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ORIGINAL INVESTIGATION
Green tea extract enhances parieto-frontal connectivity
during working memory processing
André Schmidt &Felix Hammann &Bettina Wölnerhanssen &
Anne Christin Meyer-Gerspach &Jürgen Drewe &
Christoph Beglinger &Stefan Borgwardt
Received: 7 January 2014 / Accepted: 26 February 2014
#The Author(s) 2014. This article is published with open access at Springerlink.com
Abstract
Rationale It has been proposed that green tea extract may
have a beneficial impact on cognitive functioning, suggesting
promising clinical implications. However, the neural mecha-
nisms underlying this putative cognitive enhancing effect of
green tea extract still remain unknown.
Objectives This study investigates whether the intake of green
tea extract modulates effective brain connectivity during
working memory processing and whether connectivity param-
eters are related to task performance.
Material and methods Using a double-blind, counterbalanced,
within-subject design, 12 healthy volunteers received a milk
whey-based soft drink containing 27.5 g of green tea extract or
a milk whey-based soft drink without green tea as control sub-
stance while undergoing functional magnetic resonance imaging.
Working memory effect on effective connectivity between fron-
tal and parietal brain regions was evaluated using dynamic causal
modeling.
Results Green tea extract increased the working memory in-
duced modulation of connectivity from the right superior
parietal lobule to the middle frontal gyrus. Notably, the
magnitude of green tea induced increase in parieto-frontal
connectivity positively correlated with improvement in task
performance.
Conclusions Our findings provide first evidence for the puta-
tive beneficial effect of green tea on cognitive functioning, in
particular, on working memory processing at the neural sys-
tem level by suggesting changes in short-term plasticity of
parieto-frontal brain connections. Modeling effective connec-
tivity among frontal and parietal brain regions during working
memory processing might help to assess the efficacy of green
tea for the treatment of cognitive impairments in psychiatric
disorders such as dementia.
Keywords Cognition .Working m em or y .Green tea extract .
Brain activity .Effective connectivity .Dynamic causal
modeling
Introduction
Recent research indicates that green tea extract or its main
ingredients has a beneficial impact on cognitive functioning in
humans. For instance, it has been demonstrated that the con-
sumption of green tea improved memory and attention in
subjects with mild cognitive impairments (Park et al. 2011)
and that the consumption of flavonoid-rich foods such as
green tea reduced beta-amyloid-mediated cognitive impair-
ments in Alzheimer transgenic mice, suggesting a potential
therapeutic utility in dementia (Rezai-Zadeh et al. 2008;
Williams and Spencer 2012). Furthermore, higher consump-
tion of green tea has also been associated with a lower prev-
alence of cognitive impairments in older adults (Kuriyama
et al. 2006). Comparable results were obtained in another study
investigating the association between green tea consumption
and cognition in 2,501 people aged over 55 years by showing
that the intake of green tea was significantly related to a lower
A. Schmidt :S. Borgwardt (*)
Department of Psychiatry (UPK), University of Basel, Wilhelm
Klein Str. 27, 4012 Basel, Switzerland
e-mail: Stefan.Borgwardt@usb.ch
A. Schmidt :S. Borgwardt
Medical Image Analysis Center, Schanzenstrasse 55, 4031 Basel,
Switzerland
F. Hammann :B. Wölnerhanssen :A. C. Meyer-Gerspach :
J. Drewe :C. Beglinger
Department of Gastroenterology, University Hospital Basel,
4031 Basel, Switzerland
S. Borgwardt
Department of Psychosis Studies, Institute of Psychiatry, King’s
College London, London, UK
Psychopharmacology
DOI 10.1007/s00213-014-3526-1
prevalence of cognitive impairments (Ng et al. 2008). In addi-
tion to preventing cognitive decline, green tea consumption
might even lead to better cognitive performances in
community-living older adults (Feng et al. 2010), which may
indicate a cognitive enhancing effect in healthy subjects.
More recently, a study used functional magnetic resonance
imaging (fMRI) to investigate whether this beneficial impact
of green tea on cognition could be related to altered brain
activity in regions crucially engaged during higher-order cog-
nitive functioning (Borgwardt et al. 2012). They demonstrated
relatively increased brain activation in fronto-parietal regions,
most pronounced in the right frontal cortex after the adminis-
tration of green tea extract during working memory (WM)
processing as assessed by the N-back task (Borgwardt et al.
2012). These data suggest that green tea extract may modulate
brain activity in key areas for mediating WM processing in the
human brain such as the dorsolateral prefrontal cortex
(Goldman-Rakic 1996). However, successful WM processing
during the N-back task requires a functional coupling of
parietal and frontal brain regions as shown by functional
(Owen et al. 2005; Rottschy et al. 2012) and effective con-
nectivity studies (Deserno et al. 2012;Maetal.2011). It has
been suggested that effective connectivity from the parietal
cortex to the frontal cortex may contribute to the encoding of
incoming stimuli (Ma et al. 2011), while the connections from
the frontal to the parietal cortex likely mediate the updating of
rules (e.g., 2-back condition; Gazzaley et al. 2004;Sauseng
et al. 2005). Therefore, it is conceivable that the increased
frontal activity during WM processing after green tea admin-
istration (Borgwardt et al. 2012) may have resulted from a
change in functional couplingconnectivity from the parietal to
the frontal cortex.
We thus explored in the current study whether the ad-
ministration of green tea extract changed brain connectivity
between the frontal and parietal cortex during WM process-
ing. In particular, we applied dynamic causal modeling
(DCM; Friston et al. 2003) to fMRI data from 12 healthy
subjects receiving green tea extract and a control beverage
while performing a N-back WM task. DCM can explicitly
evaluate the directional modulation effects of contextual
experimental conditions (e.g. the 2-back condition) on ef-
fective connectivity and has been successfully used to de-
tect pharmacological manipulations from fMRI data
(Grefkes et al. 2010; Schmidt et al. 2013b). Furthermore,
we tested whether the effect of green tea on the WM-
induced modulation of connectivity was related to its effect
on the task performance. Given the important functional
coupling between parietal and frontal brain regions during
the N-back task (Owen et al. 2005; Rottschy et al. 2012),
and that the intake of green tea extract increases prefrontal
activity (Borgwardt et al. 2012), we hypothesized that green
tea extract would enhance effective connectivity from the
parietal to the frontal cortex.
Material and methods
Participants
In total, 12 healthy right-handed male completed the study
(mean age 24.1 years; standard deviation 2.6). All participants
were nonsmokers. Participants were told to abstain from any
substance use for the duration of the study, and from the intake
of alcohol, caffeine, green tea products, and citrus juices for 24
and 12 h before each study day, respectively. At the start of the
study, a urine sample was collected for screening for amphet-
amines, benzodiazepines, cocaine, methamphetamine, opi-
ates, and THC using immunometric assay kits. None of the
participants were tested positive on any of the sessions. Par-
ticipants were carefully screened using a semistructured clin-
ical interview to exclude psychiatric or physical illness or a
family history of psychiatric illness. The local State Ethical
Committee (Ethikkommission Beider Basel) approved the
study and all participants gave their informed written consent
after the study procedure had been explained to them in detail.
The study was registered with clinicaltrials.gov (identifier:
NCT01615289).
Experimental design
A double-blind, vehicle-controlled, and within-subject
design with randomized order of substance administra-
tion using an established protocol was conducted over
four sessions (Bhattacharyya et al. 2012; Borgwardt et al.
2008). Participants received either 250 or 500 ml milk
whey-based soft drink containing 13.75 and 27.5 g of
green tea extract, respectively (Rivella, Rothrist, Switzer-
land), or a milk whey-based soft drink without green tea
extract as control condition. Each participant was
scanned four times with a 1-week interval between scans.
Before each scanning session, participants swallowed a
feeding tube for application of the test solutions. The
doses of 250 (that were diluted to 500 ml to control for
volume effects) and 500 ml were selected to produce an
effect on regional brain functioning without provoking
any toxic, psychiatric or physical symptoms, which
might have confounded interpretation of the fMRI data
and caused difficulties for participants to tolerate the
procedure. As the intragastric administration bypassed
the sensory systems, volunteers were prevented from
guessing which treatment they were being given. An
intravenous line was inserted in the nondominant arm
of each participant at the start of the testing session to
monitor substance whole-blood levels. All participants
were physically examined before testing and their heart
rate and blood pressure were assessed in 5-min intervals
throughout the 1-h session.
Psychopharmacology
Composition of test drinks
Rivella is a commercially available carbonated soft drink on
the basis of milk whey. In 1999, a new flavor with a 0.05 %
addition of standardized green tea extract was introduced. The
control drink is most similar to the drink of interest, apart from
the green tea extract, differs primarily in its lower carbohy-
drate content (2.5 g/100 ml difference). In detail, the test drink
contains the following ingredients: water, milk whey 35 %,
lactic acid, carbon dioxide, calcium cyclamate, acesulfame K,
and the following minerals: sodium 130 mg/l, potassium
450 mg/l, magnesium 35 mg/l, calcium 165 mg/l, and chloride
330 mg/l. Additionally, it contains the following ingredients:
green tea extract 0.05 %, ascorbic acid 120 mg/l, pyridoxine
30 mg/l, and fructose 25 g/l. Green tea extract is prepared from
the dried green leaves of Camellia sinensis with a drug:extract
ratio of 5.5:1, 47.5–52.5 % m/m polyphenols [high-pressure
liquid chromatography (HPLC)], 5.0–10.0 % m/m caffeine
(HPLC), 0.3–1.2 % m/m theobromine (HPLC), and 1.0–
3.0 % m/m theanine (HPLC). One gram of extract corre-
sponds to 5.5 g of green tea leaves. To equalize carbohydrate,
the control treatments were supplemented with 6.25 or 12.5 g
of sucrose for 250 and 500 ml, respectively. To additionally
blind volunteers to treatments, 250 ml treatments and controls
were diluted to 500 ml with 250 ml of uncarbonated spring
mineral water. This preparatory step also ensures equivalent
rates of gastric emptying. Treatments were heated to room
temperature and freed from carbon dioxide by stirring.
fMRI paradigm: N-back task
A rapid, mixed trial, event-related fMRI design was used with
jittered interstimulus intervals incorporating random event
presentation to optimize statistical efficiency (Ettinger et al.
2011). During the N-back task (Broome et al. 2009), all
subjects saw series of letters with an interstimulus interval of
2 s. Each stimulus was presented for 1 s. During a baseline (0-
back) condition, subjects were required to press the button
with the right hand when the letter „X”appeared. During 1-
back and 2-back conditions, participants were instructed to
press the button if the currently presented letter was the same
as that presented 1 (1-back condition) or 2 trials beforehand
(2-back condition). The three conditions were presented in ten
alternating 30 s blocks (2×1-back, 3×2-back and 5×0-back)
matched for the number of target letters per block (i.e., 2 or 3),
in a pseudo-random order.
Image acquisition and analysis
fMRI was performed on a 3T scanner (Siemens Magnetom
Verio, Siemens Healthcare, Erlangen, Germany) using an
echo planar sequence with a repetition time of 2.5 s, echo
time of 28 ms, matrix 76×76, 126 volumes and 38 slices with
0.5 mm interslice gap, providing a resolution of 3× 3 × 3 mm
3
,
and a field of view 228× 228 mm
2
. We analyzed fMRI data
using SPM8 (http://www.fil.ion.ucl.ac.uk/spm/). All volumes
were realigned to correct for head movements, mean adjusted
by proportional scaling, normalized into standard stereotactic
space (Montreal Neurological Institute), and smoothed using a
8 mm full-width at half-maximum Gaussian kernel. We con-
volved the onset times for each condition (0-back, 1-back, and
2-back) with a canonical haemodynamic response function.
Serial correlations were removed using a first-order
autoregressive model and a high-pass filter (128 s) was ap-
plied to remove low-frequency noise. Six movement parame-
ters were also entered as nuisance covariates to control for
movement. We focused our analysis on the 2-back >0-back
contrast (main effect of task) to capture the highest possible
WM load during the N-back task according to previous N-
back fMRI studies (Deserno et al. 2012; Schmidt et al. 2013b).
Differences in local brain activity between the different
treatment conditions have previously been reported
(Borgwardt et al. 2012); here, we extended this study by
conducting an effective connectivity analysis using DCM
(Friston et al. 2003), which was restricted to the bilateral
superior parietal lobule (SPL) and middle frontal gyrus
(MFG). As this previous analysis revealed significant differ-
ences in fronto-parietal activity especially between the 500 ml
doses (Borgwardt et al. 2012), we restricted our connectivity
analysis to these two conditions only. The selection of our
ROIs were based on the following evidences: (a) the previ-
ously published 2-back >0-back contrast of this data (Fig. 1a;
Borgwardt et al. 2012), (b) the previous functional connectiv-
ity studies emphasizing the importance of fronto-parietal cou-
pling for WM (Gazzaley et al. 2004; Sauseng et al. 2005), and
(c) the previous DCM studies of WM (Deserno et al. 2012;
Schmidt et al. 2013b). The treatment-specific fronto-parietal
network was detected using an anatomical mask taken from
the Automated Talairach atlas in the WFU Pick Atlas toolbox
(Tzourio-Mazoyer et al. 2002) consisting of the bilateral SPL
and MFG. Statistical significance was assessed at the cluster
level using the nonstationary random field theory (Hayasaka
et al. 2004). The first step of this cluster-level inference
strategy consisted of identifying spatially contiguous voxels
at a threshold of p< 0.001, without correction (cluster-forming
threshold; Petersson et al. 1999). Finally, a familywise error
(FWE)-corrected cluster-extent threshold of p< 0.05 was de-
fined to infer statistical significance.
Effective connectivity analysis: DCM
DCM10 (revision number 4290) as implemented in SPM8
was used to analyze effective fronto-parietal connectivity
during WM processing. In DCM for fMRI, the dynamics of
the neural states underlying regional BOLD response are
modeled by a bilinear differential equation that describes
Psychopharmacology
how the neural states change as a function of endogenous
interregional connections, modulatory effects on these con-
nections, and driving inputs (Friston et al. 2003; Stephan et al.
2007). The endogenous connections represent coupling
strengths in the absence of inputs to the system (task-
independent), while the modulatory effects represent
context-specific and additive changes in coupling (task-
induced alterations in connectivity). The modeled neuronal
dynamics is then related to the measured blood oxygen level-
dependent (BOLD) signal using a hemodynamic forward
model (Stephan et al. 2007). Here, we explicitly examined
how the coupling strengths between frontal and parietal re-
gions are changed by the 2-back condition (modulatory
effect).
Model design and time series extraction
Across all models tested, we assumed the same network
layout of connections between right and left SPL and MFG.
Specifically, SPL and MFG were reciprocally connected
Fig. 1 a Local maxima with the bilateral superior parietal lobule and
middle frontal gyri induced by the main effect of task (2-back >0-back
contrast) after the administration of green extract or the of the control
substance (FWE cluster level corrected at p<0.05).bModel space tested
in this study. 1right SPL, 2left SPL, 3right MFG, and 4left MFG. In
particular, we contrasted models in which the 2-back WM condition was
allowed to modulate, within both hemispheres: (F1) the parieto-frontal
connections, (F2) the fronto-parietal connections, or (F3)both.These
three intrahemispheric options were crossed with four possibilities which
interhemispheric connections might be modulated by the 2-back WM
condition, i.e., (a) none (first column of Fig. 1b), (b) the interhemispheric
connections between parietal areas (second column of Fig. 1b), (c)the
interhemispheric connections between frontal areas (third column of
Fig. 1b), or (d) both (fourth column of Fig. 1b). As a result, our model
space consisted of 12 alternative models, each of which was fitted to the
data from each individual subject
Psychopharmacology
within both hemispheres, with additional interhemispheric
connections between all regions. Similar to a recent DCM
study of WM (Ma et al. 2011), the visual input (driving)
entered the SPL bilaterally (Baizer et al. 1991; Nakashita
et al. 2008). Starting from this basic layout, a factorial struc-
tured model space was derived by considering where the
modulatory effect of the 2-back WM condition might be
expressed within both hemispheres (for a graphical summary
of the model design see Fig. 1b). Subject-specific regional
time series from the SPL and MFG were extracted from
spherical volumes of interest with 12 mm in diameter that
were centered on the condition maxima of the 2-back >0-back
contrast within the anatomical mask taken from the Automat-
ed Talairach atlas in the WFU Pick Atlas toolbox (Tzourio-
Mazoyer et al. 2002) using the first eigenvariate of voxels
above a subject-specific F-threshold of p<0.001 uncorrected.
When a subject had no voxel above threshold at the group
maxima (Fig. 1a, Table 1), we selected the nearest supra-
threshold voxel within the mask. One subject revealed no
activated voxels under these criteria and was therefore exclud-
ed from the connectivity analysis.
Bayesian model selection and Bayesian model averaging
Bayesian model selection (BMS) was used to determine the
most plausible neurophysiological network given the data as
expressed by a series of competing DCMs. BMS rests on
comparing the (log) evidence of a predefined set of models
(the model space). The model evidence is the probability of
observing the empirical data, given a model, and represents a
principled measure of model quality, derived from probability
theory (Penny et al. 2004). We used a random-effects BMS
approach for group studies, which is capable of quantifying
the degree of heterogeneity in a population while being ex-
tremely robust to potential outliers (Stephan et al. 2009b).
This method considers the model as a random variable and
estimates the parameters of a Dirichlet distribution, which
describes the probabilities of all models considered. One
common way to summarize the results of random effects
BMS is to report the exceedance probability (EP) of each
model, i.e., the probability that this model is more likely than
any other of the models tested, given the group data (Stephan
et al. 2009b). Given that different models may be found to be
optimal across treatments and statistical comparison of model
parameter estimates is only valid if those estimates stem from
the same model, Bayesian model averaging (BMA) has been
recommended as standard approach for clinical DCM studies
(Seghier et al. 2010; Stephan et al. 2010). BMA averages
posterior parameter estimates over models, weighted by the
posterior model probabilities (Penny et al. 2010). Thus,
models with a low posterior probability contribute little to
the estimation of the marginal posterior.
Statistic of DCM parameters
Following BMA, we used the resulting posterior means from
the averaged DCM for examining between-treatment differ-
ences. In this paper, we focused on WM-induced changes in
connectivity. Thus, we tested for group differences in the
modulatory effects only. We then used a paired ttest, testing
which of the connectivity parameters differed across the
500 ml treatments.
Statistics of WM performances
Beyond previous analyses of reaction times and number of
errors (Borgwardt et al. 2012), WM performances were ob-
jectively quantified using signal detection theory using the
formula d′=z(Hits)−z(FA), where FA reflects false alarms
(Macmillan and Creelman 1991). Hit and false alarm rates of
zero or one were adjusted as previously described (Macmillan
and Kaplan 1985). Paired ttest was used to assess between-
treatment differences in WM performances.
Results
Working memory performance
There was a strong trend toward a significantly improved task
performance as expressed by the sensitivity index d′after
consumption of green tea extract [mean (SD): 3.23 (0.42)]
compared with the control drink [mean (SD): 2.84 (0.45);
t(11)=2.041; p=0.066; Fig. 2].
Bayesian model selection
We first used Bayesian model selection (BMS) to compare the
model evidence for the three families of models with either
bidirectional, forward, or backward modulation of prefrontal–
Ta b l e 1 MNI coordinates (x,y,z) of the treatment maxima during working memory processing
Left MFG Right MFG Left SPL Right SPL
Green tea extract (−50, 22,34) (cluster size: 1199) (52, 26, 34) (cluster size: 740) (−30, −62, 48) (cluster size: 895) (32, −58, 54) (cluster size: 910)
Sham condition (−36, 4, 64) (cluster size: 220) (26, 14, 50)(cluster size: 215) (−34, −54, 56) (cluster size: 255) (28, −60, 50) (cluster size: 188)
Reported activations survive FWE correction at p<0.05 at peak and cluster level
Psychopharmacology
parietal connections. BMS revealed that the family with WM-
induced modulation of both forward and backward modula-
tion of prefrontal–parietal connections (F1) was superior to
the other families in the green tea (EP 63 %) and control
condition (EP 66 %). Single model inference showed that
model 12 emerged as the most likely model in the green tea
(EP 45 %) and control condition (EP 49 %). These BMS
results across both treatment conditions are summarized in
Fig. 3.
Effective connectivity results
Statistical analysis of treatment differences in connection
strengths concerned the posterior means of coupling esti-
mates, following BMA over all 12 models. Thus, in our
analysis of effective connectivity, we were able to test for
between-treatment differences in eight parameters describing
the modulation of fronto-parietal connections, within and
across hemispheres, induced by the 2-back WM condition.
Paired ttest results for all connection are summarized in
Tab le 2. A significantly increased WM-induced modulation
of connectivity from the right SPL to the right MFG was
found in the green tea condition compared with the control
beverage [t(10)=2.53; p=0.030; not corrected for multiple
comparisons; Fig. 4].
Importantly, we found a significant positive correlation
between the effect of green tea on task performance and right
SPL→right MFG connectivity (r=0.637, p=0.035; Fig. 5).
Discussion
In the present study we investigated the neural mechanisms
underlying the putative beneficial impact on green tea extract
on cognitive functioning. In particular, we explored by apply-
ing DCM to fMRI data whether green tea extract altered the
WM-induced modulation of interregional effective connectiv-
ity between the parietal and frontal cortex. The main findings
are that green tea extract increased the WM-induced modula-
tion of connectivity from the right superior parietal lobule to
the middle frontal gyrus. Furthermore, this effect of green tea
on parieto-frontal connectivity positively correlated with its
effect on task performance, suggesting a neural mechanism
for the positive effect of green tea consumption on cognitive
functioning at the system network level. Our finding of in-
creased parieto-frontal coupling during WM processing in-
duced by green tea might also explain the recently reported
increase in prefrontal brain activity after green tea administra-
tion (Borgwardt et al. 2012). Thus, these studies together
indicate that green tea extract might modulate WM processing
by increasing prefrontal brain activity as a result of enhanced
bottom-up connectivity from the parietal cortex.
Comparing competing models against the same data, we
found that the family of models considering a bidirectional
WM-induced modulation of connectivity between the parietal
and frontal cortex had fitted the data of all participants best
irrespective of treatments. This result supports previous func-
tional connectivity studies emphasizing the importance of
fronto-parietal connections for WM (Owen et al. 2005;
Rottschy et al. 2012). The N-back task requires different
cognitive processes including a continuous encoding of in-
coming visual letters and rule updating. Connections from the
parietal to the frontal cortex (bottom-up) may contribute to the
encoding of incoming stimuli (Ma et al. 2011), while the
connections from the frontal to the parietal cortex (top–down)
likely mediate the updating of rules (Gazzaley et al. 2004;
Sauseng et al. 2005). Under this perspective, we may specu-
late that our result of enhanced parieto-frontal connectivity
induced by green tea intake may indicate an improvement in
stimuli encoding during the N-back task.
Plasticity-dependent mechanism underlying the effect
of green tea on cognitive functioning
Green tea mainly consists of polyphenols, particularly cate-
chins such as (−)-epigallocatechin gallate (EGCG), caffeine,
and theanine, as well a lot of additional ingredients. In the
following, we show that these different substances share an
overlap in activity of at least one biochemical pathway, the N-
methyl-D-aspartate receptor (NMDAR) pathway, suggesting a
plasticity-dependent mechanism that may link the cognitive
effects of green tea from the micro- to the macro-level.
Studies in rodents support the idea of improved WM after
green tea administration via catechin-induced promotion of
antioxidative activity (Kaur et al. 2008). In accordance, pre-
vious studies proposed that EGCG mediates its protective
effect on cognitive functioning through antioxidant and iron-
Fig. 2 Mean of sensitivity indexes (d′) ± SE during working memory
processing for both treatment conditions. (Asterisk) indicates a between-
treatment difference at p=0.066
Psychopharmacology
chelating properties and modulation of cell-signaling and cell
survival pathways (Mandel and Youdim 2004; Weinreb et al.
2004). In other words, EGCG appears to reduce oxidative
stress (OS)-induced neurotoxicity as expressed by the gener-
ation of reactive oxygen species (ROS) generation. ROS
generation is critically mediated by NMDAR-dependent flow
of Ca
2+
ions into neurons (Lafon-Cazal et al. 1993;Schanne
et al. 1979). Treatment with green tea catechins as potent
natural antioxidants completely normalized the response to
activation of NMDAR by bath application of NMDA in the
mouse brain, suggesting the involvement of OS in abnormal
NMDAR-induced plasticity (Chepkova et al. 2013). Further-
more, EGCG promotes neural plasticity in the mouse
hippocampus (Xie et al. 2008) and a facilitation of Ca
2+
-
dependent glutamate release in rats (Chou et al. 2007). In
Alzheimer disease, oligomeric Aβattenuates NMDAR-
mediated Ca
2+
influx associated with an increase in ROS
production (He et al. 2011). Furthermore, amyloid protein
impairs synaptic plasticity by modulating an NMDA-type
glutamate receptor-dependent signaling pathway (Shankar
et al. 2007;Snyderetal.2005). Specifically, amyloid-b (Ab)
protein dimers isolated directly from Alzheimer’sbrainsdis-
rupt synaptic plasticity and memory via inhibition of long-
term potentiation and an enhancement of long-term depres-
sion (Shankar et al. 2008), both of which are critically medi-
ated by NMDARs (Bliss and Collingridge 1993; Malenka and
Fig. 3 Bayesian model selection (BMS) results on family level (upper column) and single model level (lower column) over both treatment conditions
separately. BMS results are reported in terms of exceedance probabilities
Ta b l e 2 Paired ttest results for
the between-treatment compari-
son of connectivity estimates
(modulatory effects of 2-back
WM condition)
*
Difference does not survive
Bonferroni correction for multiple
comparisons
Paired
differences mean
Standard
deviation
Standard
error mean
tValue Significance
(two-tailed)
Left to right parietal connectivity 0.00 0.37 0.11 0.02 p=0.987
Left parieto-frontal connectivity −0.01 0.22 0.07 −0.09 p=0.923
Right to left parietal connectivity 0.14 0.50 0.15 0.96 p=0.360
Right parieto-frontal connectivity 0.20 0.26 0.08 2.53 p=0.030*
Left fronto-parietal connectivity −0.05 0.37 0.11 −0.45 p=0.665
Left to right frontal connectivity −0.22 0.56 0.17 −1.31 p=0.219
Right fronto-parietal connectivity −0.08 0.51 0.15 −0.54 p=0.598
Right to left frontal connectivity 0.08 0.15 0.05 1.78 p=0.106
Psychopharmacology
Nicoll 1993; Paoletti et al. 2013). Remarkably, EGCG de-
creases Aβlevels and plaques in mice, reduced Aβmediated
cognitive impairment and modulates tau pathology in
Alzheimer transgenic mice (Lee et al. 2009; Rezai-Zadeh
et al. 2005,2008), as well as prevents Aβ-induced mitochon-
drial dysfunction, impairment of NMDA Ca
2+
influx and ROS
production (He et al. 2011). In addition to tea catechins,
theanine, which is an amino acid uniquely found in tea leaf,
may also possess neuroprotective effect (Nathan et al. 2006),
probably by its antagonistic effect on ionotropic glutamate
receptor subtypes, such as NMDARs (Kakuda 2011). More-
over, the beneficial effects of caffeine on stress-induced mem-
ory disturbance are mimicked by antagonists of adenosine
A2a receptors, likely mediated by its ability to control gluta-
matergic transmission, especially NMDAR-dependent plas-
ticity (Cunha and Agostinho 2010). Taken together, these
studies suggest that green tea extract or its ingredients coun-
teracts the OS-induced impairments in cognitive functioning
via its effect on NMDAR-dependent synaptic plasticity.
In this study, we examined, at the network connectivity
level, whether green tea intake altered the short-term plasticity
of interregional connections between the frontal and the pari-
etal cortex during WM processing by using DCM. DCM is a
generic Bayesian system identification technique that allows
inferring on NMDA-dependent synaptic plasticity by comput-
ing the dynamics of interacting neural macro-systems (Friston
et al. 2003; Stephan et al. 2006,2009a). Previous studies
demonstrated the sensitivity of DCM for NMDAR stimulation
(Moran et al. 2011) and that blocking of the NMDAR leaded to
altered synaptic plasticity of the bottom-up connectivity from
left primary auditory cortex to superior temporal gyrus during
an auditory oddball task (Schmidt et al. 2013a). Thus, we
propose that our result of an enhanced parieto-frontal connec-
tivity during WM processing induced by green tea intake might
reflect a green tea-induced modulation of NMDAR-dependent
synaptic plasticity, suggesting a mechanism at the network
level for the cognitive effect of green tea consumption.
Limitations
There are some limitations to be considered in the present
study. In contrast to the imaging results, we observed no
significant effect of green tea consumption on task perfor-
mances. However, we found a strong trend toward improved
performance, suggesting that our study sample was too small
to achieve differences on behavioural parameters. This suits
with evidence that fMRI data on small subject numbers are
relatively robust (Friston et al. 1999), while behavioral index-
es are typically underpowered and could be confounded by
many personal attributes that cannot be clearly assigned to the
cognition required for adequate task performance (Wilkinson
and Halligan 2004). A further caveat is that there is a differ-
ence between using a soft drink containing green tea and a
pure green tea extract. Oral ingestion of pure green tea extract
would have avoided any cross effects or effects of other
components as caffeine that may be involved in the positive
effect of green tea extract on cognitive performance.
Conclusions
The present study shows that green tea extract enhances
functional connectivity from the parietal to the frontal cortex
during WM processing in healthy controls. Interestingly, this
effect on effective connectivity was related to the green tea
induced improvement in cognitive performance. Our findings
Fig. 4 The modulatory effect of the 2-back WM condition on the
connection from the right SPL to the right MFG in the sham condition
and after the administration of green tea extract. The yaxis denotes the
average over all subjects and all 12 DCMs (using BMA) with regard to
the posterior mean (1/s) of the modulatory effect; this encodes changes in
connection strength induced by the 2-back WM condition. Significant
between-treatment differences at (asterisk)p<0.05. Error bars represent
standard deviations derived from Bayesian parameter averages
Fig. 5 Significant positive correlation between the effect of green tea on
task performance and SPL→MFG connectivity (green tea minus control
substance; r=0.64, p<0.05). That is, the stronger the increase in SPL→
MFG connectivity induced by green tea, the higher the improvement in
the task performance compared with the control drink
Psychopharmacology
provide first insights into the neural effect of green tea on WM
processing at the neural network level, suggesting a mecha-
nism on short-term plasticity of interregional brain connec-
tions. Our findings further suggest that the assessment of
effective connectivity among frontal and parietal brain regions
during working memory processing may provide a promising
tool to assess the efficacy of green tea or other compounds for
the treatment of cognitive impairments in psychiatric disor-
ders such as dementia.
Acknowledgments This study was supported by grants from the
Rivella. All authors have agreed to its submission in this form and we
do not have any conflict of interests that might be interpreted as influenc-
ing its content. The sponsor of the study had no role in study design,
collection, analysis, interpretation of data, writing of this report, and in the
decision to submit the paper for publication. We would like to acknowl-
edge the infrastructural support of the Medical Image Analysis Centre,
University Hospital Basel.
Open Access This article is distributed under the terms of the Creative
Commons Attribution License which permits any use, distribution, and
reproduction in any medium, provided the original author(s) and the
source are credited.
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Psychopharmacology
