Neurophysiological correlates of biased cognitive processing in addiction. Marianne Littel

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1 Neurophysiological correlates of biased cognitive processing in addiction Marianne Littel

2 Neurophysiological correlates of biased cognitive processing in addiction Marianne Littel

3 Cover photography by Valo-x-Stock Cover photo manipulation by Esra Zengin Layout by Ferdinand van Nispen, Citroenvlinder-DTP.nl, Bilthoven Printed by GVO drukkers & vormgevers B.V. Ponsen & Looijen ISBN Copyright 2012 M. Littel All rights reserved. No part of this thesis may be reproduced or transmitted in any form, by any means, electronic or mechanical, without the prior written permission of the author, or where appropriate, of the publisher of the articles. The research presented in this dissertation was supported by the Netherlands Organisation for Scientific Research (NWO, VIDI grant ).

4 Neurophysiological correlates of biased cognitive processing in addiction Neurofysiologische correlaten van verstoorde cognitieve verwerking in verslaving PROEFSCHRIFT ter verkrijging van de graad van doctor aan de Erasmus Universiteit Rotterdam op gezag van de rector magnificus Prof.dr. H.G. Schmidt en volgens besluit van het College van Promoties. De openbare verdediging zal plaatsvinden op vrijdag 1 juni 2012 om 9:30 uur door Marianneke Littel geboren te Willemstad

5 Promotiecommissie: Promotor: Prof.dr. I.H.A. Franken Overige leden: Prof.dr. J.W. van Strien Prof.dr. R. Zwaan Prof.dr. W. van den Brink

6 voor mijn ouders

7

8 Contents Neurophysiological correlates of biased cognitive processing in addiction Chapter 1 Electrophysiological indices of biased cognitive processing of substance-related cues: a meta-analysis 9 Chapter 2 Overall outline and hypotheses 41 Chapter 3 Chapter 4 Chapter 5 Chapter 6 Chapter 7 Chapter 8 Chapter 9 Chapter 10 Psychometric properties of the brief Questionnaire on Smoking Urges (QSU-Brief) in a Dutch smoker population 47 The effects of prolonged abstinence on the processing of smoking cues: an ERP study among smokers, ex-smokers and never-smokers 59 Changes in the electroencephalographic spectrum in response to smoking cues in smokers and ex-smokers 83 Implicit and explicit selective attention to smoking cues in smokers indexed by brain potentials 99 Electrophysiological correlates of associative learning in smokers: a higher-order conditioning experiment 123 Intentional modulation of the Late Positive Potential in response to smoking cues by cognitive strategies in smokers 147 Reduced cognitive processing of alcohol cues in alcoholdependent patients seeking treatment: an ERP study 173 Error-processing and response inhibition in excessive computer game players: an ERP study 189 Chapter 11 Summary and discussion 211 References 235 Samenvatting (summary in Dutch) 265 Dankwoord (acknowledgements in Dutch) 291 Curriculum Vitae 297 Publications 299

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10 Chapter 1 Electrophysiological indices of biased cognitive processing of substancerelated cues: a meta-analysis Littel, M., Euser, A.S., Munafo, M.R., & Franken, I.H.A. (submitted for publication). Electrophysiological indices of biased cognitive processing of substance-related cues: a meta-analysis.

11 Chapter 1 Abstract Several studies indicate that individuals with substance use disorders (SUD) exhibit biases in the cognitive processing of substance-related stimuli. These biases facilitate the detection and selection of substance cues and have been argued to play a causal or perpetuating role in the reactivity to substance cues. Indeed, mounting evidence suggests that these cognitive processing biases are important in addictive behaviors. Two electrophysiological indices of cognitive processing, the P300 and Late Positive Potential (LPP) components of the event-related potential (ERP), are associated with the deployment of attentional resources to motivationally relevant stimuli. In the present metaanalysis P300 and LPP amplitudes are used to investigate whether SUD persons show enhanced cognitive processing of substance cues relative to neutral cues as opposed to healthy control participants. Seventeen studies yielding 30 independent subject samples and six studies yielding ten independent samples were selected for meta-analysis assessing cue-elicited P300 and LPP responses, respectively. Results indicated the P300 and LPP amplitude effect sizes were significantly larger in addicted participants than in controls. This result can be explained by substance users motivated attention, that is, the allocation of attention and memory resources to stimuli relevant to substance users motivational states. Additional stratified moderator analyses revealed that both P300 and LPP amplitudes were not moderated by electrode site (Fz vs. Pz), type of substance used (stimulants vs. depressants), substance use status (abstinent vs. non-abstinent), age, gender and task requirements (active vs. passive paradigms). These results indicate that enhanced electrophysiological processing of substance cues appears characteristic of SUD in general, might be independent of recency of substance use, and presumably occurs irrespective of task demands. 10

12 Meta-analysis of biased cognitive processing in SUD Introduction Cognitive processing biases in addiction Addiction is a chronic, hard-to-treat condition characterized by cravings and frequently occurring relapse. Over the years, addiction has been associated with enhanced reactivity in response to substance-related stimuli such as the sight or smell of drugs and drug paraphernalia. This drug cue reactivity is comprised of a physiological component (e.g., skin conductance), a psychological component (e.g., self-reported urges: for a review, see Carter & Tiffany, 1999), and a cognitive component, i.e., individuals with substance use disorders (SUD) exhibit biases in the cognitive processing of substance-related stimuli. These biases, including biases in attention and memory, facilitate detection and selection of substance cues and have been argued to play a causal or perpetuating role in the physiological and psychological reactivity to substance cues. For example, it is hypothesized by Franken (2003) that SUD individuals automatically detect and orient attention toward substance-related stimuli. This increased attention for substance cues diminishes attention left for alternative cues, enhances substance-related cognitions, and causes subjective craving. The biological basis of these processing biases can be explained by the incentive-sensitization theory of addiction (Robinson & Berridge, 1993). This theory posits that repeated drug administration causes a sensitization of dopaminergic neurotransmission in the brain. Because of this sensitization drugs and drug-related stimuli acquire incentive motivational properties, which can alter the way they are perceived and processed. More specifically, substance-related stimuli will be perceived as particularly salient and reinforcing, and attention will be preferentially allocated to these stimuli (Franken, 2003; Robinson & Berridge, 1993; Ryan, 2002). The existence of cognitive processing biases in addiction has been repeatedly confirmed in studies using various behavioral tasks (see for reviews Field & Cox, 2008; Field, Munafò, & Franken, 2009). For example, utilizing modified Stroop tasks, it has been demonstrated that SUD individuals are slower to color-name substance-related words than neutral words relative to control participants (see W. M. Cox, Fadardi, & Pothos, 2006). In addition, using visual probe tasks, it has been shown that substance users exhibit faster reaction times to probes replacing substance-related cues than to probes replacing neutral cues (e.g., Chanon, Sours, & Boettiger, 2010; Franken, Kroon, & Hendriks, 2000; Mogg & Bradley, 2002). Results from these studies as well as from studies utilizing other attentional 11

13 Chapter 1 paradigms (e.g., eye movement studies, dual task procedures, flicker induced change blindness paradigms: see Field & Cox, 2008; Field et al., 2009), indicate that substance users display enhanced attentional processing of substancerelated stimuli. In addition to substance-related attentional bias, substancerelated memory bias has also been demonstrated. For example, Franken, Rosso and van Honk (2003) showed that after a picture color matching task, alcohol patients recalled more alcohol pictures, but not neutral pictures, than light drinkers. Using a more implicit measure of memory, i.e., a word-stem completion task, McCusker and Gettings (1997) found that after a modified Stroop task, wordstems primed more gambling-related words in pathological gamblers than in controls. Additional evidence comes from neuroimaging studies, in which it has been shown that presenting substance-related stimuli to SUD individuals not only increases activations in brain circuits involved in motivation and reward, such as the amygdala and the ventral striatum, but also in brain circuits that are normally involved in learning, memory and attention, such as the hippocampus, the prefrontal cortex and the anterior cingulate cortex (David et al., 2005; Hyman, 2005; Luijten et al., 2010; Robbins & Everitt, 1996). Mounting evidence suggests that cognitive processing biases are important in the development and maintenance of addiction. Attentional bias has been associated with poor treatment outcome (Carpenter, Schreiber, Church, & McDowell, 2006), relapse following treatment (W. M. Cox, Hogan, Kristian, & Race, 2002; Garland, Franken, & Howard, 2011; Marissen et al., 2006; Waters et al., 2003) and substance consumption behavior (W. M. Cox, Pothos, & Hosier, 2007; Fadardi & Cox, 2009; Field & Eastwood, 2005; Waters & Feyerabend, 2000), and both memory and attentional bias have been associated with self-reported craving (Field et al., 2009; Franken et al., 2003). All these studies indicate that attentional bias is an important concept in addiction; at least it seems an important predictor of craving and substance use behavior. However, direct evidence for a causal role of attentional bias in substance use is lacking (e.g., Hogarth, Dickinson, Janowski, Nikitina, & Duka, 2008; Hogarth, Dickinson, & Duka, 2009). To summarize, addiction is characterized by cognitive processing biases. SUD individuals are shown to selectively attend to substance-related stimuli and memorize these at the cost of other stimuli. Associations between these biases and craving, substance use and relapse highlight the importance of further investigation in cognitive processing biases in addiction. In this meta-analysis we will focus on 12

14 Meta-analysis of biased cognitive processing in SUD a relatively new method to asses cognitive processing of substance cues, i.e., the measurement of Event-Related Potentials (ERPs) using electroencephalography (EEG) techniques. ERP methodology provides a potentially more direct assessment of attentional processing than conventional behavioral (reaction time/ accuracy) data relying on indirect motor-responses. Event-related potentials as index for cognitive processing bias ERPs are manifestations of brain activities that occur in preparation for, or in response to discrete events (Fabiani, Gratton, & Coles, 2000). They consist of several peaks and troughs that tend to co vary in response to experimental manipulations. Positive and negative deflections that have been associated with specific information-processing operations are called components (Coles & Rugg, 1995). Components are often labeled after their polarity (e.g., positive) and relative latency (e.g., 300 ms) and vary in amplitude which presumably depicts the extent to which a processing operation is engaged (Kok, 1990). In the present meta-analysis we focus on two late positive ERP components that have been consistently associated with attentional processes and have been studied most frequently in drug cue-reactivity paradigms, namely the P3 or P300 and the Late Positive Potential (LPP). The P300 component refers to a large positive deflection of the ERP, arising about ms after stimulus presentation, which is typically maximal at medial central and parietal electrode sites (e.g., Polich & Kok, 1995b; Pontifex, Hillman, & Polich, 2009). This component has been extensively studied in oddball paradigms, in which participants have to respond to non-frequent target stimuli presented among a series of frequent non-target stimuli. In these paradigms the P300 amplitude tends to be more enhanced in response to targets as compared to nontargets (e.g., Donchin, 1981; Johnson, 1986), i.e., in response to stimuli that are task-relevant and demand attention. Furthermore, the P300 amplitude in response to targets is typically decreased when attention is directed elsewhere, for example when it is occupied by a distracter task (Duncan-Johnson & Donchin, 1977; Hillyard, Hink, Schwent, & Picton, 1973). Therefore it is generally believed that the P300 reflects the mental processes underlying the deployment of attentional resources to task-relevant stimuli (Donchin, 1981; Johnson, 1986; Polich & Kok, 1995b; Polich & Criado, 2006; Pontifex et al., 2009). Furthermore, the P300 has been associated with the evaluation of task-relevant stimuli, and the subsequent memory mechanisms engaged for these stimuli. 13

15 Chapter 1 Stimuli that are necessary for survival of the individual, i.e., stimuli that signal threat or danger as well as stimuli that signal the availability of sex and food or reward, tend to automatically attract attention (e.g., Ohman, Flykt, & Esteves, 2001). This automatic attention has been termed motivated attention, since it is believed to originate from the individual s drive state or motivation to survive and appears dependent on motivational factors (e.g., approach or avoidance tendencies; Lang, Bradley, & Cuthbert, 1997). Indeed, research has repeatedly shown that the P300 component of the ERP is enhanced in response to pleasant and unpleasant stimuli relative to neutral stimuli with most arousing pictures eliciting the largest P300 amplitudes (for a review, see Hajcak, MacNamara, & Olvet, 2010). In addition, when motivationally relevant stimuli are also task-relevant, i.e., when they are targets in an oddball task, the difference between the P300 evoked by motivationally relevant as compared to neutral stimuli has been observed to be even larger (Ferrari, Codispoti, Cardinale, & Bradley, 2008; Schupp et al., 2007), suggesting an interaction of directed and motivated attention. More recent work in this field has focused on a P300-related component of the ERP referred to as the Late Positive Potential (LPP). Whereas the P300 component appears to be transient, the LPP is typically enhanced for several seconds after the presentation of motivationally relevant stimuli (Cuthbert, Schupp, Bradley, Birbaumer, & Lang, 2000; Hajcak & Olvet, 2008). It has been argued that the P300 and the LPP are functionally similar in that they reflect the same underlying mental processes (Kok, 1997). From this point of view, the LPP reflects additional attentive processing or a continuation of attentive processing of motivationally relevant stimuli. However, research has indicated that the LPP has both a longer duration and a different topography, with activity shifting from parietal electrode sites to more fronto-central sites (Foti & Hajcak, 2008; Foti, Hajcak, & Dien, 2009; Hajcak, Dunning, & Foti, 2007), indicating that the LPP and the P300 cannot be considered entirely identical components (Foti et al., 2009). Although research on this topic is limited, it indeed appears that the LPP reflects additional involvement of memory encoding and storage (Koenig & Mecklinger, 2008) and that it is more sensitive to top-down cognitive modulation (Hajcak et al., 2010). Because of the functional overlap between the P300 and LPP, the two components have been quantified in different windows across studies. For example, amplitudes in the ms time window have been referred to as P300 in some studies (e.g., Schupp et al., 2007) and as LPP in other studies (e.g., Moser, Hajcak, Bukay, & Simons, 2006), hindering the differentiation between effects on the P300 versus 14

16 Meta-analysis of biased cognitive processing in SUD the LPP. However, recent results from an exploratory temporospatial principal components analysis conducted by Foti et al. (2009) have indicated that the spatial topography of the activity in the ms time window, often referred to as (part of) the LPP, is consistent with the P300. Furthermore, it was demonstrated that activity shifted from posterior to anterior recording sites from about 1000 ms after stimulus onset, implying that the two components are ideally separated somewhere between 600 and 1000 ms after stimulus presentation. In this article we chose to use the term P300 for positive ERP deflections between 300 and 800 ms, even if they have been termed LPP in the reviewed studies, whereas we chose to use LPP for the sustained positivity from 800 ms and onwards. In addition to the P300 ( ms), the LPP (> 800 ms) has also been found to be enhanced in response to pleasant and unpleasant stimuli, as compared to neutral stimuli (Cuthbert et al., 2000; Dunning & Hajcak, 2009; Foti & Hajcak, 2008; Hajcak et al., 2007; Hajcak & Olvet, 2008; Hajcak, Dunning, & Foti, 2009; Thiruchselvam, Blechert, Sheppes, Rydstrom, & Gross, 2011). Thus, not only do motivationally relevant stimuli capture attention and evaluation as reflected in P300 ERP potentials, they also recruit more elaborate and sustained attention and subsequent memory processes that are reflected in sustained late positive ERP potentials. Enhancement of late ERP components indicate motivated attention for substance cues in SUD populations Preferential processing of pleasant and unpleasant stimuli and stimuli signaling emotion can be considered evolutionary relevant to all individuals, and indeed motivated attention for these stimuli, as reflected by enhanced P300 and LPP amplitudes, has been replicated across different samples. At some points however, individuals can differ in their motivations, and consequently in what is motivationally relevant. These individual differences in stimulus preference have been found to be related to variation in the late ERP components. For example, enlarged P300 amplitudes have been found for hungry compared to satiated individuals in response to food cues (Nijs, Franken, & Muris, 2008; Nijs, Muris, Euser, & Franken, 2010; Stockburger, Schmalzle, Flaisch, Bublatzky, & Schupp, 2009). From these results one would expect that SUD individuals, who are highly motivated to seek out and consume drugs, would differ from healthy controls with regard to electrophysiological processing of substance-related stimuli. 15

17 Chapter 1 As early as the 80 s, electrophysiological processing of an alcohol cue was investigated in a small sample of alcoholics (Genkina & Shostakovich, 1983; Shostakovich, 1987). It was found that alcoholics displayed enlarged P300 amplitudes in response to brief presentations of the word vodka, but not to similarly presented neutral words, and that this effect was absent in control participants. Warren and McDonough (1999) continued this line of research and investigated ERP cue-reactivity in smokers. Instead of words they used pictures which were repeated several times and presented for 150 ms. Results indicated that the P300 was enlarged in response to smoking-related pictures as compared to neutral pictures. Although this discrepancy was found in both smokers and nonsmokers, it was significantly larger for smokers at frontal and central electrode sites. Comparable results were obtained in a replication study by the same researchers two years later (McDonough & Warren, 2001). Because the results were consistent with earlier studies showing that the P300 varies as a function of affective value and motivational significance of stimuli, the authors concluded that the effects were caused by the enhanced value or motivational significance of smoking-related stimuli for smokers, with enhanced attentional allocation as the underlying mechanism. Thus, the enhanced processing of smoking cues observed in smokers, as reflected by enhanced P300 amplitudes, was assumed to reflect the allocation of attentional resources to stimuli relevant to smokers smokingaddicted, motivational states. In the years that followed, several researchers have sought to extent these findings. With regard to smoking addiction, Littel and Franken (2007) confirmed the finding that the P300 amplitude in response to smoking pictures compared to neutral pictures is enhanced in smokers. In contrast to the results of Warren and McDonough (1999) and McDonough and Warren (2001), they found the effect to be absent in non-smokers, providing stronger evidence for the conclusion that enhanced attentional processing of smoking-related stimuli is uniquely driven by smokers smoking-addicted, motivational states. Furthermore, it was shown in this study that the effects are uniquely attributable to the smoking-related content of the pictures. Differential effects of physical properties of the visual stimuli were ruled out, since all smoking and neutral pictures were closely matched for all but the smoking-related features of content (i.e., the exact same backgrounds, persons and number of persons in both types of stimuli). More recently, Versace et al. (2011; 2011), demonstrated increased P300 amplitudes in response to smoking cues relative to neutral cues in larger groups of smokers (n = 116 and n = 180) 16

18 Meta-analysis of biased cognitive processing in SUD with varying dependence levels, with the most dependent smokers showing the largest amplitude differences. Similar results were obtained by Littel and Franken (2011), although no differences were observed between moderate and light smokers in the latter study. ERP cue-reactivity research has not been confined to alcoholics and smokers. Up until now, enlarged P300 amplitudes ( ms) elicited by substancerelated and neutral pictures and words have also been observed in cannabis users (Wölfling, Flor, & Grüsser, 2008), cocaine users (Dunning et al., 2011; Sokhadze et al., 2008), and heroin users (Franken, Stam, Hendriks, & van den Brink, 2003; Lubman, Allen, Peters, & Deakin, 2008) as compared to healthy controls. In some studies distinct analyses have been conducted for early and late P300 components (Herrmann et al., 2000; Littel & Franken, 2007). In these studies both early and late P300 amplitudes appeared to be enhanced in response to substance-related cues as compared to neutral cues. Enhanced LPP amplitudes (> 800 ms) in response to substance-related stimuli have been found in heroin users (Franken, Stam et al., 2003) and cocaine users (Dunning et al., 2011; Franken et al., 2008; Van de Laar, Licht, Franken, & Hendriks, 2004) relative to controls. Of all ERP cue-reactivity studies in SUD individuals, two studies failed to find significant effects. In a study by Hansenne et al. (2003), no ERP differences were found between alcoholics and controls in response to alcohol-related words relative to neutral words, whereas Jang et al. (2007) did not observe significant differences between smokers and non-smokers ERPs in response to smokingrelated pictures. Both studies, however, included only 10 participants per group, indicating that the absence of effects could have been caused by a lack of power. Furthermore, in the first study alcohol and neutral words were not matched for length, frequency or emotional valance and arousal, whereas the latter study included smokers that smoked only two cigarettes per day, implying that the smoker sample might not have been nicotine dependent. All of the above studies utilized passive paradigms in which participants were instructed to simply view presented words and pictures. There were no additional task demands and stimuli were presented with equal probabilities. An advantage of this design is that effects can neither be caused by high or low probability of certain stimuli compared to others nor by specific task instructions that dictate which stimuli are relevant. However, this passive paradigm also has a disadvantage. 17

19 Chapter 1 Although results from studies employing this paradigm have all been explained in terms of substance users motivated attention toward substance-related cues, one cannot be completely sure whether the effects are caused by the motivational significance of substance stimuli or merely (or additionally) by participants intentional viewing strategies (Lubman, Allen, Peters, & Deakin, 2007). In other words, it cannot be inferred whether the effects arise because of an implicit, involuntary capture of attention or because of an explicit, voluntary choice to focus attention on substance stimuli. In order to solve this issue, several studies have been conducted in which attention was manipulated ( active studies). In an oddball study by Lubman et al. (2007) heroin-dependent patients and controls were presented with frequent neutral and heroin-related non-target stimuli, among which infrequent neutral and heroinrelated targets were presented. Stimuli were displayed for 500 ms and participants had to respond to targets with a button press. ERPs were recorded in response to neutral and heroin-related non-targets and all oddballs combined (neutral and heroin-related targets). Results showed that heroin-dependent patients responded with increased P300 amplitude to heroin non-targets as compared to controls. These results are in line with the results from passive viewing paradigms, but provide additional evidence that enhanced processing of substance stimuli displayed by SUD individuals remains present when attention is directed elsewhere. Therefore, substance-related stimuli are shown to capture the implicit, involuntary attention of substance users. These results have been replicated in cocaine users (Sokhadze et al., 2008) and smokers (Littel & Franken, 2010). In these studies non-target substance stimuli were presented at shorter presentation durations (respectively 200 ms and 333 ms) and without perceivable stimulus intervals, making it even less probable that explicit, voluntary attention played a role in the enhanced processing of non-target, task-irrelevant substance-related stimuli. An additional comparison was made between responses to substancerelated target stimuli and neutral target stimuli in these studies. Results showed that SUD individuals additionally displayed enhanced processing of substancerelated targets as compared to neutral targets, indicating that SUD individuals also show enhanced voluntary attention toward substance cues. Similar results were obtained in an oddball study by Namkoong et al. (2004) conducted in a sample of alcoholics. In sum, motivated attention in addiction seems to be comprised of both implicit and explicit attention for substance cues, the latter operating in addition to selective attention directed by task-demands (i.e., responding to predefined 18

20 Meta-analysis of biased cognitive processing in SUD targets). This is in agreement with the earlier mentioned assumption that there exists an interaction between directed and motivated attention (Ferrari et al., 2008; Schupp et al., 2007). In addition to oddball paradigms, other active paradigms have been employed in two studies (Fehr, Wiedenmann, & Herrmann, 2006; Fehr, Wiedenmann, & Herrmann, 2007). In the first study smokers and non-smokers performed a smoking-related Stroop task in which they had to respond to the colors of the words and to ignore word content. In the other study smokers and non-smokers performed a smoking-related picture color matching task, in which they had to respond to the colors of the pictures and ignore the picture content. Results of both studies revealed that smokers, as compared to non-smokers, showed greater late ERP positivity in response to primary smoking cues (e.g., cigarettes, people smoking; Fehr et al., 2007) and secondary smoking cues (e.g., bus stops, kiosks; Fehr et al., 2006; Fehr et al., 2007). These findings are in line with those obtained in the substance-related oddball studies in that they provide evidence for enhanced processing of substance cues while attention is directed elsewhere. Despite the numerous indications of enhanced cognitive processing of substancerelated stimuli by SUD individuals on the electrophysiological level several issues need to be resolved. First of all, the overall effect size needs to be confirmed by a meta-analysis. As noted before, not all studies have found significant P300/ LPP amplitude differences between substance users and controls in response to substance and neutral cues (Hansenne et al., 2003; Jang et al., 2007). Moreover, not all studies reporting significant effects on the LPP component, have demonstrated significant effects on the P300 component (Franken et al., 2008; Van de Laar et al., 2004). These aberrant findings additionally indicate that the results of late ERP enhancements to substance cues in substance users await a meta-analytic approach. Secondly, P300 and LPP discrepancies are typically posteriorly distributed in traditional oddball tasks and ERP studies of emotion (Hajcak et al., 2010). Although this has also been found in some substance use studies (Herrmann et al., 2000; Versace et al., 2011), several substance-specific ERP studies have found frontally or fronto-centrally distributed differences between substance users and controls in response to substance-related cues relative to neutral cues (e.g., Littel & Franken, 2010; Sokhadze et al., 2008). Remarkably, some studies only observed fronto-centrally distributed effects (Fehr et al., 2006; Fehr et al., 2007; Littel & Franken, 2007; McDonough & Warren, 2001; Van de 19

21 Chapter 1 Laar et al., 2004; Warren & McDonough, 1999). Clearly, a meta-analytic approach would elucidate this topographic issue. And finally, results from the ERP studies in addiction have often been lumped together, despite differences in sample and study characteristics, such as differences in type of substance dependence and utilized paradigms. These different characteristics could have moderated the observed effects. A meta-analytic approach would additionally clarify the role of potential moderating variables. Putative moderators: sample and study characteristics With regard to sample characteristics, ERP studies have been conducted in substance-users dependent on different types of substances. Some of these studies were confined to individuals dependent on stimulants, i.e., nicotine and cocaine, whereas others investigated in substance-related processing of individuals dependent on depressants, i.e., heroin, alcohol, and cannabis. ERP differences between substance users and controls might differ between different SUD samples. In addition to type of substance use, the status of substance use varies between studies. Whereas some studies examined current substance users, other studies used participants that were abstinent at the time of testing, varying from at least one week (e.g., Franken et al., 2008) to at least one month (Van de Laar et al., 2004). Recency of substance use may relate to aberrant neural activity to substance cues (Wilson, Sayette, & Fiez, 2004). For example, Littel and Franken (2007) found the P300 to be significantly reduced in ex-smokers that were abstinent for at least six months. In addition, Dunning et al. (2011) observed that both abstinent and current cocaine users displayed enhanced P300/ LPP amplitudes in response to cocaine cues, but that this effect was no longer discernible for current users during a later LPP window. Furthermore, there is considerably variability between studies with regard to participants ages, with averages ranging from 17 (Nickerson et al., 2011) to 40 years (e.g., Sokhadze et al., 2008), as well as with regard to the relative proportion of participating males and females, with percentages ranging from 15 (e.g., Littel & Franken, 2007) to 100 percent male (e.g., Franken, Stam et al., 2003). Although the influence of age on cue-reactivity is unknown, gender appears to have some influence, with females being more sensitive to substance cues than males (e.g., Field & Duka, 2004). With regard to study characteristics, different paradigms have been employed, namely passive viewing, oddball and color matching paradigms. Differences between these paradigms might influence the ERP discrepancies between 20

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