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Volumn , Issue , 2006, Pages 461-491

Granger Causality on Spatial Manifolds: Applications to Neuroimaging

Author keywords

Applications to neuroimaging; Continuous spatial multivariate autoregressive model(sMAR); Estimation via MM algorithm; Evaluation of simulated data; Granger causality; Penalized sMAR; Spatial manifolds; Testing for spatial Granger causality; Time series analysis

Indexed keywords


EID: 84889295131     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1002/9783527609970.ch18     Document Type: Chapter
Times cited : (26)

References (74)
  • 1
    • 0028190347 scopus 로고
    • Functional and effective connectivity in neuroimaging: a synthesis
    • K. J. Friston. Functional and effective connectivity in neuroimaging: a synthesis. Hum. Brain Mapping, 2: 56-78, 1994.
    • (1994) Hum. Brain Mapping , vol.2 , pp. 56-78
    • Friston, K.J.1
  • 3
    • 0042178307 scopus 로고    scopus 로고
    • Multivariate autoregressive modeling of fMRI time series
    • L. Harrison, W. D. Penny, and K. Friston. Multivariate autoregressive modeling of fMRI time series. Neuroimage, 19:1477-1491, 2003.
    • (2003) Neuroimage , vol.19 , pp. 1477-1491
    • Harrison, L.1    Penny, W.D.2    Friston, K.3
  • 4
    • 0003607151 scopus 로고
    • Multivariate Analysis
    • Academic Press, London, San Diego, New York, Boston, Sydney, Tokyo, Toronto
    • K. V. Mardia J. T. Kent and J. M. Bibby. Multivariate Analysis. Academic Press, London, San Diego, New York, Boston, Sydney, Tokyo, Toronto, 1979.
    • (1979)
    • Mardia, K.V.1    Kent, J.T.2    Bibby, J.M.3
  • 5
    • 0000414912 scopus 로고    scopus 로고
    • A dimension-reduced approach to space-time kalman filtering
    • C. K. Wikle and N. Cressie. A dimension-reduced approach to space-time kalman filtering. Biometrika, 86: 815-829, 1999.
    • (1999) Biometrika , vol.86 , pp. 815-829
    • Wikle, C.K.1    Cressie, N.2
  • 6
    • 0000351727 scopus 로고
    • Investigating causal relations by econometric models and cross-spectral methods
    • C. W. J. Granger. Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 37:414, 1969.
    • (1969) Econometrica , vol.37 , pp. 414
    • Granger, C.W.J.1
  • 7
    • 0032833351 scopus 로고    scopus 로고
    • Investigation of cooperative cortical dynamics by multivariate autoregressive modeling of event-related local field potentials
    • S. L. Bressler, M. Z. Ding, and W. M. Yang. Investigation of cooperative cortical dynamics by multivariate autoregressive modeling of event-related local field potentials. Neurocomputing, 26-7: 625-631, 1999.
    • (1999) Neurocomputing , vol.26 , Issue.7 , pp. 625-631
    • Bressler, S.L.1    Ding, M.Z.2    Yang, W.M.3
  • 8
    • 0002537923 scopus 로고    scopus 로고
    • Estimation of parameters and eigenmodes of multivariate autoregressive models
    • A. Neumaier and T. Schneider. Estimation of parameters and eigenmodes of multivariate autoregressive models. ACM Trans. Math. Softw., 27: 27-57, 2001.
    • (2001) ACM Trans. Math. Softw. , vol.27 , pp. 27-57
    • Neumaier, A.1    Schneider, T.2
  • 9
    • 0002537922 scopus 로고    scopus 로고
    • Algorithm 808: Arfit -a matlab package for the estimation of parameters and eigenmodes of multivariate autoregressive models
    • T. Schneider and A. Neumaier. Algorithm 808: Arfit -a matlab package for the estimation of parameters and eigenmodes of multivariate autoregressive models. ACM Trans. Math. Softw., 27: 58-65, 2001.
    • (2001) ACM Trans. Math. Softw. , vol.27 , pp. 58-65
    • Schneider, T.1    Neumaier, A.2
  • 10
    • 0031518090 scopus 로고    scopus 로고
    • Fitting time series models to nonstationary processes
    • R. Dahlhaus. Fitting time series models to nonstationary processes. Ann. Stat., 25: 1-37, 1997.
    • (1997) Ann. Stat. , vol.25 , pp. 1-37
    • Dahlhaus, R.1
  • 12
    • 0033794032 scopus 로고    scopus 로고
    • Acquiring simultaneous EEG and functional MRI. Clin. Neurophysiol.
    • R. I. Goldman, J. M. Stern, J. Engel, and M. S. Cohen. Acquiring simultaneous EEG and functional MRI. Clin. Neurophysiol., 111: 1974-1980, 2000.
    • (2000) , vol.111 , pp. 1974-1980
    • Goldman, R.I.1    Stern, J.M.2    Engel, J.3    Cohen, M.S.4
  • 15
    • 0004130505 scopus 로고    scopus 로고
    • Functional Data Analysis
    • Springer, Berlin
    • J. O. Ramsay and B. W. Silverman. Functional Data Analysis. Springer, Berlin, 1997.
    • (1997)
    • Ramsay, J.O.1    Silverman, B.W.2
  • 16
    • 84894912254 scopus 로고
    • Measurement of linear-dependence and feedback between multiple time-series
    • J. F. Geweke. Measurement of linear-dependence and feedback between multiple time-series. J. Am. Stat. Assoc., 77: 304-313, 1982.
    • (1982) J. Am. Stat. Assoc. , vol.77 , pp. 304-313
    • Geweke, J.F.1
  • 17
    • 84950949120 scopus 로고
    • Measures of conditional linear-dependence and feedback between time-series
    • J. F. Geweke. Measures of conditional linear-dependence and feedback between time-series. J. Am. Stat. Assoc., 79:907-915, 1984.
    • (1984) J. Am. Stat. Assoc. , vol.79 , pp. 907-915
    • Geweke, J.F.1
  • 18
    • 0037147690 scopus 로고    scopus 로고
    • Simultaneous EEG and fMRI of the alpha rhythm
    • R. I. Goldman, J. M. Stern, J. Engel, and M. S. Cohen. Simultaneous EEG and fMRI of the alpha rhythm. Neuroreport, 13: 2487-2492, 2002.
    • (2002) Neuroreport , vol.13 , pp. 2487-2492
    • Goldman, R.I.1    Stern, J.M.2    Engel, J.3    Cohen, M.S.4
  • 19
    • 0029931242 scopus 로고    scopus 로고
    • A unified statistical approach for determining significant signals in images of cerebral activation
    • K. J. Worsley, S. Marrett, P. Neelin, A. C. Vandal, K. J. Friston, and A. C. Evans. A unified statistical approach for determining significant signals in images of cerebral activation. Hum. Brain Mapping, 4: 58-73, 1996.
    • (1996) Hum. Brain Mapping , vol.4 , pp. 58-73
    • Worsley, K.J.1    Marrett, S.2    Neelin, P.3    Vandal, A.C.4    Friston, K.J.5    Evans, A.C.6
  • 20
    • 2142732441 scopus 로고    scopus 로고
    • Large-scale simultaneous hypothesis testing: The choice of a null hypothesis
    • B. Efron. Large-scale simultaneous hypothesis testing: The choice of a null hypothesis. J. Am. Stat. Assoc., 99: 96-104, 2004.
    • (2004) J. Am. Stat. Assoc. , vol.99 , pp. 96-104
    • Efron, B.1
  • 21
    • 25444476525 scopus 로고    scopus 로고
    • Selection and estimation for large-scale simultaneous inference
    • B. Efron. Selection and estimation for large-scale simultaneous inference. 2005. URL http://www-stat.stanford.edu/people/faculty/efron/papers.html.
    • (2005)
    • Efron, B.1
  • 22
    • 6344254546 scopus 로고    scopus 로고
    • Spatio-temporal autoregressivemodels defined over brain manifolds
    • P. A. Valdes-Sosa. Spatio-temporal autoregressivemodels defined over brain manifolds. Neuroinformatics, 2: 239-250, 2004.
    • (2004) Neuroinformatics , vol.2 , pp. 239-250
    • Valdes-Sosa, P.A.1
  • 24
    • 0025970724 scopus 로고
    • Structural-analysis of neural circuits using the theory of directed-graphs
    • L. A. Baccala,M. A. L. Nicolelis, C. H. Yu, and M. Oshiro. Structural-analysis of neural circuits using the theory of directed-graphs. Comput. Biomed. Res., 24: 7-28, 1991.
    • (1991) Comput. Biomed. Res. , vol.24 , pp. 7-28
    • Baccala, L.A.1    Nicolelis, M.A.L.2    Yu, C.H.3    Oshiro, M.4
  • 25
    • 0035433274 scopus 로고    scopus 로고
    • Evaluating causal relations in neural systems: Granger causality, directed transfer function and statistical assessment of significance
    • M. Kaminski, M. Z. Ding, W. A. Truccolo, and S. L. Bressler. Evaluating causal relations in neural systems: Granger causality, directed transfer function and statistical assessment of significance. Biol. Cybern., 85: 145-157, 2001.
    • (2001) Biol. Cybern. , vol.85 , pp. 145-157
    • Kaminski, M.1    Ding, M.Z.2    Truccolo, W.A.3    Bressler, S.L.4
  • 26
    • 0042738946 scopus 로고    scopus 로고
    • The elusive concept of brain connectivity
    • B. Horwitz. The elusive concept of brain connectivity. Neuroimage, 19:466-470, 2003.
    • (2003) Neuroimage , vol.19 , pp. 466-470
    • Horwitz, B.1
  • 27
    • 0028312413 scopus 로고
    • Structural equation modeling and its applications to network analysis in functional brain imaging
    • A. R. McIntosh and F. Gonzalez-Lima. Structural equation modeling and its applications to network analysis in functional brain imaging. Hum. Brain Mapping, 2: 2-22, 1994.
    • (1994) Hum. Brain Mapping , vol.2 , pp. 2-22
    • McIntosh, A.R.1    Gonzalez-Lima, F.2
  • 28
    • 0032331836 scopus 로고    scopus 로고
    • Graphs, causality, and structural equation models
    • J. Pearl. Graphs, causality, and structural equation models. Sociol. Meth. Res., 27: 226-284, 1998.
    • (1998) Sociol. Meth. Res. , vol.27 , pp. 226-284
    • Pearl, J.1
  • 29
    • 0006104710 scopus 로고
    • The decomposition and measurement of the interdependency between 2nd-order stationary-processes
    • Y. Hosoya. The decomposition and measurement of the interdependency between 2nd-order stationary-processes. Probability Theory and Related Fields, 88: 429-444, 1991.
    • (1991) Probability Theory and Related Fields , vol.88 , pp. 429-444
    • Hosoya, Y.1
  • 30
    • 84950949120 scopus 로고
    • Measures of conditional linear-dependence and feedback between time-series
    • J. F. Geweke. Measures of conditional linear-dependence and feedback between time-series. J. Am. Stat. Assoc., 79:907-915, 1984.
    • (1984) J. Am. Stat. Assoc. , vol.79 , pp. 907-915
    • Geweke, J.F.1
  • 31
    • 0033190863 scopus 로고    scopus 로고
    • On the directionality of cortical interactions studied by structural analysis of electrophysiological recordings
    • C. Bernasconi and P. Konig. On the directionality of cortical interactions studied by structural analysis of electrophysiological recordings. Biol. Cybern., 81: 199-210, 1999.
    • (1999) Biol. Cybern. , vol.81 , pp. 199-210
    • Bernasconi, C.1    Konig, P.2
  • 32
    • 0037473234 scopus 로고    scopus 로고
    • The use of time-variant EEG granger causality for inspecting directed interdependences of neural assemblies
    • W. Hesse, E. Moller, M. Arnold, and B. Schack. The use of time-variant EEG granger causality for inspecting directed interdependences of neural assemblies. J. Neurosci. Meth., 124: 27-44, 2003.
    • (2003) J. Neurosci. Meth. , vol.124 , pp. 27-44
    • Hesse, W.1    Moller, E.2    Arnold, M.3    Schack, B.4
  • 34
    • 0032331836 scopus 로고    scopus 로고
    • Graphs, causality, and structural equation models
    • J. Pearl. Graphs, causality, and structural equation models. Sociol. Meth. Res., 27: 226-284, 1998.
    • (1998) Sociol. Meth. Res. , vol.27 , pp. 226-284
    • Pearl, J.1
  • 35
    • 1042301100 scopus 로고    scopus 로고
    • Investigating directed cortical interactions in time-resolved fMRI data using vector autoregressive modeling and granger causality mapping
    • R. Goebel, A. Roebroeck, D. S. Kim, and E. Formisano. Investigating directed cortical interactions in time-resolved fMRI data using vector autoregressive modeling and granger causality mapping. Magn. Reson. Imaging, 21:1251-1261, 2003.
    • (2003) Magn. Reson. Imaging , vol.21 , pp. 1251-1261
    • Goebel, R.1    Roebroeck, A.2    Kim, D.S.3    Formisano, E.4
  • 36
    • 84889318233 scopus 로고    scopus 로고
    • Graphical time series modelling in brain imaging
    • index issue
    • M. Eichler. Graphical time series modelling in brain imaging. Philos. Trans. R. Soc. Lond. B, index issue, 2005.
    • (2005) Philos. Trans. R. Soc. Lond. , vol.B
    • Eichler, M.1
  • 37
    • 0003410290 scopus 로고    scopus 로고
    • Time Series Analysis
    • Princeton University Press, Princeton, NJ
    • J. D. Hamilton. Time Series Analysis. Princeton University Press, Princeton, NJ, 1999.
    • (1999)
    • Hamilton, J.D.1
  • 38
    • 1442299111 scopus 로고    scopus 로고
    • Different origins of low-and high-frequency components (600 Hz) of human somatosensory evoked potentials
    • R. Goebel, T. D. Waberski, H. Simon, E. Peters, F. Klostermann, G. Curio, and H. Buchner. Different origins of low-and high-frequency components (600 Hz) of human somatosensory evoked potentials. Clin. Neurophysiol., 115: 927-937, 2004.
    • (2004) Clin. Neurophysiol. , vol.115 , pp. 927-937
    • Goebel, R.1    Waberski, T.D.2    Simon, H.3    Peters, E.4    Klostermann, F.5    Curio, G.6    Buchner, H.7
  • 39
    • 0003626140 scopus 로고
    • Analysis of average evoked potentials making use of least mean square techniques
    • D. S. Ruchkin, E. R. John, and J. Villegas. Analysis of average evoked potentials making use of least mean square techniques. Ann. New York Acad. Sci., 115:799, 1964.
    • (1964) Ann. New York Acad. Sci. , vol.115 , pp. 799
    • Ruchkin, D.S.1    John, E.R.2    Villegas, J.3
  • 40
    • 0032835345 scopus 로고    scopus 로고
    • Revealing interactions among brain systems with nonlinear pca
    • K. Friston, J. Phillips, D. Chawla, and C. Buchel. Revealing interactions among brain systems with nonlinear pca. Hum. BrainMapping, 8: 92-97, 1999.
    • (1999) Hum. BrainMapping , vol.8 , pp. 92-97
    • Friston, K.1    Phillips, J.2    Chawla, D.3    Buchel, C.4
  • 43
    • 1542784498 scopus 로고    scopus 로고
    • Variable selection via nonconcave penalized likelihood and its oracle properties
    • J. Q. Fan and R. Z. Li. Variable selection via nonconcave penalized likelihood and its oracle properties. J. Am. Stat. Assoc., 96:1348-1360, 2001.
    • (2001) J. Am. Stat. Assoc. , vol.96 , pp. 1348-1360
    • Fan, J.Q.1    Li, R.Z.2
  • 44
    • 0041445620 scopus 로고    scopus 로고
    • Computational Methods for the Analysis of Brain Connectivity, chapter 14-Hilgetag
    • Ascoli operator: Network typesetting edition
    • C. Hilgetag, R. Kotter, and K. E. Stephan. Computational Methods for the Analysis of Brain Connectivity, chapter 14-Hilgetag. Ascoli operator: Network typesetting edition, 2002.
    • (2002)
    • Hilgetag, C.1    Kotter, R.2    Stephan, K.E.3
  • 45
    • 0034749241 scopus 로고    scopus 로고
    • Multimodal characterisation of cortical areas by multivariate analyses of receptor binding and connectivity data
    • R. Kotter, K. E. Stephan, N. Palomero-Gallagher, S. Geyer, A. Schleicher, and K. Zilles. Multimodal characterisation of cortical areas by multivariate analyses of receptor binding and connectivity data. Anat. Embryol., 204: 333-350, 2001.
    • (2001) Anat. Embryol. , vol.204 , pp. 333-350
    • Kotter, R.1    Stephan, K.E.2    Palomero-Gallagher, N.3    Geyer, S.4    Schleicher, A.5    Zilles, K.6
  • 46
    • 4444318641 scopus 로고    scopus 로고
    • Organization, development and function of complex brain networks
    • O. Sporns, D. R. Chialvo, M. Kaiser, and C. C. Hilgetag. Organization, development and function of complex brain networks. Trends Cogn. Sci., 8: 418-425, 2004.
    • (2004) Trends Cogn. Sci. , vol.8 , pp. 418-425
    • Sporns, O.1    Chialvo, D.R.2    Kaiser, M.3    Hilgetag, C.C.4
  • 48
    • 0004213845 scopus 로고    scopus 로고
    • Causality
    • Cambridge University Press, Cambridge, UK
    • J. Pearl. Causality. Cambridge University Press, Cambridge, UK, 2000.
    • (2000)
    • Pearl, J.1
  • 49
    • 0347568442 scopus 로고    scopus 로고
    • Searching for the causal structure of a vector autoregression
    • S. Demiralp and K. D. Hoover. Searching for the causal structure of a vector autoregression. Oxford Bull. Econ. Stat., 65: 745-767, 2003.
    • (2003) Oxford Bull. Econ. Stat. , vol.65 , pp. 745-767
    • Demiralp, S.1    Hoover, K.D.2
  • 50
    • 0031526204 scopus 로고    scopus 로고
    • Approaches for bayesian variable selection
    • E. I. George and R. E. McCulloch. Approaches for bayesian variable selection. Statistica Sinica, 7: 339-373, 1997.
    • (1997) Statistica Sinica , vol.7 , pp. 339-373
    • George, E.I.1    McCulloch, R.E.2
  • 51
    • 0442309436 scopus 로고    scopus 로고
    • The variable selection problem
    • E. I. George. The variable selection problem. J. Am. Stat. Assoc., 95: 1304-1308, 2000.
    • (2000) J. Am. Stat. Assoc. , vol.95 , pp. 1304-1308
    • George, E.I.1
  • 53
    • 84889498650 scopus 로고    scopus 로고
    • Covariance decomposition in multivariate analysis
    • B. Jones and M. West. Covariance decomposition in multivariate analysis. http://ftp.isds.duke.edu/WorkingPapers/04-15.pdf.), 2005.
    • (2005)
    • Jones, B.1    West, M.2
  • 54
    • 24344502730 scopus 로고    scopus 로고
    • Nonconcave penalized likelihood with a diverging number of parameters
    • J. Q. Fan and H. Peng. Nonconcave penalized likelihood with a diverging number of parameters. Ann. Stat., 32: 928-961, 2004.
    • (2004) Ann. Stat. , vol.32 , pp. 928-961
    • Fan, J.Q.1    Peng, H.2
  • 55
    • 84889475049 scopus 로고    scopus 로고
    • A note on the LASSO and related procedures in model selection, URL
    • Ch. Leng, Y. Lin, and G. Wahba. A note on the LASSO and related procedures in model selection. 2005. URL http://www.stat.wisc.edu/~wahba/ftp1/tr1091rxx.pdf.
    • (2005)
    • Leng, C.1    Lin, Y.2    Wahba, G.3
  • 56
    • 29344450399 scopus 로고    scopus 로고
    • Consistent neighbourhood selection for sparse high-dimensional graphs with the LASSO
    • URL
    • N. Meinshausen and P. Bühlmann. Consistent neighbourhood selection for sparse high-dimensional graphs with the LASSO. 2004. URL http://stat.ethz.ch/research/.
    • (2004)
    • Meinshausen, N.1    Bühlmann, P.2
  • 57
    • 84942484786 scopus 로고
    • Ridge regression-biased estimation for nonorthogonal problems
    • A. E. Hoerl and R. W. Kennard. Ridge regression-biased estimation for nonorthogonal problems. Technometrics, 12:55-67, 1970.
    • (1970) Technometrics , vol.12 , pp. 55-67
    • Hoerl, A.E.1    Kennard, R.W.2
  • 58
    • 0242295767 scopus 로고    scopus 로고
    • Bayesian factor regression models in the "large p, small n" paradigm
    • Working Papers of the Institute of Statistics and Decision Science, Duke University
    • M. West. Bayesian factor regression models in the "large p, small n" paradigm. Working Papers of the Institute of Statistics and Decision Science, Duke University, 2002.
    • (2002)
    • West, M.1
  • 60
    • 67349234649 scopus 로고    scopus 로고
    • Alternative prior distributions for variable selection with very many more variales than observations
    • Technical report, Deparment of Satistic, University of Warwick, Coventry, CV4 7AL, UK
    • P. J. Brown J. E. Griffin. Alternative prior distributions for variable selection with very many more variales than observations. Technical report, Deparment of Satistic, University of Warwick, Coventry, CV4 7AL, UK, 2005.
    • (2005)
    • Brown, P.J.1    Griffin, J.E.2
  • 61
    • 0039713775 scopus 로고
    • On scale mixtures of normal-distributions
    • M. West. On scale mixtures of normal-distributions. Biometrika, 74: 646-648, 1987.
    • (1987) , vol.74 , pp. 646-648
    • West, M.1
  • 64
    • 16244401458 scopus 로고    scopus 로고
    • Regularization and variable selection via the elastic net
    • H. Zou and T. Hastie. Regularization and variable selection via the elastic net. J. R. Statisc. Soc. B, 67: 301-320, 2005.
    • (2005) J. R. Statisc. Soc. , vol.67 B , pp. 301-320
    • Zou, H.1    Hastie, T.2
  • 65
    • 1342280631 scopus 로고    scopus 로고
    • Mm algorithms for generalized bradley-terry models
    • D. R. Hunter. Mm algorithms for generalized bradley-terry models. Ann. Stat., 32: 384-406, 2004.
    • (2004) Ann. Stat. , vol.32 , pp. 384-406
    • Hunter, D.R.1
  • 66
    • 1342332031 scopus 로고    scopus 로고
    • A tutorial on mm algorithms
    • D. R. Hunter and K. Lange. A tutorial on mm algorithms. Am. Stat., 58: 30-37, 2004.
    • (2004) Am. Stat. , vol.58 , pp. 30-37
    • Hunter, D.R.1    Lange, K.2
  • 67
    • 26444617168 scopus 로고    scopus 로고
    • Variable selection using MM algorithms
    • D. R. Hunter and R. Li. Variable selection using MM algorithms. Ann. Stat., 33(4):1617-1642, 2005.
    • (2005) Ann. Stat. , vol.33 , Issue.4 , pp. 1617-1642
    • Hunter, D.R.1    Li, R.2
  • 68
    • 32044449925 scopus 로고
    • Generalized cross-validation as a method for choosing a good ridge parameter
    • G. H. Golub, M. Heath, and G. Wahba. Generalized cross-validation as a method for choosing a good ridge parameter. Technometrics, 21: 215-223, 1979.
    • (1979) Technometrics , vol.21 , pp. 215-223
    • Golub, G.H.1    Heath, M.2    Wahba, G.3
  • 69
    • 0038364283 scopus 로고    scopus 로고
    • Robbins, empirical bayes and microarrays
    • B. Efron. Robbins, empirical bayes and microarrays. Ann. Stat., 31: 366-378, 2003.
    • (2003) Ann. Stat. , vol.31 , pp. 366-378
    • Efron, B.1
  • 70
    • 27944454515 scopus 로고    scopus 로고
    • Bayesians, frequentists, and physicists
    • URL
    • B. Efron. Bayesians, frequentists, and physicists. 2005. URL http://www-stat.stanford.edu/~brad/papers/physics.pdf.
    • (2005)
    • Efron, B.1
  • 71
    • 0012216618 scopus 로고    scopus 로고
    • The sandwich (robust covariance matrix) estimation
    • Technical Report. Preprint available at
    • R. J. Carroll, S.Wang, D. G. Simpson, A. J. Stromberg, and D. Ruppert. The sandwich (robust covariance matrix) estimation. Technical Report. Preprint available at http://stat. tamu.edu/ftp/pub/rjcarroll/sandwich.pdf., 1998.
    • (1998)
    • Carroll, R.J.1    Wang, S.2    Simpson, D.G.3    Stromberg, A.J.4    Ruppert, D.5
  • 72
    • 24644494711 scopus 로고    scopus 로고
    • Fast and compact smoothing on large multidimensional grids
    • P. H. C. Eilers, I. D. Currie, and M. Durban. Fast and compact smoothing on large multidimensional grids. Comput. Stat. Data Anal., 50: 61-76, 2006.
    • (2006) Comput. Stat. Data Anal. , vol.50 , pp. 61-76
    • Eilers, P.H.C.1    Currie, I.D.2    Durban, M.3
  • 73
    • 0034790441 scopus 로고    scopus 로고
    • Interactions among neuronal systems assessed with functional neuroimaging
    • C. Buchel and K. Friston. Interactions among neuronal systems assessed with functional neuroimaging. Revue Neurologique, 157: 807-815, 2001.
    • (2001) Revue Neurologique , vol.157 , pp. 807-815
    • Buchel, C.1    Friston, K.2


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