake experiences revealed by recurrent neural networks. In: IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2018 ; Vol. 26, No. 11. pp. 2115-2125.
@article{06895b2715d846fb9fadbeb71401a06b,
title = "Large-scale circuitry interactions upon earthquake experiences revealed by recurrent neural networks",
abstract = "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.",
keywords = "Brain dynamics, fear conditioning, large-scale LFP recordings, recurrent neural network",
author = "Han Wang and Kun Xie and Zhichao Lian and Yan Cui and Yaowu Chen and Jing Zhang and Leo Xie and Tsien, {Joseph Zhuo} and Tianming Liu",
year = "2018",
month = "11",
doi = "10.1109/TNSRE.2018.2872919",
language = "English (US)",
volume = "26",
pages = "2115--2125",
journal = "IEEE Transactions on Neural Systems and Rehabilitation Engineering",
issn = "1534-4320",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "11",

}

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

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

UR - http://www.scopus.com/inward/record.url?scp=85054550545&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85054550545&partnerID=8YFLogxK

U2 - 10.1109/TNSRE.2018.2872919

DO - 10.1109/TNSRE.2018.2872919

M3 - Article

C2 - 30296236

AN - SCOPUS:85054550545

VL - 26

SP - 2115

EP - 2125

JO - IEEE Transactions on Neural Systems and Rehabilitation Engineering

JF - IEEE Transactions on Neural Systems and Rehabilitation Engineering

SN - 1534-4320

IS - 11

M1 - 8482281

ER -