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Intrahemispheric Theta Rhythm Desynchronization Impairs Working Memory

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Background: There is a growing interest in large-scale connectivity as one of the crucial factors in working memory. Correlative evidence has revealed the anatomical and electrophysiological players in the working memory network, but understanding of the effective role of their connectivity remains elusive. Objective: In this double-blind, placebo-controlled study we aimed to identify the causal role of theta phase connectivity in visual-spatial working memory. Methods: The frontoparietal network was over- or de-synchronized in the anterior-posterior direction by multi-electrode, 6 Hz transcranial alternating current stimulation (tACS). Results: A decrease in memory performance and increase in reaction time was caused by frontoparietal intrahemispheric desynchronization. According to the diffusion drift model, this originated in a lower signal-to-noise ratio, known as the drift rate index, in the memory system. The EEG analysis revealed a corresponding decrease in phase connectivity between prefrontal and parietal areas after tACS-driven desynchronization. The over-synchronization did not result in any changes in either the behavioral or electrophysiological levels in healthy participants. Conclusion: Taken together, we demonstrate the feasibility of manipulating multi-site large-scale networks in humans, and the disruptive effect of frontoparietal desynchronization on theta phase connectivity and visual-spatial working memory.
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Intrahemispheric Theta Rhythm Desynchronization Impairs Working
Memory
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Ivan Alekseichuk1,*, Stefanie Corinna Pabel1, Andrea Antal1, Walter Paulus1
1Department of Clinical Neurophysiology, University Medical Center Göttingen, Georg-August
University Göttingen, Göttingen, Germany
*Correspondence: ivan.alekseichuk@med.uni-goettingen.de
Abstract
Background: There is a growing interest in large-scale connectivity as one of the crucial factors
in working memory. Correlative evidence has revealed the anatomical and
electrophysiological players in the working memory network, but understanding of the
effective role of their connectivity remains elusive. Objective: In this double-blind, placebo-
controlled study we aimed to identify the causal role of theta phase connectivity in visual-
spatial working memory. Methods: The frontoparietal network was over- or de-synchronized
in the anterior-posterior direction by multi-electrode, 6 Hz transcranial alternating current
stimulation (tACS). Results: A decrease in memory performance and increase in reaction time
was caused by frontoparietal intrahemispheric desynchronization. According to the diffusion
drift model, this originated in a lower signal-to-noise ratio, known as the drift rate index, in
the memory system. The EEG analysis revealed a corresponding decrease in phase
connectivity between prefrontal and parietal areas after tACS-driven desynchronization. The
over-synchronization did not result in any changes in either the behavioral or
electrophysiological levels in healthy participants. Conclusion: Taken together, we
demonstrate the feasibility of manipulating multi-site large-scale networks in humans, and
the disruptive effect of frontoparietal desynchronization on theta phase connectivity and
visual-spatial working memory.
The final publication is available at IOS Press through http://dx.doi.org/10.3233/RNN-160714
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1. Introduction
Successful voluntary behavior demands timely activation and cooperation of multiple brain
regions that together form the large-scale networks. Inherent precision and coordination
within the neural networks are essential for the compound cognitive processes, such as
working memory. The human working memory is subdivided into visual-spatial and
phonological domains (Baddeley, 2012). Functional neuroanatomical studies show that
visual-spatial working memory, of interest in this present study, arises in the bilateral parietal
and prefrontal areas (Barbey, Colom, Paul, & Grafman, 2014; Müller & Knight, 2006). There is
discussion regarding the neurophysiological organization. One view allocates working
memory-related computations to the prefrontal cortex, responsible for the operations with
items and cognitive control, and memory storage area to the posterior parietal cortex (Nee
et al., 2013; Todd & Marois, 2004). The other view is that both cortexes are self-coordinating
and interacting during working memory processes, functioning as a single frontoparietal
network and sharing the responsibility (Constantinidis & Klingberg, 2016; Ester, Sprague, &
Serences, 2015). Electrophysiological measurements demonstrated involvement of
oscillatory activity in the theta and gamma bands (Hsieh & Ranganath, 2014; Roux, Wibral,
Mohr, Singer, & Uhlhaas, 2012) and theta-gamma coupling (Alekseichuk, Turi, Amador de
Lara, Antal, & Paulus, 2016; Canolty & Knight, 2010; Lisman & Jensen, 2013) during the
memory task. Commonly, weak gamma rhythms are interpreted as the local activity of fast
spiking interneurons (Buzsáki & Wang, 2012; Cardin et al., 2009), while the theta oscillations
are driven by the distant pacemakers, reflecting remote communications (Colgin, 2013;
Fujisawa & Buzsáki, 2011). This theta activity was also found to be the primary coordinator
between the areas in the frontoparietal network, mainly through the mechanism of phase
synchronization (Fell & Axmacher, 2011; Salazar, Dotson, Bressler, & Gray, 2012). The multi-
scale graph analysis of phase connectivity during the visual cognitive task showed
reorganization within the frontoparietal and occipital networks, including increase in
clustering and rich club coefficients, predominantly in the theta band (Bola & Sabel, 2015).
Nevertheless, the causal role of theta phase connectivity and the degree to which it is driving
visual working memory function is not yet clear. Active manipulation by transcranial
alternating current stimulation (tACS) makes it possible to enhance the power of brain
oscillations and entrain their phase, thus, allowing us to observe the consequences of
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alternative brain states (Antal et al., 2008; Antal & Herrmann, 2016; Thut, Miniussi, & Gross,
2012). Both power modulation and phase entrainment are frequency-specific and spatially
restricted in their primary target by the shape of the applied electrical field. The modification
of local electrical landscape with tACS affects the neuronal communications through the
mechanism of ephaptic coupling (Anastassiou & Koch, 2015; Anastassiou, Perin, Markram, &
Koch, 2011). The feasibility of such manipulations on the neural level was demonstrated by
using the in silica and in vitro models (Deans, Powell, & Jefferys, 2007; Ozen et al., 2010;
Reato, Rahman, Bikson, & Parra, 2010). It was shown that an electrical field as weak as 0.2
mV/mm can influence local neural processes. Furthermore the pioneering
electrophysiological in vivo recordings in primates and humans demonstrated the possibility
of generating such electrical field in the brain tissues during tACS (Opitz et al., 2016). From
the biophysical perspective, due to the phase entrainment, injection of in-phase alternating
current in two or more oscillating systems should lead to their synchronization, and injection
of anti-phase current to desynchronization. The synchronization between two brain regions
can be achieved, if biologically plausible, by at least three electrodes, whereby two electrodes
would act in-phase and the third in anti-phase as the “return” electrode. On the other hand,
the power modulation depends only on the electrical field density and the neurophysiological
state before and during the stimulation.
In the present study we aimed to investigate the causal role of theta connectivity for working
memory by utilizing tACS. Since the theta rhythm arises from the subcortico-cortical
interactions, and subcortical structures cannot be selectively stimulated with existing non-
invasive techniques, this study focused on the neocortical, frontoparietal theta connectivity.
It has already been shown that desynchronization of the left prefrontal cortex versus the left
parietal cortex by tACS negatively impacted reaction time in the verbal working memory task,
while their synchronization versus the central medial brain area improved the reaction time
(Polania, Nitsche, Korman, Batsikadze, & Paulus, 2012). Other researchers reported that
desynchronization of the medial prefrontal versus medial parietal cortexes led to a poorer
verbal working memory performance in one case (Chander et al., 2016), and to a higher score
in the digital span test in another (Vosskuhl, Huster, & Herrmann, 2015). The latter study,
however, utilized the individual stimulation protocols, where the frequency was tailored to
the subject specific theta peak. Thus, manipulations using theta oscillations can affect working
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memory, and the outcomes depend on the anatomical target, stimulation parameters and
the cognitive task. Nevertheless, two conceptual limitations became clear from the preceding
experiments. First, in every study only one or two regions were taken into account, leaving
questions open regarding possible phase interactions within a multisite brain network.
Second, previous studies utilized a two-electrode montage or used different electrode
montages between the conditions, thereby undermining definitive differentiation between
the power modulation and phase de/synchronization effects. Here, for the first time, we
entrained the theta phase connectivity between four neocortical sites that are hosting visual-
spatial working memory. That is, the left and right prefrontal and posterior parietal areas. We
hypothesized that (i) frontoparietal synchronization simultaneous synchronization of left
prefrontal with left parietal cortexes and right prefrontal with right parietal cortexes, will
optimize the cognitive processes during the working memory task, while (ii) the frontoparietal
desynchronization will negatively affect performance. Further we introduced the
computational behavioral model the diffusion drift model, in order to evaluate the signal-
to-noise ratio in the memory system and response conservativeness due to the frontoparietal
de/synchronization (Ratcliff, 1978; Ratcliff, Smith, Brown, & McKoon, 2016). The diffusion
drift model assumes that a simple forced-choice decision, such as recognition of a memory
item, is made within a noisy neural process that accumulates information over time from a
starting point toward one of the response boundaries. The deterministic part of this noisy
process, the drift rate, can be defined as the speed of evidence accumulation or as an index
for the signal-to-noise ratio in the system. For the memory task, the drift rate represents the
quality of match between the stimulus and probe. The response boundaries are characterize
the conservativeness or amount of evidences needed for the decision on the recognition of
memory item and therefore reflect the cognitive strategy regarding the speed-accuracy
preference. Also the model allows to extract the part of reaction time that do not relate to
the evidence accumulation process the non-decision time. We expected that the
manipulation with frontoparietal phase connectivity will affect the informational transfer
and, as the result, the signal-to-noise ratio within the working memory network, but not the
response conservativeness or non-decision time.
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2. Material and Methods
2.1. Participants
Thirty-five healthy, adult volunteers with normal or corrected-to-normal vision, without a
history of neurological or psychiatric diseases took part in the study. Ten volunteers (6
females, 24 ± 2 years old, 16.7 ± 1.8 years of education) were assigned to the natural history
group and twenty-five participants (13 females, 23.5 ± 2.9 years old, 16.6 ± 2.1 years of
education) formed the experimental group. All volunteers gave informed consent in
accordance with the Declaration of Helsinki and regulation of the Ethics Committee of Georg-
August University Göttingen.
2.2. Experimental Procedure
The volunteers participated in three sessions, performing visual-spatial working memory
tests. They were naive regarding the study a priori and were familiarized with the laboratory
and setting during the standardized introductory meeting. Participants in the natural history
group (Natural) received no intervention during any of the three sessions. Their performance
was used to assess the cognitive responses on the working memory task in the absent of real
or placebo effects and to investigate the conceivable learning effect over three sessions. The
experimental group took part in the placebo-controlled, randomized, crossover, double-blind
study which included three conditions: frontoparietal synchronization (Sync), frontoparietal
desynchronization (Desync) and sham control (Placebo). TACS was applied for the whole test
period (17-19 minutes) and was framed with the EEG recording.
2.3. Working Memory Task
The participants performed the two-back visual-spatial working memory task during each
session (Fig. 1). The task comprised two blocks of trials with a one-minute break between the
blocks. Each block contained 100 trials and required 8-9 minutes to complete. Within every
trial there were stimulus and response periods. The stimulus consisted from four dots of the
same shape and size randomly distributed across the visual field. The color altered between
purple and blue from trial to trial to facilitate the memorization in two-back fashion.
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Fig. 1. Working memory task. During the session, the volunteer performed the two-back visual-spatial working
memory test. The test comprised two 100-trials blocks. Each trial consisted of the stimulus (0.6 sec) and the
response period (up to 2.5 sec), which were separated by the variable intervals (1-2 sec). The participants
indicated whether the recent stimulus matched the two-back item by pressing the “yes” or “no” button on the
response pad.
In the response period, volunteers were requested to press the “yes” or “no” button,
indicating whether the stimulus was in the same position as two trials before. The order of
trials was pseudo-randomized across the sessions. The amount of matching and non-
matching trials per session was equal. The test was generated in PsychoPy (Peirce, 2008).
Responses were acquired by using the response pad RB-740 (Cedrus™). The test produced
two primary types of data: the responses and the reaction times. All responses across the
single session were analyzed to obtain the individual condition-specific correct hit and false
alarm rates. Further, these two parameters were combined in the working memory
performance score by subtracting the false alarm rate from the correct hit rate. The reaction
time data were log-transformed to correct for the right skewed distribution before the
statistical analysis.
To further investigate the behavioral differences between the conditions that reflected in
both the memory performance and in the reaction time, we implemented the diffusion drift
model. The fundamental idea behind the model is that prior to the response in an alternative
task the participant is accumulating evidence in favor of one or another decision. This
accumulation process has a time cost which contributes to the total reaction time. When the
amount of accumulated evidence reaches a certain critical threshold, the answer is given.
There are multiple mathematical approaches to model such a decision-making process which
vary in complexity and number of free parameters. Here we implemented the Robust-EZ drift
model (Wagenmakers, van der Maas, Dolan, & Grasman, 2008; Wagenmakers, van der Maas,
& Grasman, 2007). It is the analytical solution that combines relative simplicity and high
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precision, as compared to more parameter-rich methods (van Ravenzwaaij & Oberauer,
2009). Three main parameters were estimated in our case. The drift rate (v) indicates the
speed of evidence accumulation or, in other words, information uptake per time. The
boundary separation (a) describes the amount of evidence needed for the decision. And the
non-decision time (Ter) specifies the part of reaction time in which no evidence accumulation
is occurring. Mathematically, the Robust-EZ procedure begins with fitting the reaction time
data to the mixed model with two components: the uniform distribution of response
contaminants and exponentially modified Gaussian distribution that captures the process of
interest. The latter is the linear combination of exponential distribution with rate τ and normal
distribution with mean µ and standard deviation σ. The mixed distribution is fitted by using
the maximum likelihood procedure with quasi-Newton routine for optimization and method-
of-moments estimation of starting values:
  
where M1, M2, and M3 are the moments of first, second, and third order, respectively. Then,
the reaction time mean (MRTEG) and variance (VRTEG) can be recovered as the functions of ex-
Gaussian distribution:
  
Finally, the Robust-EZ procedure introduces the proportion of correct responses (Pc) and
scaling parameter s (by default, s = 0.1) to determine the drift rate (v), boundary separation
(a), and non-decision time (Ter) as follows:




 


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Statistical differences at the group level were assessed with the bootstrap t-test. This test
estimates the probability that the observed numerical difference between two groups comes
from the null hypothesis distribution, which is obtained by bootstrapping (resampling with
replacement) the dataset. Bootstrapping overcomes many limitations inherent in the classical
t-test, such as assumption regarding the data distribution, yet keeps high statistical power
and intuitive interpretability. The effect size was estimated in terms of Cohen’s d and reported
together with 95% confidence intervals. The order effect was tested by fitting the linear
regression to the data with “session order” as a categorical predictor.
2.4. Transcranial Alternating Current Stimulation (tACS)
Transcranial electrical stimulation and electrophysiological recording were performed using
the combined device StarStim (Neuroelectrics™). Four stimulation electrodes were positioned
over the AF3, AF4, P3, and P4 points according to the international 10-10 system (Fig. 2A, B).
The stimulation was delivered via Ag/AgCl electrodes (r = 1 cm) with electroconductive gel
during the test procedure, which lasted about 18 minutes, including 10 s fade-in and fade-out
periods. Small stimulation electrodes provided higher precision of electric field, while the skin
sensations were not inflated in comparison to larger stimulation electrodes (Turi et al., 2014).
The alternating current was applied with the frequency of 6 Hz and the intensity of 1 mA peak-
to-baseline. Impedance was kept below 10 kOhm.
Frontoparietal synchronization (Sync): The electrodes AF3 and P3 from one side, and AF4 and
P4 from the contra-lateral side were in-phase. Thus, the prefrontal and parietal electrodes
were synchronized in pairs, while left and right hemisphere electrodes were in anti-phase to
ensure the same amount of inward and outward current to the brain.
Frontoparietal desynchronization (Desync): The prefrontal electrodes AF3 and AF4, and the
parietal electrodes P3 and P4, were in phase. In this configuration, the prefrontal and parietal
electrodes were desynchronized in pairs.
Sham control (Placebo): Control intervention was delivered with the same intensity and via
the same electrode montage (AF3, AF4, P3, and P4) as for the real stimulation conditions, but
only for 10 s at the beginning and end of the session, according to fade-in/fade-out placebo
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protocol (Ambrus et al., 2012). The phase relationships between the stimulation electrodes
were randomized.
Fig. 2. Transcranial alternating current stimulation. (A-B) Multi-electrode montage for the conditions of
frontoparietal desynchronization (Desync) and frontoparietal synchronization (Sync). The electrodes were
positioned over the points AF3, AF4, P3, and P4 according to the international 10-10 EEG system. Alternating
current was delivered during the working memory task via all four electrodes at a frequency of 6 Hz and an
intensity of 1 mA peak-to-baseline. The phase of the current is color-coded. (C-D) Realistic, anisotropic, finite
element models of the current flow. The field strength is color-coded from 0 (dark blue) to 0.55 mV/mm (dark
red).
To estimate the electrical field distribution in the brain, we calculated the realistic,
anisotropic, finite element head models (Fig. 2C, D). The following compartments were
modeled by using SimNIBS 2 (Thielscher, Antunes, & Saturnino, 2015): scalp (σ = 0.465 S/m),
bone (σ = 0.010 S/m), cerebrospinal fluid (σ = 1.654 S/m), gray matter (σ = 0.275 S/m), and
white matter (σ = 0.126 S/m). Post-processing and visualization were done in Gmsh (Geuzaine
& Remacle, 2009).
2.5. Electroencephalography (EEG)
Eyes-open, resting state EEG was recorded before and after each stimulation and test
procedure for three minutes against the reference electrode on the right earlobe. Eight
Ag/AgCl electrodes were positioned over the Fpz, AF3, AF4, C3, C4, P3, P4, and Oz points
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according to the international 10-10 system. The sampling rate was equal to 500 Hz at an
analog-digital precision of 24 bits. Impedance was kept below 10 kOhm.
EEG was analyzed in MATLAB 2016a with the Fieldtrip toolbox (Oostenveld, Fries, Maris, &
Schoffelen, 2011). First, the recordings were high-pass filtered (> 1 Hz) with an 8th-order
forward-backward Butterworth filter. The line noise (50 and 100 Hz) was suppressed by using
the discrete Fourier transform filter. The data were then divided into 2-s-long trials with 50%
overlap. The trials with abnormal within-channel variance were visually inspected and
removed if a technical or muscle artifact was confirmed. On average 13.6% of trials, between
6.5% and 17.2%, were excluded. After that, we utilized the independent component analysis.
One component, related to eye movements, was rejected. The preprocessing was finished
after the low-pass filtering (< 30 Hz) with the 8th-order forward-backward Butterworth filter.
Phase connectivity was estimated for every pair of electrodes before and after the stimulation
and test procedure. Changes between pre- and post-stimulation EEG connectivity are known
as reliable and characteristic reflection of neurophysiological brain state in healthy humans
and patients (Alekseichuk et al., 2016; Bola et al., 2014; Gall et al., 2016). First, the frequency
analysis was performed using the Hanning tapers in the range from 1 to 30 Hz. The data were
separated on the frequency bands 1-4 Hz (delta), 4-8 Hz (theta), 8-12 Hz (alpha), 12-20 Hz
(low beta), and 20-30 Hz (high beta). Then, the weighted phase lag indexes (wPLI) were
calculated for every trial and frequency band, and averaged to obtain the individual,
condition-specific, pre- and post-stimulation connectivity values. This metric is known as
robust, volume conduction and noise insensitive estimator of functional connectivity in the
human brain (Vinck, Oostenveld, Van Wingerden, Battaglia, & Pennartz, 2011). It depends
only on the imaginary part of coherence and debiased with respect to the sample size:
  


Where average weight
  
 and the weight  .
The pairwise product of thresholding . Xj and Xk is
complex-valued cross-spectrums of i-th and k-th trial between 1 and N.
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Any increase or decrease in phase connectivity after the stimulation, as compared to the pre-
stimulation state, was assessed for every condition by using the bootstrap t-test (10,000
iterations). To compensate for multiple comparisons, all tests were corrected within each
condition according to the false discovery rate procedure (Benjamini & Yekutieli, 2001).
Significant changes (pcorr 0.05) in phase connectivity were visualized in BrainNet Viewer (Xia,
Wang, & He, 2013).
3. Results
First, we analyzed the working memory performance and reaction time changes (Fig. 3A, B).
Interestingly, both control stimulation (Placebo) and frontoparietal synchronization (Sync)
resulted in the similar performance score (72.8 ± 2.3% and 71.9 ± 2.0%, accordingly) and
reaction time score (5.881 ± 0.030 and 5.890 ± 0.033). Same values were observed for the
natural history group (Natural; performance: 72.1 ± 1.5% and reaction time: 5.887 ± 0.031).
However, the frontoparietal desynchronization (Desync) led to the decreased memory
performance (69.0 ± 2.4%) and slower reaction time (5.930 ± 0.035). Significant differences
were observed between Desync and Placebo conditions (performance: bootstrap t-test p =
0.018, Cohen’s d = 0.41 [0.05 0.78]; reaction time: p = 0.015, d = 0.35 [0.01 0.80]), and
between Desync and Sync conditions (performance: p = 0.019, d = 0.37 [0.03 0.89]; reaction
time: p = 0.049, d = 0.27 [-0.03 0.75]), but not between Synch and Placebo conditions
(performance: p = 0.282, d = 0.10 [-0.30 0.53]; reaction time: p = 0.339, d = 0.06 [-0.40
0.42]). Taking the crossover study design, we utilized the linear model to control for the
learning effect within the natural history group (performance: R2 = 0.05, p = 0.24; reaction
time: R2 = 0.09, p = 0.25) and session order effect within the experimental group
(performance: R2 = 0.01, p = 0.76; reaction time: R2 = 0.01, p = 0.71). Hence, learning and
session order had no significant impact on the group level statistics.
Further, we introduced the diffusion drift model to account for the simultaneous changes in
working memory performance and reaction time, and investigate what behavioral aspects
were modified (Fig. 3C, D, E).
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Fig. 3. Behavioral results. The outcomes of the working memory tests for the natural history group (Natural, n =
10) and experimental group (n = 25). The latter experienced the following conditions in a randomized order:
frontoparietal synchronization (Synch), frontoparietal desynchronization (Desync), and sham control (Placebo).
All statistical comparisons were conducted within the experimental group by using the bootstrap t-test and
depicted as horizontal brackets (* p ≤ 0.05, n.s. – p > 0.05). All values are presented as mean ± SEM. (A) Working
memory performance score. (B) Log-normalized reaction time. (C-E) Main parameters of the diffusion drift
model. According to the model, prior to the participant’s response to the question, he/she is accumulating
evidence in favor of one or another alternative answer. The drift rate (v) shows the deterministic component of
a noisy evidence accumulation process, and can be treated as the speed of accumulation or signal-to-noise ratio.
The boundary separation (a) indicates the conservativeness, i.e. the amount of accumulated evidence needed
to make a response. Finally, the non-decision time (Ter) denotes the reaction time excluding the evidence-
accumulation process.
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The drift rate (v) was impaired due to Desync stimulation (v = 0.249 ± 0.015) in statistical
comparison with Placebo condition (v = 0.273 ± 0.014; bootstrap t-test p = 0.031, Cohen’s d
= 0.47 [0.09 0.97]) as well as Sync condition (v = 0.275 ± 0.013; p = 0.017, d = 0.34 [0.04
0.78]), while two latter conditions demonstrated no differences (p = 0.44, d = 0.02 [-0.39
0.43]) and were numerically similar to Natural group (v = 0.270 ± 0.011). At the same time,
the boundary separation (a) did not differ between Placebo (a = 0.075 ± 0.004) and other
conditions: Desync (a = 0.077 ± 0.004; p = 0.16, d = 0.18 [-0.19 0.73]), Sync (a = 0.073 ±
0.004; p = 0.19, d = 0.19 [-0.22 0.62]), and Natural (a = 0.076 ± 0.003). The nondecision time
(Ter) also was not modulated by any stimulation protocols in numerical comparison to Natural
group (Ter = 0.250 ± 0.009) and statistical comparison to Placebo condition (Ter = 0.242 ±
0.006): both Desync (Ter = 0.245 ± 0.009; p = 0.29, d = 0.18 [-0.19 0.72]) and Sync
interventions (Ter = 0.247 ± 0.011, p = 0.20, d = 0.19 [-0.21 0.61]) are characterized by the
same values. Once again, the session order had no detectable impact on any parameter (v: R2
= 0.03, p = 0.39; a: R2 = 0.01, p = 0.68; Ter: R2 = 0.01, p = 0.87).
We estimated the phase connectivity difference maps in the theta range between the post-
and pre-stimulation states for every condition (Fig. 4). Significant decrease in theta
connectivity between the prefrontal (AF3, Fpz, and AF4) and parietal (P3 and P4) areas was
observed after Desync stimulation.
Fig. 4. Phase connectivity difference maps. The
phase connectivity was estimated in the
frequency band between 4 Hz and 8 Hz for the
electroencephalographic data that were
recorded before and after every experimental
session. In the figure, all electrodes that showed
a significant decrease in phase coupling are
linked with blue lines, and an increase with
green lines (bootstrap t-test, p 0.05, FDR
corrected). Orange spheres depict the
stimulation and recording electrodes, and yellow
spheres depict recording-only electrodes.
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Altogether, 21.4% of electrode-pairs (6 out of 28) indicated the lower weighted phase lag
index (bootstrap t-test p ≤ 0.05, FDR corrected). At the same time, no modification of phase
connectivity was introduced by Placebo, and only minor changes accompanied Sync
stimulation, where one pair of electrodes (C4-P4) showed an increase in theta connectivity.
No significant post-stimulation changes in phase connectivity (bootstrap t-test p ≤ 0.05, FDR
corrected) were observed for other brain rhythms, namely, the delta, alpha, low beta and
high beta frequencies.
4. Discussion
In this study we explore the effects of frontoparietal intrahemispheric synchronization and
desynchronization on visual-spatial working memory. Contrary to expectations, we did not
find a significant cognitive improvement during the synchronization between the left
prefrontal with left parietal cortexes, and right prefrontal with right parietal cortexes
(condition Sync). However, the second hypothesis could be proven: desynchronization
between prefrontal and posterior parietal cortexes (Desync) has a negative impact on both
working memory performance and reaction time. In agreement with the behavioral data, the
EEG analysis showed a decrease in theta phase connectivity between the anterior and
posterior electrodes only after Desync stimulation, and no aftereffects in any other cases.
Previously, Polania and colleagues (Polania et al., 2012) reported a faster or slower reaction
time in the verbal working memory task due to synchronizing or desynchronizing tACS
between the left hemispheric prefrontal and parietal cortexes. We not only reproduced the
impairment in reaction time, but also found a lower performance score due to our
bihemispheric desynchronization. The positive effect of frontoparietal synchronization was
not reproduced, possibly because of a different electrode montage in the previous study that
targeted not just frontal-parietal, but frontal-parietal-central areas (electrodes F3-P3 and
return over Cz, according to the international 10-10 system), leading to a differently shaped
electrical field. Here we avoided this problem by using four stimulation electrodes and
switching the current between them, which allowed to keep the electrode montage same for
every condition. Chander and colleagues (Chander et al., 2016) applied tACS to target the
frontal midline theta activity via the electrodes over the medial prefrontal versus medial
parietal areas (Fpz versus Pz). As a result, the stimulation produced a frontoparietal
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desynchronization, which negatively affected verbal working memory performance and,
according to the ongoing magnetoencephalography, reduced midline theta activity. However,
they did not examined the effect of frontoparietal synchronization.
Applying the diffusion drift modeling approach, in agreement with the prediction we
observed a lower drift rate index under the desynchronizing tACS with unaltered non-decision
time and boundary separation. The latter is interesting as it is the main mechanism by which
the speed-accuracy trade-off can arise (Wagenmakers et al., 2007). Thereby, we can
hypothesize that mild behavioral impairment in our study was not the consequence of
different response strategy, but the result of a lower quality of information, possibly due to
the disturbed encoding or comparison between the items. From the other side, no changes
in the non-decision time together with longer overall reaction time during Desync stimulation
argue in favor of weakened working memory computations rather than delays in other
physiological processes. The specific decrease in the drift rate index was also reported by
Philiastides et al. in connection with a poorer performance in a visual discriminative task due
to low-frequency repetitive transcranial magnetic stimulation (TMS) over the left prefrontal
cortex (Philiastides, Auksztulewicz, Heekeren, & Blankenburg, 2011). In line with modern
assumptions that TMS disturbs the connectivity of large scale networks, one can speculate
about the link between the stable large-scale neural networks and the drift rate index in the
working memory processes.
Taking together, our analysis shows that desynchronizing theta tACS over the prefrontal
versus parietal cortexes (Desync) worsens working memory performance and increases
reaction time due to the decline in the information uptake (i.e. drift rate), which is also
reflected in the outlasting theta rhythm desynchronization in the neocortex. At the same
time, the sham stimulation (Placebo) and frontoparietal synchronization (Sync) induced no
significant changes on the performance or electrophysiological levels. It should be
emphasized that the conditions Sync and Desync led to these distinct results by utilizing
exactly the same power, latency, and frequency of tACS with the only difference in the AC
phase. Therefore, possible online effect of power modulation per se was either not present
or overtaken by the effect of phase entrainment; in any case, it can be ruled out as a
mechanism behind the observed cognitive effect. Furthermore, the double-blind nature of
the experimental procedure ensured that the impact of subjective perception and
16
expectations by the study volunteers and investigator was kept at the minimum. Together
these points exclude any alternative explanation for the observed results and reinforce the
hypothesis of phase entrainment within the frontoparietal network.
Further interpreting the modification of theta phase connectivity, one limitation of the
present study should be mentioned. By concentrating on multiple brain regions and their
connectivity, we tackle the intricate functional relationships, because the frontoparietal
synchronization in our study can be seen as interhemispheric desynchronization, and vice
versa (Fig. 2A, B). That is why the interpretation of stimulation protocols and their outcomes
sought and found extra support in a priori expectation that theta connectivity in the anterior-
posterior direction is more prominent and thus more susceptible to the manipulation than
the between-hemispheric connectivity (Sauseng, Klimesch, Schabus, & Doppelmayr, 2005;
Summerfield & Mangels, 2005). This was further confirmed by the present EEG data, which
indicates an impact on the phase connectivity between the prefrontal and parietal electrodes
in Desync condition, but not between the left and right electrodes, supporting the anterior-
posterior view of this intervention. Our assumptions and results are also in line with the
influential hypothesis regarding visual working memory architecture that suggests a degree
of independence for informational processing and capacity limits between left and right
hemispheres (Franconeri, Alvarez, & Cavanagh, 2013; Umemoto, Drew, Ester, & Awh, 2010).
This originates in the unilateral activation of left or right occipital cortex by right- or left-sided
visual information.
The present findings offer new insights on the role of theta phase connectivity as the naturally
well-adjusted functional substrate for visual-spatial working memory. While the long-range
theta coherence has already been identified as the key phenomenon for working memory
function (Fell & Axmacher, 2011; Lisman, 2010), new results suggest that the frontoparietal
theta oscillations in healthy humans are an optimized system. This optimized system
preferentially reacts to disturbances, leading to impairment of cognitive function. However,
having our data in mind, we can speculate that the performance of the system cannot be that
easily improved if it is already in the optimal state (Furuya, Klaus, Nitsche, Paulus, & Altenmu,
2014). It still remains to be seen whether humans with minor cognitive impairment will
improve in response to synchronizing tACS. It may be interesting to consider the present
results in a broader sense as a piece in the framework of “communication through coherence”
17
in the brain (Fries, 2015). Phase connectivity may have variable roles depending on the
network state − hence continuous enhancement of frontoparietal synchronization may not
be the best way to facilitate working memory processes. Future investigations will have an
opportunity to clarify the impact of state-specific synchronization, e.g. by using closed-loop
stimulation during the encoding, maintenance, or retrieval memory stages. With this first
attempt to coordinate as many as four brain areas with tACS, we provide in addition to new
data on the working memory processes − good experimental evidence supporting the
feasibility of multi-site phase entrainment. Further research may overcome the necessity to
have anti-phase electrodes/areas and improve the current setting by introducing multiple
Laplacian electrode arrays (Helfrich et al. 2014).
From the clinical perspective, both de- and over-synchronization within the large-scale brain
networks are associated with numerous neuropsychiatric disorders, such as schizophrenia,
anxiety, depression, and autism (reviewed in Menon 2011; Voytek and Knight 2015). While it
is straightforward that the lack of between-area coordination can lead to impairment of
network-based neural functions, over-synchronization can do no less harm by diminishing the
informational transfer due to the lower computational dimensionality (i.e. lower “number of
channels” for communication). Therefore the method of soft, large-scale desynchronization
holds out definite promise for clinical practice.
In conclusion, we demonstrate that manipulation with theta phase connectivity between the
bihemispheric prefrontal and parietal areas is feasible, that tACS-induced frontoparietal
desynchronization in healthy humans decreases phase connectivity, and that a deficit in
frontoparietal theta phase connectivity impairs visual-spatial working memory.
Acknowledgments
IA conceptualized the study. IA, AA, and WP designed the experiments. SCP collected the data.
IA analyzed the data. AA and WP supervised the study. IA, SCP, AA, and WP wrote the paper.
We thank Dr. Zsolt Turi for inspiring discussions regarding the present study and Christine
Crozier for proofreading the manuscript. This work was supported by the Deutsche
Forschungsgemeinschaft (SPP 1665 to WP).
18
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... Synchronization between brain oscillations of different brain cortices is considered an essential step in cognitive and behavioral processes (Klimesch et al., 2010;Siegel et al., 2012;Kleinert et al., 2017). Therefore, the effect of manipulating the phase synchrony of theta oscillations between cerebral cortices known to be involved in WM processes has been extensively addressed in the literature (Polanía et al., 2012;Alekseichuk et al., 2017;Kleinert et al., 2017;Violante et al., 2017;Tseng et al., 2018), the results of the studies can be outlined as follows: in-phase theta tACS over right and left posterior parietal cortices enhanced visual WM task scores in low-performers, while anti-phase theta-tACS had a detrimental effect on high-performers' memory abilities (Tseng et al., 2018); Polanía et al. (2012) suggested that, in contrary to the anti-phase setting, in-phase theta tACS (between the left prefrontal and posterior parietal cortex) significantly reduced reaction times in a delayed letter discrimination task; Alekseichuk et al. (2017) induced fronto-parietal intrahemispheric desynchronization and synchronization (thetaphase) and assessed the outcomes of 2-back visuospatial WM. They found that theta phase desynchronization had a negative impact on WM outcomes (reaction time and performance) and on phase connectivity (frontal and parietal cortices), but the synchronization of theta phases between the areas of interest had no effect on WM outcomes or electroencephalography (EEG) features (Alekseichuk et al., 2017); whereas Violante et al. (2017), examined the effect of in-phase and anti-phase theta-tACS on WM in different brain cortices (right fronto-parietal middle frontal gyrus and inferior parietal cortices) and suggested that in-phase theta-tACS improved n-back task outcomes when the cognitive demands were significantly high; in contrast to the results of the above studies, Kleinert et al. (2017) found no significant effects of theta-tACS (in-phase and anti-phase) applied over right fronto-temporal regions on visuospatial WM task outcomes or EEG features. ...
... Synchronization between brain oscillations of different brain cortices is considered an essential step in cognitive and behavioral processes (Klimesch et al., 2010;Siegel et al., 2012;Kleinert et al., 2017). Therefore, the effect of manipulating the phase synchrony of theta oscillations between cerebral cortices known to be involved in WM processes has been extensively addressed in the literature (Polanía et al., 2012;Alekseichuk et al., 2017;Kleinert et al., 2017;Violante et al., 2017;Tseng et al., 2018), the results of the studies can be outlined as follows: in-phase theta tACS over right and left posterior parietal cortices enhanced visual WM task scores in low-performers, while anti-phase theta-tACS had a detrimental effect on high-performers' memory abilities (Tseng et al., 2018); Polanía et al. (2012) suggested that, in contrary to the anti-phase setting, in-phase theta tACS (between the left prefrontal and posterior parietal cortex) significantly reduced reaction times in a delayed letter discrimination task; Alekseichuk et al. (2017) induced fronto-parietal intrahemispheric desynchronization and synchronization (thetaphase) and assessed the outcomes of 2-back visuospatial WM. They found that theta phase desynchronization had a negative impact on WM outcomes (reaction time and performance) and on phase connectivity (frontal and parietal cortices), but the synchronization of theta phases between the areas of interest had no effect on WM outcomes or electroencephalography (EEG) features (Alekseichuk et al., 2017); whereas Violante et al. (2017), examined the effect of in-phase and anti-phase theta-tACS on WM in different brain cortices (right fronto-parietal middle frontal gyrus and inferior parietal cortices) and suggested that in-phase theta-tACS improved n-back task outcomes when the cognitive demands were significantly high; in contrast to the results of the above studies, Kleinert et al. (2017) found no significant effects of theta-tACS (in-phase and anti-phase) applied over right fronto-temporal regions on visuospatial WM task outcomes or EEG features. ...
... Therefore, the effect of manipulating the phase synchrony of theta oscillations between cerebral cortices known to be involved in WM processes has been extensively addressed in the literature (Polanía et al., 2012;Alekseichuk et al., 2017;Kleinert et al., 2017;Violante et al., 2017;Tseng et al., 2018), the results of the studies can be outlined as follows: in-phase theta tACS over right and left posterior parietal cortices enhanced visual WM task scores in low-performers, while anti-phase theta-tACS had a detrimental effect on high-performers' memory abilities (Tseng et al., 2018); Polanía et al. (2012) suggested that, in contrary to the anti-phase setting, in-phase theta tACS (between the left prefrontal and posterior parietal cortex) significantly reduced reaction times in a delayed letter discrimination task; Alekseichuk et al. (2017) induced fronto-parietal intrahemispheric desynchronization and synchronization (thetaphase) and assessed the outcomes of 2-back visuospatial WM. They found that theta phase desynchronization had a negative impact on WM outcomes (reaction time and performance) and on phase connectivity (frontal and parietal cortices), but the synchronization of theta phases between the areas of interest had no effect on WM outcomes or electroencephalography (EEG) features (Alekseichuk et al., 2017); whereas Violante et al. (2017), examined the effect of in-phase and anti-phase theta-tACS on WM in different brain cortices (right fronto-parietal middle frontal gyrus and inferior parietal cortices) and suggested that in-phase theta-tACS improved n-back task outcomes when the cognitive demands were significantly high; in contrast to the results of the above studies, Kleinert et al. (2017) found no significant effects of theta-tACS (in-phase and anti-phase) applied over right fronto-temporal regions on visuospatial WM task outcomes or EEG features. On the other hand, Reinhart and Nguyen (2019) stimulated two brain regions by the mean of theta-tACS in healthy older adults using different experimental setups. ...
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Working memory (WM) is a cognitive process that involves maintaining and manipulating information for a short period of time. WM is central to many cognitive processes and declines rapidly with age. Deficits in WM are seen in older adults and in patients with dementia, schizophrenia, major depression, mild cognitive impairment, Alzheimer’s disease, etc. The frontal, parietal, and occipital cortices are significantly involved in WM processing and all brain oscillations are implicated in tackling WM tasks, particularly theta and gamma bands. The theta/gamma neural code hypothesis assumes that retained memory items are recorded via theta-nested gamma cycles. Neuronal oscillations can be manipulated by sensory, invasive- and non-invasive brain stimulations. Transcranial alternating-current stimulation (tACS) and repetitive transcranial magnetic stimulation (rTMS) are frequency-tuned non-invasive brain stimulation (NIBS) techniques that have been used to entrain endogenous oscillations in a frequency-specific manner. Compared to rTMS, tACS demonstrates superior cost, tolerability, portability, and safety profile, making it an attractive potential tool for improving cognitive performance. Although cognitive research with tACS is still in its infancy compared to rTMS, a number of studies have shown a promising WM enhancement effect, especially in the elderly and patients with cognitive deficits. This review focuses on the various methods and outcomes of tACS on WM in healthy and unhealthy human adults and highlights the established findings, unknowns, challenges, and perspectives important for translating laboratory tACS into realistic clinical settings. This will allow researchers to identify gaps in the literature and develop frequency-tuned tACS protocols with promising safety and efficacy outcomes. Therefore, research efforts in this direction should help to consider frequency-tuned tACS as a non-pharmacological tool of cognitive rehabilitation in physiological aging and patients with cognitive deficits.
... This study aimed to extend previous studies that have targeted the FPN with bifocal theta tACS to improve WM performance [24,25,76,77] by using a similar setup to study the effect on MSL. This has been the first time that both the DLPFC and the PPC have been targeted with the use of bifocal theta tACS during a motor sequence learning task. ...
... The results of the N-back task showed a specific effect on reaction times, and not on hit-rate, false alarms, and accuracy. These 25], while desynchronized tACS increased the reaction time on a visual WM task [25,76]. The exact reason for the effect on reaction times but not on other parameters remains elusive. ...
... Therefore, Violante et al. suggest that the increase in neural activation in the parietal areas might have interacted with the mechanisms related to reaction times [24]. However, Alekseichuk et al. argue that the improved reaction times are network-related, as they found increased reaction times after desynchronization of the prefrontal areas from the parietal areas [76]. They argue that this is due to a decline of information uptake, reflected in the outlasting theta rhythm desynchronization in the cortex [76]. ...
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Background: Healthy older adults show a decrease in motor performance and motor learning capacity as well as in working memory (WM) performance. WM has been suggested to be involved in motor learning processes, such as sequence learning. Correlational evidence has shown the involvement of the frontoparietal network (FPN), a network underlying WM processes, in motor sequence learning. However, causal evidence is currently lacking. Non-invasive brain stimulation (NIBS) studies have focused so far predominantly on motor-related areas to enhance motor sequence learning while areas associated with more cognitive aspects of motor learning have not yet been addressed. Hypothesis: In this study, we aim to provide causal evidence for the involvement of WM processes and the underlying FPN in the successful performance of a motor sequence learning task by using theta transcranial alternating current stimulation (tACS) targeting the FPN during a motor sequence learning task. Methods: In a cohort of 20 healthy older adults, we applied bifocal tACS in the theta range to the FPN during a sequence learning task. With the use of a double-blind, cross-over design, we tested the efficacy of active compared to sham stimulation. Two versions of the motor task were used: one with high and one with low WM load, to explore the efficacy of stimulation on tasks differing in WM demand. Additionally, the effects of stimulation on WM performance were addressed using an N-back task. The tACS frequency was personalized by means of EEG measuring the individual theta peak frequency during the N-back task. Results: The application of personalized theta tACS to the FPN improved performance during the motor sequence learning task with high WM load (p < .001), but not with low WM load. Active stimulation significantly improved both speed (p < .001), and accuracy (p = .03) during the task with high WM load. In addition, the stimulation paradigm improved performance on the N-back task for the 2-back task (p = .013), but not for 1-back and 3-back. Conclusion: The performance during a motor sequence learning task can be enhanced by means of personalized bifocal theta tACS to the FPN when WM load is high, indicating that the efficacy of this stimulation paradigm is dependent on the cognitive demand during the learning task. These data provide further causal evidence for the critical involvement of WM processes and the FPN during the execution of a motor sequence learning task in healthy older. These findings open new exciting possibilities to counteract the age-related decline in motor performance, learning capacity and WM performance.
... [146][147][148][149][150] Thus, several forms of disruption such as desynchronization of oscillatory activity manifesting as either hyper-or hyposynchrony, both of which may impair communication within and between networks, and perturb function. [151][152][153][154] Importantly, some evidence points toward alterations in the levels of synaptic proteins as underlying this desynchronization. 94 Several lines of evidence link desynchronization of oscillatory activity to a variety of motor and non-motor symptoms discussed below. ...
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... Building up on that, previous NIBS studies aiming at memory neuromodulation used theta-band frequencies to entrain cortico-hippocampal circuits. Most of these studies used a single stimulationfrequency within theta band across all participants: either 4Hz (Alekseichuk et al., 2020;Bender et al., 2019), 5Hz (Kleinert et al., 2017;Vulić et al., 2021) or 6Hz (Abellaneda-Pérez et al., 2020;Alekseichuk et al., 2017;Antonenko et al., 2016;Lang et al., 2019;Lara et al., 2018;Polanía et al., 2012;Röhner et al., 2018;Tseng et al., 2018;Violante et al., 2017). However, the evidence suggests that the choice of stimulation frequency may be relevant factor that can modulate the NIBS effects. ...
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Non-invasive brain stimulation (NIBS) has gained increased interest in research and therapy of associative memory (AM) and its impairments. However, the one-size-fits-all approach yields inconsistent findings, thus putting forward the need for the development of personalized frequency-modulated NIBS protocols to increase the focality and the effectiveness of the interventions. There have been only a few attempts to deliver theta frequency-personalized tES. The current study explores the feasibility of determining dominant individual theta-band frequency (ITF) based on AM task-evoked EEG activity. In a sample of 42 healthy young adults, we extracted the frequencies (2-15 Hz, in 0.5 Hz steps) with the highest event-related spectral perturbation from the EEG recorded during successful encoding in the AM task. The developed method for extraction of the dominant theta-band frequency based on the AM-evoked EEG changes is able to reliably determine the AM-related ITF and can be used for personalization of the oscillatory NIBS techniques.
... In contrast, tACS is thought to entrain the firing rate of neurons to the frequency of the alternating current (Herrmann et al., 2013;Reato et al., 2013). Dual-site tACS has been recently introduced as technique to manipulate the phase synchronization of local oscillations in two connected cortical areas with the aim to modulate the coupling of remote neural populations (Polanía et al., 2012(Polanía et al., , 2015Helfrich et al., 2014;Alekseichuk et al., 2017Alekseichuk et al., , 2019Saturnino et al., 2017;Meier et al., 2019;Misselhorn et al., 2019;Preisig et al., , 2020Preisig et al., , 2021Reinhart and Nguyen, 2019;Schwab et al., 2019). Both tDCS and tACS have become very popular over the last two decades as they promise the possibility of causal inference about the functional role of stimulated brain regions and networks. ...
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There is considerable individual variability in the reported effectiveness of non-invasive brain stimulation. This variability has often been ascribed to differences in the neuroanatomy and resulting differences in the induced electric field inside the brain. In this study, we addressed the question whether individual differences in the induced electric field can predict the neurophysiological and behavioral consequences of gamma band tACS. In a within-subject experiment, bi-hemispheric gamma band tACS and sham stimulation was applied in alternating blocks to the participants’ superior temporal lobe, while task-evoked auditory brain activity was measured with concurrent functional magnetic resonance imaging (fMRI) and a dichotic listening task. Gamma tACS was applied with different interhemispheric phase lags. In a recent study, we could show that anti-phase tACS (180° interhemispheric phase lag), but not in-phase tACS (0° interhemispheric phase lag), selectively modulates interhemispheric brain connectivity. Using a T1 structural image of each participant’s brain, an individual simulation of the induced electric field was computed. From these simulations, we derived two predictor variables: maximal strength (average of the 10,000 voxels with largest electric field values) and precision of the electric field (spatial correlation between the electric field and the task evoked brain activity during sham stimulation). We found considerable variability in the individual strength and precision of the electric fields. Importantly, the strength of the electric field over the right hemisphere predicted individual differences of tACS induced brain connectivity changes. Moreover, we found in both hemispheres a statistical trend for the effect of electric field strength on tACS induced BOLD signal changes. In contrast, the precision of the electric field did not predict any neurophysiological measure. Further, neither strength, nor precision predicted interhemispheric integration. In conclusion, we found evidence for the dose-response relationship between individual differences in electric fields and tACS induced activity and connectivity changes in concurrent fMRI. However, the fact that this relationship was stronger in the right hemisphere suggests that the relationship between the electric field parameters, neurophysiology, and behavior may be more complex for bi-hemispheric tACS.
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A primary goal of translational neuroscience is to identify the neural mechanisms of age-related cognitive decline and develop protocols to maximally improve cognition. Here, we demonstrate how interventions that apply noninvasive neurostimulation to older adults improve working memory (WM). We found that one session of sham-controlled transcranial direct current stimulation (tDCS) selectively improved WM in older adults with more education, extending earlier work and underscoring the importance of identifying individual predictors of tDCS responsivity. Improvements in WM were associated with two distinct electrophysiological signatures. First, a broad enhancement of theta network synchrony tracked improvements in behavioral accuracy, with tDCS effects moderated by education level. Further analysis revealed that accuracy dynamics reflected an anterior-posterior network distribution regardless of cathode placement. Second, specific enhancements of theta-gamma phase-amplitude coupling (PAC) reflecting tDCS current flow tracked improvements in reaction time (RT). RT dynamics further explained inter-individual variability in WM improvement independent of education. These findings illuminate theta network synchrony and theta-gamma PAC as distinct but complementary mechanisms supporting WM in aging. Both mechanisms are amenable to intervention, the effectiveness of which can be predicted by individual demographic factors.
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Transcranial electric stimulation (TES) is an emerging technique, developed to non-invasively modulate brain function. However, the spatiotemporal distribution of the intracranial electric fields induced by TES remains poorly understood. In particular, it is unclear how much current actually reaches the brain, and how it distributes across the brain. Lack of this basic information precludes a firm mechanistic understanding of TES effects. In this study we directly measure the spatial and temporal characteristics of the electric field generated by TES using stereotactic EEG (s-EEG) electrode arrays implanted in cebus monkeys and surgical epilepsy patients. We found a small frequency dependent decrease (10%) in magnitudes of TES induced potentials and negligible phase shifts over space. Electric field strengths were strongest in superficial brain regions with maximum values of about 0.5 mV/mm. Our results provide crucial information of the underlying biophysics in TES applications in humans and the optimization and design of TES stimulation protocols. In addition, our findings have broad implications concerning electric field propagation in non-invasive recording techniques such as EEG/MEG.
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Previous, albeit correlative, findings have shown that the neural mechanisms underlying working memory critically require cross-structural and cross-frequency coupling mechanisms between theta and gamma neural oscillations. However, the direct causality between cross-frequency coupling and working memory performance remains to be demonstrated. Here we externally modulated the interaction of theta and gamma rhythms in the prefrontal cortex using novel cross-frequency protocols of transcranial alternating current stimulation to affect spatial working memory performance in humans. Enhancement of working memory performance and increase of global neocortical connectivity were observed when bursts of high gamma oscillations (80–100 Hz) coincided with the peaks of the theta waves, whereas superimposition on the trough of the theta wave and low gamma frequency protocols were ineffective. Thus, our results demonstrate the sensitivity of working memory performance and global neocortical connectivity to the phase and rhythm of the externally driven theta-gamma cross-frequency synchronization.
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Working memory - the ability to maintain and manipulate information over a period of seconds - is a core component of higher cognitive functions. The storage capacity of working memory is limited but can be expanded by training, and evidence of the neural mechanisms underlying this effect is accumulating. Human imaging studies and neurophysiological recordings in non-human primates, together with computational modelling studies, reveal that training increases the activity of prefrontal neurons and the strength of connectivity in the prefrontal cortex and between the prefrontal and parietal cortex. Dopaminergic transmission could have a facilitatory role. These changes more generally inform us of the plasticity of higher cognitive functions.
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Background: Frontal midline theta (FMT) oscillations (4-8 Hz) are strongly related to cognitive and executive control during mental tasks such as memory processing, arithmetic problem solving or sustained attention. While maintenance of temporal order information during a working memory (WM) task was recently linked to FMT phase, a positive correlation between FMT power, WM demand and WM performance was shown. However, the relationship between these measures is not well understood, and it is unknown whether purposeful FMT phase manipulation during a WM task impacts FMT power and WM performance. Here we present evidence that FMT phase manipulation mediated by transcranial alternating current stimulation (tACS) can block WM demand-related FMT power increase (FMTΔpower) and disrupt normal WM performance. Methods: Twenty healthy volunteers were assigned to one of two groups (group A, group B) and performed a 2-back task across a baseline block (block 1) and an intervention block (block 2) while 275-sensor magnetoencephalography (MEG) was recorded. After no stimulation was applied during block 1, participants in group A received tACS oscillating at their individual FMT frequency over the prefrontal cortex (PFC) while group B received sham stimulation during block 2. After assessing and mapping phase locking values (PLV) between the tACS signal and brain oscillatory activity across the whole brain, FMT power and WM performance were assessed and compared between blocks and groups. Results: During block 2 of group A but not B, FMT oscillations showed increased PLV across task-related cortical areas underneath the frontal tACS electrode. While WM task-related FMTΔpower and WM performance were comparable across groups in block 1, tACS resulted in lower FMTΔpower and WM performance compared to sham stimulation in block 2. Conclusion: tACS-related manipulation of FMT phase can disrupt WM performance and influence WM task-related FMTΔpower. This finding may have important implications for the treatment of brain disorders such as depression and attention deficit disorder associated with abnormal regulation of FMT activity or disorders characterized by dysfunctional coupling of brain activity, e.g., epilepsy, Alzheimer's or Parkinson's disease (AD/PD).
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. Transcranial alternating current stimulation (tACS) is a relatively recent method suited to noninvasively modulate brain oscillations. Technically the method is similar but not identical to transcranial direct current stimulation (tDCS). While decades of research in animals and humans has revealed the main physiological mechanisms of tDCS, less is known about the physiological mechanisms of tACS. Method . Here, we review recent interdisciplinary research that has furthered our understanding of how tACS affects brain oscillations and by what means transcranial random noise stimulation (tRNS) that is a special form of tACS can modulate cortical functions. Results . Animal experiments have demonstrated in what way neurons react to invasively and transcranially applied alternating currents. Such findings are further supported by neural network simulations and knowledge from physics on entraining physical oscillators in the human brain. As a result, fine-grained models of the human skull and brain allow the prediction of the exact pattern of current flow during tDCS and tACS. Finally, recent studies on human physiology and behavior complete the picture of noninvasive modulation of brain oscillations. Conclusion . In future, the methods may be applicable in therapy of neurological and psychiatric disorders that are due to malfunctioning brain oscillations.
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Working memory (WM) enables the storage and manipulation of information in an active state. WM storage has long been associated with sustained increases in activation across a network of frontal and parietal cortical regions. However, recent evidence suggests that these regions primarily encode information related to general task goals rather than feature-selective representations of specific memoranda. These goal-related representations are thought to provide top-down feedback that coordinates the representation of fine-grained details in early sensory areas. Here, we test this model using fMRI-based reconstructions of remembered visual details from region-level activation patterns. We could reconstruct high-fidelity representations of a remembered orientation based on activation patterns in occipital visual cortex and in several sub-regions of frontal and parietal cortex, independent of sustained increases in mean activation. These results challenge models of WM that postulate disjoint frontoparietal "top-down control" and posterior sensory "feature storage" networks. Copyright © 2015 Elsevier Inc. All rights reserved.