메뉴 건너뛰기




Volumn 1, Issue 5, 2011, Pages 389-400

A Spectral Graphical Model Approach for Learning Brain Connectivity Network of Children's Narrative Comprehension

Author keywords

Bayesian model averaging; brain development; functional magnetic resonance imaging; graphical models; narrative comprehension; spectral density matrix

Indexed keywords


EID: 84879304866     PISSN: 21580014     EISSN: 21580022     Source Type: Journal    
DOI: 10.1089/brain.2011.0045     Document Type: Article
Times cited : (4)

References (48)
  • 1
    • 0016355478 scopus 로고
    • A new look at the statistical model identification
    • Akaike H. 1974. A new look at the statistical model identification. IEEE Trans Autom Control 19:716-723.
    • (1974) IEEE Trans Autom Control , vol.19 , pp. 716-723
    • Akaike, H.1
  • 2
    • 3543119498 scopus 로고    scopus 로고
    • Learning graphical models for stationary time series
    • Bach F, Jordan M. 2004. Learning graphical models for stationary time series. IEEE Trans Signal Process 52:2189-2199.
    • (2004) IEEE Trans Signal Process , vol.52 , pp. 2189-2199
    • Bach, F.1    Jordan, M.2
  • 3
    • 84914799652 scopus 로고
    • An introduction to the structural analysis of narrative
    • Barthes R, Duisit L. 1975. An introduction to the structural analysis of narrative. New Literary Hist 6:237-272.
    • (1975) New Literary Hist , vol.6 , pp. 237-272
    • Barthes, R.1    Duisit, L.2
  • 4
    • 34250108028 scopus 로고
    • Model selection and Akaike's information criterion (AIC): the general theory and its analytical extensions
    • Bozdogan H. 1987. Model selection and Akaike's information criterion (AIC): the general theory and its analytical extensions. Psychometrika 52:345-370.
    • (1987) Psychometrika , vol.52 , pp. 345-370
    • Bozdogan, H.1
  • 5
    • 0030666829 scopus 로고    scopus 로고
    • Modulation of connectivity in visual pathways by attention: cortical interactions evaluated with structural equation modelling and fMRI
    • Buchel C, Friston K. 1997. Modulation of connectivity in visual pathways by attention: cortical interactions evaluated with structural equation modelling and fMRI. Cereb Cortex 7:768.
    • (1997) Cereb Cortex , vol.7 , pp. 768
    • Buchel, C.1    Friston, K.2
  • 7
    • 58149350434 scopus 로고    scopus 로고
    • Discrete dynamic Bayesian network analysis of fMRI data
    • Burge J, Lane T, Link H, Qiu S, Clark VP. 2009. Discrete dynamic Bayesian network analysis of fMRI data. Hum Brain Mapp 30:122-137.
    • (2009) Hum Brain Mapp , vol.30 , pp. 122-137
    • Burge, J.1    Lane, T.2    Link, H.3    Qiu, S.4    Clark, V.P.5
  • 8
    • 0034753663 scopus 로고    scopus 로고
    • A method for making group inferences from functional MRI data using independent component analysis
    • Calhoun V, Adali T, Pearlson G, Pekar J. 2001. A method for making group inferences from functional MRI data using independent component analysis. Hum Brain Mapp 14:140-151.
    • (2001) Hum Brain Mapp , vol.14 , pp. 140-151
    • Calhoun, V.1    Adali, T.2    Pearlson, G.3    Pekar, J.4
  • 10
    • 11144226380 scopus 로고    scopus 로고
    • Perisylvian language networks of the human brain
    • Catani M, Jones D. 2004. Perisylvian language networks of the human brain. Ann Neurol 57:8-16.
    • (2004) Ann Neurol , vol.57 , pp. 8-16
    • Catani, M.1    Jones, D.2
  • 12
    • 0000319411 scopus 로고    scopus 로고
    • Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence San Francisco, California, USA
    • Friedman N, Goldszmidt M. Learning Bayesian networks with local structure. In Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence, San Francisco, California, USA, 1996, pp. 252-262.
    • (1996) Learning Bayesian networks with local structure , pp. 252-262
    • Friedman, N.1    Goldszmidt, M.2
  • 13
    • 61349120207 scopus 로고    scopus 로고
    • Causal modelling and brain connectivity in functional magnetic resonance imaging
    • Friston K. 2009. Causal modelling and brain connectivity in functional magnetic resonance imaging. PLoS Biol 7:e1000033.
    • (2009) PLoS Biol , vol.7 , pp. e1000033
    • Friston, K.1
  • 16
    • 0000034390 scopus 로고
    • Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence Seattle, Washington, USA
    • Geiger D, Heckerman D. Learning Gaussian Networks. In Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence, Seattle, Washington, USA, 1994, pp. 235-243.
    • (1994) Learning Gaussian Networks , pp. 235-243
    • Geiger, D.1    Heckerman, D.2
  • 17
    • 34249761849 scopus 로고
    • Learning Bayesian networks: the combination of knowledge and statistical data
    • Heckerman D, Geiger D, Chickering D. 1995. Learning Bayesian networks: the combination of knowledge and statistical data. Mach Learn 20:197-243.
    • (1995) Mach Learn , vol.20 , pp. 197-243
    • Heckerman, D.1    Geiger, D.2    Chickering, D.3
  • 18
    • 3042594840 scopus 로고    scopus 로고
    • Validating the independent components of neuroimaging time series via clustering and visualization
    • Himberg J, Hyvarinen A, Esposito F. 2004. Validating the independent components of neuroimaging time series via clustering and visualization. Neuroimage 22:1214-1222.
    • (2004) Neuroimage , vol.22 , pp. 1214-1222
    • Himberg, J.1    Hyvarinen, A.2    Esposito, F.3
  • 21
    • 0032629347 scopus 로고    scopus 로고
    • Fast and robust fixed-point algorithms for independent component analysis
    • Hyvarinen A. 1999. Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans Neural Netw 10:626-634.
    • (1999) IEEE Trans Neural Netw , vol.10 , pp. 626-634
    • Hyvarinen, A.1
  • 22
  • 25
    • 44249123488 scopus 로고    scopus 로고
    • Dynamic Bayesian network modeling of fMRI: a comparison of group-analysis methods
    • Li J, Wang Z, Palmer S, McKeown M. 2008. Dynamic Bayesian network modeling of fMRI: a comparison of group-analysis methods. Neuroimage 41:398-407.
    • (2008) Neuroimage , vol.41 , pp. 398-407
    • Li, J.1    Wang, Z.2    Palmer, S.3    McKeown, M.4
  • 26
    • 11844277640 scopus 로고    scopus 로고
    • Language lateralization in young children assessed by functional transcranial Doppler sonography
    • Lohmann H, Drager B, Muller-Ehrenberg S, Deppe M, Knecht S. 2005. Language lateralization in young children assessed by functional transcranial Doppler sonography. Neuroimage 24:780-790.
    • (2005) Neuroimage , vol.24 , pp. 780-790
    • Lohmann, H.1    Drager, B.2    Muller-Ehrenberg, S.3    Deppe, M.4    Knecht, S.5
  • 28
    • 84950945692 scopus 로고
    • Model selection and accounting for model uncertainty in graphical models using Occam's window
    • Madigan D, Raftery A. 1994. Model selection and accounting for model uncertainty in graphical models using Occam's window. J Am Stat Assoc 89:1535-1546.
    • (1994) J Am Stat Assoc , vol.89 , pp. 1535-1546
    • Madigan, D.1    Raftery, A.2
  • 29
    • 0028312413 scopus 로고
    • Structural equation modeling and its application to network analysis in functional brain imaging
    • Mclntosh A, Gonzalez-Lima F. 1994. Structural equation modeling and its application to network analysis in functional brain imaging. Hum Brain Mapp 2:2-22.
    • (1994) Hum Brain Mapp , vol.2 , pp. 2-22
    • Mclntosh, A.1    Gonzalez-Lima, F.2
  • 32
    • 0003229133 scopus 로고    scopus 로고
    • The bayes net toolbox for matlab
    • Murphy K. 2001. The bayes net toolbox for matlab. Comput Sci Stat 33:1024-1034.
    • (2001) Comput Sci Stat , vol.33 , pp. 1024-1034
    • Murphy, K.1
  • 33
    • 7044235213 scopus 로고    scopus 로고
    • Modelling functional integration: a comparison of structural equation and dynamic causal models
    • Penny W, Stephan K, Mechelli A, Friston K. 2004a. Modelling functional integration: a comparison of structural equation and dynamic causal models. Neuroimage 23:S264-S274.
    • (2004) Neuroimage , vol.23 , pp. S264-S274
    • Penny, W.1    Stephan, K.2    Mechelli, A.3    Friston, K.4
  • 35
    • 0023856319 scopus 로고
    • Positron emission tomographic studies of the cortical anatomy of single-word processing
    • Petersen S, Fox P, Posner M, Mintun M, Raichle M. 1988. Positron emission tomographic studies of the cortical anatomy of single-word processing. Nature 331:585-589.
    • (1988) Nature , vol.331 , pp. 585-589
    • Petersen, S.1    Fox, P.2    Posner, M.3    Mintun, M.4    Raichle, M.5
  • 36
    • 0031506560 scopus 로고    scopus 로고
    • Model selection and accounting for model uncertainty in linear regression models
    • Raftery A, Madigan D, Hoeting J. 1997. Model selection and accounting for model uncertainty in linear regression models. J Am Stat Assoc 92:179-191.
    • (1997) J Am Stat Assoc , vol.92 , pp. 179-191
    • Raftery, A.1    Madigan, D.2    Hoeting, J.3
  • 37
    • 34547839743 scopus 로고    scopus 로고
    • Learning effective brain connectivity with dynamic Bayesian networks
    • Rajapakse J, Zhou J. 2007. Learning effective brain connectivity with dynamic Bayesian networks. Neuroimage 37:749-760.
    • (2007) Neuroimage , vol.37 , pp. 749-760
    • Rajapakse, J.1    Zhou, J.2
  • 38
    • 14244259417 scopus 로고    scopus 로고
    • Mapping directed influence over the brain using Granger causality and fMRI
    • Roebroeck A, Formisano E, Goebel R. 2005. Mapping directed influence over the brain using Granger causality and fMRI. Neuroimage 25:230-242.
    • (2005) Neuroimage , vol.25 , pp. 230-242
    • Roebroeck, A.1    Formisano, E.2    Goebel, R.3
  • 39
    • 34548148294 scopus 로고    scopus 로고
    • Frequency-dependent functional connectivity analysis of fMRI data in Fourier and wavelet domains
    • New York, NY Springer-Verlag
    • Salvador R, Achard S, Bullmore E. 2007. Frequency-dependent functional connectivity analysis of fMRI data in Fourier and wavelet domains. In: Jirsa V.K., McIntosh AR (eds.) Handbook of Brain Connectivity. New York, NY: Springer-Verlag, pp. 379-401.
    • (2007) Handbook of Brain Connectivity , pp. 379-401
    • Salvador, R.1    Achard, S.2    Bullmore, E.3    Jirsa, V.K.4    McIntosh, A.R.5
  • 41
    • 0035354209 scopus 로고    scopus 로고
    • Simultaneous correction of ghost and geometric distortion artifacts in EPI using a multi-echo reference scan
    • Schmithorst V, Dardzinski B, Holland S. 2001. Simultaneous correction of ghost and geometric distortion artifacts in EPI using a multi-echo reference scan. IEEE Trans Med Image 20:535.
    • (2001) IEEE Trans Med Image , vol.20 , pp. 535
    • Schmithorst, V.1    Dardzinski, B.2    Holland, S.3
  • 42
    • 1542319069 scopus 로고    scopus 로고
    • A Comparison of three methods for generating group statistical inferences from independent component analysis of fMRI data
    • Schmithorst V, Holland S. 2004. A Comparison of three methods for generating group statistical inferences from independent component analysis of fMRI data. J Magn Reson Imaging 19:365.
    • (2004) J Magn Reson Imaging , vol.19 , pp. 365
    • Schmithorst, V.1    Holland, S.2
  • 43
    • 29244481134 scopus 로고    scopus 로고
    • Cognitive modules utilized for narrative comprehension in children: a functional magnetic resonance imaging study
    • Schmithorst V, Holland S, Plante E. 2006. Cognitive modules utilized for narrative comprehension in children: a functional magnetic resonance imaging study. Neuroimage 29:254-266.
    • (2006) Neuroimage , vol.29 , pp. 254-266
    • Schmithorst, V.1    Holland, S.2    Plante, E.3
  • 46
    • 0031647592 scopus 로고    scopus 로고
    • A pyramid approach to subpixel registration based on intensity
    • Thevenaz P, Ruttimann U.E., Unser M. 1998. A pyramid approach to subpixel registration based on intensity. IEEE Trans Image Process 7:27-41.
    • (1998) IEEE Trans Image Process , vol.7 , pp. 27-41
    • Thevenaz, P.1    Ruttimann, U.E.2    Unser, M.3
  • 48
    • 33746860833 scopus 로고    scopus 로고
    • Learning functional structure from fMR images
    • Zheng X, Rajapakse JC. 2006. Learning functional structure from fMR images. Neuroimage 31:1601-1613.
    • (2006) Neuroimage , vol.31 , pp. 1601-1613
    • Zheng, X.1    Rajapakse, J.C.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.