6 Methods for Functional Connectivity Analysis
143
12. J.W. Choi, K.S. Cha, J.D. Choi et al., Difficulty-related changes in inter-regional neural synchrony are dissociated between target and non-target processing. Brain Res. 1603, 114–123
(2015). https://doi.org/10.1016/j.brainres.2015.01.031
13. J.W. Choi, K.M. Jang, K.Y. Jung et al., Reduced theta-band power and phase synchrony during
explicit verbal memory tasks in female, non-clinical individuals with schizotypal traits. PLoS
ONE 11, 1–18 (2016). https://doi.org/10.1371/journal.pone.0148272
14. J.W. Choi, D. Ko, G.T. Lee et al., Reduced neural synchrony in patients with restless legs
syndrome during a visual oddball task. PLoS ONE 7, 1–9 (2012). https://doi.org/10.1371/jour
nal.pone.0042312
15. M.X. Cohen, Analyzing Neural Time Series Data: Theory and Practice (The MIT Press, Cambridge, Massachusetts, 2014)
16. P.W. Diaconia, D. Freedman, Consistency of Bayes estimates for nonparametric regression:
normal theory. Bernoulli 4, 411–444 (1998)
17. K.J. Friston, Functional and effective connectivity: a review. Brain Connect 1, 13–36 (2011).
https://doi.org/10.1089/brain.2011.0008
18. G. Gómez-Herrero, M. Atienza, K. Egiazarian, J.L. Cantero, Measuring directional coupling
between EEG sources. Neuroimage 43, 497–508 (2008). https://doi.org/10.1016/j.neuroimag
e.2008.07.032
19. C.W.J. Granger, Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37, 424–438 (1969)
20. M. Hassan, O. Dufor, I. Merlet et al., EEG source connectivity analysis: from dense array
recordings to brain networks. PLoS ONE 9, (2014). https://doi.org/10.5281/zenodo.10498
21. S. Haufe, Towards EEG Source Connectivity Analysis (Berlin Institute of Technology, Berlin,
Germany, 2012)
22. S. Haufe, V.V. Nikulin, K.-R. Müller, G. Nolte, A critical assessment of connectivity measures
for EEG data: a simulation study. Neuroimage 64, 120–133 (2013). https://doi.org/10.1016/j.
neuroimage.2012.09.036
23. C.S. Herrmann, M.H.J. Munk, A.K. Engel, Cognitive functions of gamma-band activity: memory match and utilization. Trends Cogn. Sci. 8, 347–355 (2004). https://doi.org/10.1016/j.tics.
2004.06.006
24. A.-S. Hincapié, J. Kujala, J. Mattout et al., The impact of MEG source reconstruction method
on source-space connectivity estimation: a comparison between minimum-norm solution and
beamforming. Neuroimage 156, 29–42 (2017). https://doi.org/10.1016/j.neuroimage.2017.04.
038
25. J.F. Hipp, D.J. Hawellek, M. Corbetta et al., Large-scale cortical correlation structure of spontaneous oscillatory activity. Nat. Neurosci. (2012). https://doi.org/10.1038/nn.3101
26. R.M. Hutchison, T. Womelsdorf, E.A. Allen et al., Dynamic functional connectivity: promise,
issues, and interpretations. Neuroimage 80, 360–378 (2013). https://doi.org/10.1016/j.neuroi
mage.2013.05.079
27. M.J. Kaminski, K.J. Blinowska, A new method of the description of the information flow in
the brain structures. Biol. Cybern. 65, 203–210 (1991)
28. J. Kayser, C.E. Tenke, Principal components analysis of Laplacian waveforms as a generic
method for identifying ERP generator patterns: II. Adequacy of low-density estimates. Clin.
Neurophysiol. 117, 369–380 (2006). https://doi.org/10.1016/j.clinph.2005.08.033
29. S. Khan, A. Gramfort, N.R. Shetty et al., Local and long-range functional connectivity is
reduced in concert in autism spectrum disorders. Proc. Natl. Acad. Sci. U S A. 110, 3107–3112
(2013). https://doi.org/10.1073/pnas.1214533110
30. J.-P. Lachaux, E. Rodriguez, J. Martinerie, F.J. Varela, Measuring phase synchrony in brain
signals. Hum. Brain Mapp. 8, 194–208 (1999)
31. Y.-Y. Lee, S. Hsieh, Classifying different emotional states by means of EEG-based functional
connectivity patterns. PLoS ONE 9, (2014). https://doi.org/10.1371/journal.pone.0095415
32. L. Marzetti, C. Del Gratta, G. Nolte, Understanding brain connectivity from EEG data by
identifying systems composed of interacting sources. Neuroimage 42, 87–98 (2008). https://d
oi.org/10.1016/j.neuroimage.2008.04.250
143
12. J.W. Choi, K.S. Cha, J.D. Choi et al., Difficulty-related changes in inter-regional neural synchrony are dissociated between target and non-target processing. Brain Res. 1603, 114–123
(2015). https://doi.org/10.1016/j.brainres.2015.01.031
13. J.W. Choi, K.M. Jang, K.Y. Jung et al., Reduced theta-band power and phase synchrony during
explicit verbal memory tasks in female, non-clinical individuals with schizotypal traits. PLoS
ONE 11, 1–18 (2016). https://doi.org/10.1371/journal.pone.0148272
14. J.W. Choi, D. Ko, G.T. Lee et al., Reduced neural synchrony in patients with restless legs
syndrome during a visual oddball task. PLoS ONE 7, 1–9 (2012). https://doi.org/10.1371/jour
nal.pone.0042312
15. M.X. Cohen, Analyzing Neural Time Series Data: Theory and Practice (The MIT Press, Cambridge, Massachusetts, 2014)
16. P.W. Diaconia, D. Freedman, Consistency of Bayes estimates for nonparametric regression:
normal theory. Bernoulli 4, 411–444 (1998)
17. K.J. Friston, Functional and effective connectivity: a review. Brain Connect 1, 13–36 (2011).
https://doi.org/10.1089/brain.2011.0008
18. G. Gómez-Herrero, M. Atienza, K. Egiazarian, J.L. Cantero, Measuring directional coupling
between EEG sources. Neuroimage 43, 497–508 (2008). https://doi.org/10.1016/j.neuroimag
e.2008.07.032
19. C.W.J. Granger, Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37, 424–438 (1969)
20. M. Hassan, O. Dufor, I. Merlet et al., EEG source connectivity analysis: from dense array
recordings to brain networks. PLoS ONE 9, (2014). https://doi.org/10.5281/zenodo.10498
21. S. Haufe, Towards EEG Source Connectivity Analysis (Berlin Institute of Technology, Berlin,
Germany, 2012)
22. S. Haufe, V.V. Nikulin, K.-R. Müller, G. Nolte, A critical assessment of connectivity measures
for EEG data: a simulation study. Neuroimage 64, 120–133 (2013). https://doi.org/10.1016/j.
neuroimage.2012.09.036
23. C.S. Herrmann, M.H.J. Munk, A.K. Engel, Cognitive functions of gamma-band activity: memory match and utilization. Trends Cogn. Sci. 8, 347–355 (2004). https://doi.org/10.1016/j.tics.
2004.06.006
24. A.-S. Hincapié, J. Kujala, J. Mattout et al., The impact of MEG source reconstruction method
on source-space connectivity estimation: a comparison between minimum-norm solution and
beamforming. Neuroimage 156, 29–42 (2017). https://doi.org/10.1016/j.neuroimage.2017.04.
038
25. J.F. Hipp, D.J. Hawellek, M. Corbetta et al., Large-scale cortical correlation structure of spontaneous oscillatory activity. Nat. Neurosci. (2012). https://doi.org/10.1038/nn.3101
26. R.M. Hutchison, T. Womelsdorf, E.A. Allen et al., Dynamic functional connectivity: promise,
issues, and interpretations. Neuroimage 80, 360–378 (2013). https://doi.org/10.1016/j.neuroi
mage.2013.05.079
27. M.J. Kaminski, K.J. Blinowska, A new method of the description of the information flow in
the brain structures. Biol. Cybern. 65, 203–210 (1991)
28. J. Kayser, C.E. Tenke, Principal components analysis of Laplacian waveforms as a generic
method for identifying ERP generator patterns: II. Adequacy of low-density estimates. Clin.
Neurophysiol. 117, 369–380 (2006). https://doi.org/10.1016/j.clinph.2005.08.033
29. S. Khan, A. Gramfort, N.R. Shetty et al., Local and long-range functional connectivity is
reduced in concert in autism spectrum disorders. Proc. Natl. Acad. Sci. U S A. 110, 3107–3112
(2013). https://doi.org/10.1073/pnas.1214533110
30. J.-P. Lachaux, E. Rodriguez, J. Martinerie, F.J. Varela, Measuring phase synchrony in brain
signals. Hum. Brain Mapp. 8, 194–208 (1999)
31. Y.-Y. Lee, S. Hsieh, Classifying different emotional states by means of EEG-based functional
connectivity patterns. PLoS ONE 9, (2014). https://doi.org/10.1371/journal.pone.0095415
32. L. Marzetti, C. Del Gratta, G. Nolte, Understanding brain connectivity from EEG data by
identifying systems composed of interacting sources. Neuroimage 42, 87–98 (2008). https://d
oi.org/10.1016/j.neuroimage.2008.04.250