Chasing Memo: Investigating effects of alcohol on intentional inhibition during a continuous performance task

1

Master Brain and Cognitive Sciences

Cognitive Neuroscience

Research Project

Chasing Memo: Investigating effects of alcohol on

intentional inhibition during a continuous

performance task

by

Leonie Alexis Dühlmeyer

10437312

October, 2014

Supervisor:

Co-Assessor:

2

1. ABSTRACT

BACKGROUND: Inhibition of ongoing actions is a vital process in everyday life. So far, inhibition was mainly researched by inducing it with a cue, representing cued inhibition. However, this process does not represent real life inhibition where the motivators are internal, representing intentional inhibition. Moreover, the effect of alcohol on neural processes during inhibition is unclear. METHODS: Participants performed a motor-tracking task, providing a contextual-based design for cued and intentional inhibition of ongoing action, while EEG was recorded. The task was performed once under the influence of alcohol and once under placebo within the same subjects. RESULTS: Time-frequency analysis revealed lower-alpha band suppression 150ms preceding movement offset, only in the ‘placebo-intentional’ condition over the dorsal fronto-median cortex (dFMC). Moreover, a dynamic shift from gamma increase (-2500 to -2000ms) to decrease (-1700ms until movement offset) is visible over sensorimotor areas. Alcohol significantly modulates time-frequency responses in both inhibition conditions. DISCUSSION: Results display a vast difference between both inhibition conditions. Moreover, they suggest an anticipatory response over dFMC and an involvement of the posterior cingulate cortex, assisting the transition from inversion to extroversion during intentional inhibition. CONCLUSIONS: Alcohol modulates frequency responses of the brain in a manner that could cause miscommunication between brain areas. Importantly, frequency responses to cued and intentional inhibition differed considerably, suggesting that these mechanisms are not comparable. As a consequence, inhibition should continue to be researched with intentional inhibition designs.

2. INTRODUCTION

Most people’s daily life consists of a series of ongoing actions. Each action is at some point inhibited due to a specific reason. Take the example of brushing teeth with an electric toothbrush in the morning: for two minutes, the toothbrush is circled over your teeth. Once the stop signal appears, the circling is inhibited. This is an example of cued inhibition. On the other side, actions can also be inhibited without such a clear signal. In this case, the inhibition occurs due to internal reasons. For example: when eating from a breakfast buffet, one does not stop through external signals. Reasons to stop could be the feeling to be full, consideration of ingested calories or simply elapsed time (Mita et al., 2009). This process can be described as intentional inhibition.

External cues are more practicable to exert in experimental settings. Thus, the inhibition of an ongoing action has primarily been studied in this manner. Yet, intentional inhibition of ongoing actions is far more common in daily life. It is assumed that principally, healthy adults decide independently when to

3 abort an action rather than rely on external cues (Aron, 2011). However, not only the prevalence of intentional inhibition accentuates the relevance of deeper research in this field. As mentioned above, intentional inhibition differs from cued response inhibition by means of its motivation. This implies that these forms of inhibition represent separate mechanisms. Thus, insights in cued inhibition cannot directly be projected onto intentional inhibition. More precisely, while cued inhibition happens almost instantly upon perception of the cue (Corbetta & Shulman, 2002) and depends on external input (Passingham, Bengston & Lau, 2010), intentional inhibition is less temporally bound (Schadlen & Gold, 2004), conscious (Filevich, Kühn & Haggard, 2012) and voluntary (Aron, 2011). Moreover, it better captures the crucial operations of inhibitory control, especially in social contexts. Intentional inhibition arises because the action interferes with future goals, became inappropriate during that time or in that context (Ridderinkhof et al., 2011). Motivators for intentional inhibition are behavioral goals (Matsumoto & Tanaka, 2004), memories (Harnishfeger, 1995), or elapsed time (Mita et al., 2009). As a consequence, elucidating underlying mechanisms of self-generated decisions to inhibit an action is crucial. This applies to both scientific understanding of inhibitory control, as well as potential therapies of conditions such as impulsivity, harmful behavior or shyness (Schel et al., 2014).

Within neuroscience, cued inhibition has been extensively studied using paradigms such as stop-signal tasks (Logan and Cowan, 1984) or go/no-go tasks (Casey et al., 1997). During these paradigms, participants are instructed to inhibit an already prepared or prepotent response upon presentation of a stimulus. Successful performance relates to activation of the fronto-striatal network (Aron & Poldrack, 2006; Verbruggen & Logan, 2008; Aron, 2011; Ridderinkhof et al., 2011). Relevant for inhibiting motor responses were especially the right inferior frontal gyrus (rIFG) and the pre-supplementary motor area (pre-SMA) (Aron, Robbins & Poldrack, 2004; Chikazoe, 2010; Jahfari et al., 2011, 2012). Cued inhibition benefits from a number of methodological advantages, such as well-established experimental tasks and computational models for its dynamics and mechanisms (Aron and Poldrack, 2006)

Intentional inhibition, however, may not simply be introduced by a stimulus, since an internal decision consists of making choices. As a result, the ongoing neural processes may individually vary and require a larger time window, which impedes analysis. Using fMRI, two studies investigated intentional inhibition compared with intentional action. Participants were asked to prepare actions, and then were requested to occasionally abort the prepared actions at the latest possible moment prior to the action (Brass & Haggard, 2007; Kühn, Haggard & Brass, 2009). These studies revealed distinct neural networks being activated stronger during intentional inhibition compared to intentional action, predominantly the left dorsal fronto-median cortex (dFMC) and the left insula. Walsh et al., 2010 investigated intentional inhibition by comparing the neural time-frequency response to acting out or inhibiting a planned action.

4 The researchers found a left frontal increase in beta power during intentional inhibition, but not action trials. Although no source localization was performed, the researchers targeted sensorimotor areas by choosing “classic sensorimotor rhythm bands” (8-24 Hz) associated with motor preparation and execution, and recorded from “nine electrodes of interest over sensorimotor areas”. Intriguingly, these results for intentional inhibition oppose findings for cued inhibition, which emphasize the involvement of right frontal regions (Aron et al., 2004; Chikazoe, 2010; Jahfari et al., 2011, 2012). Only one published study attempted a direct comparison of cued and intentional inhibition (Schel et al., 2014). This fMRI study reported overlapping networks of activation, including bilateral parietal and prefrontal cortex (PFC), lateral PFC and pre-SMA. In addition, they found activation in the dFMC and left inferior frontal gyrus during intentional inhibition that depended on the previous choices. However, the two mechanisms did not correlate behaviorally – participants who performed well on the intentional inhibition tasks, did not necessarily perform well on the cued inhibition task. A likely reason for the lack of differential findings in the comparison of cued and intentional inhibition is poor temporal resolution of fMRI - it cannot capture the underlying inhibitory process during the task. Yet, timing is crucial to distinguish the moment of intentional inhibition from the previous action preparation, the following action execution (Walsh et al, 2010) and the feeling of frustration associated with failing to complete an action (Abler, Walter & Erk, 2005). For this purpose, investigating the two inhibition mechanisms with EEG could provide evidence for a distinction that does not stem from spatial activation, but rather temporal characteristics or differences in electrical neural processes. Moreover, Schel et al. (2014) used separate tasks for intentional and cued inhibition, which impedes direct comparison.

In order to shed light on these processes, we investigated electrophysiological differences between intentional and cued inhibition (Rigoni et al., manuscript in preparation) of an ongoing action. Here, both forms of inhibition were performed within the same task, which minimizes effects of task context. Moreover, inhibiting an ongoing action – instead of a planned action (Walsh et al., 2010)–awards the inhibitory process authenticity. Results show varying ERPs preceding intentional and cued response inhibition. Specifically, intentional response inhibition is preceded by a readiness potential (Kornhuber & Deecke, 1965), whereas cued inhibition is not. This finding supports the theory that intentional inhibition could be an ideomotor rather than a sensorimotor response (Ridderinkhof, Wildenberg & Brass, 2014). Here, the pragmatic idea to stop is first developed, then simulated without an action being executed. More specific, a forward model of the anticipated bodily effect of stopping is developed. Ideomotor inhibition is likely triggered by situations, such as the presence of strong contextual cues (Ridderinkhof, Wildenberg & Brass, 2014). These trigger an internal decision to inhibit the action, rather than explicit cues which directly trigger inhibition without the need for a decision. As a consequence, related preparatory processes

5 may involve ideomotor processes related to the forward model of action effects - such as the readiness potential. In the process, ideomotor inhibition is thought to engage largely overlapping networks with sensorimotor inhibition, with addition of the dFMC (Ridderinkhof, Wildenberg & Brass, 2014).

A previous project, which implemented the same paradigm used in Rigoni et al.(manuscript in preparation) and the current study, found an effect of impulsivity on intentional inhibition, but not intentional action initiation (Asschemann & Ridderinkhof, 2013). For this purpose, we decided to investigate this effect by adding a modulating factor which is common and critical in everyday life. Alcohol is known to increase impulsivity (Perry & Carrol, 2008) and reduce emotion regulation (Sher & Grekin, 2007). Furthermore, it has been shown to reduce response inhibition by means of impaired working memory (Finn et al., 1999) and impaired executive attention (Curtin & Fairchild, 2003). For this purpose, we introduce an inter-participant alcohol bias by administering alcohol during one test session and a placebo during another. We aim to elucidate the effects of alcohol on intentional response inhibition in terms of electro-physiological responses. As alcohol is a commonly used and abused drug, researching its relation to response inhibition is of great clinical value.

As mentioned above, the time window of making an intentional decision varies, which may reduce the chance of detecting ERPs. Yet, event-related changes in EEG spectral power occur over longer time scales than many ERPs and have a better signal-to-noise ratio (Pfurthschneller, 1992). The present study is the first to investigate the influence of alcohol on neural correlates of intentional inhibition of an ongoing action. In particular, time-frequency analysis is a novel approach to both the effects of alcohol and intentional inhibition of ongoing actions. To reduce modulating factors, the study was performed double blind and the manipulation occurred within the same subjects. To this end, participants performed an action inhibition task, while EEG measured electrical brain activity. The task required the consistent tracking of a fish as ongoing action, with two types of requested inhibition. Intentional inhibition comprised collecting a star in a certain time range. Participants were not informed on the available time range, but were encouraged to follow their instinct of when to stop. If the participants stopped too early or too late, no points were collected. The moment in which participants felt the urge to stop tracking and collect the star is called the W-moment. To capture this moment – instead of the moment of motor inhibition – a clock ran along with the fish stimulus. After collecting a star, participants had to insert the corresponding number of the perceived W-moment. As control condition, cued inhibition was signaled by a color change of a circle around the fish. This signal required instant inhibition after which the corresponding number was to be inserted as well. Due to metabolic effects of alcohol processing, we chose to only include men in the study.

6 We hypothesized that intentional inhibition, will manifest in the left sensorimotor area and left dFMC. In particular, beta power will increase in left frontal sensorimotor regions during the decision to inhibit, prior to movement offset. We hypothesized that alcohol will reduce this effect. Moreover, power spectrum will display a stronger involvement of the left dFMC during intentional inhibition, compared to cued inhibition. Again, alcohol will reduce this effect.

3. METHODS

A total of 20 right-handed male adults participated in this study for financial compensation, with age ranges of 21 to 28 years (mean = 24.6 years, SD = 2.3 years). All participants were students and held English skills at a fair level. Participants were recruited via the University of Amsterdam. According to self-report, they had no head injuries or neurological or psychiatric disorders. Moreover, participants were not addicted to alcohol or drugs and did not participate in excessive drinking of alcohol, use of drugs or were inexperienced with alcohol. They weighed between 61 and 95kg, but were specifically not anorexic or obese. Due to legal purposes regarding the release of participants into traffic after alcohol administration, they ether owned a driver’s license for a minimum duration of two years or did not own one at all. This study was performed in compliance with relevant laws and institutional guidelines.

3.2 QUESTIONNAIRES

All participants filled out a demographic questionnaire and The Alcohol Use Disorders Identification Test (AUDIT) (Saunders et al., 1993) prior to the testing. A score characterizing excessive use of alcohol excluded them from participation. Moreover, a session information form captured time of testing and alcohol measurements, weight of the subject, specific condition, and alcohol levels during the alcohol condition. Finally, the Self-Rating of the Effects of Alcohol (SRE) form (Schuckit, Smith & Tipp, 1997) was implemented after the second session, asking the participant to judge his level of intoxication during both sessions.

3.3 EXPERIMENTAL CONDITIONS

Each participant performed the experiment twice, two to seven days apart. On one test session, they received an alcoholic drink, on the other one a placebo. The order of experimental conditions was randomized. Drinks consisted of orange juice, Tabasco and 40% alcoholic vodka or water, respectively. The amount of vodka or water depended on the weight of the participant. We aimed for 0.05% blood

7 alcohol, which converts 1:2.3 to breathe alcohol (User Manual lion alcometer SD-400). Once the drink was mixed, the outside of the glass was sprayed with vodka and a vodka-soaked lemon was inserted. Each participant received 3 drinks: 2 before the experimental task and one 40min after the second drink. Immediately after finishing the drinks (within 2 minutes), the mouth was flushed with non-alcoholic mouthwash.

3.4 EXPERIMENTAL TASK

We developed a simple motor-tracking task called “Chasing Memo”, which provided a contextual‐based inhibition design. Participants tracked the movement of an on‐screen target fish - “Memo”. The primary goal was to remain within certain proximity of the target, where successful tracking was rewarded by points. These points had an inherent monetary value for the participant at the end of the experiment. The participant therefore had an immediate incentive motivation to continue accurate tracking. Detailed instructions and training to the participant was performed to ensure that the context of the game was fully understood to the extent that disengagement was entirely intentional and not participant to external cues. Participants were to start following Memo within ten seconds after the counter changed from orange to blue. This color change happened randomly, between 3000 and 6000ms, after the trial started. After two seconds of successful continuous tracking, a bonus item in form of a star appeared. At this point, participants had 20 seconds to collect the star to gain bonus points. In order to collect the star, participants had to stop tracking, leaving the computer mouse resting in its end-position. When the participants collected it straight away, the star had little value. Moreover, when they waited too long (exceeding 20 seconds), no tracking points nor star value were collected. The star value was determined by a fixed schedule that was normally distributed. Minimum star value is two points and maximum star value is 50 points (at ten seconds). Collecting the star ends a trial. Intentional and cued condition consisted each of 4 blocks with 10 trials, presented in randomized order.

8 Fig. 1: The tracking target Memo. The green zone indicates correct proximity to the target and is rewarded with tracing points. The tracking meter displays successful tracking: points increase exponentially with time of successful tracking. After 2s of successful tracking, a star appears and the bonus can be collected in the time range between 5s - 20s. Once the participant feels the urge to stop, he memorizes the number in the counter, which he states after collecting the bonus.

3.5 PROCEDURE

Participants received a link containing questionnaires prior to the experiment. Upon begin of the experiment, all participants read and signed informed consent for the research. Hereafter, breath alcohol was measured by a second researcher to ensure double-blindness and sobriety. Subsequently, participants were weighed and performed practice trials. Once convinced they fully understood the task, the first drink was handed out, which was drunk while the EEG cap was applied. Five minutes after finishing the first drink, participants received the second drink. Again five minutes later, breath alcohol was measured a second time. Now, the participants perform the tracking task on the computer. EEG data was recorded for the duration of the test. Following the test session, breath alcohol was measured for a third time. Participants filled out exit questionnaires and received a final breath alcohol measurement. When breathe alcohol was below 0.02%, participants were free to leave.

3.6 EEG RECORDING AND PREPROCESSING

EEG was recorded and sampled at 2048 Hz using a Bio Semi Active Two 64-channel system. Sixty-four scalp electrodes were measured, as well as 6 electrodes for horizontal and vertical eye-movements. After acquisition, EEG data was down-sampled to 512 Hz, referenced to average of all channels and filtered a high-pass filter of 0.1 Hz, a low-pass filter of 100 Hz and a notch-filter of 50 Hz. All trials were epoched -3 to 1s around the point of final disengagement in both conditions and a liner baseline correction was performed from -3 to -2.5s. Strongly artifact-laden trials (especially in baseline

9 period) and channels were manually inspected and excluded. Following, Principal Component Analysis was performed to identify EEG segments, highly contaminated with eye-blinks across recordings (spatial distribution visually determined as being eye-blinks). PCA was used to identify the topography of brain signals and artifact signals. Finally, eye-blink related artifacts and some channel or muscle artifacts were removed. All preprocessing steps were performed using EEGLAB (Delorme & Makeig, 2004). Data of one participant was rejected, because it was too noise over the whole course of recording.

3.7 EVENT-RELATED SPECTRAL PERTURBATION (ERSP) ANALYSES

The Multiple Subject Processing tools of EEGLAB were used to first interpolate all previously excluded channels and then compute channel measures for Power Spectrum and ERSPs. The activity during the baseline period from -3 to -2.5s was subtracted and the epochs averaged. Event-related spectral perturbation was plotted for electrode FC3 and FC4 responding to sensorimotor areas and F1, FZ, F2 over dFMC. Increase or decrease in power was defined relative to the baseline in the respective rhythm bands. Mixed analyses of variance (ANOVA) with factors condition (alcohol, no alcohol) and disengage (intentional, cued) and a p-value of 0.05 were performed on the data to allow statistical interferences in time-frequency space. Moreover, False Discovery Rate correction corrected for multiple comparisons.

4. RESULTS

This paper reports results of time-frequency analysis of the collected EEG data. The first section of the results states spectral perturbations of the electrodes F1, Fz and F2 over the dFMC. The second section reports spectral perturbations for electrodes FC3 and FC4 over sensorimotor areas. Within the sections, power perturbations in distinct frequency bands are related across electrodes and stated consecutively. Moreover, the experimental conditions ‘alcohol’ and ‘placebo’, as well as the movement offset conditions ‘intentional’ and ‘cued’ are compared.

4.1 Electrodes over dFMC

This section states spectral perturbations for the electrodes F1, Fz and F2. Activation clusters of distinct frequency bands are consecutively compared across electrodes and conditions.

10 Fig. 2: Power spectrum of neural activity around the time of disengagement for electrode F1 (left dFMC), under the conditions (from top left to bottom right): Alcohol (Alc), intentional inhibition (Intentional); Alcohol (Alc), cued inhibition (Cued); Placebo (noAlc), intentional inhibition (Intentional); Placebo (noAlc), cued inhibition (Cued). Color conventions can be seen on the left bar, indicating spectral power in dB. Note logarithmic ordinate.

11 Fig. 3: Power spectrum of neural activity around the time of disengagement for electrode Fz (midline dFMC), under the conditions (from top left to bottom right): Alcohol (Alc), intentional inhibition (Intentional); Alcohol (Alc), cued inhibition (Cued); Placebo (noAlc), intentional inhibition (Intentional); Placebo (noAlc), cued inhibition (Cued). Color conventions can be seen on the left bar, indicating spectral power in dB. Note logarithmic ordinate.

12 Fig. 4: Power spectrum of neural activity around the time of disengagement for electrode F2 (right dFMC), under the conditions (from top left to bottom right): Alcohol (Alc), intentional inhibition (Intentional); Alcohol (Alc), cued inhibition (Cued); Placebo (noAlc), intentional inhibition (Intentional); Placebo (noAlc), cued inhibition (Cued). Color conventions can be seen on the left bar, indicating spectral power in dB. Note logarithmic ordinate.

At F1 (over the left dFMC), exclusively the ‘Placebo-Intentional’ condition suppresses lower-alpha activity (8-10.5 Hz) from -0.15s until movement offset. Following movement offset, both intentional conditions decrease power in the alpha-band (8-13 Hz). While the alpha decrease begins, around 0.4s in the placebo condition, it is immediate, stronger and longer in the alcohol condition. At Fz, this decrease intensifies in the alcohol condition, but vanishes during placebo. Interestingly, the alpha power decrease following movement offset reappears at F2 at 0.25s and is further intensified in the alcohol condition, where it spans a larger frequency range from upper theta to alpha (7-13Hz).

While both ‘intentional’ conditions show decreased power in the alpha band following movement offset, both ‘cued’ control conditions decrease mainly in the theta band (3.5–7 Hz) at F1. This happens over a larger frequency range in the placebo condition (4-9.5 Hz), compared to the alcohol condition (6-9

13 Hz). The effect increases from F1 over Fz to F2. Moreover, the movement offset of the ‘cued’ conditions (alcohol-cued and placebo-cued) were immediately preceded by a visual stimulus. Here, strong activity in low frequency bands from delta (0.5–3.5 Hz) to lower alpha, as well as in the beta range (13-30 Hz) is present. The beta-increase, is present at all 3 electrodes, but stronger in the Placebo condition. Moreover, it is stronger delimited in the placebo condition, while it converges with a later power increase in higher beta power in the alcohol condition.

4.2 Electrodes over sensorimotor Areas

The following section compares results between the electrodes FC3 and FC4 over sensorimotor areas. As in the previous section, activation clusters of distinct frequency bands are consecutively compared across electrodes and conditions.

14 Fig. 5: Power spectrum around the time of disengagement for electrode FC3 (left sensorimotor), under the conditions (from top left to bottom right): Alcohol (Alc), intentional inhibition (Intentional); Alcohol (Alc), cued inhibition (Cued); Placebo (noAlc), intentional inhibition (Intentional); Placebo (noAlc), cued inhibition (Cued). Color conventions can be seen on the left bar, indicating spectral power in dB. Note logarithmic ordinate.

15 Fig. 6: Power spectrum around the time of disengagement for electrode FC4 (left sensorimotor), under the conditions (from top left to bottom right): Alcohol (Alc), intentional inhibition (Intentional); Alcohol (Alc), cued inhibition (Cued); Placebo (noAlc), intentional inhibition (Intentional); Placebo (noAlc), cued inhibition (Cued). Color conventions can be seen on the left bar, indicating spectral power in dB. Note logarithmic ordinate.

At electrode FC3 (over left sensorimotor area) in the ‘intentional-alcohol’ condition, scattered spots of increased power in the lower beta to higher gamma-bands range from -2.5 to -2s. From -1.7 until movement offset first higher gamma, then gradually lower gamma decreased power spots appear. Except for one high gamma spot, the decreased power ends about 0.1s after movement offset. During the ‘intentional-placebo’ condition, a similar pattern is expressed in the higher gamma band (60-100 Hz), but less suppression in the lower gamma band (30-60 Hz). Here, power decrease also appears in the beta range, as well as a decrease in the alpha-band from 9-11 Hz from -0.2s to movement offset, which is immediately followed a wider spread alpha decrease. This deactivation is comparable with the ‘placebo-intentional’ condition at F1. The cued conditions lack significant increase or decrease patterns in the

16 higher frequency bands. The activation does not differ strongly, compared with the activity measured at dFMC electrodes. To account for contralateral motor activity, we plotted activation at electrode FC4 over the right sensorimotor area. In the intentional conditions, comparable high-frequency activity is present as in FC3. In the alcohol condition, beta and alpha suppresions are far stronger and span a larger frequency range. In the intentional condition, alpha suppression prior to movement offset lacks, but is more pronounced following movement offset. Activations in the cued conditions are, aswell, in the same frequency ranges but stronger.

5. DISCUSSION

While our study explored the effects of alcohol on intentional inhibition, it was also the first to contrast frequency responses of the more commonly studied cued inhibition to intentional inhibition of an ongoing action. To this end, we designed a motor-tracking task, providing a contextual-based design for cued and intentional inhibition of an ongoing action. Depending on the conditions, participants ether ceased tracking immediately after viewing the stop-stimulus (cued-condition) or had a time window in which they followed their urge to stop (intentional inhibition). We hypothesized that intentional inhibition would manifest over the left dFMC (Brass & Haggard, 2007; Kühn, Haggard & Brass, 2009, Schel et al., 2014) and that beta power would increase over the left sensorimotor area (Walsh et al., 2010). Alcohol was expected to decrease the frequency responses in both conditions. As the neural decision-making processes for intentional inhibition took place before movement offset, we will focus on the period before time 0 for frequency response discussion. The period following movement offset will be discussed regarding alcohol effects.

5.1. Frequency response at dFMC

Over the left dFMC, we found that only the placebo condition of intentional inhibition expressed a decrease in alpha power prior to movement offset. The amplitude of alpha rhythms is considered to indicate the degree of cortical inhibition (Klimesch et al., 2007). Furthermore, alpha rhythms were found to be suppressed upon tactile stimulation (Jasper & Andrews, 1938). Thus, participants in our experiment could have become aware of the computer mouse in their hand, as they decided to let go of it. Alcohol reducing tactile sensation (Mullin & Luckhardt, 1934) could cause the lack of alpha suppression. On the other side, the dose of alcohol was not high enough to justify a complete tactile insensitivity. Conceptually, the role of left frontal alpha suppression representing reward expectancy (Sobotka, Davidson & Senulis, 1992) applies to the design of our experiment. Yet, the alpha suppression is noticeably stronger at the medial electrode Fz compared to the left F1. Another possible explanation is

17 that the power decrease resulted from voluntary or imagined movements (Gastaut, 1952; Chatrian et al., 1959). In this case, the decrease in alpha power could express the anticipated movement offset prior to its execution - possibly an ideomotor response. The lack of alpha decrease in the alcohol condition can be explained by alcohol increasing impulsivity and reducing the decision process including the imagined movement offset. The fact that the alpha power decrease is slightly stronger at Fz compared to F1 (while gone at F2) promotes an anticipatory effect. Would alpha suppression relate to contralateral touch of the mouse, the decrease should be stronger at F1 than at Fz. However, the lack of extensive literature renders an ideomotor interpretation speculative.

The strong increase in lower-frequency power before movement offset in the cued conditions likely results from the visually presented stop-stimulus (Makeig et al., 2002). The low beta-power increase, on the other hand, could reflect the selection of relevant sensory evidence for a motor action. It could stem from dorsolateral prefrontal cortex (dPFC) or motor cortex, with enhanced power predicting correctness of the participants’ choice of the evidence (Siegel, Engel & Donner, 2011). In the alcohol condition, low-beta is weaker, suggesting a lesser evaluation of the sensory evidence. Moreover, the left hemisphere appears to have a larger role in this process, as the increase in activation decreases from F1 over Fz to F2. As the intentional conditions require no sensory evidence for motor plans, increase in beta activation is not present in this condition.

5.2 Frequency response at left sensorimotor site FC3

For electrode FC3, we expected an increase in beta-power like in Walsh et al. (2010). Here, low-beta power around 14Hz increased when participants inhibited an action instead of acting it out. Our ‘intentional’ conditions, however, show no beta power increase, but some decrease during Placebo. The cued condition, on the other hand, expresses power increase in low-beta as in Walsh et al, (2010). The reason for this could be the variation in the intentional inhibition task. While in Walsh et al. (2010) participants imagined an action, which they never came to execute in the inhibition trials, our participants performed an ongoing action which they inhibited at a chosen time point. In the first scenario, the mind is set on planning the action, which is interrupted in ‘the last moment’ prior to execution. The second scenario executes the action as the default state and focusses on inhibition. For this reason, our scenario likely better represents the state of mind during inhibition. The fact that no increase in low-beta power was found in our intentional conditions, but in the ‘Cued-Placebo’ condition, suggests that the rise in beta power seen in Walsh et al. (2010) does not actually stem from the neural process of intentional inhibition, but likely another mechanism. Moreover, beta synchronization was found at left fronto-central cites in NoGo trials of a Go/NoGo paradigm (Alegre et al., 2004). This supports the increase in beta power found

18 in Walsh et al. (2010), not stemming from the intentional decision-making process of inhibiting an ongoing action, but possibly from withholding an action. However, the fact that our experimental design required an actual change in motor activity could mean that findings of increased beta activity at left frontal sites (Alegre et al., 2004, Walsh et al., 2010) in fact represent an ideomotor response, while our results are indeed sensorimotor.

Unlike over the dFMC, electrode FC3 measured strong and variable activity in the gamma band. Studies report gamma power encoding sensory evidence, perceptual decision-making and assisting in motor control (Siegel, Engel & Donner, 2011; Heekeren, Marrett & Ungerleider, 2008; van Wijk, Beek & Daffertshofer, 2012). When assisting in perceptual decision-making, gamma frequency responded with increased activity over the left inferior frontal cortex to changes in an acoustic pattern and with increased activity over the right parietal cortex to spatial changes. This reaction was more pronounced during easy than during difficult decisions (Heekeren, Marrett & Ungerleider, 2008). In the case of encoding sensory evidence or assisting in motor control, gamma activity is enhanced in visual, primary motor and premotor cortices. In the motor related areas, gamma increases contralateral to the movement e.g. button press (Siegel, Engel & Donner, 2011; van Wijk, Beek & Daffertshofer, 2012). Due to current spread, FC3 could measure activity from the motor cortex. However, FC4 on the ipsilateral side of computer mouse-use measured the same gamma activations in the intentional condition, meaning that the activation is unlikely to result from the motor action. In addition, enhanced gamma power during motor control is typically accompanied by decreased power in lower frequencies (van Wijk, Beek & Daffertshofer, 2012), which is not the case here, and gamma power in our experiment is enhanced until 1.7s before movement offset, thereafter is shifts towards decreased power. Since this activation pattern is not evident in the cued condition, it is not contralateral to motor action and the gamma increase does not happen preceding an action (movement offset), it likely results from the internal decision process. One study measuring intracranial EEG in epileptic patients, found a function of decreased gamma band activity (Ossadón et al., 2011). In a visual search task, high-frequency gamma power was suppressed over the classical “default-mode network” areas (DMN) (Raichle et al., 2001), including posterior cingulate cortex (PCC), mPFC, temporoparietal junction (TPJ), ventrolateral PFC (vlPFC), lateral temporal cortex (LTC) and right medial frontal gyrus (MFG). During the task, gamma power was longer suppressed during the difficult search condition than during the easy one and stronger gamma suppression correlated with better performance. In our experiment, the period prior to the movement offset in intentional conditions represents the time where the decision-making and the urge to inhibit is strongest. Thus, difficult searching of visual stimuli could relate to the strained unconscious evaluation of when to stop. Moreover, the highly transient nature of power suppression in the DMN network and the sequential order of power drop onset in each distinct

19 node of the network emphasize its dynamic nature. This suggests an involvement in rapid switching between different attentional states or degrees of engagement with the external world (Ossadón et al., 2011). More specific, DMN suppression is suggested to encode the quality of transition from an introspective to an exterospective state (Ossadón et al., 2011). Regarding intentional inhibition, gamma power suppression could assist in switching from the evaluation of internal motivators to the external exhibited action. As the more frontal electrodes did not pick up this activity, is could origin from the PCC. The PCC is connected to a wide range of intrinsic control networks (Leech & Sharp, 2014). Enhanced PCC activity indicates memory retrieval and planning, while suppressed activity relates to higher external attention and initiates a signal to change the behavioral strategy (Pearson et al., 2011; Leech & Sharp, 2014). In the course of our intentional inhibition task, this would mean that decision-forming took place until 1.7 seconds prior to movement offset, followed by planning to execute the decision. Next to the DMN, the PCC is involved in the dorsal attention network and the fronto parietal control network (Leech & Sharp, 2014). It is feasible that the PCC also forms a network with the sensorimotor area below FC3 and FC4 to produce an ideomotor response. However, only one study found the relation of suppressed gamma power over DMN areas (Raichle et al., 2001), further research is needed to affirm these conclusions.

5.3 Effect of alcohol on frequency response

As expected, alcohol weakened and delayed many power responses in both conditions over the dFMC. These include low-alpha suppression from -0.15s until movement offset in the ‘intentional‘ conditions at all electrodes, low-beta power increase immediately preceding movement offset in the ‘cued‘ conditions at all electrodes, as well as reducing the separation of the before mentioned beta increase from a broader activation post movement offset. An exception is increased activation in the delta (0.5-3 Hz) and low-theta band (3.5-7 Hz) in the ‘intentional’ conditions. Phase-locked delta responses are the main processing signals in the sleeping human brain, but delta power is also increased during oddball experiments (Başar, 1999). Yet, no oddball stimulus existed at the moment of movement offset and interestingly, alcohol was shown to reduce delta activity during sleep (Colrain, Turlington & Baker, 2009). However, driver fatigue was found to elicit strong delta and theta activity (Lal & Craig, 2002). Thus, the detected low frequency power possibly results from alcohol induced fatigue. Another intensifying effect of alcohol was increased suppression of alpha power following movement offset. At electrode F1, it appears as if the alpha suppression prior and following movement offset was shifted after movement offset in the alcohol condition. Interestingly, desynchronized lower-alpha reflects states such as alertness and vigilance, medium alpha expectancy (Klimesch at al., 1998). We would expect alcohol to have the opposite effect. However, in this case increased alertness could reflect the participants being

20 maladjusted to the period following movement offset. No fast or new reaction is required, so alertness is in fact inappropriate.

At FC3 and FC4, the spots of high-frequency power increase and decrease seem to be less confined to high-gamma in the alcohol condition. Alcohol was suggested to modulate the firing rate of neurons, while affecting higher frequencies stronger than lower ones (Colzato, Erasmus & Hommel, 2004). Thus, neurons could fire in low-gamma instead of high-gamma rate, leading to a shift in power in- or decrease. Likely, communication problems between neural networks result.

6. CONCLUSIONS

First, our results do not suggest a larger involvement of the left dFMC during intentional inhibition and larger involvement of the right dFMC during cued inhibition. In fact, the activations differ so strongly that comparing frequency responses is challenging. Cued inhibition displays no activity prior to the inhibition that is not related to the visual stimulus. This supports that the inhibition process should proceed to be studied with an ‘intentional’ design to come to conclusions about real life inhibition processes. Second, alcohol significantly modifies the frequency response to the decision-making process. It appears to elicit miscommunication within the brain and further consequences should be investigated. Last, for further elucidation of involved regions, a current source reconstruction or simultaneous EEG and fMRI should be performed during intentional inhibition.

21

References

Abler, B., Walter, H., Erk, S., Kammerer, H., & Spitzer, M. (2006). Prediction error as a linear function of reward probability is coded in human nucleus accumbens. Neuroimage, 31(2), 790-795.

Alegre, M., Gurtubay, I. G., Labarga, A., Iriarte, J., Valencia, M., & Artieda, J. (2004). Frontal and central oscillatory changes related to different aspects of the motor process: a study in go/no-go paradigms. Experimental Brain Research, 159(1), 14-22.

Aron, A. R. (2011). From reactive to proactive and selective control: developing a richer model for stopping inappropriate responses. Biological Psychiatry,69(12), e55-e68.

Aron, A. R., & Poldrack, R. A. (2006). Cortical and subcortical contributions to stop signal response inhibition: role of the subthalamic nucleus. The Journal of Neuroscience, 26(9), 2424-2433.

Aron, A. R., Robbins, T. W., & Poldrack, R. A. (2004). Inhibition and the right inferior frontal cortex. Trends in Cognitive Sciences, 8(4), 170-177.

Aschemann & Ridderinkhof (2013). Resisting temptation: How intentional inhibition relates to impulsivity and emotion regulation. Bachelor Project.

Başar, E., Başar-Eroğlu, C., Karakaş, S., & Schürmann, M. (1999). Are cognitive processes manifested in event-related gamma, alpha, theta and delta oscillations in the EEG?. Neuroscience letters, 259(3), 165-168.

Brass, M., & Haggard, P. (2007). To do or not to do: the neural signature of self-control. The Journal of Neuroscience, 27(34), 9141-9145.

Casey, B. J., Trainor, R. J., Orendi, J. L., Schubert, A. B., Nystrom, L. E., Giedd, J. N., ... & Rapoport, J. L. (1997). A developmental functional MRI study of prefrontal activation during performance of a go-no-go task. Journal of Cognitive Neuroscience, 9(6), 835-847.

Chatrian, G. E., Petersen, M. C., & Lazarte, J. A. (1959). The blocking of the rolandic wicket rhythm and some central changes related to movement. Electroencephalography and clinical Neurophysiology, 11(3), 497-510.

Chikazoe, J. (2010). Localizing performance of go/no-go tasks to prefrontal cortical subregions. Current Opinion in Psychiatry, 23(3), 267-272.

Colrain, I. M., Turlington, S., & Baker, F. C. (2009). Impact of alcoholism on sleep architecture and EEG power spectra in men and women. Sleep, 32(10), 1341.

Colzato, L. S., Erasmus, V., & Hommel, B. (2004). Moderate alcohol consumption in humans impairs feature binding in visual perception but not across perception and action. Neuroscience letters, 360(1), 103-105.

Corbetta, M., & Shulman, G. L. (2002). Control of goal-directed and stimulus-driven attention in the brain. Nature reviews neuroscience, 3(3), 201-215.

Curtin, J. J., & Fairchild, B. A. (2003). Alcohol and cognitive control: implications for regulation of behavior during response conflict. Journal of abnormal psychology, 112(3), 424.

22 Delorme, A., & Makeig, S. (2004). EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of neuroscience methods, 134(1), 9-21. Filevich, E., Kühn, S., Haggard, P. (2012). Intentional inhibition in human action: The power of ‘no’. Neuroscience and Biobehavioral Reviews, 36, 1107-1118.

Finn, P. R., Justus, A., Mazas, C., & Steinmetz, J. E. (1999). Working memory, executive processes and the effects of alcohol on Go/No-Go learning: testing a model of behavioral regulation and

impulsivity. Psychopharmacology, 146(4), 465-472.

Gastaut, H. (1952). Electrocorticographic study of the reactivity of rolandic rhythm. Revue neurologique, 87(2), 176.

Harnishfeger, K.K. (1995). The Development of Cognitive Inhibition: Theories, Definitions, and Research Evidence. In: Dempster, F.N., Brainerd, C.J. (ed.). Interference and Inhibition in Cognition. London: Academic Press, Inc., pp. 175 – 184.

Heekeren, H. R., Marrett, S., & Ungerleider, L. G. (2008). The neural systems that mediate human perceptual decision making. Nature Reviews Neuroscience, 9(6), 467-479.

Jahfari, S., Fonville, M., Hengeveld, P., Reusken, C., Scholte, E. J., Takken, W., ... & Sprong, H. (2012). Prevalence of Neoehrlichia mikurensis in ticks and rodents from North-west Europe. Parasit

Vectors, 5(1), 74.

Jahfari, S., Waldorp, L., van den Wildenberg, W. P., Scholte, H. S., Ridderinkhof, K. R., & Forstmann, B. U. (2011). Effective connectivity reveals important roles for both the hyperdirect (fronto-subthalamic) and the indirect (fronto-striatal-pallidal) fronto-basal ganglia pathways during response inhibition. The Journal of Neuroscience, 31(18), 6891-6899.

Jasper, H. H., & Andrews, H. L. (1938). Brain potentials and voluntary muscle activity in man. J. Neurophysiol, 1, 87-100.

Klimesch, W., Doppelmayr, M., Russegger, H., Pachinger, T., & Schwaiger, J. (1998). Induced alpha band power changes in the human EEG and attention.Neuroscience letters, 244(2), 73-76.

Kornhuber, H. H., & Deecke, L. (1965). Hirnpotentialänderungen bei Willkürbewegungen und passiven Bewegungen des Menschen: Bereitschaftspotential und reafferente Potentiale. Pflüger's Archiv für die gesamte Physiologie des Menschen und der Tiere, 284(1), 1-17.

Kühn, S., Haggard, P., & Brass, M. (2009). Intentional inhibition: How the “veto‐area” exerts control. Human brain mapping, 30(9), 2834-2843.

Lal, S. K., & Craig, A. (2002). Driver fatigue: electroencephalography and psychological assessment. Psychophysiology, 39(3), 313-321.

Leech, R., & Sharp, D. J. (2014). The role of the posterior cingulate cortex in cognition and disease. Brain, 137(1), 12-32.

23 Logan, G. D., & Cowan, W. B. (1984). On the ability to inhibit thought and action: A theory of an act of control. Psychological review, 91(3), 295.

Makeig, S., Westerfield, M., Jung, T. P., Enghoff, S., Townsend, J., Courchesne, E., & Sejnowski, T. J. (2002). Dynamic brain sources of visual evoked responses. Science, 295(5555), 690-694.

Matsumoto, K., & Tanaka, K. (2004). The role of the medial prefrontal cortex in achieving goals. Current Opinion in Neurobiology, 14, 178-185.

Mita, A., Mushiake, H., Shima, K., Matsuzaka, Y., & Tanji, J. (2009). Interval time coding by neurons in the presupplementary and supplementary motor areas. Nature neuroscience, 12(4), 502-507.

Mullin, F. J., & Luckhardt, A. B. (1934). The effect of alcohol on cutaneous tactile and pain sensitivity. Am. J. Physiol, 109, 77.

Ossandón, T., Jerbi, K., Vidal, J. R., Bayle, D. J., Henaff, M. A., Jung, J., ... & Lachaux, J. P. (2011). Transient suppression of broadband gamma power in the default-mode network is correlated with task complexity and subject performance. The Journal of Neuroscience, 31(41), 14521-14530.

Passingham, R.E., Bengtsson, S.L., Lau, H.C. (2010). Medial frontal cortex: from self-generated action to reflection on one’s own performance. Trends in Cognitive Sciences, 14, 16-21.

Pearson, J. M., Heilbronner, S. R., Barack, D. L., Hayden, B. Y., & Platt, M. L. (2011). Posterior cingulate cortex: adapting behavior to a changing world. Trends in Cognitive Sciences, 15(4), 143-151. Perry, J. L., & Carroll, M. E. (2008). The role of impulsive behavior in drug

abuse. Psychopharmacology, 200(1), 1-26.

Pfurtscheller, G. (1992). Event-related synchronization (ERS): An electrophysiological correlate of cortical areas at rest. Electroencephalography and Clinical

Neurophysiology, 83, 62–69.

Raichle, M. E., MacLeod, A. M., Snyder, A. Z., Powers, W. J., Gusnard, D. A., & Shulman, G. L. (2001). A default mode of brain function. Proceedings of the National Academy of Sciences, 98(2), 676-682. Ridderinkhof, K. R., van den Wildenberg, W. P., & Brass, M. (2014). “Don׳ t” versus “Won׳ t”: Principles, mechanisms, and intention in action inhibition. Neuropsychologia.

Ridderinkhof, K.R., Forstmann, B.U., Wylie, S.A., Burle, B., van den Wildenberg, W.P.M. (2011). Neurocognitive mechanisms of action control: resisting the call of the Sirens. Wiley Interdisciplinary Reviews: Cognitive Science, 2, 174-192.

Rueger, S. Y., McNamara, P. J., & King, A. C. (2009). Expanding the Utility of the Biphasic Alcohol Effects Scale (BAES) and Initial Psychometric Support for the Brief‐BAES (B‐BAES). Alcoholism: Clinical and Experimental Research,33(5), 916-924.

Schadlen, M.N., & Gold, J.I. (2004). The Neurophysiology of Decision Making as a Window on Cognition. In: Gazzaniga, M.S. (ed.), The Cognitive Neurosciences III (3rd edition). Cambridge: Massachusetts Institute of Technology, pp. 1229 -1241.

24 Schel, M. A., Kühn, S., Brass, M., Haggard, P., Ridderinkhof, K. R., & Crone, E. A. (2014). Neural correlates of intentional and stimulus-driven inhibition: a comparison. Frontiers in human

Neuroscience, 8.

Schuckit, M. A., Smith, T. L., & Tipp, J. E. (1997). The Self‐Rating of the Effects of Alcohol (SRE) form as a retrospective measure of the risk for alcoholism.Addiction, 92(8), 979-988.

Sher, K. J., & Grekin, E. R. (2007). Alcohol and Affect Regulation.

Siegel, M., Engel, A. K., & Donner, T. H. (2011). Cortical network dynamics of perceptual decision-making in the human brain. Frontiers in human Neuroscience, 5, 21.

Sobotka, S. S., Davidson, R. J., & Senulis, J. A. (1992). Anterior brain electrical asymmetries in response to reward and punishment. Electroencephalography and clinical Neurophysiology, 83(4), 236-247. van Wijk, B. C., Beek, P. J., & Daffertshofer, A. (2012). Neural synchrony within the motor system: what have we learned so far?. Frontiers in human Neuroscience, 6.

Verbruggen, F., & Logan, G. D. (2008). Response inhibition in the stop-signal paradigm. Trends in Cognitive Sciences, 12(11), 418-424.

Walsh, E., Kühn, S., Brass, M., Wenke, D., & Haggard, P. (2010). EEG activations during intentional inhibition of voluntary action: An electrophysiological correlate of