GMAC: A Matlab toolbox for spectral Granger causality analysis of fMRI data

Computers in Biology and Medicine

Volumn 42, Issue 10, 2012, Pages 943-956

  Tana, Maria Gabriella ^a   Sclocco, Roberta ^a   Bianchi, Anna Maria ^a  

^a POLITECNICO DI MILANO   (Italy)

Author keywords

Connectivity; FMRI; Granger causality; Network analysis; Open source toolbox

Indexed keywords

BRAIN NETWORKS; CLINICAL USERS; CONNECTIVITY; FMRI; FMRI DATA; FUNCTIONAL MAGNETIC RESONANCE IMAGING; GRANGER CAUSALITY; GRAPHICAL INTERFACE; MATLAB ENVIRONMENT; MATLAB TOOLBOXES; MULTIVARIATE AUTOREGRESSIVE; NETWORK NODE; NEURAL ACTIVITY; OPEN SOURCES; OPEN-SOURCE SOFTWARES; STATISTICAL SIGNIFICANCE; SURROGATE DATA;

ELECTRIC NETWORK ANALYSIS; MAGNETIC RESONANCE IMAGING; NEURONS; VISUALIZATION;

DATA VISUALIZATION;

ARTICLE; COMPUTER ANALYSIS; COMPUTER NETWORK; COMPUTER PROGRAM; DATA ANALYSIS SOFTWARE; FUNCTIONAL MAGNETIC RESONANCE IMAGING; GRANGER MULTIVARIATE AUTOGREGRESSIVE CONNECTIVITY; MATHEMATICAL COMPUTING; NEUROIMAGING; PRIORITY JOURNAL; STATISTICAL ANALYSIS;

ALGORITHMS; BRAIN MAPPING; CLUSTER ANALYSIS; EPILEPSY; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MALE; MULTIVARIATE ANALYSIS; SOFTWARE;

EID: 84866502612     PISSN: 00104825     EISSN: 18790534     Source Type: Journal    
DOI: 10.1016/j.compbiomed.2012.07.003     Document Type: Article

References (65)

1. Friston K.J., Frith C.D, Liddle P.F., S-Frackowiak R Functional connectivity: the principal component analysis of large (PET) data sets,. J. Cerebr. Blood Flow Metab. 1993, 13:5-14.
2. Marrelec G., Bellec P., Benali H. Exploring large-scale brain networks in functional MRI. J. Physiol.-Paris 2006, 100:171-181.
3. Friston K.J., Harrison L., Penny W. Dynamic causal modelling. NeuroImage 2003, 19:1273-1302.
4. Roebroeck A., Formisano E., Goebel R. The identification of interacting networks in the brain using fMRI: model selection, causality and deconvolution. NeuroImage 2011, 58:296-302.
5. Zhuang J., LaConte S., Peltier S, Zhang K, Hu X Connectivity exploration with structural equation modeling: an fMRI study of bimanual motor coordination. NeuroImage 2005, 25:462-470.
6. Chen G., Glen D.R., Saad Z.S., Hamilton J.P., Thomason M.E., Gotlib I.H., Cox R.W. Vector autoregression, structural equation modeling, and their synthesis in neuroimaging data analysis. Comput. Biol. Med. 2011, 41:1142-1155.
7. Friston K.J. Dynamic causal model for fMRI. Statistical Parametric Mapping 2006, The analysis of functional brain images Elsevier, London. K.J. Friston, J. Ashburner, S. Kiebel, T. Nichols, W. Penny (Eds.).
8. Deshpande G., LaConte S., James G.A., Peltier S., Xiaoping H. Multivariate Granger causality analysis of fMRI data. Hum. Brain Mapp. 2009, 30:1361-1373.
9. Granger C.W.J. Investigating casual relations by econometric models and cross-spectral methods. Econometrica 1969, 37:424-438.
10. Roebroeck A., Formisano E., Goebel R. Mapping directed influence over the brain using Granger causality and fMRI. NeuroImage 2005, 25:230-242.
11. 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.
12. Sato J.R., Fujita A., Cardoso E.F., Thomaz C.E., Brammer M.J., Amaro E. Analyzing the connectivity between regions of interest: an approach based on cluster Granger causality for fMRI data analysis. NeuroImage 2010, 53:1444-1455.
13. Ide J.S., Li C.R. A cerebellar thalamic cortical circuit for error-related cognitive control. NeuroImage 2011, 54:455-464.
14. Schippers M.B., Keysers C. Mapping the flow of information within the putative mirror neuron system during gesture observation. NeuroImage 2011, 57:37-44.
15. Seger C.A., Dennison C.S., Lopez-Paniagua D., Peterson E.J., Roark A.A. Dissociating hippocampal and basal ganglia contributions to category learning using stimulus novelty and subjective judgments. NeuroImage 2011, 55:1739-1753.
16. Deshpande G., Xiaoping H., 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.
17. Stilla R., Deshpande G., Laconte S., Hu X., Sathian K. Posteromedial parietal cortical activity and inputs predict tactile spatial acuity. J. Neurosci. 2007, 27:11091-11102.
18. Stilla R, Hanna R., Mariola E., Deshpande G., Hu X., Sathian K. Neural processing underlying tactile microspatial discrimination in the blind: a functional magnetic resonance imaging study. J. Visualization 2008, 8:13.1-13.19.
19. Havlicek M., Jan J., Brazdil M., Calhoun V.D. Dynamic Granger causality based on Kalman filter for evaluation of functional network connectivity in fMRI data. NeuroImage 2010, 53:65-77.
20. Abler B., Roebroeck A., Goebel R., Hose A., Schonfeldt-Lecuona C., Hole G., Walter H. Investigating directed influences between activated brain areas in a motor-response task using fMRI. Magn. Reson. Imaging 2006, 24:181-185.
21. Sato J.R., Junior E.A., Takahashi D.Y., 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.
22. Chen H., Liao W., Gong Q., Shen S. Evaluation of the effective connectivity of supplementary motor areas during motor imagery using Granger causality mapping. NeuroImage 2009, 47:1844-1853.
23. Gao Q., Duan X., Chen H. Evaluation of effective connectivity of motor areas during motor imagery and execution using conditional Granger causality. NeuroImage 2011, 54:1280-1288.
24. 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.
25. Liao W., Ding J., Marinazzo D., Xu Q., Wang Z., Yuan C., Zhang Z., Lu G., Chen H. Small-world directed networks in the human brain: multivariate Granger causality analysis of resting-state fMRI. NeuroImage 2011, 54:2683-2694.
26. Jiao Q., Lu G., Zhang Z., Zhong Y., Wang Z., Guo Y., Li K., Ding M., Liu Y. Granger causal influence predicts BOLD activity levels in the default mode network. Hum. Brain Mapp. 2011, 32:154-161.
27. David O. fMRI connectivity, meaning and empiricism comments on: Roebroeck et al. The identification of interacting networks in the brain using fMRI: Model selection, causality and deconvolution. NeuroImage 2011, 58:306-309.
28. Bressler S.L., Seth A.K. Wiener-Granger causality: a well established methodology. NeuroImage 2011, 58:323-329.
29. Deshpande G., Sathian K., Hu X. Effect of hemodynamic variability on Granger causality analysis of fMRI. NeuroImage 2010, 52:884-896.
30. Wang J., Wang L., Zang Y., Yang H., Tang H., Gong Q., Chen Z., Zhu C., He Y. Parcellation-dependent small-world brain functional networks: a resting-state fMRI study. Hum. Brain Mapp. 2009, 30:1511-1523.
31. Rubinov M., Sporns O. Complex network measures of brain connectivity: uses and interpretations. NeuroImage 2010, 52:1059-1069.
32. Sato J.R., Takahashi D.Y., Arcuri S.M., Sameshima K., Morettin P.A., Baccalà L.A. Frequency domain connectivity identification: an application of partial directed coherence in fMRI. Hum. Brain Mapp. 2009, 30:452-461.
33. Zalesky A., Fornito A., Harding I.H., Cocchi L., Yucel M., Pantelis C., Bullmore E.T. Whole-brain anatomical networks: does the choice of nodes matter?. NeuroImage 2010, 50:970-983.
34. Thirion B., Flandin G., Pinel P., Roche A., Ciuciu P., Poline J-B. Dealing with the shortcomings of spatial normalization: multisubject parcellation of fMRI datasets. Hum. Brain Mapp. 2006, 27:678-693.
35. Michel V., E. Eger, C. Keribin, J. Poline, B. Thirion, A supervised clustering approach for extracting predictive information from brain activation images, Proceedings of the IEEE Computer Vision and Pattern Recognition Workshops (2010) pp. 7-14.
36. Tana M.G., Bianchi A.M., Sclocco R., Franchin T., Cerutti S., Leal A. Parcel-based connectivity analysis of fMRI data for the study of epileptic seizure propagation. Brain Topogr. 2012, 10.1007/s10548-012-0225-2.
37. Mikl M., Marecek R., Hlustik P., Pavlicova M., Drastich A., Chlebus P., Brazdil M., Krupa P. Effects of spatial smoothing on fMRI group infences. Magn. Reson. Imaging 2008, 26:490-503.
38. Fox M.D., Snyder A.Z., Vincent J.L., Corbetta M., Van Essen D.C., Raichle M.E. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proceedings of the National Academy of Sciences of the United States of America 2005, 102:9673-9678.
39. Woods R.P., Dapretto M., Sicotte N.L., Toga A.W., Mazziotta J.C. Creation and use of a Talairach-compatible atlas for accurate, automated, nonlinear intersubject registration, and analysis of functional imaging data. Hum. Brain Mapp. 1999, 8:73-79.
40. Strother S Evaluating fMRI preprocessing pipelines. IEEE Eng. Med Biol Mag. 2006, 25:27-41.
41. Friston K.J., Howard S., Frackowiak R.S.J., Turner R. Movement related effects in fMRI time-series. Magn. Reson. Med. 1996, 35:346-355.
42. Seth A.K., MATLAB A toolbox for Granger connectivity analysis. J. Neurosci. Methods 2010, 186:262-273.
43. Said E, Dickey DA Testing for unit roots in autoregressive moving average models of unknown order. Biometrika 1984, 71:599-607.
44. Akaike H. A new look at the statistical model identification. IEEE Trans. Autom. Control 1974, 19:716-723.
45. Box G.E.P., Jenkins G.M., Reinsel G.C. Time Series Analysis: Forecasting and Control 1994, Prentice Hall, Englewood Cliffs, NJ. third ed.
46. Schwartz G. Estimating the dimension of a model. Ann. Stat. 1978, 5:461-464.
47. Smith S.M., Miller K.L., Salimi-Khorshidi G., Webster M., Beckmann C.F., Nichols T.E., Ramsey J.D., Woolrich M.W. Network modeling methods for fMRI. NeuroImage 2011, 54:875-891.
48. Stephan K.E. and A. Roebroeck, A short history of causal modeling of fMRI data, NeuroImage, 62 (2012) 856-863.
49. David O., I. Guillemain, S. Saillet, S. Reyt, C. Deransart, C. Segebarth, A. Depaulis, Identifying neural drivers with functional MRI: an electrophysiological validation, PLoS Biol.6 (2008) 2683-2697.
50. Rajapakse J.C., Kruggel F., Maisog J.M., Von Cramon D. Modeling haemodynamic response for analysis of functional MRI time-series. Hum. Brain Mapp. 1998, 6:283-300.
51. Kruggel F., Von Cramon D.Y. Modeling the hemodynamic response in single-trial functional MRI experiments. Magn. Reson. Med. 1999, 42:787-797.
52. Gossl C., Fahrmeir L., Auer D.P. Bayesian modeling of the hemodynamic response function in BOLD fMRI. NeuroImage 2001, 14:140-148.
53. Makni S., Idier J., Vincent V., Thirion B., Dehaene-Lambertz G., Ciuciu P. A fully Bayesian approach to the parcel-based detection-estimation of brain activity in fMRI. NeuroImage 2008, 41:941-969.
54. Vincent T., L. Risser, J. Idier, P. Ciuciu, Spatially adaptive mixture modelling for analysis of fMRI time series, (2009), Proceedings of 15th HBM, San Francisco, CA, USA.
55. Glover G.H. Deconvolution of impulse response in event-related BOLD fMRI. NeuroImage 1999, 9:416-429.
56. Kus R., Kaminski M., Blinowska K.J. Determination of EEG activity propagation: pair-wise versus multichannel estimate. IEEE Trans. Biomed. Eng. 2004, 51:1501-1510.
57. Kaminski M., Ding M., Truccolo W., Bressler S. Evaluating causal relations in neural systems: Granger causality, directed transfer function and statistical assessment of significance. Biol. Cybern. 2001, 85:145-157.
58. Korzeniewska A., Manczak M., Kaminski M., Blinowska K.J., Kasicki S. Determination of information flow direction between brain structures by a modified directed transfer function method (dDTF). J. Neurosci. Methods 2003, 125:195-207.
59. Baccalà L.A., Sameshima K. Partial directed coherence: a new concept in neural structure determination. Biol. Cybern. 2001, 84:463-474.
60. Sakkalis V. Review of advanced techniques for the estimation of brain connectivity measured with EEG/MEG. Comput. Biol. Med. 2011, 41:1110-1117.
61. Theiler J., Eubank A., Galdrikian B., Farmer D. Testing for nonlinearity in time series: the method of surrogate data. Physica D 1992, 58:77-94.
62. Van den Brink R., Gilles R.P. The outflow ranking method for weighted directed graphs. Eur. J. Oper. Res. 2009, 193:484-491.
63. Bullmore E., Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 2009, 10:186-198.
64. Aguirre G.K., Zarahn E., D'esposito M The variability of human, BOLD hemodynamic responses. NeuroImage 1998, 8:360-369.
65. Cui J., Xu L., Bressler S.L., Ding M., Liang H. BSMART: a Matlab/C toolbox for analysis of multichannel neural time series. Neural Networks 2008, 21:1094-1104.