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Serotonin 5-HT has been implicated in the regulation of impulsivity and such executive functions as decision-making cognition. The aim of this study was to examine the cognitive neurotoxicity of MDMA with regard to behavioral impulsivity and decision-making cognition. Cannabis users did not yield altered impulsivity compared with controls. In the GT, MDMA users performed significantly worse than cannabis consumers and controls, whereas cannabis users exhibited the same decision-making capacity as controls. In addition, the I-score as well as the decision-making performance was correlated with measures of MDMA intake. The I-score and the decision-making performance were also correlated. These results suggest that heavy use of MDMA may elevate behavioral impulsivity and impair decision-making cognition possibly mediated by a selective impairment of the 5-HT system. Even worldwide, MDMA has become one of the most widely used illegal psychoactive drugs, with millions of regular users Landry Imaging studies with serotonergic radioligands exhibited reduced 5-HT transporter densities in cortical and subcortical structures of the brains of MDMA users McCann et al. Furthermore, electrophysiological studies suggested alterations of the serotonergic system in regular users of MDMA Tuchtenhagen et al. In summary, MDMA seems to be a neurotoxin that selectively impairs the serotonergic system in primates, including humans. An important physiological role of the neurotransmitter 5-HT is behavioral inhibition. Indeed, evidence of low 5-HT neurotransmission was reported to be involved in the etiology of several disorders characterized by behavioral disinhibition, including alcohol dependence, suicide, bulimia, personality disorders, conduct disorder, and aggression LeMarquand et al. In addition, lowering serotonin via tryptophan depletion increases impulsive behavior in rats Harrison et al. Behavioral inhibition has been proposed as a critical component of decision-making cognition Monterosso and Ainslie ; Cardinal et al. Moreover, behavioral inhibition as well as decision making has been conceptualized as elements of executive functioning, and both were therefore linked to the functions of the prefrontal cortex Smith and Jonides ; Funahashi ; Elliott It has been postulated previously that prefrontal 5-HT further plays a crucial role in executive functions and especially decision making because patients with focal prefrontal lesions as well as tryptophan-depleted healthy volunteers demonstrated impaired decision-making cognition Rogers et al. This assumption is supported by animal data showing that repeated MDMA administration leads to an increase in inappropriate responses, indicating an elevation of impulsivity Taylor and Jentsch Studies with MDMA users applying self-reported measures of impulsivity were not discussed here because self-report inventories were not suitable for measuring the state-dependent behavioral neurotoxicity induced by illicit drugs Dougherty et al. There is evidence that MDMA use impairs memory e. In most of the aforementioned studies, memory deficits and executive dysfunction in MDMA users were assumed to be a consequence of the serotonergic neurotoxicity of MDMA. As of yet, only one study has assessed the decision-making cognition of MDMA users, although decision-making tasks involve primarily the 5-HT system, whereas classical executive function tasks e. However, Fox et al. The aim of this study was to further investigate the behavioral neurotoxicity of MDMA use with regard to different aspects of impulsivity and decision-making cognition. The comparison with a control group of cannabis users allowed us to estimate the influence of the common concomitant use of cannabis in MDMA users, which is discussed in previous works as being a strong biasing factor in research with MDMA consumers Croft et al. Because of the serotonergic neurotoxicity of MDMA and the postulated involvement of 5-HT in impulsivity and decision-making cognition, we expected elevated levels of impulsivity and a decision-making deficit in MDMA users in comparison with both control groups. Due to the assumption that the supposed behavioral deficits were caused by MDMA, we anticipated correlations of MDMA consumption with impulsivity and decision-making measures. Impulsivity and decision-making cognition were measured in three groups. MDMA users were recruited by advertisement in a techno music magazine. In addition, the use of MDMA clearly had to outweigh the consumption of any other psychotropic drug. To be eligible for inclusion in the cannabis group, participants should have no substantial previous use of amphetamine derivatives like MDMA and no substantial previous use of cocaine. Neuropsychological assessment was carried out when participants were drug-free for at least 3 days. Legitimate use of psychotropic medication; a present psychiatric illness; a family history of a severe psychiatry illness, such as schizophrenia and bipolar disorder; as well as a severe somatic illness were exclusion criteria for all groups. None of the participants had a history of migraine, epilepsy, or craniocerebral trauma. After being informed of the aim of the study by written and oral description, all participants gave written informed-consent statements. Neuropsychological assessment was carried out after written informed consent was given by all participants. Drug history and present pattern of psychotropic drug consumption were assessed by a structured interview. In addition to the impulsivity and decision-making measures see below , the neuropsychological test battery also comprised a verbal memory task the results of which will be published elsewhere Quednow et al. The whole battery took about min, including breaks as needed, and was generally well tolerated by the participants. During the psychiatric and neuropsychological assessment, the subjects could ask for a break at any time. For the assessment of the use of legal and illegal psychotropic substances, a structured interview was developed that comprised questions concerning quantity, duration, and frequency of present and past consumption of all known psychotropic substances. The quantity of drug consumption was assessed for MDMA in terms of the numbers of tablets consumed. In addition, one interview question asked for the highest single MDMA dose ever used lifetime peak dose. For cannabis, amphetamine, and other substances, the quantity was measured in terms of the number of times of use because evaluating and defining the concept of a single dose was difficult. On the basis of the actual and former substance intake, we estimated a cumulative drug dose. The data for pattern and amount of drug consumption of the groups are shown in Table 1. Stimuli, consisting of eight two-digit numbers four active, four passive, which ranged from 3 to 99 were repeated ten times in different, randomly assigned sequences for a total of 80 trials. Two different sets of eight numbers were employed one per condition. All participants completed two conditions. In the reward—punishment condition, participants began with 1. Responses to active numbers were reinforced, and responses to passive numbers were punished. In the punishment—reward condition, participants began with 1. Each condition was preceded by a trial reward pretreatment in which the ratio of active to passive numbers was This pretreatment served to establish hypothetically a dominant response set for reward Newman et al. Participants were given instructions for the GNG, the reinforcement contingencies, and the process of trial-and-error learning. With the experimenter present, participants completed eight practice trials that involved four presentations of each of the two practice stimuli one as an active number and two as a passive number. The experimenter was not present during the actual testing. The order of presentation of the two conditions was the same for all participants. The experimenter reentered the room between conditions to explain the expectations for the next condition. Dependent measures for this task included commission errors failures to inhibit responses to passive numbers and omission errors failures to respond to active numbers and total gain in DM. According to the motivational theory of Gray et al. The format of the MFFT involves simultaneous presentation of a stimulus figure and an array of eight alternatives, all except one differing in one or more details. The participants were then asked to select as quickly as possible from the alternatives the figure that exactly matched the standard. Each participant was given two practice items followed by 12 test items. If their initial selection was incorrect, they were told that they were wrong and were asked to try again. For each subject, the 12 items were scored according to the time to first response and the number of errors made before the correct match. Four dependent variables were analyzed: 1 the mean latency to first response, 2 the total number of errors committed, 3 an Impulsivity score I-score , and 4 an Efficiency score E-score. The error and latency values of all participants were standardized to the means and standard deviations of the control group. The GT is a virtual card game in which participants are told to accumulate as much play money as possible by picking one card at a time from any of the four decks A, B, C, and D until cards have been selected. The decks 40 cards each differ in representation of both the level of immediate gain and the level of penalty risk. In contrast to the original Iowa GT, the cards had either a gain or a penalty in the original version, each card yields a gain, and some cards had an additional penalty. Gain cards from decks A and B yield points, compared with points for every gain card from decks C and D. Some cards in each deck carry penalties, such that the accumulated penalties exceed the accumulated gains in decks A and B, and the accumulated penalties are smaller than the accumulated gains in decks C and D. Thus, continued choice from decks C or D leads to a net gain 1, points per deck , whereas continued choice from decks A or B leads to a net loss -1, points per deck. Performance on the GT is scored by a global outcome score net score and a score for each consecutive block of 25 cards. These scores correspond to the number of cards chosen from the advantageous decks C and D minus the number of cards chosen from the disadvantageous decks A and B. The analysis of the GT performance by blocks of 25 cards provides information about the learning capacity and strategy used by participants Bechara Before analyzing the neuropsychological data, we examined demographic variables. Neuropsychological data were initially analyzed using the GLM approach to ANOVA across all groups and t tests for independent samples for single comparisons. In addition, GT blocks were analyzed using the GLM approach to repeated-measures ANOVA with GT blocks as dependent variables fourfold and group as a between-subjects fixed factor threefold across all groups or twofold for single comparisons. Relationships between neuropsychological variables and drug consumption were analyzed only across combined drug groups. The demographic data of the groups are shown in Table 2. Overall, the three groups did not significantly differ with respect to age, length of education, and verbal intellectual performance as measured by the MWT-B for statistics, see Table 2. However, because of trends for a different verbal IQ and length of education, post hoc t tests were applied. Cannabis users and MDMA users did not differ in their smoking habits. Omission and commission errors as well as gain in each condition were also summed into total scores Table 3. Errors within the trial, reward-pretreatment phase were not included because participants had to be exposed to the stimuli at least once in order to learn which were active and passive. An initial ANOVA did not reveal any significant main effect between the groups in the dependent variables for statistics, see Table 3. Variables from the single GNG conditions were not correlated with drug consumption separately because the analysis would suffer either from capitalization on chance or from the overly conservative alpha levels that would be needed to correct for test multiplicity. Summed omission errors were not correlated with drug consumption. In both groups, only 19 participants reported an instance of amphetamine use, and only six participants reported an instance of cocaine use. Thus, statistical correlations with these substances should be interpreted with caution. The period of abstinence of any illegal drug did not correlate with the variables of the GNG. In summary, MDMA users made significantly more commission errors than did cannabis users. Thus, analyses of these variables had to be corrected for age. An initial ANOVA reaction time and I-score corrected for age revealed no significant main effect between the groups in the dependent variables for statistics, see Table 4. The period of abstinence of any illegal drug also did not correlate with the MFFT variables. Thus, acute drug effects are unlikely to account for the elevated impulsivity of the MDMA users. In addition, high single doses of MDMA were associated with higher I-scores and a faster reaction time. Performance of the experimental groups on the GT is shown in Fig. Cannabis users and control subjects did not differ in terms of the net score. Performance on the GT was not correlated with age, years of education, or verbal IQ. In the fourth block, the ratios of good and bad decks converge in all groups Fig. The explanation for this effect is that both advantageous and both disadvantageous decks have a maximum of 80 cards in total. Thus, if subjects demonstrated an early preference for only one of the two deck types, they had to draw cards from the remaining decks at the end. Thus, a good decision-making strategy will be punished and a bad strategy rewarded in the last block. Other illegal drug use patterns did not significantly correlate with the GT performance. Furthermore, the period of abstinence from any illegal drug did not correlate with the GT performance. Thus, acute drug effects are unlikely to account for the impaired decision-making cognition of the MDMA users. However, cannabis users and control subjects did not differ in their decision-making performance. In addition, a longer duration of MDMA use was associated with a worse decision-making performance. The GT net score was highly correlated with the I-score of the MFFT as well as with the commission errors in the GNG, supporting the mutual interdigitation of the impulsivity and decision-making concepts. Furthermore, GNG commission errors were also highly correlated with the MFFT I-score, suggesting that these measures tap into similar cognitive mechanisms. The aim of this study was to analyze the effect of MDMA use on impulsivity and decision-making cognition. To the best of our knowledge, our study is the first to highlight decision-making deficits in MDMA users. These associations suggest that the elevated impulsivity and the disrupted decision-making performance are a consequence of the MDMA intake. In addition, the I-score and the decision-making performance were correlated, indicating that the decision-making deficit of MDMA users may be attributed to an acquired lack of inhibitory control. It has to be emphasized that the MDMA users examined in the present study were mostly heavy users at least 50 times of use, with an apparent mean of over lifetime uses. This dissociation indicates that these tasks reflect different aspects of cognitive impulsivity. It was proposed that several behavioral impulsivity paradigms describe different facets of impulsivity Monterosso and Ainslie ; Moeller et al. According to the taxonomy of Moeller et al. However, both tasks indeed seem to measure facets of the same construct because the MFFT I-score and the GNG commission errors were positively correlated. Thus, also across previous studies, the dissociation of both impulsivity measures appeared. This finding may be explained by the fact that Morgan and Morgan et al. The lower reliability of the item version could explain the different effect sizes. Future studies should therefore use the longer item version. Functional imaging studies suggested that several brain regions are involved in behavioral inhibition. In these studies, the prefrontal, the inferior parietal, and the cingulate cortices were consistently activated during inhibition of reactions Liddle et al. Furthermore, patients with wide frontal lesions exhibit an increase in impulsive behavior Miller and Milner ; Miller , , and it was recently shown that patients with selective orbitofrontal lesions revealed an increased impulsivity as measured with the MFFT Berlin et al. Thus, the elevated impulsivity of MDMA users may be ascribed to an impairment of frontal, inferior parietal, or cingulate regions. This view is supported by results of animal studies showing that the frontal regions in particular exhibit a sustained loss of 5-HT axon terminals after MDMA exposure Fischer et al. In addition, immunohistochemical studies indicate that MDMA appears to cause a selective degeneration of fine-diameter serotonergic axons with small varicosities that arise from the dorsal raphe nucleus and that project particularly into the forebrain. In addition, no correlation between impulsivity measures and period of abstinence was found. Furthermore, we found no alteration of impulsivity in chronic but presently abstinent cannabis users. To the best of our knowledge, this is the first published study involving cannabis users and behavioral measures of impulsivity. However, it is unlikely that the elevated impulsivity of MDMA users is caused by postacute cannabis effects because we found no increased impulsivity in the cannabis group, although that group had a smaller mean duration of cannabis abstinence as well as a more intensive cannabis consumption than the MDMA group. In contrast to the findings of Fox et al. However, there are some differences between the studies: In this study, the cumulative MDMA dose of the users was 2. Otherwise, the decision-making tasks in both studies were very different. The Decision-Making Task of Rogers et al. In a previous study analyzing three decision-making tasks, the Rogers task was strongly correlated with IQ, whereas no association of the Bechara task with IQ was found Monterosso et al. We also could not determine a correlation between GT performance and verbal IQ. Another difference is that no learning of reinforcement contingencies by trial-and-error is needed in the Rogers task; thus, the Rogers task rather measures risk-taking behavior in association with analytical skills and less so the ability for long-term maximization of profit as the Bechara task does. In addition, Monterosso et al. A comparable finding was reported by Paulus et al. Paulus et al. The authors interpreted their findings as a consequence of methamphetamine neurotoxicity. Accumulating evidence suggests that several amphetamine derivates can cause decision-making deficits by sustained modulation of serotonergic and dopaminergic projections in frontolimbic and frontostriatal circuits Jentsch and Taylor ; Rogers et al. A decrease in inhibitory control would therefore lead to an impaired decision-making performance. Thus, the decision-making deficit of MDMA users is probably because of the loss of inhibitory control. Again, acute drug effects were unlikely to account for the decision-making deficits of MDMA users because the mean period of abstinence of MDMA and other drugs was too long see above and we found no correlation between the GT performance and the period of drug abstinence. In contrast to a previous study Whitlow et al. However, the heavy cannabis users described by Whitlow et al. However, it is unlikely that postacute cannabis effects influenced the decision-making cognition of our MDMA group because 1 our participants had to abstain from cannabis use for at least 3 days before testing and 2 we found no alterations in decision-making performance in the cannabis group, although they had a smaller mean duration 7. Cognitive functions have been proposed as sensitive markers of neurotoxicity Paule A fundamental concept of toxicology is the dose-response relationship, which states that there is a direct relationship between the amount of a toxic noxa to which an individual or a group is exposed and a toxic behavioral effect Rosenberg Thereby, the dose of a toxic drug can be measured in different ways, e. In this study, it was demonstrated that the significantly elevated impulsivity of MDMA users and the significantly disturbed decision-making cognition were associated with the lifetime peak dose of MDMA and the years of MDMA intake, respectively. These dose-response relationships suggest that the cognitive deficits are a consequence rather than a predisposition. It was plausibly argued that people may start and continue to abuse drugs just because they are more impulsive, are risk seekers, and are bad decision makers. Thus, higher impulsivity and worse decision-making cognition would be vulnerability factors for every type of drug abuse Bechara et al. However, we could not show impulsivity and decision-making deficits in regular cannabis users, a finding that disproves the assumption that drug abuse in general had to be associated with higher levels of impulsivity or dysfunctional decision making. Our data, instead, support the notion that an increase in impulsivity and a decline in decision-making cognition in amphetamine derivate users resulted from the serotonergic or dopaminergic neurotoxicity of these substances Jentsch and Taylor ; Rogers et al. With respect to cocaine, Bolla et al. Maybe this applies also for the amphetamine derivates. However, cross-sectional designs, such as the one used in this study, are less suitable to prove both assumptions adequately. Longitudinal designs measuring behavioral impulsivity and decision-making cognition in MDMA users ideally before the onset of stimulant use or after long-term abstinence are required to determine whether impulsive behavior leads to stimulant use or vice versa. Furthermore, cross-sectional designs cannot answer the question regarding the reversibility of the neurotoxic effects of MDMA. Again, only longitudinal designs can enlighten this issue. However, Taffe et al. In addition, it was shown that MDMA-treated monkeys revealed strong behavioral deficits under an additional challenge to the 5-HT system Frederick et al. But how can the discrepancy between the dramatic decrease in 5-HT brain levels and the lack of behavioral consequences under normal conditions be explained? Thus, further animal studies on cognitive neurotoxicity of MDMA should apply cognitive test batteries which are more similar to the tests with which cognitive deficits in MDMA users have been shown. One limitation of this study is that the history of drug consumption was assessed using only subjective reports. A drug-usage screening would be desirable to better control for acute and postacute drug effects within a few days of the assessment. However, Stuerenburg et al. Nevertheless, our MDMA users also reported some use of amphetamine. Thus, we think that MDMA and not amphetamine is the denominator with respect to the shown deficits in impulsivity and decision-making cognition. In summary, our results provide, to the best of our knowledge, the first evidence of a decision-making deficit on the basis of an increased behavioral impulsivity in heavy but presently abstinent users of MDMA. Furthermore, the concomitant cannabis use of the MDMA users could not account for the behavioral deficits because 1 cannabis users did not show changes in impulsivity or decision-making performance and 2 parameters of cannabis use were not statistically correlated with impulsivity and decision-making measures. Our data also suggest that the elevated impulsivity and the decision-making deficits of MDMA users could likely be attributed to a dysfunction of regions within the frontal cortex. Thus, the cognitive deficits of MDMA users could not be explained exclusively because of an impairment of temporal regions, as has been proposed by some researchers Fox et al. Neurotoxicol Teratol — Psychopharmacology Berl — CAS Google Scholar. J Clin Psychol — Bechara A Neurobiology of decision-making: risk and reward. Semin Clin Neuropsychiatry — Bechara A, Damasio H Decision-making and addiction part I : impaired activation of somatic states in substance dependent individuals when pondering decisions with negative future consequences. Neuropsychologia — PubMed Google Scholar. Cognition — Science — Brain — Psychol Med — J Neuropsychiatry Clin Neurosci — J Nucl Med — Busemeyer JR, Stout JC A contribution of cognitive decision models to clinical assessment: decomposing performance on the Bechara gambling test. Psychol Assess — Teilband: Illegale Drogen. Dev Psychol — Google Scholar. Ann N Y Acad Sci — Christophersen AS Amphetamine designer drugs-an overview and epidemiology. Toxicol Lett —— Arch Gen Psychiatry — Am J Psychiatry — An overview of evidence and of methodological problems in research. Neuropsychobiology — Hum Psychopharmacol — Psychopharmacology Berl 4 — Article PubMed Google Scholar. J Int Neuropsychol Soc — Elliott R Executive functions and their disorders. Br Med Bull — Neuropsychopharmacology — Eslinger P, Damasio AR Behavioral disturbances associated with rupture of anterior communication artery aneurysms. Semin Neurol — Evenden JL The pharmacology of impulsive behaviour in rats, VII: the effects of serotonergic agonists and antagonists on responding under a discrimination task using unreliable visual stimuli. J Neurosci — J Psychopharmacol — Neurosci Biobehav Rev — Funahashi S Neuronal mechanisms of executive control by the prefrontal cortex. Neurosci Res — Neuroimage — Biol Psychiatry — Prog Neuropsychopharmacol Biol Psychiatry — In: Zuckerman M ed Biological bases of sensation seeking, impulsivity, and anxiety. Erlbaum, Hillsdale, pp — Wiley, London. Behav Brain Res — Ther Drug Monit — J Pharmacol Exp Ther — Jentsch JD, Taylor JR Impulsivity resulting from frontostriatal dysfunction in drug abuse: implications for the control of behavior by reward-related stimuli. Kagan J Reflection—impulsivity: the generality and dynamics of conceptual tempo. J Abnorm Psychol — Psychol Monogr 78 1 Percept Mot Skills — J Psychoactive Drugs — Neurosci Lett — Hum Brain Mapp — Life Sci — J Abnorm Child Psychol — Lancet — Miller L Cognitive risk-taking after frontal or temporal lobectomy-I: the synthesis of fragmented visual information. Miller L Impulsivity, risk-taking, and the ability to synthesize fragmented information after frontal lobectomy. Miller L, Milner B Cognitive risk-taking after frontal or temporal lobectomy-I: the synthesis of phonemic and semantic information. Monterosso J, Ainslie G Beyond discounting: possible experimental models of impulse control. Addiction — Newman JP Reaction to punishment in extroverts and psychopaths: implications for the impulsive behavior of disinhibited individuals. J Res Pers — Pers Individ Differ — Neuropharmacology — Paule MG Approaches to utilizing aspects of cognitive functions as indicators of neurotoxicity. Academic, San Diego, pp — Neuroscience — J Psychopharmacol. Robbins TW Chemical neuromodulation of frontal—executive functions in humans and other animals. Exp Brain Res — Rosenberg NL Basic principles of clinical neurotoxicology. Hum Dev — Article Google Scholar. Synapse — Br J Psychiatry — Behav Brain Sci — Neuroendocrinol Lett — Sensitivity to serotonergic challenge. Pharmacol Biochem Behav — Br J Clin Psychol — Drug Alcohol Depend — Wilson MA, Ricaurte GA, Molliver ME Distinct morphological classes of serotonergic axons in primates exhibit differential vulnerability to the psychotropic drug 3,4-methylenedioxymethamphetamine. Med Sci Monit — Download references. We would like to thank two anonymous reviewers for comments on an earlier version of the manuscript. Furthermore, Dr. Boris B. You can also search for this author in PubMed Google Scholar. Correspondence to Boris B. Reprints and permissions. Quednow, B. Psychopharmacology , — Download citation. Received : 09 August Accepted : 30 September Published : 20 January Issue Date : January Anyone you share the following link with will be able to read this content:. Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative. Psychopharmacology Aims and scope Submit manuscript. Download PDF. Objective The aim of this study was to examine the cognitive neurotoxicity of MDMA with regard to behavioral impulsivity and decision-making cognition. Conclusion These results suggest that heavy use of MDMA may elevate behavioral impulsivity and impair decision-making cognition possibly mediated by a selective impairment of the 5-HT system. Noradrenergic contributions to cue-driven risk-taking and impulsivity Article 02 March Use our pre-submission checklist Avoid common mistakes on your manuscript. Materials and methods Participants Impulsivity and decision-making cognition were measured in three groups. Procedure Neuropsychological assessment was carried out after written informed consent was given by all participants. Interview for psychotropic drug consumption For the assessment of the use of legal and illegal psychotropic substances, a structured interview was developed that comprised questions concerning quantity, duration, and frequency of present and past consumption of all known psychotropic substances. Table 1 Pattern and amount of illegal drug use: results of the psychotropic drug interview Full size table. Results Demographics The demographic data of the groups are shown in Table 2. Table 2 Demographic data Full size table. Full size image. Discussion The aim of this study was to analyze the effect of MDMA use on impulsivity and decision-making cognition. Decision-making cognition In contrast to the findings of Fox et al. Neurotoxicity or predisposition? Semin Neurol — Google Scholar Evenden JL The pharmacology of impulsive behaviour in rats, VII: the effects of serotonergic agonists and antagonists on responding under a discrimination task using unreliable visual stimuli. Dev Psychol — Google Scholar Miller L Cognitive risk-taking after frontal or temporal lobectomy-I: the synthesis of fragmented visual information. Psychopharmacology Berl — CAS Google Scholar Newman JP Reaction to punishment in extroverts and psychopaths: implications for the impulsive behavior of disinhibited individuals. J Psychopharmacol —83 Google Scholar Paule MG Approaches to utilizing aspects of cognitive functions as indicators of neurotoxicity. Acknowledgements We would like to thank two anonymous reviewers for comments on an earlier version of the manuscript. Quednow View author publications. View author publications. Rights and permissions Reprints and permissions. About this article Cite this article Quednow, B. Copy to clipboard. 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Ecstasy and Gateway Drugs: Initiating the Use of Ecstasy and Other Drugs

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Official websites use. Share sensitive information only on official, secure websites. The main purposes of this study are to examine if, and to what extent, ecstasy use serves as a gateway to the use of hard drugs such as cocaine, heroin, and methamphetamine and to compare the age of onset of alcohol and marijuana use and subsequent use of cocaine, heroin, and methamphetamine among young adult ecstasy users. Face-to-face surveys were conducted with young adult ecstasy users in Atlanta, Georgia. Subjects were solicited using the community identification process, including targeted sampling and guided recruitment. Data analysis involved discrete-time, event history analysis. Results suggest that the age of onset of ecstasy use influences the initiation of cocaine and methamphetamine for our sample of active ecstasy users. In addition, alcohol and marijuana use precedes the initiation of cocaine and methamphetamine, but only marijuana influences the initiation of heroin. The sequential progression of drug use proposed in the gateway literature is not immutable. Researchers must take into account the changing popularity of drugs over time, such as the emergence of ecstasy use, when identifying patterns of drug use onset. The nature of the developmental progression of drug use trajectories remains a hotly debated topic, often focusing on the validity of the gateway theory 1. This theory assumes that the use of specific drugs is related to a sequential pattern of initiation, although the causal mechanisms that drive this sequential trajectory are often unclear 2. Other scholars have provided further support for the gateway theory as it pertains to other drug pathways 7 — Studies guided by the gateway theory are not without critics. One critique is that such studies are often derived from population-based samples, which are known to exhibit low prevalence of hard drug use 12 , 16 — When examining findings from targeted samples of drug users, results show that hard drug users often do not initiate their trajectory with alcohol use, but with marijuana 18 — A second critique of studies based on the gateway theory is that most are limited to adolescents. There is little evidence that adolescents who initiate the use of hard drugs necessarily become regular users 16 , A final critique of the theory is that there is substantial ambiguity over how research findings should be interpreted, with similar findings leading to divergent conclusions 2. Despite critiques of research guided by gateway theory, the theory is used to set policies and develop educational campaigns that emphasize alcohol and tobacco abstinence to prevent future use of and addiction to hard drugs 19 , Drug researchers have critiqued such policies for failing to consider the complex underlying causal mechanisms that lead to both hard drug use and subsequent addiction 2 , Further, such policies inadequately recognize the impact of local drug markets, which to a large extent determine what drugs are available and how they are used 17 , Finally, policies typically do not respond to period and cohort effects in drug use initiation 20 , As the drug scene evolves, gateway drugs may change as well In this paper we focus on ecstasy 3,4-Methylenedioxymethamphetamine or MDMA and whether it serves as a gateway in the path to other drug use within a sample of young adult ecstasy users. We do not argue that ecstasy use may physiologically cause more serious drug use, however the social context of ecstasy use may set the stage for the subsequent use of other drugs. Within the social context of drug use, ecstasy is largely perceived to be a safe drug, with roughly one-half of adults age 19 to 30 years old seeing little risk in ecstasy In addition, there is a relatively high prevalence of ecstasy use. Among individuals between the ages of 18 and 25, Between September and April , a cross-sectional survey was conducted of active ecstasy users between the ages of 18 and 25 in Atlanta, Georgia. To be eligible, respondents had to have used ecstasy at least three times in the past 90 days on separate occasions , not be in drug treatment or another institutional setting, and not be intoxicated or otherwise cognitively impaired at the time of interview. Initial participant recruitment involved the community identification CID process, a mapping method to record epidemiological indicators of the prevalence of ecstasy use, expert opinions, and ethnographic information from local researchers 28 — It is important to note that this sample is not generalizable to the general population or even to a population of active ecstasy users. As has been done in similar studies 30 , we concentrated on a specific sample of ecstasy users to reinforce the internal validity of our data. Our subsequent results must be interpreted taking into account the specificity of our sample. Further, since all of our respondents use ecstasy, we cannot establish causality as we may have been able to had our sample included non-users. However, this sample is important in its own right, despite these weaknesses, because drug use trajectories of active ecstasy users may differ from those of experimental or non-users Using a short form, potential respondents were initially screened in the setting where they were actively recruited such as at clubs and near college dorms. In addition, we actively solicited respondents via referrals. We limited referral chains to two referrals per chain. Further, we limited the number of respondents from any one setting to eight in order to avoid saturation. Passive recruitment, involving the posting of flyers in locales with heavier concentrations of young adults, was also utilized. Passively recruited respondents were initially screened, when they called the phone number listed on the flyers, using the same short form as actively recruited respondents. Prior to each interview, a follow-up screening with a detailed focus on ecstasy use was conducted. Questions were asked about the local price of ecstasy, the local ecstasy taxonomy, and the ecstasy high. Very few individuals failed the second screening. Individuals who passed both screenings were provided with information on the nature of the study, including details about informed consent and other confidentiality procedures. Data collection involved an interviewer-administered computerized survey. The interviews took place at a mutually convenient location such as one of the project offices or a local coffee shop. The average length of time to complete the survey was two hours. We use logistic regression of discrete-time models 31 — 32 , to examine the effects of the timing of the onset of ecstasy use on the onset of cocaine, heroin, and methamphetamine use. Discrete-time event history analysis is the most appropriate approach to studying processes in which individuals change from one state to another e. Discrete-time event history models require a unique type of data. The data used in these analyses were collected as a cross-sectional dataset in which each case represents a single individual. To estimate these models using discrete-time analysis, the original data were transformed into a person-year dataset in which each case represents a separate observation of each individual for each year in which they were at risk of having used a given drug. Admittedly using years as a unit of time is a rough interval, however it is often used in social research due to a lack of more discrete measures and to avoid recall problems endemic of retrospectively collected data For each person-year case, a dummy variable, indicating whether or not the individual used a drug in that year, was added, along with a dichotomous event indicator to specify whether the event of interest occurred at the time of the last observation or the case was censored. This event indicator was regressed using logistic regression on the predictor variables and the series of dummy variables in each person-year. Every respondent within our sample either experienced the onset of a given drug or was censored at the time of the survey, within a very small number of possible years. Hence there are many cases that are tied i. The presence of ties leads to bias in continuous-time models but not discrete-time models We chose to use discrete-time models in lieu of continuous-time models for two reasons. First discrete-time models can approximate continuous-time models The transition from never using a given drug to having used that drug can occur at any finite point in time. Using retrospective data, however, limits the ability of the respondent to recall the precise timing of their transition from one state to another. The data used in this study were collected so that the respondents indicated their age when they first used a given drug. We, therefore, use discrete-time event history analysis to model a phenomenon that is in reality continuous, but that is measured discretely. Second, continuous-time models assume that an event does not happen at precisely the same time for multiple subjects. This equation predicts the conditional log-odds that a given event using a certain drug will occur in a given time period, j. In interpreting the coefficients from discrete-time survival models, the estimated percentage change in the hazard rate of having experienced a given event for a positive change in each covariate is calculated as: The three dependent variables in this analysis are the risk of using cocaine , methamphetamine , and heroin i. The dependent variable in each model is coded 0 for each year in which a respondent is at risk of using a drug and is censored for those years that the respondent is no longer at risk because the event has already occurred i. Our key independent variable is a time-dependent measure of ecstasy use. We also include measures of the timing of first alcohol use and marijuana use. We integrate time-varying independent variables into our models via the method of episode splitting Episode splitting is used when covariates change their value at some discrete point in time, such as over the course of a given year as is the case here. We have integrated episode splitting by substituting each original episode with a set of sub-episodes that split whenever the respondent experienced the onset of one of the independent variables. Hence, in our analyses, each time-varying independent variable is coded categorically as onset having occurred or not having occurred, as was the dependent variable Beyond time-dependent covariates, we include a static measure of self-control to assess differences in propensity for drug use see Table 1. We measured low self-control with a modified version of Grasmick et al. We also include a number of demographic characteristics as control variables. We conducted diagnostic analyses of our data, with special attention to issues of multicollinearity. The highest variance inflation factor in our models is 1. The onsets of alcohol and marijuana use occur at earlier ages than the onset of any other substance use among our respondents Table 2. Moreover, in this sample the onset of marijuana use precedes the onset of alcohol use by almost one year. Further, a larger proportion of our respondents reports never having used alcohol 5. Although all respondents are active ecstasy users, the age of onset of ecstasy use occurs quite late in the drug use queue. On average, the respondents report using cocaine Methamphetamine has the oldest age of onset at Univariate Kaplan-Meier survival curves for each of the drug use variables show the probability of not experiencing the onset of a given behavior at a given age Figure 1. As expected from the results presented above, the probability of not initiating alcohol and marijuana use appear lower at younger ages than the probability of not using any hard drug, including ecstasy. At that age, the use of all other drugs is just emerging. This trend is followed by cocaine and methamphetamine at approximate ages of 19 and 22, respectively. Our multivariate analyses indicate that the estimated risk of using cocaine is influenced by a number of factors Table 3. Of greatest interest to this study, however, is the use of ecstasy and the so-called gateway substances, alcohol and marijuana. As would be expected from the gateway literature, later onset of alcohol and marijuana is associated with later onset of cocaine use in our sample. These results are expected from the literature and from the survival curves presented in Figure 1. However, neither clearly indicated that delaying the onset of ecstasy use might also influence the onset of cocaine use among our respondents. That appears to be the case in these multivariate analyses. While the effect is not of the same magnitude as the effects of alcohol and marijuana, ecstasy appears to hold a position in the sequential progression of cocaine use. Alcohol, marijuana, and ecstasy onset have similar effects on the onset of methamphetamine use Table 4. As was the case for the initiation of cocaine use, the initiation of methamphetamine use appears to be influenced by ecstasy. Alcohol, marijuana, and ecstasy use do not have the same effects on heroin use as they did on cocaine and methamphetamine use among our respondents Table 5. In fact, only marijuana use has a significant effect on heroin use. Neither the onset of ecstasy use or the onset of alcohol use has a significant effect on the onset of heroin use. We explored this proposition by using discrete-time hazard models to examine how the timing of initial onset of ecstasy use influenced the onset of other hard drugs in a unique sample of active ecstasy users. And to our knowledge no other research has specifically examined the effects of the age of onset of ecstasy use on the age of onset of other hard drugs. Therefore our findings regarding ecstasy are revealing. The analyses indicate that ecstasy may have an influence on the use of other hard drugs, specifically cocaine and methamphetamine. While those in our sample of active ecstasy users tend to initiate ecstasy at a later age than many other substances, once key variables are controlled for, it appears that initiating ecstasy at a later age may lower the risk of initiating the use of cocaine and methamphetamine, although not heroin. There are a number of reasons to expect that ecstasy use may trigger the initiation of other hard drug use. While not without risk, ecstasy tends to be perceived by drug users as less risky than other hard drugs Because perception of risk influences drug use 43 , individuals may be more likely to try ecstasy. In the early years of ecstasy, use tended to be restricted to a rave-scene that was purist in nature with participants restricting the use of drugs to ecstasy However, in recent years, ecstasy use has expanded far beyond this specific drug culture The expanded use of ecstasy may have lessened its potential protection against the use of other, more stigmatized, hard drugs. Further, the current use of ecstasy in a poly-drug culture may have increased the exposure of ecstasy users to other hard drugs Users may begin to use ecstasy because they perceive it as having less risk than other hard drugs. However the initiation of ecstasy use may also be an initiation into a poly-drug culture where the availability of other hard drugs is greater and the use such drugs is normalized. In other words, individuals begin to use ecstasy because they perceive it to be low-risk, but they then put themselves at risk of using other drugs because they have now entered an environment where poly-drug use is common, and an environment where ecstasy may be adulterated with a variety of other substances unbeknownst to the user. It is important to note that our research simply establishes the existence of temporal trajectories from ecstasy use to the use of other drugs. Our research cannot explicate the causal mechanisms by which this occurs. The ideas we present as to how ecstasy may operate as a gateway to the use of other drugs are offered simply for discussion. We leave to other research the task of identifying the causal mechanisms that underpin this drug sequence. We also leave to other research the task of exploring how ecstasy operates as part of the temporal progression of drug use within other populations. Our research is based upon a unique sample of young adult, active ecstasy users. For this reason our results cannot be generalized to other drug users or to the population in general. Ecstasy may not operate in the same way in the general population where there are a greater number of people who may have experimented with ecstasy, but would not be considered active ecstasy users. Finally, our sample was restricted to those ages 25 or younger. It may be that some of our respondents initiated the use of certain drugs at some later point in life outside the bounds of this study. In summary, this research suggests that the sequential progression of drug use proposed in the gateway literature is not immutable. As different drugs rise and fall in popularity, it is important to revisit the patterns of initiation of substance use. Policy and prevention efforts based on drug sequences identified in the literature of the past may be misidentifying the typical progression of those initiating drug use in the contemporary context. Hence the initiator role of ecstasy and more recent entries into youth drug cultures, such as methamphetamine, must be investigated further. The views presented in this paper are those of the authors and do not represent those of the funding agency. We thank Miriam W. Boeri for the coordination of the data collection and all the field staff and the participants who made this study possible. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. As a library, NLM provides access to scientific literature. Ann Epidemiol. Published in final edited form as: Ann Epidemiol. Issue date Jan. PMC Copyright notice. The publisher's version of this article is available at Ann Epidemiol. Open in a new tab. Similar articles. Add to Collections. Create a new collection. Add to an existing collection. Choose a collection Unable to load your collection due to an error Please try again. Add Cancel.

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