The integration of information between functionally specialized and widely distributed
brain regions (i.e., connectivity) is fundamentally important for cognition (Bressler
and Menon, 2010), and can be estimated using electroencephalography (EEG) (Horwitz,
2003). However, the interpretation of connectivity measures from sensor-level EEG
recordings is not straightforward. Instead, the neuroanatomical sources of recorded
sensor-level data can be reconstructed by first finding the scalp potentials that
result from hypothetical current distributions inside the head (i.e., the forward
problem), then applying this to the actual EEG data to estimate back the sources that
fit the measurements (i.e., the inverse problem). Several publicly available software
packages have been developed for analysis of source-level EEG data. There exist many
different models for solving the forward and inverse problems, and these can be implemented
using several analysis packages. For source-level EEG to be a reliable tool for measuring
connectivity, source estimates should be consistent across these different combinations
of commonly used model parameters and software packages. If not, this would have important
implications for reproducibility of EEG studies reconstructing brain activation and
connectivity from neuronal sources.
Recently, Mahjoory et al. (2017) investigated the consistency of source localization
and connectivity estimates across a host of widely used analysis pipelines. They tested
14 pipelines consisting of different combinations of (1) software packages including
Brainstorm, FieldTrip and Berlin Toolbox; (2) inverse models including weighted minimum-norm
estimate (WMNE), exact low resolution electromagnetic tomography (eLORETA) and linearly
constrained minimum-variance (LCMV); and (3) forward models including boundary element,
finite element and spherical harmonics expansions methods. Resting EEG data (eyes
closed) was recorded from 65 healthy subjects as part of two different experiments;
one on attentional processes (Fasor data) and the other as part of a brain-computer
interface study (Würzburg data). In the latter, two sessions on separate days were
conducted per subject. Source localization and connectivity estimates were computed
for alpha-band (8–13 Hz) oscillations. Connectivity measures included imaginary coherence
(Nolte et al., 2004), reflecting temporal correlations of neural activity between
brain areas (i.e., functional connectivity), and phase slope index (Nolte et al.,
2008), reflecting directed interactions (i.e. effective connectivity). To evaluate
consistency across pipelines they computed grand-average correlations between localization
or connectivity results for all pairs of pipelines. To evaluate between-study consistency
they computed grand-average correlations between results of the two experiments for
each pipeline. To evaluate within-subjects consistency they computed correlation between
the two sessions of the Würzburg experiment for each subject and pipeline then averaged
across subjects. To evaluate between-subjects consistency they computed correlations
between datasets of distinct subjects separately for the Fasor and Würzburg experiments
then averaged across pairs of subjects.
Mahjoory et al. (2017) showed that: connectivity patterns between EEG electrodes vary
depending on the choice of electrical reference, supporting the use of source reconstruction
for connectivity analyses. Source localization estimates, mapped onto cortical surface,
have a smaller maximum and are more focally concentrated in the occipital region when
LCMV is used than when eLORETA or WMNE is used. Patterns of interactions estimated
from the reconstructed sources vary when different inverse methods are used. Across
all pipelines source localization estimates are more consistent than functional connectivity
estimates, followed by effective connectivity estimates. Average correlation across
different combinations of forward models is higher than when varying inverse methods
or software packages. For source localization and connectivity, correlation between
WMNE and eLORETA based estimates exceeds correlation between LCMV and either eLORETA
or WMNE based estimates. Source localization and connectivity results are most consistent
between-studies, followed by within-subjects and then between-subjects. Between-study,
within-subjects and between-subjects consistencies are highest for source localization
estimates followed by functional and then effective connectivity estimates. Between-study,
within-subjects and between-subjects consistencies, computed at the sensor-level,
are in general similar to the average source-level results.
There are several important issues to bear in mind when considering the consistency
of EEG measures in the source-space, not least of which is the physiological state
of participants at the time of recording. The resting condition is a challenging state
for source-level analysis, and likely represents a major source of inconsistency in
the source localization and connectivity outcomes. Related to this is the arousal
state of the subjects. This has been shown to affect physiological significance of
source localization and alter connectivity estimates using various methods and modalities
(Kaufmann et al., 2005; Massimini et al., 2005; Murphy et al., 2009; Ventouras et
al., 2010). Therefore particularly important for within-subjects and between-subjects
analysis, variability in the subjects' arousal state likely account for some of the
variability in the estimates.
Based on the findings of Mahjoory et al. (2017), we highlight the importance of utilizing
a priori assumptions based on physiological information determined using other modalities.
Magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) are
non-invasive recording techniques used to study human brain activity. It is suggested
that combining EEG with these modalities may produce more accurate source localization
estimates than either modality alone (Liu et al., 2002; Groening et al., 2009), presumably
improving the consistency of source localization and connectivity estimates. Additionally,
transcranial magnetic stimulation (TMS) is a technique that enables non-invasive manipulation
of neural activity. TMS combined with EEG can be used to provide priors for EEG connectivity
analysis and validate the connectivity results (Bortoletto et al., 2015).
There are several limitations of Mahjoory et al. (2017), many of which were addressed
in the original work. However, one additional point to consider is the statistical
approach for assessing consistency. In Mahjoory et al. (2017) consistency is quantified
by reporting Pearson correlation coefficient. However, other measures may be suited
to estimate consistency, such as intraclass correlation coefficient or the standard
error of measurement (Bédard et al., 2000).
Mahjoory et al. (2017) presented the first comprehensive assessment of consistency
of EEG source localization and connectivity estimates across widely used forward and
inverse methods. Their study is an important contribution toward a consensus about
source-level methodologies. Their findings highlight the need to take caution when
interpreting source-level outcomes, particularly in clinical settings. However, this
should not discourage researchers from studying EEG recordings in the source-space.
Author contributions
All authors listed have made a substantial, direct and intellectual contribution to
the work, and approved it for publication.
Conflict of interest statement
The authors declare that the research was conducted in the absence of any commercial
or financial relationships that could be construed as a potential conflict of interest.