Biological Psychology 96 (2014) 42–47

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Increasing working memory capacity with theta transcranial alternating current stimulation (tACS) Norbert Jauˇsovec ∗ , Ksenija Jauˇsovec Univerza v Mariboru, Filozofska fakulteta, Koroˇska 160, Slovenia

a r t i c l e

i n f o

Article history: Received 10 July 2013 Accepted 19 November 2013 Available online 27 November 2013 Keywords: Working memory tACS ERP Theta frequency P300 Scope of attention

a b s t r a c t The study aimed to investigate the influence of transcranial alternating current stimulation (tACS) on working memory’s (WM) storage capacity. Sham/verum tACS with individually determined theta frequency was applied to the left parietal (target electrode = P3) or frontal (target electrode = F3) brain areas (return electrode above the right eyebrow). After sham and verum stimulation, 24 respondents solved a task measuring the scope of attention while their electroencephalogram (EEG) was recorded. Verum tACS with the target electrode positioned over the left parietal brain area significantly increased WM storage capacity, as compared to sham tACS. No such influence was observed for tACS with the target electrode positioned over the left frontal area. Increased WM storage capacity was accompanied by event-related potential (ERP) P300 latency decrease in the left hemisphere. The obtained behavioral and neuroelectric data emphasize the causal relationship between WM storage capacity and theta frequency oscillations in the left parietal brain area. © 2013 Elsevier B.V. All rights reserved.

1. Introduction The psychological construct of working memory (WM) refers to a system that temporarily holds or manipulates information that we have just experienced or retrieved from long-term memory (Cowan, 2001; Miyake & Shah, 1999). Most definitions of WM encompass both storage and processing components, although distinguishing them is the main problem. As stressed by Curtis and D’Esposito (2003), cognitive neuroscientists are searching for ways to separately analyze working memory components and their neural underpinnings. Recently, Cowan et al. (2005) suggested a new theoretical framework to study WM storage capacity, the core of which represents two constructs, the focus and scope of attention. The focus of attention reflects the conscious awareness and the scope of attention measures its capacity. Therefore, an effective measure of the scope of attention must prevent rehearsal and grouping processes, allowing a clearer estimate of the number of separate chunks of information that the focus of attention can hold. From the neuropsychological perspective, the fronto-parietal network has been associated with working memory tasks (e.g., Chein & Fiez, 2010; Jonides et al., 2008; Palva, Monto, Kulashekhar, & Palva, 2010; Posner, 1990). It has been further suggested that the central executive function of WM is linked to frontal lobes

∗ Corresponding author at: Univerza v Mariboru, Filozofska fakulteta, Koroˇska 160, 2000 Maribor, Slovenia. Tel.: +386 26821651; fax: +386 2258180. E-mail address: [email protected] (N. Jauˇsovec). 0301-0511/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.biopsycho.2013.11.006

whereas the WM storage component is associated with parietal areas (Champod & Petrides, 2010; Collette & Van der Linden, 2002; Olson & Berryhill, 2009; Sauseng, Griesmayr, Freunberger, & Klimesch, 2010). Based on the evidence from several brain imaging studies (Cowan, 2011; Cowan et al., 2011; Majerus et al., 2006, 2010; Todd & Marois, 2004; Xu & Chun, 2006), the left intraparietal sulcus (LIS) has been identified as unique for amodal or multimodal storage of information. Support for a frontoparietal distinction related to the WM functions of processing and storing of information comes also from research employing neuroelectric brain imaging methods (Klimesch, 1999, 2012; Klimesch, Freunberger, Sauseng, & Gruber, 2008; Sauseng et al., 2010). These studies showed that theta oscillations relate to working memory processes and that theta synchronizes during WM processes as well as acts as a gating mechanism, providing optimal neural conditions for specific processing (Sauseng et al., 2010). An alternative perspective has suggested that the dorsolateral region of the prefrontal cortex (DLPFC) supports storage as well as processing of WM functions (Courtney, 2004; Leung, Seelig, & Gore, 2004; Pessoa, Gutierrez, Bandettini, & Ungerleider, 2002) and that short term storage and manipulation of information actually activate the same brain areas (Veltman, Rombouts, & Dolan, 2003). Several neuroimaging studies (Narayanan et al., 2005; Veltman et al., 2003; Zarahn, Rakitin, Abela, Flynn, & Stern, 2005) have supported this viewpoint. To date, it seems that neuroimaging studies cannot clarify the conflicting perspectives on the role of the frontal and parietal brain areas in WM storage capacity.

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The recent rediscovered noninvasive brain stimulation techniques for inducing reversible changes in brain activity have become a valuable tool to investigate the underlying mechanisms of human cognition (Kuo & Nitsche, 2012). Thus far, three main forms of low-intensity transcranial electrical stimulation have been used (for review, see Kuo & Nitsche, 2012; Utz, Dimova, Oppenländer, & Kerkhoff, 2010; Zaghi, Acar, Hultgren, Boggio, & Fregni, 2009): transcranial direct current stimulation (tDCS; a method in which low-intensity constant current is applied to the head), transcranial alternating current stimulation (tACS in which low-intensity alternating current is applied to the head), and transcranial random noise stimulation (tRNS in which electrical stimulation is applied within a broad frequency spectrum (0.1–640 Hz) with random noise distribution). The most often used method is tDCS. It is assumed that tDCS modifies the transmembrane neuronal potential and thus influences the level of excitability and modulates the firing rate of individual neurons. The delivered current appears to modulate the spontaneous neuronal activity in a polarity-dependent fashion: anodal tDCS increases the excitability of the underlying cortex whereas cathodal tDCS decreases it (Bindman, Lippold, & Redfearn, 1964). On the other hand, tACS is a newly developed stimulation technique that modulates cortical activity by affecting neuronal membrane potentials with oscillatory electrical stimulation in specific frequency bands. It is thought that tACS interacts with ongoing rhythmic cortical activities during cognitive processes (Kuo & Nitsche, 2012). It has been suggested that tACS could influence brain oscillations via interference or entrainment of ongoing oscillations (Thut & Miniussi, 2009), as recently demonstrated with rhythmic transcranial magnetic stimulation (rTMS) in the alpha band (Thut et al., 2011). This can provide insight into the functional significance of brain oscillations and their relation to cognition. Most of the transcranial stimulation studies investigating the effects on WM performance stimulated the left DLPFC (Andrews, Hoy, Enticott, Daskalakis, & Fitzgerald, 2011; Boggio et al., 2006, 2008; Fregni et al., 2005; Fregni, Boggio, Nitsche, Rigonatti, & Pascual-Leone, 2006; Mulquiney, Hoy, Daskalakis, & Fitzgerald, 2011; Zaehle, Sandmann, Thorne, Jäncke, & Herrmann, 2011; for a review Utz et al., 2010). Stimulation increased WM performance on different storage-and-processing tasks (e.g., n-back speed of performance and accuracy; accuracy in a visual recognition and in a sequential-letter working memory task). To our knowledge, just few studies (Andrews et al., 2011; Fregni et al., 2006) investigated the influence of anodal tDCS of the LDLPFC on WM storage capacity (digit span forward and backward). tDCS significantly increased performance on digit-span forward and backward tasks (Fregni et al., 2006), or just on digit-span forward tasks (Andrews et al., 2011). However, it is difficult to generalize this finding with regard to the relationship between the left DLPFC and WM storage capacity for several reasons. First, the respondents who participated in the study by Fregni et al. (2006) were patients with depression, second, the individuals were not exposed to verum and sham conditions. A third issue, that relates also to the Andrews et al. (2011) study, was that the digit-span task is not a pure measure of WM storage capacity that would provide no opportunity for rehearsal and grouping strategies (Cowan et al., 2005). Much less research has been conducted on stimulating parietal brain areas. Jacobson, Ezra, Berger, and Lavidor (2012), used anodal tDCS over the left intraparietal sulcus, which had a positive effect on a verbal recognition memory task. In a second study, Polanía, Nitsche, Korman, Batsikadze, and Paulus (2012) could show that left frontoparietal coupling in the theta band (0◦ phase tACS stimulation) increased the speed of performance on a delayed letter discrimination task as compared to a sham condition as well as to an experimental condition of left frontoparietal decoupling in the theta band (180◦ phase tACS stimulation).

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Table 1 Means, SD, and t-tests for differences in WM storage capacity: forward and backward Corsi block-tapping tasks (Corsi FW/BW); the forward and backward digit-span tasks (Digit FW/BW), of the frontal and parietal groups. Test

Parietal group

Frontal group

M

SD

M

SD

df (22)

Corsi FW Corsi BW

6.21 5.88

1.12 1.17

6.25 6.00

1.08 0.80

t = 0.09 p < 0.92 d = 0.04 t = 0.31 p < 0.76 d = 0.12

Digit FW Digit BW

7.25 6.75

0.87 1.29

6.83 7.08

1.62 1.92

t = 0.77 p < 0.45 d = 0.32 t = 0.50 p < 0.62 d = 0.20

The aim of the present study was to examine the causal association between left parietal/frontal brain activity and WM storage capacity. It was expected that theta tACS applied to the left parietal brain area would have a more pronounced influence on WM storage capacity compared to theta tACS applied to the left DLPFC. This assumption was based on recent neuroimaging studies showing that LIS demonstrated load-dependent activity for visual stimuli or visual and auditory stimuli (Cowan, 2011; Cowan et al., 2011). Additionally, it was reported that LIS activity is closely associated with capacity-limited working memory maintenance (Majerus et al., 2006, 2010; Todd & Marois, 2004; Xu & Chun, 2006) and that disruptive rTMS had a more pronounced influence on WM storage capacity when applied to parietal areas than when applied to DLPFC (Postle et al., 2006). By analyzing differences between sham/verum settings in ERP components (N1–P1, P300 latency and amplitude) and their relation to global stages of information processing, we attempted to determine the neurobiological underpinnings of WM storage capacity. Evidence suggests that the P1 amplitude is associated with the suppression of irrelevant information while the N1 is associated with the processing of attended, relevant information. Thus, it was suggested that the P1 reflects inhibitory and the N1 excitatory processes (for review, see Klimesch et al., 2004). The P300 has been related to the closure of the perceptual process (Verleger, 1988) and to an update of the information in the working memory (Donchin & Coles, 1988). P300 represents the last phase in the identification of a relevant stimulus caused by its significance or by the requirements of a task (Hillyard & Münte, 1984). As stressed by Kok (2001), the P300 amplitude reflects the degree of match between the stimulus presented and the internal representation of the stimulus relevant for the task. Because we predicted a beneficial influence of tACS applied to the left parietal area on WM storage capacity, it was expected that this would also be reflected in the P1–N1 and P300 ERP components during the solution of a task measuring the scope of attention, showing higher amplitude and shorter latency as compared to tACS applied to the left DLPFC.

2. Methods 2.1. Subjects The sample included 24 right-handed individuals (16 females; average age = 20 years and 7 months; SD = 5.25 months). They were recruited from a group of students participating in a large-scale resting eyes closed EEG study. They were divided into two groups – frontal and parietal – receiving tACS with target electrodes placed over left frontal or parietal sites. The respondents in the two groups were matched with respect to gender and performance on WM tests administered prior to the experiment: (1) the forward and backward Corsi block tapping task, a measure of spatial WM capacity (Corsi, 1972); and (2) the forward and backward digit-span tasks, a measure of verbal WM capacity (Wechsler, 1981). The results are summarized in Table 1. The respondents had similar educational background, did not take medication, and reported no medical treatments or health problems. The experiment was undertaken with the understanding and written consent of each subject. It followed the recommendations of the ethics committee of the Slovene Psychological Association.

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2.2. Tasks and procedure Respondents participated in 2 sessions, a sham and verum tACS settings, which were counterbalanced. The sham and verum settings were separated by 28 days. This time delay was needed to ensure that females in sham and verum settings were tested on the same day of their menstrual cycle. It was shown that the release of sexual hormones in different phases of the menstrual cycle affected cognitive responses of females (e.g., Amin, Epperson, Constable, & Canli, 2006; Berman et al., 1997). The duration of the menstrual cycle was determined with a questionnaire administered after the first tACS session. Most female participants had a regular 28 days menstrual cycle (M = 28.5 days; SD = 0.80 days). Prior to tACS the respondents’ EEG, while resting with eyes closed, was recorded for 5 min. This EEG recording was used to determine the individual alpha peak frequency (IAF) needed to adjust the theta frequency employed in tACS. In each setting, the respondents were exposed to sham/verum tACS for 15 min. After stimulation individuals solved the visual-array comparison task of Luck and Vogel (1997) while their EEG was recorded. The visual-array comparison task is supposed to measure the scope of attention, that is, the capacity of the focus of attention (Cowan et al., 2005). On every trial, an array of colored squares was presented for 400 ms, following an inter-stimulus interval of 1000 ms, a second array similar to the first one was presented for 2000 ms. One square in the second array was encircled and the respondent had to press “2” on the response pad if the square differed in color from the square in the previously presented array, or press “1” if it did not differ in color. Three series with 4, 6, and 8 simultaneously presented colored squares were used. Each series consisted of 30 trials. A memory capacity score was determined for each respondent (Pashler, 1988). The STIM2 stimulator (Compumedics Neuroscan Systems, Charlotte, NC, USA) generated all task items. At the end of the session respondents answered a questionnaire about their sensations during stimulation.

Fig. 1. Average memory capacity score (Pashler, 1988), on the visual-array comparison task in sham and verum tACS settings of respondents of the frontal (target electrode = F3) and parietal group (target electrode = P3). Error bars represent 1 SD.

2.3. Electrical stimulation tACS was applied via two sponge electrodes (approx. 5 cm × 7 cm/35 cm2 ; Neuroconn, Ilmenau, Germany) attached to the head underneath an EEG recording cap, using a battery-operated stimulator system (DC-stimulator plus, Neuroconn, Ilmenau, Germany). The target electrode was placed over the left parietal location (P3) or the left frontal location (F3), and the return electrode was placed above the right eyebrow. The frequency of the theta stimulation was adjusted to IAF (see EEG recording). The waveform was sinusoidal without DC offset and a 0◦ relative phase. The impedance was kept below 10 k. The study was a single-blinded, sham-controlled experiment. In the verum condition, tACS was applied for 15 min. The current was ramped up and down in the first and last 15 s of stimulation. In the sham condition, the procedure and stimulation characteristics (current magnitude and frequency) were the same as in the verum condition, except for the duration of stimulation, which was applied for just 30 s and then turned off automatically (Gandiga, Hummel, & Cohen, 2006; Nitsche et al., 2008). The magnitude of the stimulating current was based on individually determined thresholds for skin sensations and phosphenes induced by tACS. To determine the thresholds, we applied tACS stimulation at the individual theta frequency for 1 min (ramping up and down for 15 ms) at a time and increased the amplitude stepwise by 250 ␮A starting with 1000 ␮A and reaching a maximum of 2250 ␮A. Participants were asked to keep their eyes closed and indicate the presence of a sensation. For the remaining experiment, stimulation intensity was kept 250 ␮A below the lower threshold for skin sensations and induced phosphenes (parietal group: modus = 1750 ␮A peak-to-peak; range = 1000–2000 ␮A; frontal group: modus = 1750 ␮A peak-to-peak; range = 1000–2250 ␮A).

(amplitude and latency) were collapsed for different electrode locations, distinguishing the hemispheres as well as frontal and parietal brain areas. The electrode positions were aggregated as follows: frontal left (Fp1, F3, F7), frontal right (Fp2, F4, F8), parieto-occipital left (T5, P3, O1), and parieto-occipital right (T6, P4, O2). The resting EEG data used to determine the theta stimulating frequency were divided into 11-s epochs (11,132 data points), and automatically screened for artifacts. Excluded were all epochs showing amplitudes above ±100 ␮V. A fast Fourier transformation was performed on artifact-free 11-s chunks of data in order to derive estimates of absolute spectral power. Power estimates were obtained for frequency steps of 0.05 Hz. The stimulating theta frequency was adjusted to the individual alpha peak frequency averaged over 19 electrodes (Klimesch, 1999) (stimulating theta = IAF − 5 Hz). On average, this method resulted in a theta stimulating frequency of M = 5.07; SD = 1.25 in the parietal group, and M = 4.69; SD = 0.69 in the frontal group. 2.5. Statistical analysis Sham–verum differences in behavioral and ERP data were determined with a GLM for repeated measures. In the ERP analysis (P1, N1, P300, amplitude and latency), tACS (sham/verum), HEMISPHERE (left versus right), and LOCATION (frontal and parieto-occipital) were treated as within-subjects variables, whereas GROUP (parietal versus frontal) was handled as a between-subjects variable. In the analysis of performance scores, tACS (sham/verum) was treated as a within subjects variable and GROUP (parietal versus frontal) as a between-subjects variable.

2.4. EEG recording

3. Results

EEG was recorded using a Quick-Cap with sintered (Silver/Silver Chloride; 8 mm diameter) electrodes. Using the Ten-twenty Electrode Placement System of the International Federation, the EEG activity was monitored over nineteen scalp locations (Fp1, Fp2, F3, F4, F7, F8, T3, T4, T5, T6, C3, C4, P3, P4, O1, O2, Fz, Cz, and Pz). All leads were referenced to linked mastoids (A1 and A2), and a ground electrode was applied to the forehead. Additionally, vertical eye movements were recorded with electrodes placed above and below the left eye. Electrode impedance was maintained below 5 k. The digital EEG data acquisition and analysis system (SynAmps RT) had a bandpass of 0.15–100.00 Hz. At cutoff frequencies, the voltage gain was approximately −6 dB. The 19 EEG traces were digitized online at 1000 Hz with a gain of 10× (accuracy of 29.80 nV/LSB in a 24 bit A to D conversion) and stored on a hard disk. The ERP analysis was performed on EEG data based on the common average reference. Before ERP analysis, the signals were low-pass filtered (30 Hz, −24 dB/octave), and a correction for ocular artifacts was performed. Epochs comprised of 1000 ms preceding and 1000 ms following the stimulus presentation (display of the encircled square – see description of the visual-array comparison task). The average voltages in the 200 ms pre-stimulus period served for baseline correction. The epochs were then automatically screened for artifacts. All epochs showing amplitudes above ±100 ␮V were excluded. Peak-to-baseline amplitudes and latencies were determined automatically using the following time windows: P1 (40–120 ms), N1 (120–220 ms), and P300 (250–600 ms). The ERP values

3.1. Behavioral data The questionnaire data was analyzed with a Wilcoxon signed ranks test (sham/verum). The test showed no significant difference between sham and verum tACS settings in respondents’ sensations (Z(23) = 0.52; pexact < 0.63). The average test scores obtained during sham/verum stimulation are shown in Fig. 1. The GLM revealed a significant main effect of tACS (F(1,22) = 8.26; p < 0.009; 2 = 0.27) and an interaction effect between tACS and GROUP (F(1,22) = 6.89; p < 0.015; 2 = 0.24). In general, verum stimulation significantly increased performance on the visual-array comparison task as compared to sham stimulation. This increase was much more pronounced for the parietal position of the target electrode than for the frontal position, as indicated by subsequent paired-sample t-tests (Bonferroni corrected), which indicated no significant increase in performance (sham/verum comparison) among respondents of the frontal group (t(11) = 0.17; p < 0.87; d = 0.05). After verum stimulation, they

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Fig. 2. (A) Topographic ERP maps (325–400 ms) while respondents were solving the visual-array comparison task after sham/verum tACS with the target electrode positioned over the left parietal brain area (P3). (B) Dorsal view of sLORETA (Pascual-Marqui, 2007; Tadel, Baillet, Mosher, Pantazis, & Leahy, 2011), source reconstruction for ERP (325–400 ms) while respondents were solving the visual-array comparison task after sham/verum tACS. (C) Grand average (digitally filtered at 30 Hz) for sham/verum tACS during the solution of the visual-array comparison task for the P3 electrode (solid line = verum tACS; dotted line = sham tACS).

correctly solved an approximately equal number of task items, as after sham stimulation. By contrast, after verum tACS, respondents in the parietal group showed a significant increase in WM storage capacity, as compared to sham stimulation (t(11) = 3.94; p < 0.002; d = 1.14). In contrast, a paired-sample t-test for sham/verum differences in reaction time was not significant (t(11) = 1.08; p < 0.30; d = 0.31). 3.2. ERP data The only significant difference in ERP values between sham and verum settings was observed for the P300 latency, indicating significant interactions among tACS, HEMISPHERE, and GROUP (F(1,22) = 21.48; p < 0.0001; 2 = 0.49) and among tACS, LOCATION, and GROUP (F(1,11) = 6.09; p < 0.02; 2 = 0.22). To gain a detailed insight into the influence of tACS on changes in brain activity, we analyzed the data separately for each group (parietal and frontal), with a GLM for repeated measures – tACS (sham/verum) × HEMISPHERE (left/right) × LOCATION (frontal, parieto-occipital). The GLM conducted for the parietal group showed significant interactions between tACS and HEMISPHERE (F(1,11) = 5.69; p < 0.036; 2 = 0.34) and between tACS and LOCATION (F(1,11) = 12.67; p < 0.004; 2 = 0.54). As can be seen in Fig. 2, shorter P300 latency in the verum setting as compared to the sham setting was mainly observed in the left parieto-occipital location. In the frontal group, only an interaction effect between tACS and HEMISPHERE was observed (F(1,11) = 18.53; p < 0.001; 2 = 0.63). Theta tACS increased P300 latency in the left hemisphere and decreased P300 latency in the right hemisphere as compared with the sham condition (see Fig. 3).

4. Discussion The aim of the study was to explore the influence of different tACS protocols on WM storage capacity and in that way establish a causative relation between brain activation patterns and WM storage capacity. In analyzing differences in ERP components between sham/verum settings while asking respondents to solve a task measuring the scope of attention, we attempted to determine the neurobiological underpinnings of WM storage capacity. The central finding of the study was that verum tACS with the target electrode positioned over the left parietal area significantly increased WM storage capacity as compared to sham tACS. On the other hand, no such influence was observed for verum stimulation when the target electrode was positioned over the left frontal area. This supports the hypothesis that the left parietal brain area plays a central role in WM storage capacity. This finding has been confirmed in several neuroimaging studies (Champod & Petrides, 2010; Collette & Van der Linden, 2002; Cowan et al., 2011; Klimesch, 1999, 2012; Klimesch et al., 2008; Majerus et al., 2006, 2010; Olson & Berryhill, 2009; Sauseng et al., 2010; Todd & Marois, 2004; Xu & Chun, 2006) as well as in a recent rTMS study (Postle et al., 2006). To our knowledge, this is the first reported evidence for a causal relation between left parietal theta activity and WM storage capacity. Additional proof for the significance of the left parietal area for WM storage capacity was provided by analyzing ERP components while respondents were solving the visual-array comparison task. This analysis has shown that theta tACS in respondents of the parietal group decreased P300 latency in left parietal brain areas. Given that P300 latency is an index of classification speed, which is proportional to the time required to detect and evaluate a target stimulus (Polich, 2007), it can be hypothesized that theta tACS

Fig. 3. (A) Topographic ERP maps (325–400 ms) while respondents were solving the visual-array comparison task after sham/verum tACS with the target electrode positioned over the left frontal brain area (F3). (B) Dorsal view of sLORETA (Pascual-Marqui, 2007; Tadel et al., 2011), source reconstruction for ERP (325–400 ms) while respondents were solving the visual-array comparison task after sham/verum tACS. (C) Grand average (digitally filtered at 30 Hz) for sham/verum tACS during the solution of the visual-array comparison task for the P3 electrode (solid line = verum tACS; dotted line = sham tACS).

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increased the respondents’ capability to allocate resources needed to solve the WM task more rapidly. The decrease in P300 latency was not reflected in shorter reaction times but only in the scope of attention. This indicates that theta tACS mainly influenced cognitive processes and to a lesser extent motor and executive processes, which is in line with the general claim that P300 is generated in the service of memory storage (Polich, 2007). The finding that tACS had no influence on the earlier occurring ERP components of P1 and on N1 associated with inhibitory and excitatory processes involved in sensory processing of stimuli (Klimesch et al., 2004), provided further support for the explanation that theta tACS mainly influenced cognitive processes. In comparison, verum stimulation of the left DLPFC had an opposite effect on P300 latency, increasing latency in the left hemisphere and decreasing latency in the right hemisphere, which did not influence WM storage capacity. This result lends further support for the unique importance of the left parietal brain area for the temporary storage of information. However, some limitations of the obtained results that restrict the generalization of conclusions must be mentioned. First, the investigated sample was rather small, and second, a more essential constraint was the predominantly female structure of the sample. Given the immense body of evidence with regard to sex related differences in general as well as specific abilities (e.g., Johnson & Bouchard, 2007; Nyborg, 2005), this might have had some influence on the results obtained. In conclusion, the results of the present study are consistent with the assumption that the left parietal brain area is more important for the short-term storage of information than is the left dorsolateral prefrontal cortex. Furthermore, the observed behavioral and neuroelectric data revealed a causal relation between WM storage capacity and theta frequency oscillations in the left parietal brain area and illustrated the value that electric stimulation techniques can bring to systems-level analyses of cognitive functions. References Amin, Z., Epperson, C. N., Constable, R. T., & Canli, T. (2006). Effects of estrogen variation on neural correlates of emotional response inhibition. NeuroImage, 32(1), 457–464. Andrews, S. C., Hoy, K. E., Enticott, P. G., Daskalakis, Z. J., & Fitzgerald, P. B. (2011). Improving working memory: The effect of combining cognitive activity and anodal transcranial direct current stimulation to the left dorsolateral prefrontal cortex. Brain Stimulation, 4(2), 84–89. Berman, K. F., Schmidt, P. J., Rubinow, D. R., Danaceau, M. A., Van Horn, J. D., Esposito, G., et al. (1997). Modulation of cognition-specific cortical activity by gonadal steroids: A positron-emission tomography study in women. Proceedings of the National Academy of Sciences of the United States of America, 94(16), 8836–8841. Bindman, L., Lippold, O., & Redfearn, J. W. T. (1964). The action of brief polarizing currents on the cerebral cortex of the rat (1) during current flow and (2) in the production of long-lasting after-effects. Journal of Physiology, 72, 369–382. Boggio, P. S., Ferrucci, R., Rigonatti, S. P., Covre, P., Nitsche, M., Pascual-Leone, A., et al. (2006). Effects of transcranial direct current stimulation on working memory in patients with Parkinson’s disease. Journal of the Neurological Sciences, 249(1), 31–38. Boggio, P. S., Sultani, N., Fecteau, S., Merabet, L., Mecca, T., Pascual-Leone, A., et al. (2008). Prefrontal cortex modulation using transcranial DC stimulation reduces alcohol craving: A double-blind, sham-controlled study. Drug and Alcohol Dependence, 92(1–3), 55–60. Champod, A. S., & Petrides, M. (2010). Dissociation within the frontoparietal network in verbal working memory: A parametric functional magnetic resonance imaging study. Journal of Neuroscience, 30(10), 3849–3856. Chein, J. M., & Fiez, J. A. (2010). Evaluating models of working memory through the effects of concurrent irrelevant information. Journal of Experimental Psychology: General, 139(1), 117–137. Collette, F., & Van der Linden, M. (2002). Brain imaging of the central executive component of working memory. Neuroscience and Biobehavioral Reviews, 26(2), 105–125. Corsi, P. M. (1972). Human memory and the medial temporal region of the brain. Dissertation Abstracts International, 34. Courtney, S. M. (2004). Attention and cognitive control as emergent properties of information representation in working memory. Cognitive, Affective & Behavioral Neuroscience, 4, 501–516. Cowan, N. (2001). The magical number 4 in short-term memory: A reconsideration of mental storage capacity. Behavioral and Brain Sciences, 24(1), 87–114.

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