Bram de Jong
Bachelor Thesis University of Amsterdam
Faculty of Social and Behavioural Sciences, Department of Psychology Thesis Advisors: Dr. I. G. Sligte, Drs. A. Laufer
May 29, 2015 10303553
Abstract
Transcranial neural stimulation is a popular low risk way of influencing and improving cognitive functioning. Recent findings have shown visual working memory (VWM) to be susceptible to external influence of the right dorsolateral prefrontal cortex (DLPFC). In the present study, feasibility of transcranial direct-current stimulation (tDCS) of the right DLPFC in improving VWM performance was assessed using 16 healthy participants. VWM was measured using a partial report change-detection paradigm. Influence of tDCS was compared to a double-blind sham-stimulation condition. No performance difference was found between tDCS and sham-stimulation. A placebo effect was found for sham-stimulation, but no tDCS effect was found. These findings contrast previous studies showing both general working memory and visual improvement through tDCS. An explanation is posited in the form of interference in other visual processing areas resulting from tDCS. Possible implications of non-blind experimenters in previous tDCS studies are also discussed. While tDCS has shown improvement in isolated cognitive functions, the current study shows DLPFC stimulation does not seem to result in a net effect on combined functioning of working memory and visual processing.
Introduction
Retrieving memories about celebrations or the face of a loved one can induce vivid visual experiences. However not all visual experiences are stored in long-term memory, as incoming visual information is either actively maintained or consequently lost during cognitive process-ing (Ranganath, Cohen, & Brozinsky, 2005). This process of maintainprocess-ing optical information is the domain of visual working memory (VWM). A common way of measuring this type of memory is by using a change-detection task (Luck & Vogel, 2013). In this task an array of items is shown for a short duration before disappearing, and after a retention period a single probe item is shown and participants are tasked to decide if that item conserved the initial orientation. Capacity of the VWM system is limited, and it is known that the complexity of visual features of items can negatively influence task performance (Luck & Vogel, 1997). Task performance also drops steeply for item sets larger than four, indicating that this is the maximum amount of objects that can be properly maintained (Alvarez & Cavanagh, 2004).
Improving cognitive functions such as VWM has seen an increase of interest recently, specifically with a technique called transcranial direct-current stimulation (tDCS). The tDCS procedure involves electrodes that are placed on the scalp after which a small direct current is sent through to stimulate underlying cortical areas. Using tDCS it is possible to directly modulate neurons in these areas (Antal, Kincses, Nitsche, Bartfai, & Paulus, 2004). In ef-fect the technique can be used to induce changes on the physiological and cognitive level. However, it is currently unknown if tDCS can improve performance of the VWM system. Recent studies on tDCS have already shown improved performance in attention, learning, and general working memory capacities (Coffman, Clark, & Parasuraman, 2014). As VWM differs from these cognitive functions by being less continuous in capacity as shown by a distinct performance plateau at four consecutive items, it is unsure if this system will respond to tDCS in the same fashion.
A requirement to perform tDCS is a clearly delineated cortical area involved in the cog-nitive function to be influenced. Multiple areas involved in VWM functioning are known, including the dorsolateral prefrontal cortex (DLPFC), dorsomedial prefrontal cortex, and
ventral prefrontal cortex (Mottaghy, Gangitano, Sparing, Krause, & Pascual-Leone, 2002). Previous studies involving VWM have found that disruption of the right DLPFC decreases performance on the change-detection task (Sligte, Wokke, Tesselaar, Scholte, & Lamme, 2011). The current study was performed to look at the possibility of improving the VWM system through electrical stimulation. As disrupting the DLPFC decreased VWM task performance, stimulation of this area is expected to increase performance compared to sham-stimulation. Possible improvement of VWM with the non-invasive tDCS procedure can prove useful in both clinical and non-clinical populations to assist in tasks such as rehabilitation (Jo et al., 2009), information retention, and demanding visual tasks such as used in air-traffic controlling.
Methods
Participants
For this experiment 18 healthy participants (13 females) were recruited, 16 were students at the University of Amsterdam. 2 female participants did not complete the full experiment due to intolerance to tDCS or scheduling issues, and were subsequently excluded from analysis. Age of participants was between 18 and 24 (M = 20.44, SD = 1.79). Participants were given course credit if applicable, but no financial rewards were given. Exclusion criteria were (a) left-handedness, due to possible lateralization issues with tDCS, (b) color blindness or vision problems uncorrectable by lenses or glasses, (c) family or personal history of epilepsy, neurological damage or other risk factors. Informed consent was given by all participants, and the experiment was approved by the ethics committee of the department of Psychology of the University of Amsterdam.
Change-Detection Paradigm
Tasks were performed in a lab setting at the University of Amsterdam using Presentation (NeuroBehavioral Systems Inc., 2014) software version 17.2. A 24 inch LCD monitor with a resolution of 1920 by 1080 pixels was used with the participant sitting 57 cm away from the screen. Visual angle per pixel was 1◦. Background color values for all screens were 224 (R), 224 (G), and 224 (B).
A black fixation cross on the center of the screen was used measuring 20 by 4 pixels. As illustrated in figure 1, the fixation cross was shown 1000 ms at the start of each trial. To indicate an upcoming memory array the fixation cross turned green 500 ms before showing the memory array. The memory array contained 8 black rectangles with a size of 25 by 100 pixels. Rectangles were positioned in a circle with increasing 1/8th π intervals starting at 0. For radius size 15% of the maximum screen width was used. Rectangle angles were selected randomly from either 0◦, 45◦, 90◦, or 135◦, with a maximum of 3 rectangles using the same angle. The memory array was shown for 250 ms after which only the black fixation cross was
Black Fixation Cross
1000 ms
Green Fixation Cross
500 ms
Memory Array
250 ms
Black Fixation Cross
1000 ms
Probe Rectangle
3000 ms −−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−→
Figure 1. The change-detection task with timing for each screen. Rectangles are angled either 0◦, 45◦, 90◦, or
135◦. As the probe rectangle is changed 90◦from the rectangle in the memory array, a change trial is shown here. visible for 1000 ms. After this retention period a single pseudo-random probe rectangle was chosen which was either flipped 90◦(change trial) or had the same orientation as the memory array rectangle in that position (no-change trial). The goal of the task was to detect whether the probe rectangle changed or not. Probe rectangle locations and change or no-change trial possibilities were drawn from a randomized set in which those variables were represented in equal amounts. The probe rectangle was shown for 3000 ms while participants decided on either change or no-change options. A press on a red marked z key was used to indicate a no-change trial, and a green marked m key for change trials. An audio cue was given after correct answers. If no input was given during this time the trial was marked as missed. Making a choice did not start the upcoming trial earlier to normalize total time spent, with each trial lasting 5750 ms. To transform scores to a VSM performance measure Cowan’s K (Cowan, 2001) was used. Cowan’s K is defined as:
K =(percentage correct - guessing chance) * N
100 - guessing chance
In this formula N equals the number of items in the memory array. The score measures items held in memory with a maximum of N and corrects for guessing.
tDCS and Sham-Stimulation
To achieve stimulation a direct current of 1 mA was applied transcranially by an anodal 9 cm2electrode placed on area F4 corresponding to the right DLPFC on the 10-20 coordinate system. A cathodal 35 cm2 electrode was placed on the contralateral supraorbital area.
solution. A NeuroConn DC-Stimulator was used to power the electrodes. Ramp-up and -down periods of 60 seconds were used in which the current was scaled to the intended level.
In order to perform the experiment double-blind the tDCS device was activated with a unique code for each session, without the experimenter knowing which code corresponded to (sham-)stimulation. Sham-stimulation was achieved by performing the normal ramp-up period, after which the ramp-down period immediately commenced. Near the end of the sham-stimulation block the current was ramped up again prior to the final ramp-down. Sham-stimulation has been shown to be an adequate control procedure in previous studies without the participants being aware of the manipulation (Gandiga, Hummel, & Cohen, 2006).
Procedure
Before the experimental sessions participants were invited to a 1-hour training session. In this session participants had the opportunity to practise the task and were screened for tolerance to tDCS. After this training 2 experimental sessions were planned with a minimum of 72 hours between each session. Each session consisted of 3 blocks of 20 minutes, corresponding to pre-manipulation, (sham-)stimulation, and post-manipulation, for a total of 1 hour per session. After every 5 minutes a pause of 90 seconds was issued, with an additional 90-second pause between blocks. A random set of 160 change-detection trials was created for each block after which the trial order was randomized again. When the (sham-)stimulation block commenced the tDCS device was switched on using the aforementioned codes. After the final session participants were debriefed and had the opportunity to ask questions.
For statistical analysis and graphics MATLAB (The MathWorks Inc., 2014) software version 8.4.0.150421 (R2014b) was used. Repeated measures ANOVA were performed to compare conditions, blocks, and learning effects. To test for gender and location effects t- and χ2 tests were used.
Results
Results for each session were distributed normally according to Shapiro-Wilk tests, which were p = .73, p = .81 and p = .63 respectively for each session. Missed trials were removed before analysis was performed. Sphericity assumptions were met according to Mauchly’s test, p = .51, thus uncorrected repeated measures ANOVA p-values are reported. No outliers were present. Females performed better (M = 2.71, SD = 0.56) than males (M = 1.96, SD = 0.31), t8 = 4.47, p = .002. Mean K and accuracy scores for each block can be found in table 1. Test results did not differ between accuracy percentage and K scores. Figure 1 shows a learning effect between the first blocks of training and subsequent sessions, F2,32 = 4.97, p = .01, η2
p = 0.25. Experimental conditions were divided equally over sessions 2 and 3 between subjects for counterbalancing of learning effects. K scores of the pre-sham and pre-stimulation blocks did not differ significantly, showing both conditions started with comparable performance, t15 = 0.15, p = .89. In contrast with hypothesized results, K scores did not show an interaction effect between (sham-)stimulation conditions and blocks, F2,62 = 0.49, p = .62. In consequence, this shows there was no difference in performance between conditions. Results of the (sham-)stimulation sessions are shown in figure 3.
Training Session 2 Session 3
Cowan's K 1.5 2 2.5 3 3.5
Figure 2. Mean K scores ± SEM indicating VWM performance
for the first block of each session. A learning effect is observable between training and experimental sessions.
Pre During Post
Cowan's K 1.5 2 2.5 3 3.5 Stimulation Sham-Stimulation
Figure 3. Mean K scores ± SEM indicating VWM performance
before, during, and after (sham-)stimulation. No interaction effect between conditions and blocks was found.
Block 1 Block 2 Block 3 Pre During Post Pre During Post
Accuracy 0.61(0.05) 0.62(0.06) 0.65(0.08) 0.64(0.08) 0.69(0.09) 0.68(0.07) 0.64(0.05) 0.67(0.09) 0.68(0.09)
Cowan’s K 1.75(0.81) 1.99(0.96) 2.48(1.27) 2.27(1.23) 3.04(1.37) 2.89(1.14) 2.30(0.85) 2.73(1.41) 2.88(1.43)
Table 1. Mean K score and accuracy for each block, including the standard deviation in parentheses.
To look for placebo influence, the effect of blocks in the sham-stimulation condition was tested and found, F2,32 = 4.41, p = .02, ηp2 = 0.23. A block effect was not seen for stimulation, indicating stimulation did not change performance between pre-, during-, and post-stimulation, F2,32 = 2.78, p = .08. An effect was found for probe rectangle location, χ27 = 17.82, p = .01, ϕ = .042. No effect was found between the upper and lower 3 rectangles, χ2
1 = 2.70, p = .10.
Following the unexpected results during (sham-)stimulation and receiving reports from participants of the stimulation being distracting, an additional exploratory analysis was made on the post (sham-)stimulation block. Previous research showing longer term effects from tDCS (Clark, Coffman, Trumbo, & Gasparovic, 2011) could also be investigated in this way. Qualitative analysis included graphing the progression of accuracy scores during all trials in the post (sham-)stimulation block. The results are shown in figure 4.
Post (Sham-)stimulation Trials
0 20 40 60 80 100 120 140 160 Mean Accuracy 0.66 0.67 0.68 0.69 Stimulation Sham-Stimulation
Figure 4. Mean accuracy development per trial during the post (sham-)stimulation block. These results are only
Discussion
In the present study tDCS and sham-stimulation were applied to the right DLPFC, a brain area that has been shown to be involved in VWM functioning. Recent findings have shown improvement to both general working memory and visual processing using tDCS. The results show that, in contrast to expectations, stimulation of the DLPFC did not affect VWM performance compared to both sham-stimulation and baseline. A placebo effect did appear for sham-stimulation compared to baseline. The effect found for rectangle location had a miniscule effect size combined with a large number of observations, and will not be discussed further as the effect is likely to be a statistical artifact.
While it seems intuitive to find a placebo effect using elaborate sham-stimulation such as performed in this study, this effect is not commonly found in studies involving sham-stimulation (Fregni et al., 2005; Ragert, Vandermeeren, Camus, & Cohen, 2008; Iyer et al., 2005; Hummel et al., 2005; Chen et al., 2014). This difference could possibly be due to influence from lack of experimenter blinding, resulting in a reduction of placebo effects. Non-blind experimenters are conducting sham-stimulation in the majority of tDCS studies, including all of the previously mentioned studies which did not report placebo effects, while in the current study experimenters were fully blind to conditions. If placebo effects are dampened by non-blind experimenters, the opposite effect, namely inflation of experimental findings due to experimenter influence, is also possible to occur. Changes in participant performance can arise from subtle experimenter behavior such as verbal and nonverbal cues, without both the experimenter and participant being aware of these actions (Rosenthal, 1966). Non-blind experimenters can thus possibly affect the results twofold: experimental manipulations are not compared to a true placebo condition, or experimental manipulations themselves are inflated. Both factors are caused by a change in experimenter behavior between conditions, can occur within the same experiment, and can impact experimental results. While participants do not seem to notice difference in sham- versus actual stimulation (Gandiga, Hummel, & Cohen, 2006), experimenter blinding and its effects are factors that should be taken into consideration when conducting sham-controlled tDCS studies.
inferred from earlier research which showed a decrease in VWM performance when DLPFC function was disrupted (Sligte, Wokke, Tesselaar, Scholte, & Lamme, 2011). Even though it has been shown tDCS stimulation of the DLPFC can increase other cognitive functions (Fregni et al., 2005), this does not seem to extend to an improvement on all functions in which the DLPFC is involved. As the results indicate VWM performance cannot be improved with stimulation of the DLPFC, an explanation seems necessary to reconcile these findings with previous research. The differences in outcome could be the result of inherent properties of the visual component of VWM. Visual processing is widespread across the brain, including the temporal, parietal, and occipital lobes (Goodale & Milner, 1992). It has also been shown that tDCS in one cortical area can interfere with cognitive functioning elsewhere, even in the contralateral hemisphere (Vines, Nair, & Schlaug, 2006). Thus while tDCS may be able to stimulate general working memory in the DLPFC (Andrews, Hoy, Enticott, Daskalakis, & Fitzgerald, 2011), it may also have inadvertently caused interference with visual or working memory processing in other areas. Fragility to distant tDCS has also been shown by a decrease of general working memory performance during cerebellar tDCS (Ferrucci et al., 2008). This would explain why no stimulatory effect was found in the current study, while other tDCS studies focusing solely on visual function show improvement with tDCS of the visual cortex, (Antal, Kincses, Nitsche, & Paulus, 2003), as only an isolated cognitive function is stimulated and measured. Focus on a single function thus avoids possible negative effects in other cognitive processes.
Exploratory qualitative analysis of performance directly after (sham-)stimulation shows signs of longer term influence from tDCS. While there were no differences in mean scores of post (sham-)stimulation blocks, performance is seen increasing steadily after tDCS but is variable after sham-stimulation, as indicated by figure 4. This rise in performance can possibly be attributed to post-stimulation neurochemical involvement, as found in other research (Clark, Coffman, Trumbo, & Gasparovic, 2011). As participants reported that the (sham-)stimulation was distracting, release from distraction and possible visual processing interference could have occurred in the post (sham-)stimulation block. This release, combined with the influence of enhancing post-stimulation effects is a possible mechanism for the increasing performance.
A possible shortcoming of this study is the low average performance rate compared to other studies using the same VWM paradigm (Sligte, Wokke, Tesselaar, Scholte, & Lamme, 2011; Makovski, 2012). An explanation for this low performance could be lack of extensive training, however while there was an effect for sessions, this was only found between training and experimental sessions, not between the two experimental sessions. The performance gap could also be the result of differences in experimenter blinding, as specified in this discussion. Lack of financial rewards causing low motivation is another possible explanation of this difference. Exclusion of participants based on performance was no option due to statistical power requirements.
Implications of the current study are that cognitive function enhancement using tDCS is not as straightforward as previously thought, as VWM improvement was not found while stimulating an associated cortical area. Furthermore, consideration needs to be taken when performing sham-stimulation with non-blind experimenters, as this can possibly dampen placebo effects or inflate experimental findings. Stimulating the DLPFC might have unforeseen consequences on cognitive processes elsewhere. Additional research separating working memory and visual processing during tDCS seems warranted to explore possible interactions between both systems. As of now, there seems to be no indication transcranial electrical stimulation can improve the performance of demanding visual working memory tasks.
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