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 amphetamines, benzodiazepines, cocaine, methamphetamine, opiates, and THC using immunometric assay kits. None of the participants were tested positive on any of the sessions. Participants were carefully screened using a semistructured clinical 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 administration 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, Switzerland), 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.

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 carbohydrate 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 corresponds 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 mm3, and a field of view 228 × 228 mm2. 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 convolved 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 applied to remove low-frequency noise. Six movement parameters 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 differences 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 previously published 2-back >0-back contrast of this data (Fig. 1a; Borgwardt et al. 2012), (b) the previous functional connectivity studies emphasizing the importance of fronto-parietal coupling 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 defined to infer statistical significance.

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). b Model space tested in this study. 1 right SPL, 2 left SPL, 3 right MFG, and 4 left 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 Full size image

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 how the neural states change as a function of endogenous interregional connections, modulatory effects on these connections, 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 regions 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 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 structured 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 Automated 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 excluded from the connectivity analysis.

Table 1 MNI coordinates (x, y, z) of the treatment maxima during working memory processing Full size table

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 extremely 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 differences. 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 t test, 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 objectively 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 t test was used to assess between-treatment differences in WM performances.