Chapter 6
Methods for Functional Connectivity
Analysis
Jeong Woo Choi and Kyung Hwan Kim
Abstract The purpose of this chapter is to provide comprehensive and useful guidelines for the methods of the functional connectivity analysis (FCA) for electroencephalogram (EEG) and its application. After presenting the detailed procedure for
the FCA, we described various methods for quantifying functional connectivity. The
problem of volume conduction and the means to diminish its confounding effects on
the FCA was thoroughly reviewed. As a useful preprocessing for the FCA, spatial filtering of the time-series measured on the scalp or transformation to current densities
on cortical surface were described. We also 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.
6.1 Introduction
Cognition and behavior is enabled by coordinated and integrated activities of neuronal populations of relevant regions in the brain. Beyond spatial and temporal pattern
of brain activation, investigating the interaction between those neuronal populations,
i.e., the functional connectivity analysis (FCA), is essential for proper understanding
of human brain function [6, 17, 49, 56]. Now the FCA is regarded as one of the major
tools for functional brain imaging.
In functional neuroimaging studies, mainly using functional magnetic resonance
imaging (fMRI), intrinsic cortical networks such as default mode and saliency networks, have been identified during both resting state and task performance by the
FCA [7, 17, 55]. The functional brain network is obviously dynamic although most
fMRI-based FCA studies so far implicitly assumed static functional connectivity
J. W. Choi · K. H. Kim (B)
Department of Biomedical Engineering, Yonsei University, Wonju, South Korea
e-mail: khkim0604@yonsei.ac.kr
© Springer Nature Singapore Pte Ltd. 2018
C.-H. Im (ed.), Computational EEG Analysis, Biological and Medical Physics,
Biomedical Engineering, https://doi.org/10.1007/978-981-13-0908-3_6
125
Methods for Functional Connectivity
Analysis
Jeong Woo Choi and Kyung Hwan Kim
Abstract The purpose of this chapter is to provide comprehensive and useful guidelines for the methods of the functional connectivity analysis (FCA) for electroencephalogram (EEG) and its application. After presenting the detailed procedure for
the FCA, we described various methods for quantifying functional connectivity. The
problem of volume conduction and the means to diminish its confounding effects on
the FCA was thoroughly reviewed. As a useful preprocessing for the FCA, spatial filtering of the time-series measured on the scalp or transformation to current densities
on cortical surface were described. We also 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.
6.1 Introduction
Cognition and behavior is enabled by coordinated and integrated activities of neuronal populations of relevant regions in the brain. Beyond spatial and temporal pattern
of brain activation, investigating the interaction between those neuronal populations,
i.e., the functional connectivity analysis (FCA), is essential for proper understanding
of human brain function [6, 17, 49, 56]. Now the FCA is regarded as one of the major
tools for functional brain imaging.
In functional neuroimaging studies, mainly using functional magnetic resonance
imaging (fMRI), intrinsic cortical networks such as default mode and saliency networks, have been identified during both resting state and task performance by the
FCA [7, 17, 55]. The functional brain network is obviously dynamic although most
fMRI-based FCA studies so far implicitly assumed static functional connectivity
J. W. Choi · K. H. Kim (B)
Department of Biomedical Engineering, Yonsei University, Wonju, South Korea
e-mail: khkim0604@yonsei.ac.kr
© Springer Nature Singapore Pte Ltd. 2018
C.-H. Im (ed.), Computational EEG Analysis, Biological and Medical Physics,
Biomedical Engineering, https://doi.org/10.1007/978-981-13-0908-3_6
125