ArticlePDF Available

Abstract and Figures

It is debated whether cognitive training of specific executive functions leads to far transfer effects, such as improvements in fluid intelligence (Gf). Within this context, transcranial direct current stimulation and recently also novel protocols such as transcranial random noise and alternating current stimulation are being investigated with regards to their ability to enhance cognitive training outcomes. We compared the effects of four different transcranial electrical brain stimulation protocols in combination with nine daily computerized training sessions on Gf. 82 participants were randomly assigned to receive transcranial direct current stimulation (tDCS), random noise stimulation (tRNS), multifocal alternating current stimulation at 40Hz (mftACS), or multifocal tDCS (mftDCS) in combination with an adaptive and synergistic executive function (EF) training, or to a no-contact control group. EF training consisted of gamified tasks drawing on isolated as well as integrated executive functions (working memory, inhibition, cognitive flexibility). Transfer was assessed with a combined measure of Gf including three established tests (Bochumer Matrizentest - BOMAT, Raven's Advanced Progressive Matrices - RAPM, and Sandia Matrices). We found significant improvements in Gf for the tDCS, mftDCS, and tRNS groups when compared with the no-contact group. In contrast, the mftACS group did not improve significantly and showed a similar pattern as the no-contact group. Mediation analyses indicated that the improvement in Gf was mediated through game progression in the mftDCS and tRNS group. Electrical brain stimulation in combination with sustained EF training can lead to transfer effects in Gf, which are mediated by training progression.
Content may be subject to copyright.
1
Research Article
Modulating fluid intelligence performance through combined cognitive training and
brain stimulation
Anna-Katharine Brema, b, 1, Jessamy Norton-Ford Almquistc, Karen Mansfielda, Franziska
Plessowb,2, Francesco Sellaa, Emiliano Santarnecchib, Umut Orhanc, James McKannad, Misha
Paveld, Santosh Mathanc, Nick Yeunga, Alvaro Pascual-Leoneb, Roi Cohen Kadosha, on
behalf of Honeywell SHARP Team authorsa,b,c,d,e
aDepartment of Experimental Psychology, University of Oxford, Oxford, United Kingdom
bBerenson-Allen Center for Noninvasive Brain Stimulation and Division for Cognitive
Neurology, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard
Medical School, Boston, MA, United States
cHoneywell Labs, Honeywell Aerospace, Redmond, WA, United States
dElectrical and Computer Engineering Department, Northeastern University, Boston, MA,
United States
eSimcoach Games, Pittsburgh PA, USA
Honeywell SHARP Team Authors: Oxford: Anna-Katharine Brem, Roi Cohen Kadosh,
Karen Mansfield, Nick Yeung; Harvard Medical School: Franziska Plessow, Emiliano
Santarnecchi, Alvaro Pascual-Leone; Honeywell: Jessamy Norton-Ford Almquist, Michael
Dillard, Umut Orhan, Santosh Mathan; Northeastern University: James McKanna, Deniz
Erdogmus, Misha Pavel; Simcoach Games: Garrett Kimball, Eben Myers
1"Present address: Max-Planck Institute of Psychiatry, Munich, Germany
2 Present address: Neuroendocrine Unit, Massachusetts General Hospital, Boston, MA, USA;
Department of Medicine, Harvard Medical School, Boston, MA, USA
2
Corresponding authors: Anna-Katharine Brem and Roi Cohen Kadosh
Anna-Katharine Brem
Max-Planck Institute of Psychiatry, Department of Neuropsychology, Kraepelinstrasse 2-10,
80804 Munich, Germany
Fax: +49 89 30622 605 Phone: +49 89 30622 285
E-mail address: anna-katharine_brem@psych.mpg.de
Roi Cohen Kadosh
Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom
Phone: +44 1865 271 385
E-mail address: roi.cohenkadosh@psy.ox.ac.uk
Words in abstract: 247
Words in the manuscript: 4658
Number of figures: 3
Number of tables: 1
3
ABSTRACT
It is debated whether cognitive training of specific executive functions leads to far transfer
effects, such as improvements in fluid intelligence (Gf). Within this context, transcranial
direct current stimulation and recently also novel protocols such as transcranial random noise
and alternating current stimulation are being investigated with regards to their ability to
enhance cognitive training outcomes.
We compared the effects of four different transcranial electrical brain stimulation protocols in
combination with nine daily computerized training sessions on Gf.
82 participants were randomly assigned to receive transcranial direct current stimulation
(tDCS), random noise stimulation (tRNS), multifocal alternating current stimulation at 40 Hz
(mftACS), or multifocal tDCS (mftDCS) in combination with an adaptive and synergistic
executive function (EF) training, or to a no-contact control group. EF training consisted of
gamified tasks drawing on isolated as well as integrated executive functions (working
memory, inhibition, cognitive flexibility). Transfer was assessed with a combined measure of
Gf including three established tests (Bochumer Matrizentest - BOMAT, Raven’s Advanced
Progressive Matrices - RAPM, and Sandia Matrices). We found significant improvements in
Gf for the tDCS, mftDCS, and tRNS groups when compared with the no-contact group. In
contrast, the mftACS group did not improve significantly and showed a similar pattern as the
no-contact group. Mediation analyses indicated that the improvement in Gf was mediated
through game progression in the mftDCS and tRNS group. Electrical brain stimulation in
combination with sustained EF training can lead to transfer effects in Gf, which are mediated
by training progression.
Keywords (max 6 keywords): Transcranial electrical stimulation (tES); cognitive training;
fluid intelligence; cognitive enhancement; executive functions.
4
1. INTRODUCTION
Fluid intelligence (Gf), first defined by Cattell (1963), is the ability to cope with novelty, to
think rapidly and flexibly, to see relations amongst items independent of acquired knowledge
and is predictive of important life outcomes such as income, work performance, and health
(Au et al., 2014; Sternberg, 2012). Gf is theorized to draw on neural processes that overlap
with executive functions (EFs) such as working memory, inhibition, and cognitive flexibility
(Bastian and Oberauer, 2014; Burgess et al., 2011; Colzato et al., 2006; Diamond, 2013;
Witthöft et al., 2009) as well as the broader concept of creativity (Benedek et al., 2014), and
has hence inspired researchers to explore interventions aimed at improving Gf via executive
function pathways.
Cognitive training of individual EFs such as response inhibition (Enge et al., 2014) or
working memory (e.g., Jaeggi et al., 2008; Redick et al., 2013; Rudebeck et al., 2012) has
yielded diverging results with regards to transfer effects to Gf in healthy subjects. Recent
meta-analyses mirror these inconsistent findings in the domain of working memory. While
some (Au et al., 2014) support far transfer effects of working memory training to Gf, others
conclude that working memory training does not “generalize to measures of “real-world”
cognitive skills” (Melby-Lervåg et al., 2016). Inconsistent findings have been suggested to
partly result from methodological issues (Shipstead et al., 2012) as well as the aggregation of
a broad range of test and training tasks. Notably, reported effect sizes for far transfer to Gf are
mostly small, which leaves the question of how to optimize cognitive training in order to
achieve greater transfer effects. One approach is the adaptation of training protocols to imitate
more life-like tasks, which commonly require the engagement of several cognitive functions
simultaneously. For example, specific EFs are rarely used singularly, but mostly in
conjunction with other cognitive functions (Taatgen, 2013). Therefore, training regimes
should ideally train multiple functions concomitantly in an effort to imitate real-life
challenges.
5
One potential tool to enhance training outcomes and promote transfer effects is transcranial
electrical stimulation (tES). The most prominent form of tES is transcranial direct current
stimulation (tDCS), and the latest additions are transcranial alternating current (tACS) and
random noise stimulation (tRNS). These techniques do not induce neuronal firing, but rather
modulate brain excitability and task-related neuronal activity, which in turn is thought to lead
to neuroplastic changes, and hence physiological mechanisms that are similar to those
involved in learning (Cooke and Bliss, 2006; Paulus, 2004; Pelletier and Cicchetti, 2015).
Synergistic effects might therefore arise through the combination of cognitive training and
tES. A number of studies support the assumption that noninvasive brain stimulation during
cognitive training can enhance cognitive functions in healthy subjects (for an overview, see
Krause and Cohen Kadosh, 2013; Santarnecchi et al., 2015). These effects might be attributed
to stimulation of brain regions that become active during the cognitive training, and therefore
to the ability to impact the neuronal substrates of the desired cognitive function more
effectively based on the idea of state-dependent brain stimulation (Cohen Kadosh et al., 2010;
Feurra et al., 2013; Romei et al., 2016; Silvanto et al., 2007). Moreover, a recent meta-
analysis stated that stimulation effects are stronger for training than for performance
(Simonsmeier et al., 2018), and theoretical frameworks have been proposed to account for
functional enhancement as well as costs (Brem et al., 2014).
Most tES approaches so far have used two stimulation electrodes. However, to increase focus
on single brain regions as well as targeting multiple regions of a network, devices are
available that allow simultaneous stimulation of multiple brain areas with multiple electrodes
(Ruffini et al., 2014). These multifocal protocols differ from recently developed high-
definition protocols in that they are not trying to increase focality by suppressing activity in
surrounding areas of the region of interest. Furthermore, in comparison to “classic” protocols
such as the bifocal tDCS montage, they do not only focus on one brain area that is known to
be implicated in a targeted function, but try to modulate a whole network associated with it
6
and therefore imitate “natural” network activation. The optimization of stimulation protocols
poses a challenge in this research field. Here we have exploited these recent developments
and investigated the combined effects of different tES protocols in combination with
cognitive training on Gf.
We compared four different stimulation protocols in their efficiency in enhancing Gf
performance relative to a no-contact group (NC): bifocal tDCS (tDCS), bifocal tRNS (tRNS),
multifocal tDCS (mftDCS), and multifocal tACS (mftACS), the latter two being novel
protocols developed on the basis of functional imaging literature related to EF (see Methods).
Note that our main goal was to compare the different stimulation protocols. The tES methods
were applied in combination with a long-term (9 sessions) cognitive training approach
integrating several EFs concomitantly. We hypothesised that progression in the game would
predict Gf post, however, we did not preview a hypothesis regarding the predominance of any
of the stimulation protocols.
7
2. MATERIAL AND METHODS
2.1. Subjects and study design
In this randomized, controlled, single-blind study eighty-seven healthy subjects (out of
ninety-eight enrolled) completed all study procedures. Eleven subjects were excluded from
the analysis due to non-compliance with the test administration on at least two independent
measures (i.e., >2 standard deviations). The attrition rate (n =11) was similar across
stimulation groups (before intervention: n =6; during intervention: mftACS: n=1, mftDCS: n
=1, bitDCS: n =2; bitRNS: n =1). The study took place at two difference sites (University of
Oxford and Harvard). The 82 (Oxford N=45; Harvard N=37) were randomized to receive
tDCS [n =17, age 28.41±11.58; 11 males], mftDCS [n =15, age 27.88±11.58; 9 males], tRNS
[n = 16, age 29.19±10.39; 9 males], or mftACS [n = 17, age 30.73±13.17; 9 males] combined
with cognitive training. In addition we included a NC group [n = 17, age 30.88±13.30; 8
males]. A stratified randomization process taking into account age, education, and gender was
applied to control for the influence of these baseline characteristics. Subjects were
remunerated for their participation (£10 or $15 per hour in the UK and the US respectively).
Subjects with any current or past history of psychiatric illness, unstable medical condition,
epilepsy or family history of epilepsy, and active or past substance abuse considered a
potential hazard for the application of tES were precluded from participating in the study.
Subjects were instructed to sleep at least 6 hours each night, abstain from alcohol during the
entire study duration, and refrain from caffeine for 1 hour before study visits. The respective
ethics committees (Oxford: NRES Committee South Central Berkshire; Harvard: CCI/IRB,
Beth Israel Deaconess Medical Center) approved the study and all participants gave written
informed consent prior to study onset according to the Declaration of Helsinki.
The study design is depicted in Fig. 1. We compared multiple types of stimulation protocols
with a NC group in order to assess the most effective regimen. Each training participant
8
received 9 cognitive training sessions on consecutive days (except weekends), each lasting 30
minutes, combined with one of the four tES protocols. Transfer effects were assessed with a
combined measure of three established Gf tests, which were assessed before and after the
training in a non-randomized approach with the following test sequence: the Bochumer
Matrizentest (BOMAT; Hossiep et al., 1999), Raven’s Advanced Progressive Matrices
(RAPM(Raven et al., 1998), and Sandia (www.sandia.gov; Matzen et al., 2010).
Subjects were pretested within 2 weeks before the start of the intervention and post-tested on
the day after the last training visit.
Figure 1. Cognitive training intervention and study design. A: Cognitive training
intervention: The intervention setup is depicted in the top middle panel. Easier cognitive
training blocks (top left panel) focus on cues for a single EF task (in this case, matching n-
back on numbers). The most difficult blocks (top right panel) contain three EF tasks; in this
case, using the background colour to determine when to switch between tasks (switching
component) involving matching n-back on number (working memory component) and
inhibiting on pictures of flying things that are not birds (inhibition component with a semantic
operator). B: Study Design: After consenting and randomization, subjects came in for the pre-
test followed by 9 sessions of combined cognitive training and stimulation and the posttest.
Subjects randomized to the no-contact control group attended the pre- and posttest sessions
only.
Pre-test
- Fluid intelligence:
BOMAT
Sandia
Raven's APM
Cognitive training combined
with stimulation
- 9 sessions (30 minutes)
- 5 groups: tRNS, tDCS,
mftDCS, mftACS, NC
Consent
Randomization
Cognitive
training
Post-test
- Fluid intelligence:
BOMAT
Sandia
Raven's APM
A
B
9
2.2. Transcranial electrical stimulation
Current was delivered via gel-filled pi-electrodes (3.14 cm2), which were inserted into a
neoprene cap (Starstim®, Neuroelectrics, Barcelona, Spain) in accordance to the international
10-20 EEG system. In all protocols 8 electrodes were mounted (Fig. 2) and spare positions
were used to collect EEG data. Stimulation onset coincided with training onset and the
current was ramped up and down for the first and last 30 s of stimulation. Participants were
monitored at all times to ensure their safety.
TDCS and tRNS are already established stimulation protocols. The chosen parameters were
hence based on previous literature that has shown enhancement of various cognitive functions
with the specific montages used in the present study (i.e., F3-Fp2 for tDCS and F3-F4 for
tRNS), including effects on functions that are strongly related to Gf (see Figure 2 for
stimulation details and (Santarnecchi et al., 2015) for a review on tES to enhance cognition).
The amplitude of 1 mA for tRNS implies that 99% of the amplitude values were between +/-
500 µA(Terney et al., 2008). Alternating current stimulation for mftACS was sinusoidal and
set to 40 Hz as this frequency was previously shown to modulate complex problem solving
(Santarnecchi et al., 2016). The multifocal stimulation templates (mftDCS, mftACS; Fig. 2)
were newly developed for the present study through Activation Likelihood Estimation (ALE)
analysis (Eickhoff et al., 2012; Turkeltaub et al., 2012) and based on a meta-analysis of
imaging literature investigating the three major EFs engaged during FAST with the following
tasks: 1) inhibition (go-nogo task, stop-signal task), cognitive flexibility (task switching), and
working memory (n-back task, AX version of the continuous performance test). The literature
on these functions suggests a more distributed pattern of activation, usually involving both
frontal and parietal lobes bilaterally. Studies that did not report MNI/Talairach coordinates, or
provided an insufficient description of results or experimental paradigms, were excluded. An
average map of all activation foci for each EF was created and a threshold was applied using
a False Discovery Rate of .05 (cluster mm3=1000), assuming independence of foci/studies.
This average map was used as a seed-region in a following functional connectivity analysis.
10
As expected, a fronto-parietal network was obtained by averaging the fMRI activation maps
for each EF and provided the basis for the mftACS and mftDCS protocols (Fischer et al.,
2017). These two montages were therefore optimized to focus on bilateral frontal and parietal
regions, hereby targeting the entire executive function network, using a genetic algorithm
testing any possible combination of electrode positions in the 10-10 EEG system. For the
multifocal stimulation protocols we chose tDCS, being the most used enhancement technique,
as well as tACS in the gamma band, which was previously shown to be effective in
modulating abstract reasoning by our group (Santarnecchi et al. 2013, 2016).
mftDCS
mftACS (40 Hz)
tRNS
tDCS
1
F3 (anode, 703µA)
F3 (675µA)
F3 (1000µA, 100-500Hz)
F3 (anode, 1250µA)
2
Fz (cathode, -1000µA)
Fz (850µA, 180°)
F4 (1000µA, 100-500Hz)
AF8 (cathode, 1250µA)
3
F4 (anode, 860µA)
F4 (675µA)
C3 (EEG)
C3 (EEG)
4
T7 (cathode, -341µA)
PO7 (925µA, 180°)
Cz (EEG)
Cz (EEG)
11
5
T8 (cathode, -412µA)
PO8 (925µA, 180°)
C4 (EEG)
C4 (EEG)
6
P3 (anode, 603µA)
P3 (675µA)
PO7 (EEG)
PO7 (EEG)
7
P4 (anode, 587µA)
P4 (675µA)
PO8 (EEG)
PO8 (EEG)
8
Oz (cathode, -1000µA)
Pz (EEG)
Pz (EEG)
Pz (EEG)
30 min
30 min
30 min
20 min
Figure 2. Stimulation parameters. In all four stimulation protocols (mftDCS, mftACS,
tDCS, tRNS) 8 electrodes were mounted and spare positions were used to collect EEG.
Stimulation onset coincided with training onset. StimWeaver (Neuroelectrics, Barcelona,
Spain) was used to optimize the stimulation montages according to the specified targets
(Ruffini et al., 2013). Electric field calculations (component of the electric field orthogonal to
the cortical surface En [V/m]) were performed using a realistic head model (Miranda et al.,
2013). Positive values indicate that the field is directed into the cortical surface. Note that the
tRNS and mftACS protocols only depict one instance of stimulation given that polarity
changes continually during stimulation.
2.3. Cognitive training
Cognitive training (Fig. 1) was presented on a laptop (screen size 17”) while subjects listened
to custom-designed music and game-specific sounds via earphones. The training consisted of
gamified tasks drawing on isolated as well as integrated EFs (working memory, inhibition,
and cognitive flexibility). The task setting was a robot factory, in which the participants were
employed. They had to work through 2-minute task blocks (15 blocks per session), which
were preceded by time-limited (30 s) instruction screens, and received feedback on every trial
as well as at the end of each block. Difficulty levels were increased whenever subjects passed
80% of a training block, which ensured an individually adapted and constant level of
challenge. Reaching a level of 80% correct in a block was considered a successful completion
of that block. When a participant reached 50-80% correct in a given block, difficulty would
remain the same, while difficulty was decreased when a subject reached <50% correct.
Subjects started with the training of single sub-functions (working memory or inhibition or
12
cognitive flexibility) and continued with combinations of two or all three sub-functions.
Subjects never performed the same task twice in a row. At level 1, the trials were
differentiated according to stimulus material (numbers, pictures, words, spatial locations) and
EF component (working memory, cognitive flexibility, inhibition). At level 2, the trials
involved unique pairwise combinations of all three EF components. At level 3, tasks from
Levels 1 and 2 were extended to include four logical (identity, AND and (exclusive) OR). As
an example, a task involving exclusive OR might require that participants respond only to
numbers less than 10 or to purple shapes, but not both. The most difficult training blocks (4a)
contained all three EF tasks. For example, one block might consist of two different tasks
(switching component), each involving n-back operations (working memory component), as
well as a separate cue to not respond (inhibition component). A final, slightly different
complexity level (4b) required subjects to partially deduce the rules themselves, as the
instructions intentionally obfuscated some information so that subjects had to use a trial-and-
error approach. For example, the instruction would state that the task contains an inhibition
cue, but would not state what this cue consisted of. This regime forces participants to learn
general skills rather than training-specific stimulus-response relationships. Difficulty was
furthermore increased by reducing the time available to respond, or by increasing the number
of items to remember, while changing training parameters in the opposite direction decreased
difficulty. Progress in the game was defined as the number of tasks passed of which the
participant achieved 80% or better accuracy on the most challenging difficulty level. The
development of the cognitive training was effected in collaboration with Simcoach Games
(Pittsburgh, PA, USA) and is reported elsewhere (Almquist Norton-Ford et al., in press).
2.4. Gf tests
Gf tests are thought to capture domain-independent nonverbal abilities underlying
performance on various cognitive tasks. In the current study, three of the most common tests
were administered, which specifically assess logical-deductive reasoning: BOMAT (Hossiep
et al., 1999), RAPM (Raven et al., 1998), and Sandia (www.sandia.gov; Matzen et al., 2010).
13
They have a common structure in that stimuli are presented as matrices of patterns in which
one pattern is missing. Subjects then have to point out the missing pattern from an array of
patterns. The BOMAT presents a 5x3 matrix with a choice of 6 possible answers, whereas the
RAPM and Sandia each present 3x3 matrices with a choice of 8 possible answers. Each test
was presented for 15 minutes and subjects were instructed to solve as many of the presented
problems as possible.
For the current experiment both the BOMAT and the RAPM were divided into 2 parallel
versions with 14 and 17 stimuli, respectively. Odd and even numbered stimuli were used for
BOMAT, while we divided the RAPM stimuli by approximating an even-odd distribution
taking into account item difficulty provided in the test manual. A similar approach has been
adopted in previous studies (Jaeggi et al., 2008; Rudebeck et al., 2012; Thompson et al.,
2013). The Sandia overcomes the issue of a limited number of stimuli by providing the option
to choose from a pool of approximately 3000 matrices, obtained through the combination of
different stimulus features such as shape, colour and orientation (Matzen et al., 2010).
Experimental matrices belong to 4 different classes based on the type and number of
analogical operations required for a correct solution (1-, 2-, 3- relations and logic matrices).
Parallel versions of the Sandia with 42 stimuli each were based on stimuli classes and
difficulty levels (Santarnecchi et al., 2013, 2016), and presentation time was limited to 1
minute per stimulus.
2.5. Statistical analyses
Data was analyzed using SPSS (21.0 for Mac) and the statistical computing software R
(2015). Given the nature of our data as a series of successes (correct responses) and failures
(incorrect responses or items not attempted), we modeled subjects’ performance on Gf tests as
a binomial process, employing a logistic regression analysis approach. In this analysis,
outcome variables were the binomial distribution of correct and incorrect responses from each
participant at posttest defined by the total number of items on the tests. We modeled subjects
accuracy across all Gf tests at posttest, given their (1) experimental condition (mftACS, tDCS,
14
mftDCS, tRNS, NC), (2) baseline ability in all Gf tests, and (3) age. We used the general
linear model (GLM) function to estimate the logistic regression model. In this case, the linear
predictor was associated to the outcome with the link function logit. In the first equation the
reference category for Condition was NC. In a post-hoc analysis we used the stimulation
group with the weakest effect as the reference category to further shed light on the current
effect, and the differences between the groups.
We further clarify whether progression in the game was mediating Gf post in the different
stimulation groups by conducting a mediation analysis [bootstrapped with 10,000 samples
and 90% confidence intervals (CIs) given the directional hypothesis between progress in the
game and improved Gf] in each of the stimulation groups using the PROCESS module in
SPSS (Hayes, 2013). In order to integrate the different Gf measures, we aggregated the
standardized scores of the three Gf tests at pre- and posttest. The mediation analysis verified
whether the effect of an independent variable (X, in this case Gf pre) on a dependent variable
(Y, Gf post), denominated c path, is mediated by a mediator variable (M, game progression).
The c path corresponds to the beta regression coefficient of the linear regression with Y as
dependent variable and X as predictor. The connection between the independent variable (X)
and the mediator (M) is a denominated a path, whereas the connection between the mediator
(M) and the dependent variable (Y) is a denominated b path. The a path corresponds to the
beta coefficient of the linear regression with the mediator (M) as outcome variable and the
independent variable (X) as predictor. The b path, instead, corresponds to the beta coefficient
of the linear regression with the dependent variable (Y) as outcome and the mediator (M) as
predictor when the contribution of the independent variable (X) is controlled by including it
into the model. Therefore the mediation analysis can elegantly demonstrate a link between
several variables in our experiment. The product of the a and b paths represents the indirect
effect of the independent variable (X) on the dependent variable (Y) through the mediator (M)
and its significance is evaluated adopting a resampling technique to obtain a bootstrapped
distribution of ab products. Despite the a and b paths may be themselves statistically
significant, a robust way to establish a specific indirect effect of the independent variable (X)
15
on the dependent variable (Y) through the mediator (M) is to observe when the 90% CIs of
the bootstrapped distribution of the ab products does not overlap zero (Hayes, 2013; Hayes
and Scharkow, 2013). The mediation model can elucidate the role of game progression, which
was hypothesized to act as a mediator in the relationship between the predictor X (Gf pre) and
the outcome Y (Gf post). In this respect mediation analyses allow a better view of the
mechanisms of change due to the intervention (Fairchild and MacKinnon, 2009). In addition,
the mediation analysis allows us to examine several hypotheses within one analytic
framework. That is, the a path allows us to examine the link between pre Gf and training
progression, as would be predicted by current theories (Diamond, 2013), unless stimulation
might alter this expected relationship. The b path allows us to examine if progression in the
cognitive training is transferred to post Gf scores, while controlling for baseline (pre) Gf.
16
3. RESULTS
At baseline, groups did not differ significantly with regards to age (F(4,81)=0.21, p=.934, η2p
=.01), gender (χ2(4, N=82)=1.68, p =.795), and education (F(4,81)=0.50, p =.736, η2p =.03).
As expected, education was positively associated with baseline Gf [controlling for age]
(r=.261, p =.038, 95% CI [.03, .48]). However, education was negatively correlated with the
change in Gf [controlling for age] (r=-.327, p =.008, 95% CI [-.57, -.01]) indicating that
subjects with lower education showed a higher increase in Gf over all cognitive training
groups.
No major side effects were reported and all stimulation groups showed similar progress
(number of tasks where subjects achieved 80% or better accuracy) in the training (F(3,63)<1,
p =.818, η2p =.08).
3.1. Logistic regression
In a first model, the reference category for Condition was NC (Table 1, model 1). We found
that all stimulation protocols, except the mftACS (p=.510) protocol, improved significantly
more in Gf after the training intervention (all ps <.05).
In a second model we then chose the weakest stimulation group (mftACS) as reference
category (Table 1, model 2). This model showed that the mftACS group was performing
significantly weaker than the tDCS group (p=.029) and the mftDCS group (p=.043), and
marginally weaker than the tRNS group (p=.067). Furthermore, we found a significant
negative correlation between age and Gf scores at pretest (r=-.592, p <.001, 95% CI [-.71, -
.44]) as well as posttest (r=-.562, p <.001, 95% CI [-.70, -.39]). However, the change in Gf
score was not correlated with age (r=-.150, p =.178, 95% CI [-.40, .11]).
b
se
z ratio
p
.057
.086
0.66
.510
17
.235
.088
2.67
.008
.239
.086
2.79
.005
.212
.086
2.46
.014
-.057
.086
-.66
.510
.179
.088
2.03
.043
.182
.083
2.19
.029
.156
.085
1.83
.067
a Gf baseline: b = .050 (SE = .005), p<.001; Age: b = -.025 (SE = .003), p<.001
b Gf baseline: b = .050 (SE = .005), p<.001; Age: b = -.025 (SE = .003), p<.001
Table 1. Logistic regression analysis of subjects’ accuracy across all Gf tests.
3.2. Mediation analyses
Mediation analyses elucidate the mechanisms that are hypothesised to underlie the
relationship between independent and dependent variables (changes from pre- to post-training
Gf scores), via the inclusion of an explanatory variable - the mediator variable (progression in
the game). As expected, regressing Gf post onto the Gf pre (c path) resulted in a significant
association between the variables in all groups (all ps<.001). Participants with a high Gf pre
score were therefore more likely to achieve a high Gf post score. We then entered the
mediator, progression in the game, into the model.
For the mftACS group (Fig. 3), Gf pre was not significantly associated with progression (p
=.125). Similarly, progression was not significantly associated with Gf post (p =.29). The
bias-corrected bootstrapped confidence interval for the specific indirect effect of the mapping
of Gf pre on Gf post through progression crossed zero (0.02, 90% CI [-.02 to .24]).
For the tDCS group (Fig. 3), Gf pre was significantly associated with progression (p <.001).
However, progression was not significantly associated with Gf post (p =.14). The bias-
corrected bootstrapped confidence interval for the specific indirect effect of the mapping of
Gf pre on Gf post through progression crossed zero (0.22, 90% CI [-.23 to .64]).
For the mftDCS group (Fig. 3), Gf pre was significantly associated with progression (p
=.045). Progression, in turn, was significantly associated with Gf post (p =.003). The bias-
18
corrected bootstrapped confidence interval for the specific indirect effect of the mapping of
Gf pre on Gf post through progression remained above zero (0.21, 90% CI [.02 to .46])
indicating a significant mediation effect.
For the tRNS group (Fig. 3), Gf pre was significantly associated with progression (p =.01).
Progression was also significantly associated with Gf post (p =.028). The bias-corrected
bootstrapped confidence interval for the specific indirect effect of the mapping of Gf pre on
Gf post through progression remained above zero (0.16, 90% CI [.03 to .47]) indicating,
again, a significant mediation effect.
These results support an indirect effect of Gf pre on Gf post through the progression in the
game for the groups receiving mftDCS and tRNS. Interestingly, mediation effects in the tDCS
group did not reach significance, even though coefficients were high and their pattern similar
to the mftDCS and tRNS groups. This could be explained by increased variance in the b path
in this group compared to the others.
19
Figure 3. Mediation analyses to examine whether the relation between pre- and posttest
Gf is mediated by game progression in each stimulation group. Standardized regression
coefficients, standard errors, and 90% confidence intervals (in parentheses), bootstrapped
with 10,000 resamples are reported. Solid lines indicate significant paths. Due to our
directional hypothesis based on previous literature (Au et al., 2014; Jaeggi et al., 2008;
Taatgen, 2013) these are one-tailed tests.
tRNS
Training
Progression
Gf PostGf Pre
=.57, SE=.22[.18, .96]
=.73, SE=.13[.49, .97]
=.28, SE=.13[.04, .52]
mftDCS
Training
Progression
Gf PostGf Pre
=.22, SE=.29[-.31, .75]
=.64, SE=.14[.39, .90]
=.50, SE=.14[.24, .76]
tDCS
Training
Progression
Gf PostGf Pre
=.80, SE=.15[.53, 1.07]
=.60, SE=.24[.18, 1.03]
=.28, SE=.24[-.15, .70]
mftACS
Training
Progression
Gf PostGf Pre
=.31, SE=.25[-.14, .75]
=.86, SE=.14[.61, 1.10]
=.08, SE=.14[-.16, .32]
20
4. DISCUSSION
This study compared four tES protocols with sustained 9-session integrative EF training in
terms of transfer to Gf. Although we observed greater improvements in Gf for three protocols
(tRNS, tDCS, mftDCS) compared to NC, mftACS did not demonstrate similar benefits.
In order to elucidate underlying mechanisms, we investigated the mediating role of game
progression in Gf outcome. Notably, game progression only mediated Gf outcome in the
groups that received mftDCS or tRNS; though the tDCS group showed a similar pattern with
similar effect size.
This study did not include a sham group or an active control group, which could have
elucidated the specific contribution of cognitive training alone, which was not the aim of the
current study, but rather to compare the differential effects of the four stimulation protocols.
However, it is known that cognitive training alone shows small transfer effects to Gf (Jaeggi
et al., 2011). Given that transfer effects were observed for the tRNS and mftDCS groups, we
doubt that such transfer effects might be epiphenomena, induced by other factors, such as
motivation, remuneration, or the so-called ‘Hawthorne effect’ (the interaction with the
experimenters). Previous studies have already revealed long-term transfer with tRNS
(Cappelletti et al., 2013; Snowball et al., 2013). Using a similar stimulation protocol,
Snowball and colleagues (Snowball et al., 2013) demonstrated that behavioural improvements
were associated with hemodynamic responses specifically within the left DLPFC, although
the exact mechanisms of tRNS-related effects in the current study might differ.
As the mftDCS protocol is a novel approach, we can only speculate about the underlying
mechanisms. We suspect that simultaneous stimulation of brain areas that are relevant for the
trained EF, specifically frontal and parietal areas, buttressed any advantageous effects.
Electric field modelling was used to ensure that indeed the intended areas were stimulated
whilst reducing shunting effects due to mounting several (though small) electrodes in
21
relatively close proximity. The application of only one frequency (40 Hz) of alternating
currents over several brain regions in our mftACS protocol, however, may not be sufficient to
account for the complex interplay of oscillatory activity in these brain areas and possibly even
had a detrimental effect on training outcome. It is premature, based on one stimulation
protocol, to conclude that mftACS cannot be effective for cognitive learning and transfer, and
future studies will need to further examine the effect of alternative montages, different timing
of stimulation with regard to the training (online vs. offline), frequencies, or amplitudes on
behavioural outcomes. Importantly, even though the present montage was optimized to
homogeneously distribute current intensity across the electrode array, the phase of oscillatory
stimulation was not completely balanced due to physical constraints induced by the proximity
of some electrodes. Given the relevance of phase information for tACS applications (Polanía
et al., 2012), this might have constituted an undesired source of noise and should be carefully
addressed in future investigations.
It should be noted that multifocal solutions allow for stimulation of a larger number of
regions, approximating stimulation of entire cortical networks. However, given the limitation
in overall stimulation intensity imposed by tES safety guidelines, solutions including more
than two electrodes usually result in a more distributed but lower stimulation intensity, which
might in turn result in a sub-threshold stimulation. This could apply to both the mftDCS and
the mftACS protocol. Moreover, given that mftACS effects might be driven by both local
entrainment of specific oscillatory activity as well as by synchronization of different areas via
in-phase stimulation (i.e., 0 degrees phase difference between stimulation electrodes), the
mftACS protocol used in the present study might have elicited desynchronization of the
targeted fronto-parietal network, thus inducing segregation of activity in frontal and parietal
regions.
Finally, age was negatively correlated with pretest as well as posttest Gf, but the change in Gf
was independent of age. Previous studies in young healthy adults have shown that subjects
with lower baseline abilities may profit more from brain stimulation interventions aiming to
improve cognitive functions (Foroughi et al., 2014; Hsu et al., 2014; Liang et al., 2014; Looi
22
et al., 2016; Tseng et al., 2012). Lower functioning subjects should have a higher margin for
improvement, as there is room for optimization of cognitive processes, while higher
functioning subjects already perform at an optimal physiological level, which might prevent
further improvement (Brem et al., 2014; Hsu et al., 2014; Krause et al., 2013). However, with
advancing age, functional and structural changes might, in return, modify underlying
mechanisms and lead to differential outcomes (Berryhill and Jones, 2012; Learmonth et al.,
2015; Li et al., 2015). Recent research suggests that individual traits and momentary state
predict behavioural outcomes after stimulation (Benwell et al., 2015; London and Slagter,
2015; Sarkar et al., 2014). Future studies should address the question of whether cognitive
training combined with brain stimulation might lead to an assimilation of high and low
functioning individuals by targeting low functioning individuals more effectively.
In sum, after nine sessions of brain stimulation combined with cognitive training we found
transfer effects to Gf measures for the mftDCS and tRNS groups. Future studies should
explore whether individualized intervention protocols based on trait (e.g., age, education,
genetic polymorphisms) and momentary state can enhance individual training effects.
23
FUNDING
This research was supported by the Intelligence Advanced Research Projects Activity
(IARPA) via contract #2014413121700007. The United States Government is authorized to
reproduce and distribute reprints for Governmental purposes notwithstanding any copyright
annotation thereon. The funders had no role in study design, data collection and analysis,
decision to publish, or preparation of the manuscript. The views and conclusions contained
herein are those of the authors and should not be interpreted as necessarily representing the
official policies or endorsements, either expressed or implied, of IARPA, the Office of the
Director of National Intelligence, or the U.S. Government.
CONFLICT OF INTEREST STATEMENT
RCK serves on the scientific advisory boards for InnoSphere Inc., The Cognitive
Enhancement Foundation (non-profit organization), and Neuroelectrics Inc. APL serves on
the scientific advisory boards for Nexstim, Neuronix, Starlab Neuroscience, Neuroelectrics
Inc., Axilum Robotics, Magstim Inc., and Neosync; and is listed as an inventor on several
issued and pending patents on the real-time integration of transcranial magnetic stimulation
with electroencephalography and magnetic resonance imaging. The authors declare no
competing interests and do not hold any interests in the training software or Simcoach Games
beyond scientific interest.
ACKNOWLEDGMENT
We would like to thank Giulia Maistrello, Amar Sarkar, Ruairidh Battleday, and Kalaiyashni
Puvanendran from University of Oxford; Dennis Cornhill, Sandi White, Zane Thimmesch-
Gill, and Forrest Olsen from Honeywell Aerospace; Erica Levenbaum and Alexandra
Emmendorfer from Harvard Medical School; Asieh Ahani, Sadegh Salehi, Yeganeh Marghi,
and Seyhmus Güler from Northeastern University; Adam Chizmar, Michael Wagner, and
Jessica Trybus from Simcoach Games for their valuable help.
24
REFERENCES
Almquist Norton-Ford, J., Brem, A.-K., Plessow, F., McKanna, J., Santarnecchi, E.,
Pascual-Leone, A., Cohen Kadosh, R., Pavel, M., Yeung, N., n.d. FAST: A
novel, executive function-based approach to cognitive enhancement. PLoS
ONE.
Au, J., Sheehan, E., Tsai, N., Duncan, G.J., Buschkuehl, M., Jaeggi, S.M., 2014.
Improving fluid intelligence with training on working memory: a meta-
analysis. Psychon. Bull. Rev. https://doi.org/10.3758/s13423-014-0699-x
Bastian, C.C. von, Oberauer, K., 2014. Effects and mechanisms of working memory
training: a review. Psychol. Res. 78, 803–820. https://doi.org/10.1007/s00426-
013-0524-6
Benedek, M., Jauk, E., Sommer, M., Arendasy, M., Neubauer, A.C., 2014.
Intelligence, creativity, and cognitive control: The common and differential
involvement of executive functions in intelligence and creativity. Intelligence
46, 73–83. https://doi.org/10.1016/j.intell.2014.05.007
Benwell, C.S.Y., Learmonth, G., Miniussi, C., Harvey, M., Thut, G., 2015. Non-linear
effects of transcranial direct current stimulation as a function of individual
baseline performance: Evidence from biparietal tDCS influence on lateralized
attention bias. Cortex J. Devoted Study Nerv. Syst. Behav. 69, 152–165.
https://doi.org/10.1016/j.cortex.2015.05.007
Berryhill, M.E., Jones, K.T., 2012. tDCS selectively improves working memory in
older adults with more education. Neurosci. Lett. 521, 148–151.
https://doi.org/10.1016/j.neulet.2012.05.074
Brem, A.-K., Fried, P.J., Horvath, J.C., Robertson, E.M., Pascual-Leone, A., 2014. Is
neuroenhancement by noninvasive brain stimulation a net zero-sum
proposition? NeuroImage 85 Pt 3, 1058–1068.
https://doi.org/10.1016/j.neuroimage.2013.07.038
Burgess, G.C., Gray, J.R., Conway, A.R.A., Braver, T.S., 2011. Neural mechanisms
of interference control underlie the relationship between fluid intelligence and
working memory span. J. Exp. Psychol. Gen. 140, 674–692.
https://doi.org/10.1037/a0024695
Cappelletti, M., Gessaroli, E., Hithersay, R., Mitolo, M., Didino, D., Kanai, R., Cohen
Kadosh, R., Walsh, V., 2013. Transfer of Cognitive Training across
Magnitude Dimensions Achieved with Concurrent Brain Stimulation of the
Parietal Lobe. J. Neurosci. 33, 14899–14907.
https://doi.org/10.1523/JNEUROSCI.1692-13.2013
Cattell, R.B., 1963. Theory of fluid and crystallized intelligence: A critical
experiment. J. Educ. Psychol. 54, 1–22. https://doi.org/10.1037/h0046743
Cohen Kadosh, R., Soskic, S., Iuculano, T., Kanai, R., Walsh, V., 2010. Modulating
neuronal activity produces specific and long-lasting changes in numerical
competence. Curr. Biol. CB 20, 2016–2020.
https://doi.org/10.1016/j.cub.2010.10.007
Colzato, L.S., van Wouwe, N.C., Lavender, T.J., Hommel, B., 2006. Intelligence and
cognitive flexibility: fluid intelligence correlates with feature “unbinding”
across perception and action. Psychon. Bull. Rev. 13, 1043–1048.
Cooke, S.F., Bliss, T.V.P., 2006. Plasticity in the human central nervous system.
Brain J. Neurol. 129, 1659–1673. https://doi.org/10.1093/brain/awl082
Diamond, A., 2013. Executive functions. Annu. Rev. Psychol. 64, 135–168.
https://doi.org/10.1146/annurev-psych-113011-143750
25
Eickhoff, S.B., Bzdok, D., Laird, A.R., Kurth, F., Fox, P.T., 2012. Activation
likelihood estimation meta-analysis revisited. NeuroImage 59, 2349–2361.
https://doi.org/10.1016/j.neuroimage.2011.09.017
Enge, S., Behnke, A., Fleischhauer, M., Küttler, L., Kliegel, M., Strobel, A., 2014. No
evidence for true training and transfer effects after inhibitory control training
in young healthy adults. J. Exp. Psychol. Learn. Mem. Cogn. 40, 987–1001.
https://doi.org/10.1037/a0036165
Fairchild, A.J., MacKinnon, D.P., 2009. A general model for testing mediation and
moderation effects. Prev. Sci. Off. J. Soc. Prev. Res. 10, 87–99.
https://doi.org/10.1007/s11121-008-0109-6
Feurra, M., Pasqualetti, P., Bianco, G., Santarnecchi, E., Rossi, A., Rossi, S., 2013.
State-dependent effects of transcranial oscillatory currents on the motor
system: what you think matters. J. Neurosci. Off. J. Soc. Neurosci. 33, 17483–
17489. https://doi.org/10.1523/JNEUROSCI.1414-13.2013
Fischer, D.B., Fried, P.J., Ruffini, G., Ripolles, O., Salvador, R., Banus, J.,
Ketchabaw, W.T., Santarnecchi, E., Pascual-Leone, A., Fox, M.D., 2017.
Multifocal tDCS targeting the resting state motor network increases cortical
excitability beyond traditional tDCS targeting unilateral motor cortex.
NeuroImage 157, 34–44. https://doi.org/10.1016/j.neuroimage.2017.05.060
Foroughi, C.K., Blumberg, E.J., Parasuraman, R., 2014. Activation and inhibition of
posterior parietal cortex have bi-directional effects on spatial errors following
interruptions. Front. Syst. Neurosci. 8, 245.
https://doi.org/10.3389/fnsys.2014.00245
Hayes, A.F., 2013. Introduction to mediation, moderation, and conditional process
analysis: A regression-based approach. Guilford Press.
Hayes, A.F., Scharkow, M., 2013. The relative trustworthiness of inferential tests of
the indirect effect in statistical mediation analysis: does method really matter?
Psychol. Sci. 24, 1918–1927. https://doi.org/10.1177/0956797613480187
Hossiep, R., Turck, D., Hasella, M., 1999. Bochumer Matrizentest: BOMAT–
Advanced–Short Version. Hogrefe, Göttingen.
Hsu, T.-Y., Tseng, P., Liang, W.-K., Cheng, S.-K., Juan, C.-H., 2014. Transcranial
direct current stimulation over right posterior parietal cortex changes
prestimulus alpha oscillation in visual short-term memory task. NeuroImage
98, 306–313. https://doi.org/10.1016/j.neuroimage.2014.04.069
Jaeggi, S.M., Buschkuehl, M., Jonides, J., Perrig, W.J., 2008. Improving fluid
intelligence with training on working memory. Proc. Natl. Acad. Sci. U. S. A.
105, 6829–6833. https://doi.org/10.1073/pnas.0801268105
Jaeggi, S.M., Buschkuehl, M., Jonides, J., Shah, P., 2011. Short- and long-term
benefits of cognitive training. Proc. Natl. Acad. Sci. U. S. A. 108, 10081–
10086. https://doi.org/10.1073/pnas.1103228108
Krause, B., Cohen Kadosh, R., 2013. Can transcranial electrical stimulation improve
learning difficulties in atypical brain development? A future possibility for
cognitive training. Dev. Cogn. Neurosci. 6, 176–194.
https://doi.org/10.1016/j.dcn.2013.04.001
Krause, B., Márquez-Ruiz, J., Cohen Kadosh, R., 2013. The effect of transcranial
direct current stimulation: a role for cortical excitation/inhibition balance?
Front. Hum. Neurosci. 7, 602. https://doi.org/10.3389/fnhum.2013.00602
Learmonth, G., Thut, G., Benwell, C.S.Y., Harvey, M., 2015. The implications of
state-dependent tDCS effects in aging: Behavioural response is determined by
26
baseline performance. Neuropsychologia 74, 108–119.
https://doi.org/10.1016/j.neuropsychologia.2015.01.037
Li, L.M., Uehara, K., Hanakawa, T., 2015. The contribution of interindividual factors
to variability of response in transcranial direct current stimulation studies.
Front. Cell. Neurosci. 9, 181. https://doi.org/10.3389/fncel.2015.00181
Liang, W.-K., Lo, M.-T., Yang, A.C., Peng, C.-K., Cheng, S.-K., Tseng, P., Juan, C.-
H., 2014. Revealing the brain’s adaptability and the transcranial direct current
stimulation facilitating effect in inhibitory control by multiscale entropy.
NeuroImage 90, 218–234. https://doi.org/10.1016/j.neuroimage.2013.12.048
London, R.E., Slagter, H.A., 2015. Effects of Transcranial Direct Current Stimulation
over Left Dorsolateral pFC on the Attentional Blink Depend on Individual
Baseline Performance. J. Cogn. Neurosci. 27, 2382–2393.
https://doi.org/10.1162/jocn_a_00867
Looi, C.Y., Duta, M., Brem, A.-K., Huber, S., Nuerk, H.-C., Cohen Kadosh, R., 2016.
Combining brain stimulation and video game to promote long-term transfer of
learning and cognitive enhancement. Sci. Rep. 6, 22003.
https://doi.org/10.1038/srep22003
Matzen, L.E., Benz, Z.O., Dixon, K.R., Posey, J., Kroger, J.K., Speed, A.E., 2010.
Recreating Raven’s: software for systematically generating large numbers of
Raven-like matrix problems with normed properties. Behav. Res. Methods 42,
525–541. https://doi.org/10.3758/BRM.42.2.525
Melby-Lervåg, M., Redick, T.S., Hulme, C., 2016. Working Memory Training Does
Not Improve Performance on Measures of Intelligence or Other Measures of
“Far Transfer”: Evidence From a Meta-Analytic Review. Perspect. Psychol.
Sci. J. Assoc. Psychol. Sci. 11, 512–534.
https://doi.org/10.1177/1745691616635612
Miranda, P.C., Mekonnen, A., Salvador, R., Ruffini, G., 2013. The electric field in the
cortex during transcranial current stimulation. NeuroImage 70, 48–58.
https://doi.org/10.1016/j.neuroimage.2012.12.034
Paulus, W., 2004. Outlasting excitability shifts induced by direct current stimulation
of the human brain. Suppl. Clin. Neurophysiol. 57, 708–714.
Pelletier, S.J., Cicchetti, F., 2015. Cellular and molecular mechanisms of action of
transcranial direct current stimulation: evidence from in vitro and in vivo
models. Int. J. Neuropsychopharmacol. Off. Sci. J. Coll. Int.
Neuropsychopharmacol. CINP 18. https://doi.org/10.1093/ijnp/pyu047
Polanía, R., Nitsche, M.A., Korman, C., Batsikadze, G., Paulus, W., 2012. The
importance of timing in segregated theta phase-coupling for cognitive
performance. Curr. Biol. CB 22, 1314–1318.
https://doi.org/10.1016/j.cub.2012.05.021
Raven, J.C., Raven, J., Court, J.H., 1998. Manual for Raven’s progressive matrices
and vocabulary scales. Section 4: Advanced progressive matrices. Pearson,
San Antonio, TX.
Redick, T.S., Shipstead, Z., Harrison, T.L., Hicks, K.L., Fried, D.E., Hambrick, D.Z.,
Kane, M.J., Engle, R.W., 2013. No evidence of intelligence improvement after
working memory training: a randomized, placebo-controlled study. J. Exp.
Psychol. Gen. 142, 359–379. https://doi.org/10.1037/a0029082
Romei, V., Thut, G., Silvanto, J., 2016. Information-Based Approaches of
Noninvasive Transcranial Brain Stimulation. Trends Neurosci. 39, 782–795.
https://doi.org/10.1016/j.tins.2016.09.001
27
Rudebeck, S.R., Bor, D., Ormond, A., O’Reilly, J.X., Lee, A.C.H., 2012. A potential
spatial working memory training task to improve both episodic memory and
fluid intelligence. PloS One 7, e50431.
https://doi.org/10.1371/journal.pone.0050431
Ruffini, G., Fox, M.D., Ripolles, O., Miranda, P.C., Pascual-Leone, A., 2014.
Optimization of multifocal transcranial current stimulation for weighted
cortical pattern targeting from realistic modeling of electric fields.
NeuroImage 89, 216–225. https://doi.org/10.1016/j.neuroimage.2013.12.002
Ruffini, G., Wendling, F., Merlet, I., Molaee-Ardekani, B., Mekonnen, A., Salvador,
R., Soria-Frisch, A., Grau, C., Dunne, S., Miranda, P.C., 2013. Transcranial
current brain stimulation (tCS): models and technologies. IEEE Trans. Neural
Syst. Rehabil. Eng. Publ. IEEE Eng. Med. Biol. Soc. 21, 333–345.
https://doi.org/10.1109/TNSRE.2012.2200046
Santarnecchi, E., Brem, A.-K., Levenbaum, E., Thompson, T., Kadosh, R.C., Pascual-
Leone, A., 2015. Enhancing cognition using transcranial electrical stimulation.
Curr. Opin. Behav. Sci. 4, 171–178.
https://doi.org/10.1016/j.cobeha.2015.06.003
Santarnecchi, E., Muller, T., Rossi, S., Sarkar, A., Polizzotto, N.R., Rossi, A., Cohen
Kadosh, R., 2016. Individual differences and specificity of prefrontal gamma
frequency-tACS on fluid intelligence capabilities. Cortex J. Devoted Study
Nerv. Syst. Behav. 75, 33–43. https://doi.org/10.1016/j.cortex.2015.11.003
Santarnecchi, E., Polizzotto, N.R., Godone, M., Giovannelli, F., Feurra, M., Matzen,
L., Rossi, A., Rossi, S., 2013. Frequency-dependent enhancement of fluid
intelligence induced by transcranial oscillatory potentials. Curr. Biol. CB 23,
1449–1453. https://doi.org/10.1016/j.cub.2013.06.022
Sarkar, A., Dowker, A., Cohen Kadosh, R., 2014. Cognitive enhancement or
cognitive cost: trait-specific outcomes of brain stimulation in the case of
mathematics anxiety. J. Neurosci. Off. J. Soc. Neurosci. 34, 16605–16610.
https://doi.org/10.1523/JNEUROSCI.3129-14.2014
Shipstead, Z., Redick, T.S., Engle, R.W., 2012. Is working memory training
effective? Psychol. Bull. 138, 628–654. https://doi.org/10.1037/a0027473
Silvanto, J., Muggleton, N.G., Cowey, A., Walsh, V., 2007. Neural adaptation reveals
state-dependent effects of transcranial magnetic stimulation. Eur. J. Neurosci.
25, 1874–1881. https://doi.org/10.1111/j.1460-9568.2007.05440.x
Simonsmeier, B.A., Grabner, R.H., Hein, J., Krenz, U., Schneider, M., 2018.
Electrical brain stimulation (tES) improves learning more than performance: A
meta-analysis. Neurosci. Biobehav. Rev. 84, 171–181.
https://doi.org/10.1016/j.neubiorev.2017.11.001
Snowball, A., Tachtsidis, I., Popescu, T., Thompson, J., Delazer, M., Zamarian, L.,
Zhu, T., Cohen Kadosh, R., 2013. Long-Term Enhancement of Brain Function
and Cognition Using Cognitive Training and Brain Stimulation. Curr. Biol.
23, 987–992. https://doi.org/10.1016/j.cub.2013.04.045
Sternberg, R.J., 2012. Intelligence. Dialogues Clin. Neurosci. 14, 19–27.
Taatgen, N.A., 2013. The nature and transfer of cognitive skills. Psychol. Rev. 120,
439–471. https://doi.org/10.1037/a0033138
Terney, D., Chaieb, L., Moliadze, V., Antal, A., Paulus, W., 2008. Increasing human
brain excitability by transcranial high-frequency random noise stimulation. J.
Neurosci. Off. J. Soc. Neurosci. 28, 14147–14155.
https://doi.org/10.1523/JNEUROSCI.4248-08.2008
28
Thompson, T.W., Waskom, M.L., Garel, K.-L.A., Cardenas-Iniguez, C., Reynolds,
G.O., Winter, R., Chang, P., Pollard, K., Lala, N., Alvarez, G.A., Gabrieli,
J.D.E., 2013. Failure of working memory training to enhance cognition or
intelligence. PloS One 8, e63614.
https://doi.org/10.1371/journal.pone.0063614
Tseng, P., Hsu, T.-Y., Chang, C.-F., Tzeng, O.J.L., Hung, D.L., Muggleton, N.G.,
Walsh, V., Liang, W.-K., Cheng, S., Juan, C.-H., 2012. Unleashing potential:
transcranial direct current stimulation over the right posterior parietal cortex
improves change detection in low-performing individuals. J. Neurosci. Off. J.
Soc. Neurosci. 32, 10554–10561. https://doi.org/10.1523/JNEUROSCI.0362-
12.2012
Turkeltaub, P.E., Eickhoff, S.B., Laird, A.R., Fox, M., Wiener, M., Fox, P., 2012.
Minimizing within-experiment and within-group effects in Activation
Likelihood Estimation meta-analyses. Hum. Brain Mapp. 33, 1–13.
https://doi.org/10.1002/hbm.21186
Witthöft, M., Sander, N., Süss, H.-M., Wittmann, W.W., 2009. Adult age differences
in inhibitory processes and their predictive validity for fluid intelligence.
Neuropsychol. Dev. Cogn. B Aging Neuropsychol. Cogn. 16, 133–163.
https://doi.org/10.1080/13825580802348554
!
... These include a wide range of high-level cognitive functions, spreading from attention 13 , working memory 6,14 , inhibition [15][16][17] , to mathematical skills [18][19][20][21] and multitasking 22 . Noteworthy, the effects of tRNS on high-level cognitive functions are heterogeneous across studies with some studies showing no effects 15,23,24 and several others showing significant effects 6,18,21,22,[25][26][27] . For instance, Brem et al. 25 showed that groups that received either tRNS or tDCS coupled with cognitive training (by means of a video game) had better transfer effect on fluid intelligence tasks compared to a sham stimulation. ...
... Noteworthy, the effects of tRNS on high-level cognitive functions are heterogeneous across studies with some studies showing no effects 15,23,24 and several others showing significant effects 6,18,21,22,[25][26][27] . For instance, Brem et al. 25 showed that groups that received either tRNS or tDCS coupled with cognitive training (by means of a video game) had better transfer effect on fluid intelligence tasks compared to a sham stimulation. In the same way, another study 26 , using an executive function training protocol coupled with tES, showed that the tRNS group had better improvements than the sham group. ...
... Based on previous literature that showed that tRNS may increase performance in complex cognitive tasks 21,22,25,26 , we expected (hypothesis 1) that the stimulated groups (SD-tRNS and HD-tRNS) would have a better learning rate in the SF task compared to the control group (sham). This higher learning rate would result in higher performance during both short-term, that is the day right after the training, (hypothesis 2.a.) and longterm evaluation, 10 days after the training (hypothesis 2.b.). ...
Article
Full-text available
Interest for neuromodulation, and transcranial random noise stimulation (tRNS) in particular, is growing. It concerns patients rehabilitation, but also healthy people who want or need to improve their cognitive and learning abilities. However, there is no consensus yet regarding the efficacy of tRNS on learning and performing a complex task. In particular, the most effective electrode montage is yet to be determined. Here, we examined the effect of two different tRNS montages on learning rate, short- and long-term performance in a video game (Space Fortress) that engages multiple cognitive abilities. Sixty-one participants were randomly assigned to one of three groups (sham vs. simple-definition tRNS vs. high-definition tRNS) in a double-blind protocol. Their performance on the Space Fortress task was monitored during a 15-day experiment with baseline (day 1), stimulation (day 2 to 4), short- (day 5) and long-term (day 15) evaluations. Our results show that the high-definition tRNS group improved more on the long term than simple-definition tRNS group, tended to learn faster and had better performance retention compared to both simple-definition tRNS and sham groups. This study is the first to report that high-definition tRNS is more effective than conventional simple-definition tRNS to enhance performance in a complex task.
... Briefly, they cover a wide range of cognitive functions spreading from attention (Lema et al., 2021), working memory Mulquiney et al., 2011), inhibition (Brauer et al., 2018;Brevet-Aeby et al., 2019;Dondé et al., 2019), mathematical skills (Bieck et al., 2018;Looi et al., 2017;Popescu et al., 2016;Snowball et al., 2013) to multitasking (Harty & Cohen Kadosh, 2019). Noteworthy, the effects of tRNS on high-level cognitive functions are also heterogeneous across studies, with some studies showing no effects (Holmes et al., 2016;Brauer et al., 2018;Parkin et al., 2019) and several others showing significant effects (Bieck et al., 2018;Brem et al., 2018;Harty & Cohen Kadosh, 2019;Murphy et al., 2020;Almquist et al., 2019;Snowball et al., 2013;Pasqualotto, 2016). ...
... For instance, Brem et al. (2018) showed that groups that received either tRNS or tDCS coupled with cognitive training (by means of a video game) had better transfer effect on fluid intelligence tasks compared to a sham stimulation. In the same way, another study (Almquist et al., 2019), using an executive function training protocol coupled with tRNS, showed that the tRNS group had better improvements than the sham group. ...
... Of particular importance, the results of the longitudinal training studies seem to be moving in a common direction, with an almost systematic effect of the tRNS over long-term measurements (from one week to six months). A positive effect with improved performance (and transfer) in various cognitive tasks was found in five of the six studies that used a longitudinal training protocol (Snowball et al., 2013;Popescu et al., 2016;Almquist et al., 2019;Brevet-Aeby et al., 2019;Brem et al., 2018, see Table 2.2). ...
Thesis
Full-text available
The ability to perform complex tasks is emblematic of human behavior. It is essential in many aspects of daily life, which is particularly true in contexts where the safety of people depends on this ability (e.g. airline pilots) or for people with cognitive impairments. The objective of this thesis was twofold: (1) to develop tools for predicting complex task performance in healthy people based on neurophysiological data; and (2) to develop protocols to improve complex task performance in healthy people or daily-living task in brain-damaged patients. To achieve these objectives, we first selected a task qualified as complex (Space Fortress) because it was described as involving high-level cognitive functions such as executive functions. We demonstrated for the first time that this task has solid psychometric qualities, being highly sensitive, reliable and valid, making it a suitable task that relies on global executive functions. The first research axis focused on the prediction of performance on this task from neurophysiological measures. Using two different neuroimaging techniques (functional near-infrared spectroscopy and electroencephalography), we were able to demonstrate that the intrinsic (i.e. resting state) brain activity of the fronto-parietal network could predict a part of the overall performance on this task. A major perspective of this work is that there may be intrinsic neural markers of more complex tasks (e.g. flying a plane or driving). In the context of neuroergonomics, such markers could be used either as a predictive tool for selection purposes or as an opportunity to elaborate individualized cognitive training. A second line of research focused on improving performance with the use of non-invasive brain stimulation coupled with cognitive training. Our results also show that a certain type of montage (high-definition transcranial random noise stimulation) would promote not the speed of learning, but the maintenance of long-term performance. Similar results, which remain to be confirmed, have been obtained in dysexecutive brain-damaged patients, with an improvement of performance in some of their daily tasks (e.g. planning errands, managing an email box, cooking a recipe). The results obtained during this thesis could lead to the development of new targeted cognitive training programs. Such programs could allow the improvement of the quality of training and care of certain people, whether they are healthy, facing complex situations, or brain-damaged, facing daily challenges.
... A statistically significant overall trend for improvement was found in Subtest AB of the RCPM, a nontrained measure that indicates Gf (nonverbal intelligence) improvement (t ð11Þ = 1:82, p = 0:048), while the overall score resulted in positive nonsignificant treatment effects. These findings are consistent with research showing that significant improvements in Gf resulting from cognitive intervention combined with different transcranial electrical brain stimulation protocols [70]. Our findings support the notion that Gf can be improved with DLPFC stimulation [70] and WM training [10,[71][72][73]. ...
... These findings are consistent with research showing that significant improvements in Gf resulting from cognitive intervention combined with different transcranial electrical brain stimulation protocols [70]. Our findings support the notion that Gf can be improved with DLPFC stimulation [70] and WM training [10,[71][72][73]. Considering the fact that a combination of treatments was used and it is still controversial whether WM training leads to Gf improvement [74], the findings are inconclusive as to whether improvement was due to the treatment combination or to the DLPFC stimulation. ...
Article
Full-text available
Background: Traditionally, people with aphasia (PWA) are treated with impairment-based language therapy to improve receptive and expressive language skills. In addition to language deficits, PWA are often affected by some level of working memory (WM) impairments. Both language and working memory impairments combined have a negative impact on PWA's quality of life. The aim of this study was to investigate whether the application of intermittent theta-burst stimulation (iTBS) combined with computerized WM training will result in near-ransfer effects (i.e., trained WM) and far-transfer effects (i.e., untrained language tasks) and have a positive effect on the quality of life of PWA. Methods: The participant was a 63-year-old Greek-Cypriot male who presented with mild receptive aphasia and short-term memory difficulties. Treatment was carried out using a multiple baseline (MB) design composed of a pretherapy or baseline testing phase, a therapy phase, and a posttherapy/follow-up phase. The treatment program involved iTBS application to the left dorsolateral prefrontal cortex (DLPFC), an area responsible for WM, for 10 consecutive sessions. The participant received a 3-minute iTBS application followed by 30-minute computer-assisted WM training. Outcome measures included a WM screening test, a standardized aphasia test, a nonverbal intelligence test, story-telling speech samples, a procedural discourse task, and a questionnaire addressing quality of life. These measures were performed three times before the treatment, immediately upon completion of the treatment, and once during follow-up testing at 3 months posttreatment. Results: We found a beneficial effect of iTBS and WM training on naming, reading, WM, reasoning, narrative, communication efficiency, and quality of life (QoL). Implications for Rehabilitation. Noninvasive brain stimulation combined with computerized WM training may be used in aphasia rehabilitation to improve WM and generalize to language improvement.
... Moreover, there is research showing that older adults' performance on intelligence measures such as the Wechsler intelligence test significantly improved after a 40-week Tai Chi training intervention (Mortimer et al., 2012). However, unlike executive functions, fluid intelligence represents a higher-order ability that is assumed to tap a number of basic cognitive processes (Brem et al., 2018). This suggests that improvements in fluid intelligence caused by Tai Chi training may firstly result from improvements in basic cognitive processes. ...
... Indeed, previous research on cognitive training and its effect on fluid intelligence has mainly focused on training executive functions (e.g., updating and inhibition) and has observed improvements in fluid intelligence (Brem et al., 2018). This transfer effect is especially noticeable in older adults who are experiencing cognitive declines. ...
Article
Executive function and fluid intelligence are two of the crucial frontal lobe functions susceptible to aging. Tai Chi as a mind-body exercise has been proposed for preventing cognitive aging. However, whether Tai Chi has similar effects on multiple components of executive functions in older adults remains underspecified. Moreover, it is not currently known whether Tai Chi exercise training improvements in lower-order executive functions are able to explain improvements in higher-order fluid intelligence. Study 1 included a cross-sectional design based on a large sample of healthy older adults (55–79 years old). Modeling results showed that experience of Tai Chi training exerted significantly positive effects on fluid intelligence, inhibition and updating after controlling for age and education. Both inhibition and updating mediated effects of Tai Chi on fluid intelligence. Study 2 carried out a 12-month Tai Chi training intervention on a novice group and an experienced group of older adults. Results indicated that only the novices exhibited significant improvements in inhibition and fluid intelligence. In addition, improvements in fluid intelligence were no longer significant after controlling for improvements in inhibition. These results shed lights on the mechanism by which mind-body training enhances intelligence.
... The results seemed contradictory because of the assumption that electric field intensity in a brain area directly associates with the behavioral effect of tDCS (Evans et al., 2020). One highly possible explanation may lie in that efficient execution of brain function is based on networks of brain areas rather than individual brain regions (Hoogman et al., 2017;Ester and Kullmann, 2021); and multitarget stimulation tries to modulate the associated brain network and may result in additive effects of tDCS on performance compared with singletarget stimulation (Brem et al., 2018;Ester and Kullmann, 2021;Friehs et al., 2021a;Gregoret et al., 2021). Additionally, there is some evidence for the potentially inverted U-shaped nature of tDCS interactions with behavior performance, in which an intensity may lead to better performance when it lies closer to the peak of the inverted-U curve (Ehrhardt et al., 2021). ...
Article
Full-text available
Prior studies have focused on single-target anodal transcranial direct current stimulation (tDCS) over the right inferior frontal gyrus (rIFG) or pre-supplementary motor area (pre-SMA) to improve response inhibition in healthy individuals. However, the results are contradictory and the effect of multitarget anodal stimulation over both brain regions has never been investigated. The present study aimed to investigate the behavioral and neurophysiological effects of different forms of anodal high-definition tDCS (HD-tDCS) on improving response inhibition, including HD-tDCS over the rIFG or pre-SMA and multitarget HD-tDCS over both areas. Ninety-two healthy participants were randomly assigned to receive single-session (20 min) anodal HD-tDCS over rIFG + pre-SMA, rIFG, pre-SMA, or sham stimulation. Before and immediately after tDCS intervention, participants completed a stop-signal task (SST) and a go/nogo task (GNG). Their cortical activity was recorded using functional near-infrared spectroscopy (fNIRS) during the go/nogo task. The results showed multitarget stimulation produced a significant reduction in stop-signal reaction time (SSRT) relative to baseline. The pre-to-post SSRT change was not significant for rIFG, pre-SMA, or sham stimulation. Further analyses revealed multitarget HD-tDCS significantly decreased SSRT in both the high-performance and low-performance subgroups compared with the rIFG condition which decreased SSRT only in the low-performance subgroup. Only the multitarget condition significantly improved neural efficiency as indexed by lower △oxy-Hb after stimulation. In conclusion, the present study provides important preliminary evidence that multitarget HD-tDCS is a promising avenue to improve stimulation efficacy, establishing a more effective montage to enhance response inhibition relative to the commonly used single-target stimulation.
... For instance, Curtin et al. (2019) combined cognitive training with transcranial magnetic stimulation to the left dorsolateral prefrontal cortex (DLPFC) to show causal effects of DLPFC functioning on the performance in speed of processing tasks. Other studies used transcranial direct current stimulation (tDCS) alone (Sellers et al., 2015) or in conjunction with cognitive training (Brem et al., 2018) to directly modulate performance during an intelligence test (see also Santarnecchi et al., 2015Santarnecchi et al., , 2016Santarnecchi et al., , 2019. However, large heterogeneity in study protocols, stimulation sides, small sample sizes, and the lack of replication limit comprehensive conclusions, but also suggest room for improvement in future investigations. ...
Article
The scientific study of the biological basis of intelligence has been contributing to our understanding of individual differences in cognitive abilities for decades. In particular, the ongoing development of electrophysiological, neuroimaging, and genetic methods has created new opportunities to gain insights into pressing questions, allowing the field to come closer towards a comprehensive theory that explains how genotypes exert their influence on human intelligence through intermediate biological and cognitive endophenotypes. The aim of this article is to provide a focused overview of empirical benchmark findings on biological correlates of intelligence. Specifically, we summarize benchmark findings from electrophysiological, neuroimaging, and genetic research. Moreover, we discuss four open questions: (1) The robustness of research findings; (2) the relation between neural parameters and cognitive processes; (3) promising methodological developments; and (4) theory development. The aim of this paper is to assemble the most important and robust findings on the biological basis of intelligence to stimulate future research and to contribute to theory development.
... Several studies have investigated the effects of tRNS paired with learning tasks (Brem et al., 2018;Cappelletti et al., 2013;Contemori et al., 2019), focusing in particular on designs in which tRNS is applied during learning and performance is tested after learning. One study focused on visual perceptual learning (Fertonani et al., 2011). ...
Article
Van der Groen, O., Potok, W., Wenderoth, N., Edwards., G., Mattingley, J.B. and Edwards, D. Using noise for the better: the effects of transcranial random noise stimulation on the brain and behavior. NEUROSCI BIOBEHAV REV X (X) XXX-XXX 2021.- Transcranial random noise stimulation (tRNS) is a non-invasive electrical brain stimulation method that is increasingly employed in studies of human brain function and behavior, in health and disease. tRNS is effective in modulating perception acutely and can improve learning. By contrast, its effectiveness for modulating higher cognitive processes is variable. Prolonged stimulation with tRNS, either as one longer application, or multiple shorter applications, may engage plasticity mechanisms that can result in long-term benefits. Here we provide an overview of the current understanding of the effects of tRNS on the brain and behavior and provide some specific recommendations for future research.
Article
Zusammenfassung. Die menschliche Intelligenz gehört zu den bestuntersuchten psychologischen Merkmalen, in denen interindividuelle Differenzen bestehen. Die mehr als 100jährige Forschungsgeschichte hat einen hoch belastbaren Wissensstand hervorgebracht; dieser umfasst die Definition, die Psychometrie, die (ontogenetische) Entwicklung, die Struktur, die Vorhersagekraft für real-life-Variablen, das Wissen über elementar-kognitive, verhaltensgenetische und neurobiologische Grundlagen der Intelligenz, u.v.m. Jüngst steht zudem die Frage des ‚enhancements‘ der Intelligenz im Fokus, eine Frage, die nicht zuletzt durch die aktuelle philosophische Strömung des Transhumanismus stark an Bedeutung gewinnt. Der Transhumanismus nimmt eine substanzielle Erhöhung (enhancement) von Fähigkeiten und anderen (auch) psychologischen Eigenschaften des Menschen ins Zentrum und postuliert, dass ein soziokultureller Fortschritt – und letztlich das Überlegen des Homo Sapiens und unseres Planeten – erst durch technologischen Fortschritt ermöglicht werde. Viele Transhumanisten stellen eine substanzielle Steigerung der Intelligenz in den Vordergrund, die primär durch (neuro–)‌technologische und pharmakologische Maßnahmen zu bewerkstelligen seien. Diese Debatten sind jedoch oft gekennzeichnet durch übertrieben optimistische Annahmen der Möglichkeiten moderner neurowissenschaftlicher Methoden bei gleichzeitiger Vernachlässigung der potenziellen negativen Folgen für das Individuum, für die Gesellschaften und insgesamt für unsere Spezies. Im gegenständlichen Überblicksbeitrag werden behaviorale, neuroelektrische und pharmakologische Methoden im Hinblick auf ihr aktuelles Potenzial einer Steigerung der individuellen Intelligenz analysiert. Die zwischenzeitlich zu diesen Fragen vorliegenden experimentellen Studien, sowie verfügbare Metaanalysen lassen allerdings den Schluss zu, dass bislang keine der gegenwärtig verfügbaren Methoden das Potenzial haben, die individuelle Intelligenz substanziell zu steigern. Und selbst falls solche möglicherweise in absehbarer Zeit zur Verfügung stünden, müssen zuvor sowohl individuelle als auch gesellschaftliche (negative) Konsequenzen einer kritischen Analyse unterzogen werden. Diese sind Gegenstand einer abschließenden Diskussion.
Article
Full-text available
Training, learning, and practical experiences are included in sustainable education in formal and informal settings, increasing the community involvement and personal development of students with special needs. The fourth goal of sustainable development clearly described the surety of quality of education for all. Priority is not only given to the normal students but also included students with special needs. With suitable pedagogy for sustainable educational development, every aspect of students' lives with special needs can be promoted. This study aimed to identify the important components for sustainable educational development for students with special needs. A self-developed questionnaire comprised 20 statements was used to collect the data against the Likert scale (Cronbach Alpha .87). Fifty teachers of special education were selected as a sample to collect the data. Data were analyzed through SPSS using descriptive and inferential statistical tools. The study concluded that according to the perceptions and responses of teachers' poverty reduction, appropriateness of curriculum, active participation of the special students, least restrictive environment, and special students in general setup are the necessary components for the educational sustainable development. It was recommended that teachers should adapt the curriculum and pedagogies to meet the specific learning needs of students with special needs.
Article
Full-text available
Attempts to enhance human memory and learning ability have a long tradition in science. This topic has recently gained substantial attention because of the increasing percentage of older individuals worldwide and the predicted rise of age-associated cognitive decline in brain functions. Transcranial brain stimulation methods, such as transcranial magnetic (TMS) and transcranial electric (tES) stimulation, have been extensively used in an effort to improve cognitive functions in humans. Here we summarize the available data on low-intensity tES for this purpose, in comparison to repetitive TMS and some pharmacological agents, such as caffeine and nicotine. There is no single area in the brain stimulation field in which only positive outcomes have been reported. For self-directed tES devices, how to restrict variability with regard to efficacy is an essential aspect of device design and function. As with any technique, reproducible outcomes depend on the equipment and how well this is matched to the experience and skill of the operator. For self-administered non-invasive brain stimulation, this requires device designs that rigorously incorporate human operator factors. The wide parameter space of non-invasive brain stimulation, including dose (e.g., duration, intensity (current density), number of repetitions), inclusion/exclusion (e.g., subject’s age), and homeostatic effects, administration of tasks before and during stimulation, and, most importantly, placebo or nocebo effects, have to be taken into account. The outcomes of stimulation are expected to depend on these parameters and should be strictly controlled. The consensus among experts is that low-intensity tES is safe as long as tested and accepted protocols (including, for example, dose, inclusion/exclusion) are followed and devices are used which follow established engineering risk-management procedures. Devices and protocols that allow stimulation outside these parameters cannot claim to be “safe” where they are applying stimulation beyond that examined in published studies that also investigated potential side effects. Brain stimulation devices marketed for consumer use are distinct from medical devices because they do not make medical claims and are therefore not necessarily subject to the same level of regulation as medical devices (i.e., by government agencies tasked with regulating medical devices). Manufacturers must follow ethical and best practices in marketing tES stimulators, including not misleading users by referencing effects from human trials using devices and protocols not similar to theirs.
Conference Paper
Full-text available
The present study introduces a novel cognitive intervention aimed at improving fluid intelligence (Gf), based on what we call the FAST framework: Flexible, Adaptive, Synergistic Training. FAST leverages a combination of novel executive function-based training and transcranial electrical stimulation, with aims to synergistically activate and strengthen mechanisms of cognitive control critical to Gf. The question of whether targeted training of cognitive skills can enhance Gf is a topic of intense debate, and is one of substantial practical and theoretical importance. This study aimed to assess whether the FAST intervention could lead to enhancement of Gf, relative to both a no-contact and an active control condition. To test our intervention we collected three Gf measures from 113 participants (BOMAT, Raven’s Advanced Progressive Matrices, and matrices similar to those in Raven’s, generated by Sandia labs), prior to and following one of three interventions: (1) the FAST intervention, a combination of 30 minutes of daily training with our novel training game, Robot Factory, and 20 minutes of concurrent transcranial random noise stimulation applied to bilateral dorsolateral prefrontal cortex, (2) an adaptively difficult active control intervention comprised of visuospatial tasks that specifically do not target Gf, or (3) a no-contact control condition. Logistic regression analyses of performance at posttest, controlling for pretest performance and age, found that the FAST group performed significantly (vs. No-contact) or marginally better (vs. Active Control) in terms of number of items answered correctly across all three Gf posttests, and significantly better in terms of accuracy (number of correct responses/number of items attempted) against both controls. This enhanced performance was found to be driven by significantly better performance for FAST in the BOMAT (and to a lesser extent Raven’s), empirically the most difficult of our tests, indicating performance improvements from potential transfer may be most apparent on more difficult Gf test items. A final analysis of the FAST group alone found a significant correlation between progress in Robot Factory and posttest performance, implicating the role of FAST training in Gf enhancement.
Article
Full-text available
It has been claimed that working memory training programs produce diverse beneficial effects. This article presents a meta-analysis of working memory training studies (with a pretest-posttest design and a control group) that have examined transfer to other measures (nonverbal ability, verbal ability, word decoding, reading comprehension, or arithmetic; 87 publications with 145 experimental comparisons). Immediately following training there were reliable improvements on measures of intermediate transfer (verbal and visuospatial working memory). For measures of far transfer (nonverbal ability, verbal ability, word decoding, reading comprehension, arithmetic) there was no convincing evidence of any reliable improvements when working memory training was compared with a treated control condition. Furthermore, mediation analyses indicated that across studies, the degree of improvement on working memory measures was not related to the magnitude of far-transfer effects found. Finally, analysis of publication bias shows that there is no evidential value from the studies of working memory training using treated controls. The authors conclude that working memory training programs appear to produce short-term, specific training effects that do not generalize to measures of “real-world” cognitive skills. These results seriously question the practical and theoretical importance of current computerized working memory programs as methods of training working memory skills.
Article
Full-text available
Cognitive training offers the potential for individualised learning, prevention of cognitive decline, and rehabilitation. However, key research challenges include ecological validity (training design), transfer of learning and long-term effects. Given that cognitive training and neuromodulation affect neuroplasticity, their combination could promote greater, synergistic effects. We investigated whether combining transcranial direct current stimulation (tDCS) with cognitive training could further enhance cognitive performance compared to training alone, and promote transfer within a short period of time. Healthy adults received real or sham tDCS over their dorsolateral prefrontal cortices during two 30-minute mathematics training sessions involving body movements. To examine the role of training, an active control group received tDCS during a non-mathematical task. Those who received real tDCS performed significantly better in the game than the sham group, and showed transfer effects to working memory, a related but non-numerical cognitive domain. This transfer effect was absent in active and sham control groups. Furthermore, training gains were more pronounced amongst those with lower baseline cognitive abilities, suggesting the potential for reducing cognitive inequalities. All effects associated with real tDCS remained 2 months post-training. Our study demonstrates the potential benefit of this approach for long-term enhancement of human learning and cognition.
Article
Researchers have recently started evaluating whether stimulating the brain noninvasively with a weak and painless electrical current (transcranial Electrical Stimulation, tES) enhances physiological and cognitive processes. Some studies found that tES has weak but positive effects on brain physiology, cognition, or assessment performance, what has attracted massive public interest. We present the first meta-analytic test of the hypothesis that tES in a learning phase is more effective than tES in an assessment phase. The meta-analysis included 246 effect sizes from studies on language or mathematical competence. The effect of tES was stronger when stimulation was administered during a learning phase (d = 0.712) as compared to stimulation administered during test performance (d = 0.207). The overall effect was stimulation-dosage specific and, as found in a previous meta-analysis, significant only for anodal stimulation and not for cathodal. The results provide evidence for the modulation of long-term synaptic plasticity by tES in the context of practically relevant learning tasks and highlight the need for more systematic evaluations of tES in educational settings.
Article
Scientists and clinicians have traditionally targeted single brain regions with stimulation to modulate brain function and disease. However, brain regions do not operate in isolation, but interact with other regions through networks. As such, stimulation of one region may impact and be impacted by other regions in its network. Here we test whether the effects of brain stimulation can be enhanced by simultaneously targeting a region and its network, identified with resting state functional connectivity MRI. Fifteen healthy participants received two types of transcranial direct current stimulation (tDCS): a traditional two-electrode montage targeting a single brain region (left primary motor cortex [M1]) and a novel eight-electrode montage targeting this region and its associated resting state network. As a control, 8 participants also received multifocal tDCS mismatched to this network. Network-targeted tDCS more than doubled the increase in left M1 excitability over time compared to traditional tDCS and the multifocal control. Modeling studies suggest these results are unlikely to be due to tDCS effects on left M1 itself, however it is impossible to completely exclude this possibility. It also remains unclear whether multifocal tDCS targeting a network selectively modulates this network and which regions within the network are most responsible for observed effects. Despite these limitations, network-targeted tDCS appears to be a promising approach for enhancing tDCS effects beyond traditional stimulation targeting a single brain region. Future work is needed to test whether these results extend to other resting state networks and enhance behavioral or therapeutic effects.
Article
Progress in cognitive neuroscience relies on methodological developments to increase the specificity of knowledge obtained regarding brain function. For example, in functional neuroimaging the current trend is to study the type of information carried by brain regions rather than simply compare activation levels induced by task manipulations. In this context noninvasive transcranial brain stimulation (NTBS) in the study of cognitive functions may appear coarse and old fashioned in its conventional uses. However, in their multitude of parameters, and by coupling them with behavioral manipulations, NTBS protocols can reach the specificity of imaging techniques. Here we review the different paradigms that have aimed to accomplish this in both basic science and clinical settings and follow the general philosophy of information-based approaches.
Article
Emerging evidence suggests that transcranial alternating current stimulation (tACS) is an effective, frequency-specific modulator of endogenous brain oscillations, with the potential to alter cognitive performance. Here, we show that reduction in response latencies to solve complex logic problem indexing fluid intelligence is obtained through 40 Hz-tACS (gamma band) applied to the prefrontal cortex. This improvement in human performance depends on individual ability, with slower performers at baseline receiving greater benefits. The effect could have not being explained by regression to the mean, and showed task and frequency specificity: it was not observed for trials not involving logical reasoning, as well as with the application of low frequency 5 Hz-tACS (theta band) or non-periodic high frequency random noise stimulation (101-640 Hz). Moreover, performance in a spatial working memory task was not affected by brain stimulation, excluding possible effects on fluid intelligence enhancement through an increase in memory performance. We suggest that such high-level cognitive functions are dissociable by frequency-specific neuromodulatory effects, possibly related to entrainment of specific brain rhythms. We conclude that individual differences in cognitive abilities, due to acquired or developmental origins, could be reduced during frequency-specific tACS, a finding that should be taken into account for future individual cognitive rehabilitation studies.