A critical assessment of connectivity measures for EEG data: A simulation study

NeuroImage

Volumn 64, Issue 1, 2013, Pages 120-133

  Haufe, Stefan ^a,b   Nikulin, Vadim V ^c,d   Müller, Klaus Robert ^a,b,e   Nolte, Guido ^f  

^a TECHNISCHE UNIVERSITÄT BERLIN   (Germany)

^b Bernstein Focus: Neurotechnology   (Germany)

^c CHARITÉ UNIVERSITÄTSMEDIZIN BERLIN   (Germany)

^d BERNSTEIN CENTER FOR COMPUTATIONAL NEUROSCIENCE   (Germany)

^e Korea University   (South Korea)

^f UNIVERSITY MEDICAL CENTER HAMBURG EPPENDORF   (Germany)

Author keywords

EEG; Effective connectivity; GC; Inverse source reconstruction; LCMV; PDC; PSI; S FLEX; WMN

Indexed keywords

ANALYTICAL PARAMETERS; ARTICLE; ARTIFACT; BRAIN ELECTROPHYSIOLOGY; BRAIN SIZE; CONCEPTUAL FRAMEWORK; DATA ANALYSIS; ELECTROENCEPHALOGRAM; ELECTROENCEPHALOGRAPHY; NERVE CELL; NEUROANATOMY; NEUROPHYSIOLOGY; PRIORITY JOURNAL; PROCEDURES; RESPONSE TIME; SIMULATION; STATISTICAL ANALYSIS;

BRAIN; BRAIN MAPPING; COMPUTER SIMULATION; CONNECTOME; ELECTROENCEPHALOGRAPHY; HUMANS; MODELS, NEUROLOGICAL; NERVE NET; NEURAL PATHWAYS;

EID: 84867314481     PISSN: 10538119     EISSN: 10959572     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2012.09.036     Document Type: Article

References (64)

1. Astolfi L., Cincotti F., Mattia D., Salinari S., Babiloni C., Basilisco A., Rossini P.M., Ding L., Ni Y., He B., Marciani M.G., Babiloni F. Estimation of the effective and functional human cortical connectivity with structural equation modeling and directed transfer function applied to high-resolution EEG. Magn. Reson. Imaging 2004, 22:1457-1470.
2. Astolfi L., Cincotti F., Mattia D., De Vico Fallani F., Salinari S., Ursino M., Zavaglia M., Marciani M.G., Babiloni F. Estimation of the cortical connectivity patterns during the intention of limb movements. IEEE Eng. Med. Biol. 2006, 25:32-38.
3. Astolfi L., Cincotti F., Mattia D., Marciani M.G., Baccala L.A., de Vico Fallani F., Salinari S., Ursino M., Zavaglia M., Ding L., Edgar J.C., Miller G.A., He B., Babiloni F. Comparison of different cortical connectivity estimators for high-resolution EEG recordings. Hum. Brain Mapp. Feb 2007, 28(2):143-157.
4. Babiloni C., Ferri R., Moretti D.V., Strambi A., Binetti G., Dal Forno G., Ferreri F., Lanuzza B., Bonato C., Nobili F., Rodriguez G., Salinari S., Passero S., Rocchi R., Stam C.J., Rossini P.M. Abnormal fronto-parietal coupling of brain rhythms in mild Alzheimer's disease: a multicentric EEG study. Eur. J. Neurosci. 2004, 19:2583-2590.
5. Babiloni F., Cincotti F., Babiloni C., Carducci F., Mattia D., Astolfi L., Basilisco A., Rossini P.M., Ding L., Ni Y., Cheng J., Christine K., Sweeney J., He B. Estimation of the cortical functional connectivity with the multimodal integration of high-resolution EEG and fMRI data by directed transfer function. Neuroimage 2005, 24:118-131.
6. Baccalá L.A., Sameshima K. Partial directed coherence: a new concept in neural structure determination. Biol. Cybern. 2001, 84:463-474.
7. Baillet S., Mosher J.C., Leahy R.M. Electromagnetic brain mapping. IEEE Signal Process. Mag. 2001, 18:14-30.
8. Blankertz B., Tomioka R., Lemm S., Kawanabe M., Müller K.-R. Optimizing spatial filters for robust EEG single-trial analysis. IEEE Signal Process. Mag. 2008, 25:41-56.
9. Blinowska K., Kus R., Kaminski M., Janiszewska J. Transmission of brain activity during cognitive task. Brain Topogr. 2010, 23:205-213.
10. Brockwell P.J., Davis R.A. Time Series: Theory and Methods (Springer Series in Statistics) 1998, Springer.
11. Brookes M.J., Hale J.R., Zumer J.M., Stevenson C.M., Francis S.T., Barnes G.R., Owen J.P., Morris P.G., Nagarajan S.S. Measuring functional connectivity using MEG: methodology and comparison with fcMRI. Neuroimage Jun 2011, 56(3):1082-1104.
12. Chatrian G.E., Lettich E., Nelson P.L. Modified nomenclature for the "10%" electrode system. J. Clin. Neurophysiol. Apr 1988, 5(2):183-186.
13. Friston K.J. Functional and effective connectivity in neuroimaging: a synthesis. Hum. Brain Mapp. 1994, 2:56-78.
14. Gómez-Herrero G., Atienza M., Egiazarian K., Cantero J.L. Measuring directional coupling between EEG sources. Neuroimage 2008, 43:497-508.
15. Gosset W.S. The probable error of a mean. Biometrika 1908, 6:1-25. (originally published under the pseudonym "Student").
16. Gow D.W., Segawa J.A., Ahlfors S.P., Lin F.H. Lexical influences on speech perception: a Granger causality analysis of MEG and EEG source estimates. Neuroimage 2008, 43:614-623.
17. Granger C. Investigating causal relations by econometric models and cross-spectral methods. Econometrica 1969, 37:424-438.
18. Grave de Peralta-Menendez R., Gonzalez-Andino S.L. A critical analysis of linear inverse solutions to the neuroelectromagnetic inverse problem. IEEE Trans. Biomed. Eng. 1998, 45:440-448.
19. Hämäläinen M., Ilmoniemi R. Interpreting magnetic fields of the brain: minimum norm estimates. Med. Biol. Eng. Comput. 1994, 32:35-42.
20. Haufe, S., 2011. Towards EEG source connectivity analysis. Ph.D. thesis, Berlin Institute of Technology.
21. Haufe S., Nikulin V., Ziehe A., Müller K.-R., Nolte G. Combining sparsity and rotational invariance in EEG/MEG source reconstruction. Neuroimage 2008, 42:726-738.
22. Haufe S., Nikulin V.V., Ziehe A., Müller K.-R., Nolte G. Estimating vector fields using sparse basis field expansions. Advances in Neural Information Processing Systems 2009, 21:617-624. MIT Press. D. Koller, D. Schuurmans, Y. Bengio, L. Bottou (Eds.).
23. Haufe S., Nolte G., Müller K.-R., Krämer N. Sparse causal discovery in multivariate time series. JMLR W&CP 2010, vol. 6:97-106. I. Guyon, D. Janzing, B. Schölkopf (Eds.).
24. Haufe S., Tomioka R., Nolte G., Müller K.-R., Kawanabe M. Modeling sparse connectivity between underlying brain sources for EEG/MEG. IEEE Trans. Biomed. Eng. 2010, 57:1954-1963.
25. Haufe S., Tomioka R., Dickhaus T., Sannelli C., Blankertz B., Nolte G., Müller K.-R. Large-scale EEG/MEG source localization with spatial flexibility. Neuroimage 2011, 54:851-859.
26. Haufe S., Nikulin V.V., Nolte G. Alleviating the influence of weak data asymmetries on granger-causal analyses. Lecture Notes in Computer Science 2012, vol. 7191:25-33. Springer, Berlin/ Heidelberg. F. Theis, A. Cichocki, A. Yeredor, M. Zibulevsky (Eds.).
27. Holmes C.J., Hoge R., Collins L., Woods R., Toga A., Evans A.C. Enhancement of MR images using registration for signal averaging. J. Comput. Assist. Tomogr. 1998, 22:324-333.
28. Horwitz B. The elusive concept of brain connectivity. Neuroimage 2003, 19:466-470.
29. Ioannides A.A., Bolton J.P.R., Clarke C.J.S. Continous probabilistic solutions to the biomagnetic inverse problem. Inverse Prob. 1990, 6:523-542.
30. Jeffs B., Leahy R.M., Singh M. An evaluation of methods for neuromagnetic image reconstruction. IEEE Trans. Biomed. Eng. 1987, 34:713-723.
31. Jirsa V., McIntosh A. Handbook of brain connectivity. Understanding complex systems 2007, Springer.
32. Kamiński 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.
33. Kamiński M., Blinowska K., Szelenberger W. Topographic analysis of coherence and propagation of EEG activity during sleep and wakefulness. Electroencephalogr. Clin. Neurophysiol. 1997, 102:216-227.
34. Kamiński 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.
35. Kayser J., Tenke C.E. Principal components analysis of Laplacian waveforms as a generic method for identifying ERP generator patterns: I. Evaluation with auditory oddball tasks. Clin. Neurophysiol. 2006, 117:348-368.
36. Marinazzo D., Pellicoro M., Stramaglia S. Kernel method for nonlinear Granger Causality. Phys. Rev. Lett. 2008, 100:144103.
37. Martino J., Honma S.M., Findlay A.M., Guggisberg A.G., Owen J.P., Kirsch H.E., Berger M.S., Nagarajan S.S. Resting functional connectivity in patients with brain tumors in eloquent areas. Ann. Neurol. Mar 2011, 69(3):521-532.
38. Marzetti L., Nolte G., Perrucci M.G., Romani G.L., Del Gratta C. The use of standardized infinity reference in EEG coherency studies. Neuroimage 2007, 36:48-63.
39. Matsuura K., Okabe Y. Selective minimum-norm solution of the biomagnetic inverse problem. IEEE Trans. Biomed. Eng. 1995, 42:608-615.
40. Neumaier A., Schneider T. Estimation of parameters and eigenmodes of multivariate autoregressive models. ACM Trans. Math. Softw. 2001, 27:27-57.
41. Nolte G., Dassios G. Analytic expansion of the EEG lead field for realistic volume conductors. Phys. Med. Biol. 2005, 50:3807-3823.
42. Nolte G., Müller K.-R. Localizing and estimating causal relations of interacting brain rhythms. Front. Hum. Neurosci. 2010, 4:209.
43. Nolte G., Bai O., Wheaton L., Mari Z., Vorbach S., Hallett M. Identifying true brain interaction from EEG data using the imaginary part of coherency. Clin. Neurophysiol. 2004, 115:2292-2307.
44. Nolte G., Meinecke F.C., Ziehe A., Müller K.-R. Identifying interactions in mixed and noisy complex systems. Phys. Rev. E 2006, 73:051913.
45. Nolte G., Ziehe A., Nikulin V.V., Schlögl A., Krämer N., Brismar T., Müller K.R. Robustly estimating the flow direction of information in complex physical systems. Phys. Rev. Lett. 2008, 100:234101.
46. Nolte G., Ziehe A., Krämer N., Popescu F., Müller K.-R. Comparison of Granger causality and phase slope index. JMLR W&CP 2010, vol. 6:267-276. I. Guyon, D. Janzing, B. Schölkopf (Eds.).
47. Nunez P., Srinivasan R. Electric fields of the brain: the neurophysics of EEG 2006, Oxford University Press.
48. Nunez P.L., Srinivasan R., Westdorp A.F., Wijesinghe R.S., Tucker D.M., Silberstein R.B., Cadusch P.J. EEG coherency. I: Statistics, reference electrode, volume conduction, Laplacians, cortical imaging, and interpretation at multiple scales. Electroencephalogr. Clin. Neurophysiol. 1997, 103:499-515.
49. Nunez P.L., Silberstein R.B., Shi Z., Carpenter M.R., Srinivasan R., Tucker D.M., Doran S.M., Cadusch P.J., Wijesinghe R.S. EEG coherency II: experimental comparisons of multiple measures. Clin. Neurophysiol. 1999, 110:469-486.
50. Roebroeck A., Formisano E., Goebel R. Mapping directed influence over the brain using Granger causality and fMRI. Neuroimage 2005, 25:230-242.
51. Rubner Y., Tomasi C., Guibas L.J. The earth mover's distance as a metric for image retrieval. Int. J. Comput. Vision 2000, 40:99-121.
52. Schlögl A., Supp G. Analyzing event-related EEG data with multivariate autoregressive parameters. Prog. Brain Res. 2006, 159:135-147.
53. Schoffelen J.M., Gross J. Source connectivity analysis with MEG and EEG. Hum. Brain Mapp. 2009, 30:1857-1865.
54. Seth A.K. A MATLAB toolbox for Granger causal connectivity analysis. J. Neurosci. Methods 2010, 186:262-273.
55. Silberstein R.B. Dynamic sculpting of brain functional connectivity and mental rotation aptitude. Prog. Brain Res. 2006, 159:63-76.
56. Silfverhuth M.J., Hintsala H., Kortelainen J., Seppanen T. Experimental comparison of connectivity measures with simulated EEG signals. Med. Biol. Eng. Comput. Jul 2012, 50(7):683-688.
57. Srinivasan R., Winter W.R., Ding J., Nunez P.L. EEG and MEG coherence: measures of functional connectivity at distinct spatial scales of neocortical dynamics. J. Neurosci. Methods 2007, 166:41-52.
58. Supp G.G., Schlögl A., Trujillo-Barreto N., Müller M.M., Gruber T. Directed cortical information flow during human object recognition: analyzing induced EEG gamma-band responses in brain's source space. PLoS One 2007, 2:e684.
59. Theiler J., Prichard D. Using 'surrogate surrogate data' to calibrate the actual rate of false positives in tests for nonlinearity in time series. Fields Inst. Commun. 1997, 11:99-113.
60. Theiler J., Eubank S., Longtin A., Galdrikian B., Farmer J.D. Testing for nonlinearity in time series: the method of surrogate data. Phys. D: Nonl. Phenomena 1992, 58(1-4):77-94.
61. Valdes-Sosa P.A., Roebroeck A., Daunizeau J., Friston K. Effective connectivity: influence, causality and biophysical modeling. NeuroImage 2011, 58(2):339-361.
62. Van Veen B.D., van Drongelen W., Yuchtman M., Suzuki A. Localization of brain electrical activity via linearly constrained minimum variance spatial filtering. IEEE Trans. Biomed. Eng. 1997, 44:867-880.
63. Vicente R., Wibral M., Lindner M., Pipa G. Transfer entropy-a model-free measure of effective connectivity for the neurosciences. J. Comput. Neurosci. Feb 2011, 30(1):45-67.
64. Wibral M., Rahm B., Rieder M., Lindner M., Vicente R., Kaiser J. Transfer entropy in magnetoencephalographic data: quantifying information flow in cortical and cerebellar networks. Prog. Biophys. Mol. Biol. Mar 2011, 105(1-2):80-97.