Introduction
Numerous studies have shown that transcranial electrical stimulation (tES) can modulate
a wide-range of behavioral processes (Coffman et al., 2014; Harty et al., 2014; Sarkar
et al., 2014; Pasqualotto, 2016), and ameliorate deficits in several neuropsychiatric
disorders (for reviews see Kekic et al., 2016; Lefaucheur et al., 2017). These promising
outcomes, in conjunction with the fact that the approach is safe and inexpensive,
have generated enthusiasm for its viability as both an investigative and neuroenhancement
tool. However, concerns about the variability and reproducibility of tES effects have
constrained progression with its application (Jacobson et al., 2012; Berlim et al.,
2013; Horvath et al., 2015). Many factors may contribute to the variability and poor
reproducibility of findings. Some of these have already been discussed elsewhere such
as insufficient statistical power, methodological differences across studies, experimenter
error, inadequate sensitivity and test-retest reliability of the outcome measures
(Horvath et al., 2015; Open Science Collaboration, 2015). However, one factor that
we believe has received insufficient consideration to date concerns the extent to
which the assumptions relating to the targeted brain region are supported (Bikson
and Rahman, 2013; Miniussi et al., 2013; Plewnia et al., 2015; Harty et al., 2017).
In the present article, we highlight the importance of accounting for states and traits
of the neurophysiological milieu when assessing the effects of interventions such
as tES on behavior. We present hypothetical scenarios relating to the use of transcranial
direct current stimulation (tDCS), but the discussed logic equally applies to other
electrical and magnetic stimulation techniques. We additionally propose that mediation
and moderation analyses constitute valuable and elegant statistical approaches for
assessing the dynamic interaction between these interventions, the brain, and behavior.
A fundamental assumption of tDCS research
The primary objective of most tDCS studies is to establish an association between
the application of weak electric currents to specified locations on the scalp and
changes in a behavioral index of interest. An implicit assumption of this approach
is that the electric currents modulate neural activity in the regions beneath the
scalp locations and accordingly affect behaviors supported by these neural regions.
A corollary of this assumption has been that the efficacy of tDCS for modulating behavior
has typically been evaluated by assessing the direct effect of tDCS (Active vs. Sham)
on the behavior of interest (Figure 1A, top panel). A limitation of this approach
is that it disregards the fact that the impact of tDCS on behavioral outcomes will
inevitably depend on how the neurophysiological milieu of each individual responds
to the tDCS. This is a particularly pertinent consideration given the growing literature
demonstrating how various states and traits of the neurophysiological milieu can influence
the impact of tDCS on behavior (Krause and Cohen Kadosh, 2014; Li et al., 2015). Accordingly,
both tDCS and the neurophysiological milieu should be regarded as critical antecedents
to tDCS-related behavioral effects. Given the pivotal role of the neurophysiological
milieu in determining tDCS-related behavior outcomes, we propose that relevant neurophysiological
measures should be acquired and accounted for more routinely when examining the efficacy
of tDCS for modulating a given behavior. We furthermore advance that the mediating
and moderating roles of the neurophysiological milieu can be efficiently evaluated
using mediation and moderation analyses (Hayes, 2009).
Figure 1
Schematics for mediation and moderation analyses. (A) Upper panel: A linear regression
examining the relationship between transcranial direct current stimulation (tDCS;
Active vs. Sham) and the behavioral outcome measure (c path). One can proceed to the
analyses in the lower panel regardless of whether a significant relationship is observed
here. Lower panel: The effect of tDCS condition on the neurophysiological index is
evaluated with a linear regression (a path). The relationship between the neurophysiological
index and the outcome variable is evaluated with another linear regression, which
also includes the tDCS condition as a predictor (b path). The effect of tDCS condition
on the outcome variable is re-evaluated using a linear regression that also includes
the neurophysiological mediator as a predictor (c' path) The bar chart for the c'
path represents the adjusted means when the impact of the neurophysiological mediator
is controlled for. Finally, the mediation hypothesis is evaluated through one of the
three approaches outlined in the main text; (B) Upper panel: A linear regression examining
the relationship between tDCS condition and the behavioral outcome measure (c path).
Lower panel: In accordance with standard convention for moderation analyses (Aiken
and West, 1991), the estimated value of the outcome variable for each condition is
reported at the mean, one standard deviation below the mean and one standard deviation
above the mean, of the proposed moderating variable. This example shows a significant
moderation effect: the impact of the tDCS condition on the behavioral outcome changes
according to the value of the neurophysiological index (i.e., the moderator). Note
that the heatmap shown for the neurophysiological index in both (A,B) is for illustrative
purposes only, and does not reflect neural activity obtained from a neurophysiological
assay.
Conceptual overview of mediation and moderation analyses
Mediation analysis
Mediation analysis is a form of regression that can be used to simultaneously evaluate
the direct effect of tDCS on behavior and the indirect effect of stimulation on behavior
through the brain. In the simplest version of this statistical model, the tDCS condition
(e.g., Active vs. Sham) is the independent variable, an implicated brain index constitutes
the mediating variable, and the behavioral index of interest constitutes the outcome
variable (Figure 1A).
First, we determine whether there is a significant difference in the behavioral index
for each tDCS condition (called c path), as indexed by simple linear regression. This
relationship represents the direct effect of stimulation on behavior, and the majority
of tDCS studies to date have focused solely on this bivariate relationship. Second,
we investigate whether there is a significant difference in the brain index for each
tDCS condition (called a path). A significant relationship here implies that the implicated
brain index was significantly modulated by tDCS. Next, we evaluate whether the brain
index (mediating variable) is a significant predictor of the behavioral index (b path)
when tDCS condition is also included in the model (called c' path).
Finally, the mediation hypothesis is evaluated. The three most common approaches for
determining whether there is a mediation effect are the following: (1) establish that
the regression coefficients for the a path and the b path are both significant different
from zero (test of joint significance; Kenny et al., 1998); (2) use bootstrapping
with replacement to derive a distribution of the product of the a path and b path
regression coefficients, and confirm that the 95% confidence intervals of the distribution
do not overlap zero (Hayes, 2009; Mackinnon and Fairchild, 2009); or (3) determine
that the product of the regression coefficients from the a path and b path is significantly
different from zero when evaluated using the Sobel test (for details, see Sobel, 1986).
If a mediation effect is established, it can be claimed that the proposed mediating
brain index mediates the relationship between tDCS condition and behavior.
To underscore the value of measuring theoretically implicated neural indices and including
them in mediation analyses, we provide the following simplified hypothetical research
scenario. Let us assume that we are interested in determining the effect of tDCS applied
to the dorsolateral prefrontal cortex (dlPFC) on working memory, which is assumed
to rely on the dlPFC (Brunoni and Vanderhasselt, 2014). The prevailing approach to
determine an effect in this context would be to examine the effect of tDCS (Active
vs. Sham) on working memory performance using some form of a bivariate analysis. This
analysis may or may not reveal a tDCS-related change in working memory performance.
The lack of a behavioral change will likely leave us pondering several different possibilities
about why no effect was observed. For example, we might wonder whether or not we succeeded
to stimulate the targeted brain region. And, if not, was this attributable to one
of the many parameters of the tDCS protocol (e.g., intensity or duration of the stimulation,
size or location or the electrodes) being unsuitable? We might also have doubts regarding
our assumption about the involvement of the targeted region to begin with. We might
question whether our potential to pick up on a main effect was hampered by variability
in the flow and distribution of the electric current across subjects, or by one of
the many other inter-individual differences that are known to affect responsiveness
to tDCS (e.g., Krause and Cohen Kadosh, 2014; Li et al., 2015). Similarly, we might
contemplate whether different individuals within the study sample could have employed
different cognitive strategies, and in turn different brain regions, to carry out
the task. Variation in strategy use is a particularly pertinent consideration when
appraising the effects of tDCS on behavior as we know that the currents involved in
tDCS will not elicit neural firing. Rather, they only modulate the likelihood of firing
within populations of neurons that are already naturally engaged by ongoing activity.
Therefore, any tDCS-related effects on behavior are critically contingent on subjects'
intrinsic recruitment of the target brain region to perform the task. We are thus
left with several different questions that cannot be resolved when behavioral indices
are our only outcome measure.
In contrast, by quantifying the response of the dlPFC to tDCS with an appropriate
neurophysiological assay (e.g., pre- to post- change in blood-oxygen level-dependent
(BOLD) response), and including this in a mediation analysis we can gain insights
to inform many of these questions. For instance, the assumption about the role of
the dlPFC in working memory, the assumption that the employed tDCS protocol is successfully
modulating this area, and the extent to which this is common across subjects can all
be verified. It is important to underscore that an initial significant direct effect
(c path) is not a critical requisite for advancing with a mediation analysis (Hayes,
2009). For instance, we may not observe a direct effect of tDCS on working memory,
but by pursuing with the mediation analysis we may find that tDCS was associated with
an increase in activity in the dlPFC (a path) and this change in activity was in turn
associated with an improvement in working memory (b path). If we substantiate the
mediation hypothesis through one of the aforementioned approaches, we can formally
infer that the tDCS-related change in dlPFC activity mediated the tDCS-related change
in working memory. This hypothetical example serves to demonstrate how it is imperative
to be cautious about drawing conclusions about the efficacy of tDCS for modulating
behavior with the prevailing bivariate analyses. This point is particularly relevant
when only small to medium sample sizes are under question, which has been the case
for the vast majority of tDCS studies to date. Furthermore, this example highlights
how the systematic assessment of theoretically implicated brain indices and their
inclusion in a mediation model could reduce the chances of spurious conclusions in
tDCS research.
Moderation analysis
Moderation analysis is also a form of regression analysis, but here the objective
is to determine whether the relationship between the independent and dependent variables
changes as a function of a third variable (i.e., statistical interaction), known as
the moderator (Figure 1B). Thus, while mediation analyses can provide insight on how
behavioral effects are achieved (e.g., a change in activity within the neurophysiological
milieu), moderation analyses can determine particular conditions for which the effects
will hold. In the context of the hypothetical experiment described above, it is plausible
that an effect of tDCS, or lack thereof, on working memory performance may be driven
by a subset of subjects who had particular baseline neurophysiological characteristics,
such as, for example, lower than average gray matter (GMD) density in the dlPFC. Here,
moderation analyses could provide an elegant unified framework for demonstrating that
the relationship between tES and behavior is moderated by individual differences in
the GMD of the targeted region. Accordingly, we would be able to make a more refined
interpretation regarding the efficacy of tDCS: the reported effect of tDCS applied
over dlPFC on working memory was particular to a select group of individuals with
low GMD in the target region. Identifying these kinds of caveats has important implications
for the translational potential of tDCS research and the development of individualized
protocols.
In summary, most tDCS research is based on the assumption that weak direct currents
applied to the scalp will stimulate the underlying brain regions, resulting in a detectable
change in associated behavioral indices. However, we have argued that this and other
assumptions need to be formally verified by acquiring data regarding the actual states
and traits of the targeted neural region. We suggest that the inclusion of theoretically
implicated neurophysiological indices in mediation and moderation models constitute
valuable approaches for enhancing the inferential power of tDCS research, by revealing
how and for whom tDCS is effective. Exploiting these approaches should also yield
information for guiding the design of more effective and personalized tDCS protocols.
More generally, the nuanced insights that these approaches afford should reduce the
likelihood of spurious conclusions, and accordingly improve the prospects for reproducibility
in the field.
On a final note, mediation and moderation analysis can be readily implemented using
open source plug-ins for common statistical software packages such as SAS, SPSS (e.g.,
Process by Hayes, 2013, MEMORE by Montoya and Hayes, 2017), and R (e.g., The Lavaan
package; Rosseel, 2012). In addition to the basic forms of the mediation and moderation
models we discussed here, these plug-ins provide other analysis templates with varying
levels of complexity, including moderated mediation and mediated moderation, as well
as options to incorporate multiple mediators, moderators and covariates.
Author contributions
SH wrote the article and helped to conceive the opinion. FS helped to conceive the
opinion and provided feedback on drafts of the article. RK helped to conceive the
opinion and provided feedback on drafts of the article.
Funding
This work was supported by grants from the James S. McDonnell Foundation 21st Century
Science Initiative in Understanding Human Cognition and the European Research Council
(Learning and Achievement; 338065).
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.