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Time-varying dynamic Bayesian network model and its application to brain connectivity using electrocorticograph

Guo Miao-Miao Wang Yu-Jing Xu Gui-Zhi Griffin Milsap Nitish V. Thakor Nathan Crone

Time-varying dynamic Bayesian network model and its application to brain connectivity using electrocorticograph

Guo Miao-Miao, Wang Yu-Jing, Xu Gui-Zhi, Griffin Milsap, Nitish V. Thakor, Nathan Crone
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  • Cortical networks for speech production are believed to be widely distributed and highly organized over temporal, parietal, and frontal lobes areas in the human brain cortex. Effective connectivity demonstrates an inherent element of directional information propagation, and is therefore an information dense measure for the relevant activity over different cortical regions. Connectivity analysis of electrocorticographic (ECoG) recordings has been widely studied for its excellent signal-to-noise ratio as well as high temporal and spatial resolutions, providing an important approach to human electrophysiological researches. In this paper, we evaluate two patients undergoing invasive monitoring for seizure localization, in which both micro-electrode and standard clinical electrodes are used for ECoG recordings from speech-related cortical areas during syllable reading test. In order to explore the dynamics of speech processing, we extract the high gamma frequency band (70-110 Hz) power from ECoG signals by the multi-taper method. The trial-averaged results show that there is a consistent task-related increase in high gamma response for micro-ECoG electrodes for patient 1 and standard-ECoG electrodes for both patients 1 and 2. We demonstrate that high gamma response provides reliable speech localization compared with electrocortical stimulation. In addition, a directed connectivity network is built in single trial involving both standard ECoG electrodes and micro-ECoG arrays using time-varying dynamic Bayesian networks (TV-DBN). The TV-DBN is used to model the time-varying effective connectivity between pairs of ECoG electrodes selected by high gamma power, with less parameter optimization required and higher computational simplicity than short-time direct directed transfer function. We observe task-related connectivity modulations of connectivity between large-scale cortical networks (standard ECoG) and local cortical networks (micro-ECoG), as well as between large-scale and local cortical networks. In addition, cortical connectivity is modulated differently before and after response articulation onset. In other words, electrodes located over sensorimotor cortex show higher connectivity before articulation onset, while connectivity appears gradually between sensorimotor and auditory cortex after articulation onset. Also, the connectivity patterns observed during articulation are significantly different for three different places of articulation for the consonants. This study offers insights into preoperative evaluation during epilepsy surgery, dynamic real-time brain connectivity visualization, and assistance to understand the dynamic processing of language pronunciation in the language cortex.
      Corresponding author: Xu Gui-Zhi, gzxu@hebut.edu.cn
    • Funds: Project supported by the National Natural Science Foundation of China (Grant No. 50877023) and the Specialized Research Fund for the Doctoral Program of Higher Education, China (Grant No. 20121317110002).
    [1]

    Lachaux J P, Axmacher N, Mormann F, Halgren E, Crone N E 2012 Prog. Neurobiol. 98 279

    [2]

    Qian T Y, Wang Y J, Zhou W J, Gao S K, Hong B 2013 J. Tsinghua Univ. (Sci. Technol.) 53 1334 (in Chinese) [钱天翼, 王昱婧, 周文静, 高上凯, 洪波 2013 清华大学学报(自然科学版) 53 1334]

    [3]

    Crone N E, Hao L, Hart J, Boatman D, Lesser R P, Irizarry R, Gordon B 2001 Neurology 57 2045

    [4]

    Crone N E, Sinai A, Korzeniewska A 2006 Prog. Brain Res. 159 275

    [5]

    Towle V L, Yoon H A, Castelle M, Edgar J C, Biassou N M, Frim D M, Spire J P, Kohrman M H 2008 Brain 131 2013

    [6]

    Crone N E, Boatman D, Gordon B, Hao L 2001 Clin. Neurophysiol. 112 565

    [7]

    Miller K J, Leuthardt E C, Schalk G, Rao R P N, Anderson N R, Moran D W, Miller J W, Ojemann J G 2007 J. Neurosci. 27 2424

    [8]

    Kellis S S, House P A, Thomson K E, Brown R, Greger B 2009 Neurosurg. Focus 27 E9

    [9]

    Bouchard K E, Mesgarani N, Johnson K, Chang E F 2013 Nature 495 327

    [10]

    Leuthardt E C, Freudenberg Z, Bundy D, Roland J 2009 Neurosurg. Focus 27 E10

    [11]

    Conant D, Bouchard K E, Chang E F 2014 Curr. Opin. Neurobiol. 24 63

    [12]

    Indefrey P, Levelt W J M 2004 Cognition 92 101

    [13]

    Hickok G, Poeppel D 2007 Nat. Rev. Neurosi. 8 393

    [14]

    Brown S, Laird A R, Pfordresher P Q, Thelen S M, Turkeltaub P, Liotti M 2009 Brain Cogn. 70 31

    [15]

    Price C J 2000 J. Anat. 197 335

    [16]

    Li L, Jin Z L, Li B 2011 Chin. Phys. B 20 038701

    [17]

    Yi G S, Wang J, Han C X, Deng B, Wei X L, Li N 2013 Chin. Phys. B 22 028702

    [18]

    Yin N, Xu G Z, Zhou Q 2013 Acta Phys. Sin. 62 118704 (in Chinese) [尹宁, 徐桂芝, 周倩 2013 物理学报 62 118704]

    [19]

    Yang J, Chen S S, Huangfu H R 2015 Acta Phys. Sin. 64 058701 (in Chinese) [杨剑, 陈书燊, 皇甫浩然 2015 物理学报 64 058701]

    [20]

    Zou C, Denby K J, Feng J 2009 BMC Bioinform. 10 122

    [21]

    Korzeniewska A, Crainiceanu C M, Kus R, Franaszczuk P J, Crone N E 2008 Hum. Brain Mapp. 29 1170

    [22]

    Qiang B, Wang Z Z 2008 J. Biomed. Engineer. Res. 27 145 (in Chinese) [强波, 王正志 2008 生物医学工程研究 27 145]

    [23]

    Rajapakse J C, Zhou J 2007 NeuroImage 37 749

    [24]

    Benz H L, Zhang H J, Bezerianos A, Acharya S, Crone N E, Zheng X, Thakor N V 2012 IEEE Trans. Neural Syst. Rehabil. Eng. 20 143

    [25]

    Song L, Kolar M, Xing E P 2009 Adv. Neural Infor. Proc. Syst. 22 1732

  • [1]

    Lachaux J P, Axmacher N, Mormann F, Halgren E, Crone N E 2012 Prog. Neurobiol. 98 279

    [2]

    Qian T Y, Wang Y J, Zhou W J, Gao S K, Hong B 2013 J. Tsinghua Univ. (Sci. Technol.) 53 1334 (in Chinese) [钱天翼, 王昱婧, 周文静, 高上凯, 洪波 2013 清华大学学报(自然科学版) 53 1334]

    [3]

    Crone N E, Hao L, Hart J, Boatman D, Lesser R P, Irizarry R, Gordon B 2001 Neurology 57 2045

    [4]

    Crone N E, Sinai A, Korzeniewska A 2006 Prog. Brain Res. 159 275

    [5]

    Towle V L, Yoon H A, Castelle M, Edgar J C, Biassou N M, Frim D M, Spire J P, Kohrman M H 2008 Brain 131 2013

    [6]

    Crone N E, Boatman D, Gordon B, Hao L 2001 Clin. Neurophysiol. 112 565

    [7]

    Miller K J, Leuthardt E C, Schalk G, Rao R P N, Anderson N R, Moran D W, Miller J W, Ojemann J G 2007 J. Neurosci. 27 2424

    [8]

    Kellis S S, House P A, Thomson K E, Brown R, Greger B 2009 Neurosurg. Focus 27 E9

    [9]

    Bouchard K E, Mesgarani N, Johnson K, Chang E F 2013 Nature 495 327

    [10]

    Leuthardt E C, Freudenberg Z, Bundy D, Roland J 2009 Neurosurg. Focus 27 E10

    [11]

    Conant D, Bouchard K E, Chang E F 2014 Curr. Opin. Neurobiol. 24 63

    [12]

    Indefrey P, Levelt W J M 2004 Cognition 92 101

    [13]

    Hickok G, Poeppel D 2007 Nat. Rev. Neurosi. 8 393

    [14]

    Brown S, Laird A R, Pfordresher P Q, Thelen S M, Turkeltaub P, Liotti M 2009 Brain Cogn. 70 31

    [15]

    Price C J 2000 J. Anat. 197 335

    [16]

    Li L, Jin Z L, Li B 2011 Chin. Phys. B 20 038701

    [17]

    Yi G S, Wang J, Han C X, Deng B, Wei X L, Li N 2013 Chin. Phys. B 22 028702

    [18]

    Yin N, Xu G Z, Zhou Q 2013 Acta Phys. Sin. 62 118704 (in Chinese) [尹宁, 徐桂芝, 周倩 2013 物理学报 62 118704]

    [19]

    Yang J, Chen S S, Huangfu H R 2015 Acta Phys. Sin. 64 058701 (in Chinese) [杨剑, 陈书燊, 皇甫浩然 2015 物理学报 64 058701]

    [20]

    Zou C, Denby K J, Feng J 2009 BMC Bioinform. 10 122

    [21]

    Korzeniewska A, Crainiceanu C M, Kus R, Franaszczuk P J, Crone N E 2008 Hum. Brain Mapp. 29 1170

    [22]

    Qiang B, Wang Z Z 2008 J. Biomed. Engineer. Res. 27 145 (in Chinese) [强波, 王正志 2008 生物医学工程研究 27 145]

    [23]

    Rajapakse J C, Zhou J 2007 NeuroImage 37 749

    [24]

    Benz H L, Zhang H J, Bezerianos A, Acharya S, Crone N E, Zheng X, Thakor N V 2012 IEEE Trans. Neural Syst. Rehabil. Eng. 20 143

    [25]

    Song L, Kolar M, Xing E P 2009 Adv. Neural Infor. Proc. Syst. 22 1732

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  • Received Date:  04 September 2015
  • Accepted Date:  10 November 2015
  • Published Online:  05 February 2016

Time-varying dynamic Bayesian network model and its application to brain connectivity using electrocorticograph

    Corresponding author: Xu Gui-Zhi, gzxu@hebut.edu.cn
  • 1. Department of Biomedical Engineering, Hebei University of Technology, Tianjin 300130, China;
  • 2. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA;
  • 3. Department of Neurology, Johns Hopkins University, Baltimore, MD 21287, USA;
  • 4. Fischell Department of Bioengineering, University of Maryland College Park, College Park, MD 20742, USA
Fund Project:  Project supported by the National Natural Science Foundation of China (Grant No. 50877023) and the Specialized Research Fund for the Doctoral Program of Higher Education, China (Grant No. 20121317110002).

Abstract: Cortical networks for speech production are believed to be widely distributed and highly organized over temporal, parietal, and frontal lobes areas in the human brain cortex. Effective connectivity demonstrates an inherent element of directional information propagation, and is therefore an information dense measure for the relevant activity over different cortical regions. Connectivity analysis of electrocorticographic (ECoG) recordings has been widely studied for its excellent signal-to-noise ratio as well as high temporal and spatial resolutions, providing an important approach to human electrophysiological researches. In this paper, we evaluate two patients undergoing invasive monitoring for seizure localization, in which both micro-electrode and standard clinical electrodes are used for ECoG recordings from speech-related cortical areas during syllable reading test. In order to explore the dynamics of speech processing, we extract the high gamma frequency band (70-110 Hz) power from ECoG signals by the multi-taper method. The trial-averaged results show that there is a consistent task-related increase in high gamma response for micro-ECoG electrodes for patient 1 and standard-ECoG electrodes for both patients 1 and 2. We demonstrate that high gamma response provides reliable speech localization compared with electrocortical stimulation. In addition, a directed connectivity network is built in single trial involving both standard ECoG electrodes and micro-ECoG arrays using time-varying dynamic Bayesian networks (TV-DBN). The TV-DBN is used to model the time-varying effective connectivity between pairs of ECoG electrodes selected by high gamma power, with less parameter optimization required and higher computational simplicity than short-time direct directed transfer function. We observe task-related connectivity modulations of connectivity between large-scale cortical networks (standard ECoG) and local cortical networks (micro-ECoG), as well as between large-scale and local cortical networks. In addition, cortical connectivity is modulated differently before and after response articulation onset. In other words, electrodes located over sensorimotor cortex show higher connectivity before articulation onset, while connectivity appears gradually between sensorimotor and auditory cortex after articulation onset. Also, the connectivity patterns observed during articulation are significantly different for three different places of articulation for the consonants. This study offers insights into preoperative evaluation during epilepsy surgery, dynamic real-time brain connectivity visualization, and assistance to understand the dynamic processing of language pronunciation in the language cortex.

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