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J. W. Choi and K. H. Kim
pattern. Dynamic FCA of fMRI blood oxygen level-dependent (BOLD) signals is
currently under active investigation [26].
Considering the intrinsic limitation in temporal resolution of fMRI, electrophysiological recordings of neural activity are better suited for the dynamic FCA, especially for the investigation of short-term neural phenomena with temporal resolution
of millisecond scale. Either invasive or noninvasive recording techniques can be
used for the FCA. However, noninvasive methods, i.e. electroencephalogram (EEG)
and magnetoencephalogram (MEG), are to be used for human behavioral/cognitive
neuroscience studies under experimental task or task-free resting state.
The EEG/MEG signals are obtained from an array of sensors placed on the scalp,
so the spread of electromagnetic fields prohibits direct interpretation of spatial origin of the signals from a single channel. Localization of cortical current sources is
obtained by solving an electromagnetic inverse problem [4, 33, 41], and it may be
applied prior to the FCA to investigate the connectivity between specific brain regions
[20, 46]. This may be even crucial for valid interpretation of the FCA results in that
functional connection between specific cortical regions can be identified. Source
imaging techniques using distributed source models are being combined with various measures of the FC, providing significant results on cognitive, behavioral, and
clinical results [1, 2, 11, 25, 29, 39]. The high temporal resolution of EEG/MEG
can also be utilized to investigate coupling between different rhythms in various
frequency bands present within neural activities.
The FC measures should reflect the association of neural activities in different
brain regions. Hence, they should quantify the correlation and/or causality between
the time-series of neural activities of multiple brain areas [6, 15, 42, 47]. Linear
correlation coefficient is still one of the most commonly used measure of the FCA
for fMRI. Various measures have origins from various disciplines such as statistical
signal processing, nonlinear dynamics, and information theory, and they have been
adopted for the FCA analysis in order to deal with complicated interaction between
neuronal populations [6, 15, 42, 47].
It is recognized that oscillatory neural activities represent formation of local neuronal populations [9], and underlie dynamic coordination of brain function and synaptic plasticity [6, 50, 60]. Therefore, the interaction between oscillatory rhythmic
activities should provide valuable insights on inter-regional communication among
neuronal population, and MEG and EEG are the most suitable for this purpose. Novel
measures for better analysis of the couplings between rhythmic activities are under
active research and being applied for the FCA of EEG/MEG [5, 20, 24], exploiting the
high resolution of these electrophysiological signals. Beyond coupling of rhythms
within a single frequency band, cross-frequency couplings have been explored by
quantifying either phase-phase or phase-amplitude couplings [10, 45, 59].
The purpose of this review article is to provide comprehensive and useful guidelines on the methods and to illustrate application of the FCA for EEG. Although
the target is on EEG, the contents may be useful for the FCA of MEG as well. The
focus is on how the FCA can be properly applied to cognitive neuroscience studies
J. W. Choi and K. H. Kim
pattern. Dynamic FCA of fMRI blood oxygen level-dependent (BOLD) signals is
currently under active investigation [26].
Considering the intrinsic limitation in temporal resolution of fMRI, electrophysiological recordings of neural activity are better suited for the dynamic FCA, especially for the investigation of short-term neural phenomena with temporal resolution
of millisecond scale. Either invasive or noninvasive recording techniques can be
used for the FCA. However, noninvasive methods, i.e. electroencephalogram (EEG)
and magnetoencephalogram (MEG), are to be used for human behavioral/cognitive
neuroscience studies under experimental task or task-free resting state.
The EEG/MEG signals are obtained from an array of sensors placed on the scalp,
so the spread of electromagnetic fields prohibits direct interpretation of spatial origin of the signals from a single channel. Localization of cortical current sources is
obtained by solving an electromagnetic inverse problem [4, 33, 41], and it may be
applied prior to the FCA to investigate the connectivity between specific brain regions
[20, 46]. This may be even crucial for valid interpretation of the FCA results in that
functional connection between specific cortical regions can be identified. Source
imaging techniques using distributed source models are being combined with various measures of the FC, providing significant results on cognitive, behavioral, and
clinical results [1, 2, 11, 25, 29, 39]. The high temporal resolution of EEG/MEG
can also be utilized to investigate coupling between different rhythms in various
frequency bands present within neural activities.
The FC measures should reflect the association of neural activities in different
brain regions. Hence, they should quantify the correlation and/or causality between
the time-series of neural activities of multiple brain areas [6, 15, 42, 47]. Linear
correlation coefficient is still one of the most commonly used measure of the FCA
for fMRI. Various measures have origins from various disciplines such as statistical
signal processing, nonlinear dynamics, and information theory, and they have been
adopted for the FCA analysis in order to deal with complicated interaction between
neuronal populations [6, 15, 42, 47].
It is recognized that oscillatory neural activities represent formation of local neuronal populations [9], and underlie dynamic coordination of brain function and synaptic plasticity [6, 50, 60]. Therefore, the interaction between oscillatory rhythmic
activities should provide valuable insights on inter-regional communication among
neuronal population, and MEG and EEG are the most suitable for this purpose. Novel
measures for better analysis of the couplings between rhythmic activities are under
active research and being applied for the FCA of EEG/MEG [5, 20, 24], exploiting the
high resolution of these electrophysiological signals. Beyond coupling of rhythms
within a single frequency band, cross-frequency couplings have been explored by
quantifying either phase-phase or phase-amplitude couplings [10, 45, 59].
The purpose of this review article is to provide comprehensive and useful guidelines on the methods and to illustrate application of the FCA for EEG. Although
the target is on EEG, the contents may be useful for the FCA of MEG as well. The
focus is on how the FCA can be properly applied to cognitive neuroscience studies