142
J. W. Choi and K. H. Kim
6.7 Conclusions
In this review, we tried to describe essential factors for successful FCA using multichannel EEG (and MEG as well) time-series. After illustrating the detailed procedure
for the FCA, we presented various methods for quantifying functional connectivity. Especially, the FC measures based on oscillatory interactions among neuronal
population was described comprehensively, due to its importance for elucidating
coordinated activities of brain networks and synaptic plasticity. The problem of volume conduction and the means to diminish its confounding effects on the FCA was
thoroughly reviewed. Spatial filtering of the time-series measured on the scalp or
transformation to current densities on cortical surface, which are performed as a preprocessing for the FCA, were described. Also, we reviewed ongoing efforts toward
developing FC measures which are inherently robust to the volume conduction problem. Finally, we illustrated the procedures for determining significance of the FC
among specific pair of regions, which exploit surrogate data generation or the characteristics of event-related data. We hope that this review would provide guidelines
for the better application of the FCA and the development of novel methods.
References
1. A.R. Anwar, M. Muthalib, S. Perrey et al., Effective connectivity of cortical sensorimotor networks during finger movement tasks: a simultaneous fNIRS, fMRI, EEG study. Brain Topogr.
29, 645–660 (2016)
2. F. Babiloni, F. Cincotti, C. Babiloni et al., Estimation of the cortical functional connectivity
with the multimodal integration of high-resolution EEG and fMRI data by directed transfer
function. Neuroimage 24, 118–131 (2004)
3. L.A. Baccalá, K. Sameshima, Partial directed coherence: a new concept in neural structure
determination. Biol. Cybern. (2001). https://doi.org/10.1007/PL00007990
4. S. Baillet, J.C. Mosher, R.M. Leahy, Electromagnetic brain mapping. IEEE Signal Process.
Mag. 18, 14–30 (2001). https://doi.org/10.1109/79.962275
5. E. Barzegaran, M.G. Knyazeva, Functional connectivity analysis in EEG source space: the
choice of method. PLoS ONE 12, e0181105 (2017). https://doi.org/10.1371/journal.pone.018
1105
6. A.M. Bastos, J.-M. Schoffelen, A tutorial review of functional connectivity analysis methods
and their interpretational pitfalls. Front. Syst. Neurosci. 9, 175 (2016). https://doi.org/10.338
9/fnsys.2015.00175
7. R.L. Buckner, F.M. Krienen, B.T.T. Yeo, Opportunities and limitations of intrinsic functional
connectivity MRI. Nat. Neurosci. 16, 832–837 (2013). https://doi.org/10.1038/nn.3423
8. E. Bullmore, O. Sporns, Complex brain networks: graph theoretical analysis of structural and
functional systems. Nat. Rev. Neurosci. 10, 186–198 (2009). https://doi.org/10.1038/nrn2575
9. G. Buzsáki, A. Draguhn, Neuronal oscillations in cortical networks. Science 304, 1926–1929
(2004)
10. R.T. Canolty, R.T. Knight, The functional role of cross-frequency coupling. Trends Cogn. Sci.
14, 506–515 (2010). https://doi.org/10.1016/j.tics.2010.09.001
11. L. Canuet, R. Ishii, R.D. Pascual-Marqui et al., Resting-state EEG source localization and
functional connectivity in schizophrenia-like psychosis of epilepsy. PLoS ONE 6. https://doi.
org/10.1371/journal.pone.0027863
J. W. Choi and K. H. Kim
6.7 Conclusions
In this review, we tried to describe essential factors for successful FCA using multichannel EEG (and MEG as well) time-series. After illustrating the detailed procedure
for the FCA, we presented various methods for quantifying functional connectivity. Especially, the FC measures based on oscillatory interactions among neuronal
population was described comprehensively, due to its importance for elucidating
coordinated activities of brain networks and synaptic plasticity. The problem of volume conduction and the means to diminish its confounding effects on the FCA was
thoroughly reviewed. Spatial filtering of the time-series measured on the scalp or
transformation to current densities on cortical surface, which are performed as a preprocessing for the FCA, were described. Also, we reviewed ongoing efforts toward
developing FC measures which are inherently robust to the volume conduction problem. Finally, we illustrated the procedures for determining significance of the FC
among specific pair of regions, which exploit surrogate data generation or the characteristics of event-related data. We hope that this review would provide guidelines
for the better application of the FCA and the development of novel methods.
References
1. A.R. Anwar, M. Muthalib, S. Perrey et al., Effective connectivity of cortical sensorimotor networks during finger movement tasks: a simultaneous fNIRS, fMRI, EEG study. Brain Topogr.
29, 645–660 (2016)
2. F. Babiloni, F. Cincotti, C. Babiloni et al., Estimation of the cortical functional connectivity
with the multimodal integration of high-resolution EEG and fMRI data by directed transfer
function. Neuroimage 24, 118–131 (2004)
3. L.A. Baccalá, K. Sameshima, Partial directed coherence: a new concept in neural structure
determination. Biol. Cybern. (2001). https://doi.org/10.1007/PL00007990
4. S. Baillet, J.C. Mosher, R.M. Leahy, Electromagnetic brain mapping. IEEE Signal Process.
Mag. 18, 14–30 (2001). https://doi.org/10.1109/79.962275
5. E. Barzegaran, M.G. Knyazeva, Functional connectivity analysis in EEG source space: the
choice of method. PLoS ONE 12, e0181105 (2017). https://doi.org/10.1371/journal.pone.018
1105
6. A.M. Bastos, J.-M. Schoffelen, A tutorial review of functional connectivity analysis methods
and their interpretational pitfalls. Front. Syst. Neurosci. 9, 175 (2016). https://doi.org/10.338
9/fnsys.2015.00175
7. R.L. Buckner, F.M. Krienen, B.T.T. Yeo, Opportunities and limitations of intrinsic functional
connectivity MRI. Nat. Neurosci. 16, 832–837 (2013). https://doi.org/10.1038/nn.3423
8. E. Bullmore, O. Sporns, Complex brain networks: graph theoretical analysis of structural and
functional systems. Nat. Rev. Neurosci. 10, 186–198 (2009). https://doi.org/10.1038/nrn2575
9. G. Buzsáki, A. Draguhn, Neuronal oscillations in cortical networks. Science 304, 1926–1929
(2004)
10. R.T. Canolty, R.T. Knight, The functional role of cross-frequency coupling. Trends Cogn. Sci.
14, 506–515 (2010). https://doi.org/10.1016/j.tics.2010.09.001
11. L. Canuet, R. Ishii, R.D. Pascual-Marqui et al., Resting-state EEG source localization and
functional connectivity in schizophrenia-like psychosis of epilepsy. PLoS ONE 6. https://doi.
org/10.1371/journal.pone.0027863