Dynamic Granger causality based on Kalman filter for evaluation of functional network connectivity in fMRI data

NeuroImage

Volumn 53, Issue 1, 2010, Pages 65-77

  Havlicek, Martin ^a,b,d   Jan, Jiri ^a   Brazdil, Milan ^c   Calhoun, Vince D ^b,d  

^a BRNO UNIVERSITY OF TECHNOLOGY   (Czech Republic)

^b MIND RESEARCH NETWORK   (United States)

^c ST ANNE S UNIVERSITY HOSPITAL   (Czech Republic)

^d University of New Mexico   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; AUDITORY CORTEX; AUDITORY DISCRIMINATION; COMPUTER SIMULATION; CONTROLLED STUDY; FUNCTIONAL MAGNETIC RESONANCE IMAGING; INDEPENDENT COMPONENT ANALYSIS; INTERMETHOD COMPARISON; KALMAN FILTER; MATHEMATICAL COMPUTING; MULTIVARIATE AUTOREGRESSIVE MODEL; NERVE CELL NETWORK; PRIORITY JOURNAL; SENSORIMOTOR FUNCTION; SIGNAL PROCESSING; TIME SERIES ANALYSIS;

ADULT; ALGORITHMS; AUDITORY CORTEX; AUDITORY PERCEPTION; EVOKED POTENTIALS, AUDITORY; FEMALE; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MALE; NERVE NET; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SIGNAL PROCESSING, COMPUTER-ASSISTED;

EID: 77955306267     PISSN: 10538119     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2010.05.063     Document Type: Article

References (70)

1. Aguirre G.K., Zarahn E., D'esposito M. The variability of human, BOLD hemodynamic responses. Neuroimage 1998, 8:360-369.
2. Akaike H. A new look at the statistical model identification. IEEE Trans. Automatic Control 1974, 19:716-723.
3. Ancona N., Marinazzo D., Stramaglia S. Radial basis function approach to nonlinear Granger causality of time series. Phys. Rev. E 2004, 70:056221.
4. Arnold M., Milner X.H.R., Witte H., Bauer R., Braun C. Adaptive AR modeling of nonstationary time series by means of Kalman filtering. IEEE Trans. Biomed. Eng. 1998, 45:553-562.
5. Baccala L.A., Sameshima K. Partial directed coherence: a new concept in neural structure determination. Biol. Cybern. 2001, 84:463-474.
6. Baccala, L.A., Sameshima, K., Takahashi, D.Y., Arnaldo, A.D., 2007. Generalized partial directed coherence. In: IEEE (Ed.), Proceedings of the 15th International Conference on Digital Signal Processing, pp. 162-166.
7. Baccala L.A., Takahashi D.Y., Sameshima K. Computer intensive testing for the influence between time series. Handbook of Time Series Analysis 2006, Wiley-Vch Verlag GmbH & Co. KGaA, Weinheim. B. Schelter, M. Winterhalder, J. Timmer (Eds.).
8. Bell A.J., Sejnowski T.J. An information-maximization approach to blind separation and blind deconvolution. Neural Comput. 1995, 7:1129-1159.
9. Berns G.S., Song A.W., Mao H. Continuous functional magnetic resonance imaging reveals dynamic nonlinearities of "dose-response" curves for finger opposition. J. Neurosci. 1999, 19:17.
10. Calhoun V.D., Adali T. Unmixing fMRI with independent component analysis. IEEE Eng. Med. Biol. Mag. 2006, 25:79-90.
11. Calhoun V.D., Adali T., Pearlson G.D., Pekar J.J. A method for making group inferences from functional MRI data using independent component analysis. Hum. Brain Mapp. 2001, 14:140-151.
12. Calhoun V.D., Adali T., Pearlson G.D., Pekar J.J. Spatial and temporal independent component analysis of functional MRI data containing a pair of task-related waveforms. Hum. Brain Mapp. 2001, 13:43-53.
13. Calhoun V.D., Adali T., Pekar J.J., Pearlson G.D. Latency (in)sensitive ICA Group independent component analysis of fMRI data in the temporal frequency domain. Neuroimage 2003, 20:1661-1669.
14. Chang C., Thomason M.E., Glover G.H. Mapping and correction of vascular hemodynamic latency in the BOLD signal. Neuroimage 2008, 43:90-102.
15. Chen Y., Bressler S.L., Ding M. Frequency decomposition of conditional Granger causality and application to multivariate neural field potential data. J. Neurosci. Meth. 2006, 150:228-237.
16. Clark V.P., Fannon S., Lai S., Benson R., Bauer L. Responses to rare visual target and distractor stimuli using event-related fMRI. J. Neurophysiol. 2000, 83:3133-3139.
17. Cordes D., Haughton V.M., Arfanakis K., Carew J.D., Turski P., Moritz C.H., Quigley M.A., Meyerand M.E. Frequencies contributing to functional connectivity in the cerebral cortex in "resting-state" data. Am. J. Neuroradiol. 2001, 22:1326-1333.
18. David O., Guillemain I., Saillet S., Reyt S., Deransart C., Segebarth C., Depaulis A. Identifying neural drivers with functional MRI: an electrophysiological validation. PLoS Biol. 2008, 6:2683-2697.
19. Demirci O., Stevens M.C., Andreasen N.C., Michael A., Liu J., White T., Pearlson G.D., Clark V.P., Calhoun V.D. Investigation of relationships between fMRI brain networks in the spectral domain using ICA and Granger causality reveals distinct differences between schizophrenia patients and healthy controls. Neuroimage 2009, 46:419-431.
20. Deshpande G., Hu X., Stilla R., Sathian K. Effective connectivity during haptic perception: A study using Granger causality analysis of functional magnetic resonance imaging data. Neuroimage 2008, 40:1807-1814.
21. Deshpande G., LaConte S., James G.A., Peltier S., Hu X. Multivariate Granger causality analysis of fMRI data. Hum. Brain Mapp. 2009, 30:1361-1373.
22. Deshpande G., Sathian K., Hu X. Effect of hemodynamic variability on Granger causality analysis of fMRI. NeuroImage 2010, 52:884-896.
23. Dhamala M., Rangarajan G., Ding M. Analyzing information flow in brain networks with nonparametric Granger causality. Neuroimage 2008, 41:354-362.
24. Florin E., Gross J., Pfeifer J., Fink G.R., Timmermann L. The effect of filtering on Granger causality based multivariate causality measures. Neuroimage 2010, 50:577-588.
25. Fraser D., Potter J. The optimum linear smoother as a combination of two optimum linear filters. IEEE Trans. Automatic Control 1969, 14:387-390.
26. Freiwald W.A., Valdes P., Bosch J., Biscay R., Jimenez J.C., Rodriguez L.M., Rodriguez V., Kreiter A.K., Singer W. Testing non-linearity and directedness of interactions between neural groups in the macaque inferotemporal cortex. J. Neurosci. Meth. 1999, 94:105-119.
27. Friston K.J. Functional and effective connectivity in neuroimaging: a synthesis. Hum. Brain Mapp. 1994, 2:56-78.
28. Friston K.J., Harrison L., Penny W. Dynamic causal modelling. Neuroimage 2003, 19:1273-1302.
29. Gaschler-Markefski B., Baumgart F., Tempelmann C., Schindler F., Stiller D., Heinze H.J., Scheich H. Statistical methods in functional magnetic resonance imaging with respect to nonstationary time-series: auditory cortex activity. Magn. Reson. Med. 1997, 38:811-820.
30. Geweke J. Measurement of linear dependence and feedback between multiple time series. J. Am. Stat. Assoc. 1982, 77:304-313.
31. Geweke J. Measures of conditional linear dependence and feedback between time series. J. Am. Stat. Assoc. 1984, 79:907-915.
32. Goebel R., Roebroeck A., Kim D.S., Formisano E. Investigating directed cortical interactions in time-resolved fMRI data using vector autoregressive modeling and Granger causality mapping. Magn. Reson. Imaging 2003, 21:1251-1261.
33. Granger C. Investigating causal relations by econometric models and cross-spectral methods. Economet.: J. Econ. Soc. 1969, 37:424-438.
34. Guo S., Seth A.K., Kendrick K.M., Zhou C., Feng J. Partial Granger causality-eliminating exogenous inputs and latent variables. J. Neurosci. Meth. 2008, 172:79-93.
35. Handwerker D., Ollinger J., D'Esposito M. Variation of BOLD hemodynamic responses across subjects and brain regions and their effects on statistical analyses. Neuroimage 2004, 21:1639-1651.
36. Haykin S. Adaptive Filter Theory 2002, Prentice Hall.
37. Hesse W., Möller E., Arnold M., Schack B. The use of time-variant EEG Granger causality for inspecting directed interdependencies of neural assemblies. J. Neurosci. Meth. 2003, 124:27-44.
38. Jafri M.J., Pearlson G.D., Stevens M., Calhoun V.D. A method for functional network connectivity among spatially independent resting-state components in schizophrenia. Neuroimage 2008, 39:1666-1681.
39. Kalman R.E. A new approach to linear filtering and prediction problems. J. Basic Eng. 1960, 82:35-45.
40. Kaminski M., Ding M., Truccolo W.A., Bressler S.L. Evaluating causal relations in neural systems: Granger causality, directed transfer function and statistical assessment of significance. Biol. Cybern. 2001, 85:145-157.
41. Kaminski M.J., Blinowska K.J. A new method of the description of the information flow in the brain structures. Biol. Cybern. 1991, 65:203-210.
42. Kiehl K.A., Stevens M.C., Laurens K.R., Pearlson G., Calhoun V.D., Liddle P.F. An adaptive reflexive processing model of neurocognitive function: supporting evidence from a large scale (n=100) fMRI study of an auditory oddball task. Neuroimage 2005, 25:899-915.
43. Korzeniewska A., Manczak M., Kaminski M., Blinowska K.J., Kasicki S. Determination of information flow direction among brain structures by a modified directed transfer function (dDTF) method. J. Neurosci. Meth. 2003, 125:195-207.
44. Li Y.O., Adali T., Calhoun V.D. Estimating the number of independent components for functional magnetic resonance imaging data. Hum. Brain Mapp. 2007, 28:1251-1266.
45. Liao W., Mantini D., Zhang Z., Pan Z., Ding J., Gong Q., Yang Y., Chen H. Evaluating the effective connectivity of resting state networks using conditional Granger causality. Biol. Cybern. 2010, 102:57-69.
46. Liu J., Kiehl K.A., Pearlson G., Perrone-Bizzozero N.I., Eichele T., Calhoun V.D. Genetic determinants of target and novelty-related event-related potentials in the auditory oddball response. Neuroimage 2009, 46:809-816.
47. Londei A., D'Ausilio A., Basso D., Belardinelli M.O. A new method for detecting causality in fMRI data of cognitive processing. Cogn. Process. 2006, 7:42-52.
48. Londei A., D'Ausilio A., Basso D., Sestieri C., Del Gratta C., Romani G.L., Olivetti Belardinelli M. Brain network for passive word listening as evaluated with ICA and Granger causality. Brain Res. Bull. 2007, 72:284-292.
49. Lütkepohl H. New Introduction to Multiple Time Series Analysis 2005, Springer.
50. Marinazzo D., Pellicoro M., Stramaglia S. Kernel method for nonlinear Granger causality. Phys. Rev. Lett. 2008, 100:144103.
51. McIntosh A.R. Towards a network theory of cognition. Neural Netw. 2000, 13:861-870.
52. McKeown M.J., Makeig S., Brown G.G., Jung T.P., Kindermann S.S., Bell A.J., Sejnowski T.J. Analysis of fMRI data by blind separation into independent spatial components. Hum. Brain Mapp. 1998, 6:160-188.
53. McKiernan K.A., Kaufman J.N., Kucera-Thompson J., Binder J.R. A parametric manipulation of factors affecting task-induced deactivation in functional neuroimaging. J. Cogn. Neurosci. 2003, 15:394-408.
54. Oikonomou V.P., Tzallas A.T., Fotiadis D.I. A Kalman filter based methodology for EEG spike enhancement. Comput. Meth. Programs Biomed. 2007, 85:101-108.
55. Pyka M., Beckmann C., Schöning S., Hauke S., Heider D., Kugel H., Arolt V., Konrad C. Impact of working memory load on fMRI resting state pattern in subsequent resting phases. PLoS ONE 2009, 4:e7198.
56. Rauch H.E., Tung F., Striebel C.T. Maximum likelihood estimates of linear dynamic systems. AIAA J. 1965, 3:1445-1450.
57. Riera J., Watanabe J., Kazuki I., Naoki M., Aubert E., Ozaki T., Kawashima R. A state-space model of the hemodynamic approach: nonlinear filtering of BOLD signals. Neuroimage 2004, 21:547-567.
58. Roebroeck A., Formisano E., Goebel R. Mapping directed influence over the brain using Granger causality and fMRI. Neuroimage 2005, 25:230-242.
59. Roebroeck, A., Formisano, E., Goebel, R., 2009. The identification of interacting networks in the brain using fMRI: model selection, causality and deconvolution. Neuroimage In press.
60. Sato J.R., Junior E.A., Takahashi D.Y., de Maria Felix M., Brammer M.J., Morettin P.A. A method to produce evolving functional connectivity maps during the course of an fMRI experiment using wavelet-based time-varying Granger causality. Neuroimage 2006, 31:187-196.
61. Sato J.R., Takahashi D.Y., Arcuri S.M., Sameshima K., Morettin P.A., Baccala L.A. Frequency domain connectivity identification: an application of partial directed coherence in fMRI. Hum. Brain Mapp. 2009, 30:452-461.
62. Schlogl, A., Roberts, S.J., Pfurtscheller, G., 2000. A criterion for adaptive autoregressive models. In: IEEE (Ed.), Proceedings of the 22nd IEEE International Conference on Engineering in Medicine and Biology, pp. 1581-1582.
63. Schneider T., Neumaier A. Algorithm 808: ARfit-a Matlab package for the estimation of parameters and eigenmodes of multivariate autoregressive models. ACM Trans. Math. Software 2001, 27:58-65.
64. Schwarz G. Estimating the dimension of a model. Ann. Stat. 1978, 6:461-464.
65. Seth A. A MATLAB toolbox for Granger causal connectivity analysis. J. Neurosci. Meth. 2009, 186:262-273.
66. Sun F.T., Miller L.M., D'Esposito M. Measuring interregional functional connectivity using coherence and partial coherence analyses of fMRI data. Neuroimage 2004, 21:647-658.
67. Vakarin V.A., Krakovska O.O., Borowsky R., Sarty G.E. Inferring neural activity from BOLD signals through nonlinear optimization. Neuroimage 2007, 38:248-260.
68. Van de Ven V.G., Formisano E., Prvulovic D., Roeder C.H., Linden D.E.J. Functional connectivity as revealed by spatial independent component analysis of fMRI measurements during rest. Hum. Brain Mapp. 2004, 22:165-178.
69. Wink A.M., Roerdink J. BOLD noise assumptions in fMRI. Int. J. Biomed. Imaging 2006, 2006:1-11.
70. Zhou Z., Chen Y., Ding M., Wright P., Lu Z., Liu Y. Analyzing brain networks with PCA and conditional Granger causality. Hum. Brain Mapp. 2008, 30:2197-2206.