A systematic framework for functional connectivity measures

Frontiers in Neuroscience

Volumn 8, Issue DEC, 2014, Pages

  Wang, Huifang E ^a,b   Bénar, Christian G ^a,b   Quilichini, Pascale P ^a,b   Friston, Karl J ^c   Jirsa, Viktor K ^a,b   Bernard, Christophe ^a,b  

^a AIX MARSEILLE UNIVERSITÉ   (France)

^b INSERM   (France)

^c UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

Evaluation framework; fMRI; Functional connectivity; Granger causality; Neural mass models

Indexed keywords

ANALYTIC METHOD; ARTICLE; CLINICAL ASSESSMENT; CLINICAL EVALUATION; CONCEPTUAL FRAMEWORK; ELECTROENCEPHALOGRAM; ELECTROENCEPHALOGRAPHY; FUNCTIONAL MAGNETIC RESONANCE IMAGING; HEMODYNAMIC PARAMETERS; NEUROLOGIC EXAMINATION; NONLINEAR SYSTEM; RADIOLOGICAL PARAMETERS; RECEIVER OPERATING CHARACTERISTIC; SIGNAL NOISE RATIO; TASK PERFORMANCE; TIME SERIES ANALYSIS;

EID: 84920618449     PISSN: 16624548     EISSN: 1662453X     Source Type: Journal    
DOI: 10.3389/fnins.2014.00405     Document Type: Article

References (41)

1. Ansari-Asl, K., Senhadji, L., Bellanger, J. J., and Wendling, F. (2006). Quantitative evaluation of linear and nonlinear methods characterizing interdependencies between brain signals. Phys. Rev. E 74, 31916. doi: 10.1103/PhysRevE.74.031916
2. Baccalá, L. (2007). "Generalized partial directed coherence," in Digital Signal Processing, 2007 15th International Conference on (Baccald, LA: IEEE), 163-166. doi: 10.1109/ICDSP.2007.4288544
3. Baccalá, L., and Sameshima, K. (2001). Partial directed coherence: a new concept in neural structure determination. Biol. Cybern. 84, 463-474. doi: 10.1007/PL00007990
4. Baccalá, L., Sameshima, K., Ballester, G., Do Valle, A. C., and Timo-loria, C. (1998). Studying the interaction between brain structures via directed coherence and Granger causality. Appl. Signal Process. 5, 40-48. doi: 10.1007/s005290050005
5. Baccalá, L., Takahashi, D., and Sameshima, K. (2006). "Computer intensive testing for the influence between time series," in Handbook of Time Series Analysis, eds B. Schelter, M. Winterhalder, and J. Timmer (Weinheim: Wiley-VCH), 411.
6. Barnett, L., Barrett, A. B., and Seth, A. K. (2009). Granger causality and transfer entropy are equivalent for gaussian variables. Phys. Rev. Lett. 103, 238701. doi: 10.1103/PhysRevLett.103.238701
7. Bullmore, E., and Sporns, O. (2012). The economy of brain network organization. Nat. Rev. Neurosci. 13, 336-349.
8. Chicharro, D. (2011). On the spectral formulation of Granger causality. Biol. Cybern. 105, 331-347. doi: 10.1007/s00422-011-0469-z
9. David, O., Cosmelli, D., and Friston, K. J. (2004). Evaluation of different measures of functional connectivity using a neural mass model. Neuroimage 21, 659-673. doi: 10.1016/j.neuroimage.2003.10.006
10. Fox, M. D., Snyder, A. Z., Vincent, J. L., Corbetta, M., Van Essen, D. C., and Raichle, M. E. (2005). The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc. Natl. Acad. Sci. U.S.A. 102, 9673-9678. doi: 10.1073/pnas.0504136102
11. Friston, K. J. (1994). Functional and effective connectivity in neuroimaging: a synthesis. Hum. Brain Mapp. 2, 56-78. doi: 10.1002/hbm.460020107
12. Friston, K. J., Harrison, L., and Penny, W. (2003). Dynamic causal modelling. Neuroimage 19, 1273-1302. doi: 10.1016/S1053-8119(03)00202-7
13. Friston, K. J., Kahan, J., Biswal, B., and Razi, A. (2014). A DCM for resting state fMRI. Neuroimage 94, 396-407. doi: 10.1016/j.neuroimage.2013.12.009
14. Geweke, J. F. (1982). Measurement of linear dependence and feedback between multiple time series. J. Am. Stat. Assoc. 77, 304-313. doi: 10.1080/01621459.1982.10477803
15. Geweke, J. F. (1984). Measures of conditional linear dependence and feedback between time series. J. Am. Stat. Assoc. 79, 907-915. doi: 10.1080/01621459.1984.10477110
16. Granger, C. W. J. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econom. J. Econom. Soc. 37, 424-438. doi: 10.2307/1912791
17. Grassberger, P., Schreiber, T., and Schaffrath, C. (1991). Nonlinear time sequence analysis. Int. J. Bifurc. Chaos 1, 521-547. doi: 10.1142/S0218127491000403
18. Grinsted, A., Moore, J. C., and Jevrejeva, S. (2004). Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear Process. Geophys. 11, 561-566. doi: 10.5194/npg-11-561-2004
19. Hénon, M. (1976). A two-dimensional mapping with a strange attractor. Commun. Math. Phys. 50, 69-77. doi: 10.1007/BF01608556
20. Hinich, M. J., and Clay, C. S. (1968). The application of the discrete Fourier transform in the estimation of power spectra, coherence, and bispectra of geophysical data. Rev. Geophys. 6, 347-363. doi: 10.1029/RG006i003p00347
21. Jansen, B. H., and Rit, V. G. (1995). Biological Cybernetics in a mathematical model of coupled cortical columns. Biol. Cybern. 366, 357-366.
22. Kaminski, M., Ding, M., Truccolo, W. A., and Bressler, S. L. (2001). Evaluating causal relations in neural systems: granger causality, directed transfer function and statistical assessment of significance. Biol. Cybern. 85, 145-157. doi: 10.1007/s004220000235
23. Kaminski, M. J., and Blinowska, K. J. (1991). A new method of the description of the information flow in the brain structures. Biol. Cybern. 65, 203-210. doi: 10.1007/BF00198091
24. Kaminski, M., and Liang, H. (2005). Causal influence: advances in neurosignal analysis. Crit. Rev. Biomed. Eng. 33, 347. doi: 10.1615/CritRevBiomedEng.v33.i4.20
25. Korzeniewska, A., Mañczak, M., Kamiñski, M., Blinowska, K. J., and Kasicki, S. (2003). Determination of information flow direction among brain structures by a modified directed transfer function (dDTF) method. J. Neurosci. Methods 125, 195-207. doi: 10.1016/S0165-0270(03)00052-9
26. Lopes da Silva, F., Pijn, J. P., and Boeijinga, P. (1989). Interdependence of EEG signals: linear vs. nonlinear associations and the significance of time delays and phase shifts. Brain Topogr. 2, 9-18. doi: 10.1007/BF01128839
27. Mclntosh, A. R., and Gonzalez-Lima, F. (1994). Structural equation modeling and its application to network analysis in functional brain imaging. Hum. Brain Mapp. 2, 2-22. doi: 10.1002/hbm.460020104
28. Moran, R., Pinotsis, D., and Friston, K. (2013). Neural masses and fields in dynamic causal modeling. Front. Comput. Neurosci. 7:57. doi: 10.3389/fncom.2013.00057
29. Paluš, M., Komárek, V., Hrnèíø, Z., and Štìrbová, K. (2001). Synchronization as adjustment of information rates: detection from bivariate time series. Phys. Rev. E63, 46211. doi: 10.1103/PhysRevE.63.046211
30. Pereda, E., Quiroga, R. Q., and Bhattacharya, J. (2005). Nonlinear multivariate analysis of neurophysiological signals. Prog. Neurobiol. 77, 1-37. doi: 10.1016/j.pneurobio.2005.10.003
31. Quiroga, R. Q., Kraskov, A., Kreuz, T., and Grassberger, P. (2002). Performance of different synchronization measures in real data: a case study on electroencephalographic signals. Phys. Rev. E 65, 41903. doi: 10.1103/PhysRevE.65.041903
32. Ramsey, J., Zhang, J., and Spirtes, P. (2006). "Adjacency-faithfulness and conservative causal inference," in Proceedings of the 22nd Annual Conference on Uncertainty in Artificial Intelligence (Cambridge, MA).
33. Rodgers, J. L., and Nicewander, W. A. (1988). Thirteen ways to look at the correlation coefficient. Am. Stat. 42, 59-66. doi: 10.2307/2685263
34. Rössler, O. E. (1979). Continuous chaos: four prototype equations. Ann. N.Y. Acad. Sci. 316, 376-392. doi: 10.1111/j.1749-6632.1979.tb29482.x
35. Schreiber, T. (2000). Measuring information transfer. Phys. Rev. Lett. 85, 461-464. doi: 10.1103/PhysRevLett.85.461
36. Seth, A. K. (2010). A MATLAB toolbox for Granger causal connectivity analysis. J. Neurosci. Methods 186, 262-273. doi: 10.1016/j.jneumeth.2009.11.020
37. Smith, S. M., Miller, K. L., Salimi-Khorshidi, G., Webster, M., Beckmann, C. F., Nichols, T. E., et al. (2011). Network modelling methods for FMRI. Neuroimage 54, 875-891. doi: 10.1016/j.neuroimage.2010.08.063
38. Stephan, K. E., Weiskopf, N., Drysdale, P. M., Robinson, P. A., and Friston, K. J. (2007). Comparing hemodynamic models with DCM. Neuroimage 38, 387-401. doi: 10.1016/j.neuroimage.2007.07.040
39. Wendling, F., and Ansari-Asl, K. (2009). From EEG signals to brain connectivity: a model-based evaluation of interdependence measures. J. Neurosci. Methods 183, 9-18. doi: 10.1016/j.jneumeth.2009.04.021
40. Wendling, F., Bartolomei, F., Bellanger, J. J., and Chauvel, P. (2001). Interpretation of interdependencies in epileptic signals using a macroscopic physiological model of the EEG. Clin. Neurophysiol. 112, 1201-1218. doi: 10.1016/S1388-2457(01)00547-8
41. Zweig, M., and Campbell, G. (1993). Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin. Chem. 39, 561-577.