Cognitive impulsivity in cocaine and heroin polysubstance abusers

Addictive Behaviors 32 (2007) 950 – 966

Cognitive impulsivity in cocaine and heroin polysubstance abusers Antonio J. Verdejo-García a,b,⁎, José C. Perales c , Miguel Pérez-García a,b a

c

Dept. Personalidad, Evaluación y Tratamiento Psicológico, Universidad de Granada, Campus de Cartuja, s/n, 18071 Granada, Spain b Institute of Neurosciences F. Olóriz, Universidad de Granada, Spain Dept. Psicología Experimental y Fisiología del Comportamiento, Universidad de Granada, Spain

Abstract Rationale: Chronic abuse of psychoactive substances produces significant deficits in executive control functions (ECF). These deficits are prominent in different domains associated with cognitive impulsivity, including response inhibition and decision-making. The extent and nature of these deficits may depend on the principal substance of abuse. Objectives: To analyse response inhibition and decision-making performance in abstinent polysubstance abusers (PSA) of cocaine and heroin, and healthy participants. Methods: We used univariate and multivariate analyses of variance to compare the performances of cocaine and heroin PSA and healthy controls on several well-validated measures of response inhibition (Stroop, 5-Digit Test and Go/No Go Task) and decision-making (Iowa Gambling Task). Post-hoc exploratory analyses of the results from the Go/No Go task were conducted to examine specific effects of task switching on the pattern of omission/ commission errors in the PSA groups. Results: Cocaine but not heroin PSA showed significant deficits on several measures of response inhibition, when compared to controls. Reversal of task contingencies in the Go/No Go task primarily altered the commission error rate in cocaine PSA, and the omission error rate in heroin PSA. In contrast, both cocaine and heroin PSA showed poorer performance on decision-making compared to controls. Conclusions: Assuming the relevance of polysubstance involvement, cocaine abuse seems to differentially correlate with motor impulsivity skills, while both substances seem to be equally linked to the inability to decide

⁎ Corresponding author. Departamento de Personalidad, Evaluación y Tratamiento Psicológico, Universidad de Granada, Campus de Cartuja, s/n, 18071 Granada, Spain. E-mail address: [email protected] (A.J. Verdejo-García). 0306-4603/$ - see front matter © 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.addbeh.2006.06.032

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advantageously in complex decision-making tasks. Possible discrepancies in the neurological and psychological effects of these drugs are discussed. © 2006 Elsevier Ltd. All rights reserved. Keywords: Cocaine; Executive control functions; Decision-making; Heroin; Impulsivity; Polysubstance abuse; Response inhibition

1. Introduction Recent works show that chronic use of psychoactive substances produce significant deficits in executive control functions (ECF), and, particularly, in domains related with response inhibition and decision making (Rogers & Robbins, 2001; Verdejo-García, López-Torrecillas, Orozco, & Pérez-García, 2004). These deficits have been associated with anterior cingulate (ACC)/prefrontal cortex (PFC) dysfunction (Ersche, Fletcher, et al., 2005; Fishbein et al., 2005; Goldstein et al., 2004). Accordingly, recent neuroscientific models of addictive behavior have proposed a critical role of PFC circuitry in the breakdown of inhibitory control that characterizes transition from casual-recreational drug use to substance dependence (Goldstein & Volkow, 2002; Jentsch & Taylor, 1999). The PFC circuitry form a very broad system that can be affected in different ways by different substances (Bechara, 2005), as shown by recent evidence suggesting that the extent and nature of ECF deficits (including different forms of impulsivity) may depend on the principal substance of abuse (Ersche, Clark, London, Robbins, & Sahakian, 2006; Ornstein et al., 2000; Rogers et al., 1999). Impulsivity is not itself an unitary construct and it is argued to be composed of several independent factors that can be assessed with different measures (Evenden, 1999). One form of impulsivity is the inability to suppress pre-potent responses as measured by response inhibition tasks. In these tasks, participants must force themselves to inhibit an automatic or learned habit response (such as mentally reading a word when presented with it, or emitting a consistently reinforced response in the presence of its discriminative stimulus) and give an alternative response in accordance with task demands instead. A different form of impulsivity is related to the inability to delay gratification when faced with conflictive choices that are immediately followed by some type of reward, but are also followed by delayed punishment. This latter form of impulsive behavior is modelled by complex decision-making tasks, such as the Iowa Gambling Task (IGT, Bechara, Damasio, Damasio, & Anderson, 1994; Bechara et al., 2001). In this study we have assessed response inhibition and decision-making processes by means of several neuropsychological measures in a group of healthy controls, and two groups of abstinent polysubstance abusers of cocaine and heroin (users of several substances with a marked subjective preference for either cocaine or heroine), two widely abused substances (European Monitoring Centre for Drugs and Drug Addiction, 2005) with distinctive pharmacological effects on the central nervous system (CNS) (Leri, Bruneau, & Stewart, 2003; Robbins, 2000). Previous studies have demonstrated that cocaine and amphetamine PSA usually present increased response latencies and a higher number of commission errors in tests of response inhibition, such as the Stroop, Go/No Go and Stop-Signal tasks (Fillmore & Rush, 2002; Monterosso, Aron, Cordova, Xu, & London, 2005; Salo et al., 2002), when compared against healthy controls and users of other substances. Furthermore, cocaine users also show steeper discounts of delayed rewards on delay-discounting tasks (Coffey, Gudlesky, Sladin, & Brady 2003) and a poorer ability to make decisions based on long-term

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goals rather than short-term rewards in complex decision-making measures (Stout, Busemeyer, Lin, Grant, & Bonson, 2004). Heroin polysubstance users, on the other hand, have shown lack of forethought and impulsivity signs in future orientation, delay-discounting and decision-making-related tests (Kirby, Petry, & Bickel, 1999; Madden, Bickel, & Jacobs, 1999; Mintzer & Stitzer, 2002; Petry, Bickel, & Arnett, 1998; Rotheram-Fuller, Shoptaw, Berman, & London, 2004). However, evidence of response inhibition deficits in abstinent opioid abusers is sparse. Very few studies have directly compared the performances of opioids and psychostimulants abusers in ECF functioning tasks (including response inhibition and decision-making measures). Ornstein et al. (2000) found an intriguing double dissociation between performance of heroin and amphetamine users on an attentional set-shifting task (ID/ED task). Heroin users' performance was selectively impaired during the intra-dimensional (ID) phase of the task (prior to the extra-dimensional shift), whereas amphetamine users showed a poorer performance level during the extra-dimensional (ED) phase [although Ersche et al. (2006) failed to replicate these findings, and reported amphetamine users to show a poorer performance than heroin users in both phases of the ID/ED task, and in the Tower of London Task]. In a similar vein, Rogers et al. (1999) found that amphetamine users performed worse than opioids users in a decision-making task; and Lejuez, Bornovalova, Daughters, and Curtin (2005) found primary users of crack-cocaine to present higher levels of impulsivity and increased sexual risk behaviors than primary users of street heroin. On the other hand, Kirby and Petry (2004) showed that both heroin and cocaine polysubstance users present comparable higher rates of discount of delayed rewards, compared to alcohol users and normal controls, in a delay-discounting measure. Studies delimiting separable patterns of impulsivity in different substance abusing populations may have important clinical implications, as such studies could point out to important differential treatment targets, and help to identify specific obstacles for rehabilitation efficacy. Different types of substancerelated impulsivity can be attributed to different neural circuits and different neurocognitive mechanisms. In spite of scarcity of concluding evidence, the results commented above suggest that abstinent psychostimulant abusers present more evident signs of response inhibition deficits, whereas both psychostimulants and heroin abusers present poorer performance levels on complex tests of delaydiscounting and decision-making, when compared against matched healthy controls. In accordance with these preliminary data, we hypothesise that (i) primary users of cocaine will present a poorer performance on measures of response inhibition than primary heroin users and controls; and (ii) that both cocaine and heroin users will present significant deficits in a complex decision-making task, if compared against healthy participants.

2. Method 2.1. Participants 76 polysubstance abusers (PSA) – 64 of which were considered for further analyses – and 30 healthy comparison participants took part in this study. PSA were all inpatients of the “Proyecto Hombre” and “Cortijo Buenos Aires” rehabilitation centers in Granada (Spain). Selection criteria for PSA in this study were (i) meeting the DSM-IV criteria for substance dependence, (ii) absence of documented co-morbid mood or personality disorders, (iii) absence of documented head injury or neurological disorders, and (iv) minimum abstinence duration of 15 days before testing. Urine analyses for cannabis, benzodiazepines,

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Table 1 Descriptive scores and independent-sample t-tests for socio-demographic variables including age, years of education, laterality and estimation of premorbid IQ in the drug users and healthy participants groups Socio-demographic variables

Cocaine PSA, n = 39 [mean (S.D.)]

Heroin PSA, n = 25 [mean (S.D.)]

Healthy participants, n = 30 [mean (S.D.)]

F(2,91)

Age Years of education Word accentuation test Laterality (Briggs Index)

28.18 (5.49) 10.00 (2.70) 21.00 (4.96) 43.57 (12.47)

29.83 (3.45) 9.42 (2.41) 20.91 (4.44) 41.76 (13.22)

31.80 (7.37) 10.40 (1.92) 22.67 (3.94) 46.27 (11.74)

2.42 (ns) 2.84 (ns) 1.68 (ns) .86 (ns)

ns: non-significant.

cocaine, and heroin metabolites were routinely conducted in order to confirm the abstinence period. Healthy participants were recruited by means of bill posting, and were subsequently selected to meet the conditions that (1) they had not taken psychoactive substances (excluding alcohol) in the past; (2) consumption of alcohol was far from the clinical level; and (3) they had no documented psychiatric or neurological disorders. All participants provided their informed consent prior to their inclusion in the study. The two groups were matched in terms of pre-morbid IQ (estimated by means of the word accentuation test, Test de Acentuación de Palabras, TAP), gender, age, laterality, and years of education (see Table 1). Before any testing, drug users were interviewed to collect information about their drug of choice (“which drug has caused more problems to you in the past?”). The Interview for Research on Addictive Behavior (IRAB; López-Torrecillas, Pérez-García, Godoy, & Sánchez-Barrera, 2000) was used to examine the severity of drug use. This measure has been described in detail elsewhere (Verdejo-García, López-Torrecillas, Aguilar de Arcos, & Pérez-García, 2005a). The interview evaluates three parameters: the intensity (average dosing), frequency (consumption episodes by month), and duration (years of duration) of the use of a series of substances, including alcohol, amphetamines, cannabis, cocaine, heroin, MDMA, and methadone, the drugs abused in this sample. For every substance the subject had actually used, the following information was requested: (i) average amount of each target drug ingested in each consumption episode (number of joints for cannabis, number of pills for MDMA, number of grams for cocaine and heroin, number of pills/g for amphetamines, and number of units for alcohol, considering that a glass of scotch equals 1 unit, while a glass of beer or wine equals .5 units); (ii) the frequency of these consumption episodes per month (daily, between one and three times a week, once a week, between one and three times a month, or once a month); and (iii) the number of years that had elapsed since the onset of the drug use (years of duration). This way we obtained scores of quantity (intensity × frequency) and duration for each substance abused. These measures have previously been shown to correlate with several variables relevant to addiction, including stress, coping (López-Torrecillas et al., 2000), maladaptive explanatory styles (Verdejo-García, López-Torrecillas, Aguilar de Arcos, & Pérez-García, 2005b) and executive dysfunction (Verdejo-García et al., 2005a). Self-reported drug of choice was used to label the two different subgroups of PSA (cocaine and heroin users). Specifically, 76 drug users were interviewed; 39 of them were classified as cocaine users, 25 were classified as heroin users, 8 were classified as alcohol users, and 4 were unable to express any precise preference. Alcohol users and non-classifiable participants were discarded from further analysis. A final sample of 64 drug users and 30 healthy controls was used for all of the analyses reported in this paper. Quantity and duration measures were standardized by subtracting the average score for the whole group of PSA (n = 64) from the direct score (duration minus average duration, and quantity minus

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average quantity), and dividing the result by the group standard deviation. Standardized quantity and standardized duration were simply added upon each other to compute a composite severity score for each participant and each drug (see Verdejo-García et al., 2005a for a more detailed rationale of the use of this composite measure). Table 2 displays mean severity scores for each of the drugs under assessment in the IRAB in the two groups of PSA. Please note that the substance defining the group was in the two cases the most severely consumed drug (see the severity value in bold for each group). In other words, there is a close correspondence between the drug that was subjectively perceived by the participant as the one causing him or her more problems, and the drug estimated to be most severely consumed. Table 2 also shows descriptive raw scores for quantity and duration of each substance use in the two groups of PSA. Both groups were matched on severity of use of alcohol, benzodiazepines, amphetamines and MDMA. However, heroin polysubstance users had significantly greater use of cannabis (p = .009) and methadone (p = .001). Cocaine polysubstance users had been in recovery for a mean of 17.18 weeks, while heroin users had been in recovery for a mean of 25.04 weeks (the difference is non-significant). 2.2. Testing Participants went through 4 different neuropsychological tests, administered in a fixed order. The word accentuation test (Test de Acentuación de palabras, González-Montalvo, 1991) consists of a list of 30 Spanish words, which must be added the tilde sign (´) over the right vowel, in accordance with Spanish accentuation rules. Performance is assessed as the number of correct responses. This measure has been proved to be a good indicator of pre-morbid IQ level in the Spanish population (Del Ser, GonzálezMontalvo, Martínez-Espinosa, Delgado-Villapalos, & Bermejo, 1997; Del Ser & Peña-Casanova, 1994). The Stroop color-word test (Golden, 1978; adapted for Spanish population, TEA ediciones, 1999) is a well-known task of cognitive conflict in the emission of a verbal response (reading/naming the color). The main dependent variable used in this test was the interference score (IS) (Golden, 1978). The 5-Digit Test (5DT) (Sedó, 2005) is another conflict-based task, described as an alternative to the Stroop test, not dependent on reading skills (Sedó, Levenson, & Leonard, 1995), composed of 4 subtests. In Part 1 (Reading), a number of card-looking text boxes are presented. Each box contains as many instances of the same digit as the digit itself indicates, and the participant is asked to read the digit. In Part 2 (Counting), a number of boxes containing one or more asterisks are presented to the participants, and their task in this case is just counting the number of asterisks in each box. In Part 3 (Focusing), the boxes are similar to the ones in Part 1, but the identity of the digit in each box does not correspond to the number of digits in the box, and participants are asked to count the number of digits, and to ignore their identity (for example, the right answer for a box with four 5's is 4). Finally, in Part 4 (Switching), an extra clue (a thick or narrow box border) indicates whether the participant must report the number of digits or their identity (reading or counting). In the four cases, performance is measured in terms of the time required to complete each task, and the number of errors committed. A computer-based implementation of the Go/No Go task (Perales, 2003, cited by Verdejo-García, Bechara, Recknor, & Pérez-García, 2006) was also run. The task consisted of 40 trials. In the first 20 trials (pre-switch), participants were asked to press a key as quickly as they could whenever the GO stimulus was presented, and to withhold the response when the NO-GO stimulus was presented. In the second 20 trials of the task (post-switch), the participant was asked to switch the assignation of the response from the GO to the NO-GO stimulus. The inter-stimulus interval (ISI) was set at 900 ms, and each stimulus was

Cocaine

Heroin

Methadone

MDMA

Alcohol

Amphetamine

5.91 0.39 −.838 52.40 ⁎ 9.22 ⁎ 1.808 ⁎

23.07 0.02 −.519 639.64 ⁎ 1.44 ⁎ 1.082⁎

15.69 .047 19.60 .261

Severity is a composite measure computed as the sum of the standardized scores of quantity and duration. ⁎ Independent-sample t-tests showed significant differences between groups (p < .05).

56.33 ⁎ 9.72 .545 ⁎ 32.71 7.53 −.123

2.32

1.83

355.67 6.86 −.320 222.60 8.90 −.188

4.39 .032 6.36 .017

.56

.74

Quantity Duration Quantity Duration Quantity Duration Quantity Duration Quantity Duration Quantity Duration Quantity Duration (joints) (years) (g) (years) (g) (years) (mg) (years) (pills) (years) (U) (years) (g) (years)

Cannabis

Cocaine 175.76 4.74 − .240 Heroin 201.24 10.80 ⁎ .902 ⁎

Group

Table 2 Mean scores for quantity, duration and severity of use (in italics) of seven drugs of abuse for the two groups in the present study

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presented during 1000 ms. Auditory feedback (one of two distinctive sounds) was provided after each response to indicate whether that response had been right or wrong. Finally, we used the Iowa Gambling Task (IGT, Bechara et al., 1994) to evaluate decision-making processes. We used a computer version of this task (see Bechara et al., 2001 for a detailed account) that was administered according to the procedures described by the Iowa group. The primary dependent measure for this task was the difference in the number of cards selected from the advantageous versus the disadvantageous decks: [(C + D) − (A + B)] across five blocks of 20 trials.

3. Results For all analyses, p = .05 was adopted as the significance criterion. For multiple comparisons, LSD posthoc tests were used, and, again p = .05 was adopted as the significance criterion. 3.1. Stroop task A one-way (Drug use: Control, Cocaine, Heroin) ANOVA yielded a significant effect of drug use on the interference score (IS), F(2,90) = 6.31; MSE = 69.16, p = .003. LSD post-hoc analyses yielded significantly lower levels of performance for cocaine users (p = .001) and heroin users (p = .006), if compared to controls. The two PSA groups did not differ between them. Mean scores for the three groups were 5.16, −1.54, and −1.14, for controls, cocaine users, and heroin users, respectively. 3.2. 5DT The four standard subtests from the 5-Digit Test (reading, counting, focusing, and switching) were run. With regard to performance time scores, we conducted analyses for two composite measures (the difference between focusing and reading, foc − read, and the difference between switching and reading, switch − read). These two differential scores were computed in order to discount the possible influence of reading ability and speed of processing from the performance level in the focusing and switching subtests. This makes the foc − read measure presumably similar to the IS from the Stroop test, in the sense that (1) level of performance decrease as the interference from the pre-potent response (reading/naming the number) grows, (2) reading demands are discounted from the score. The switch − read score, on the other hand, is also sensitive to attentional processes, but these are more related to complex control and cognitive load than to inhibition and impulsivity. Separate ANOVAs showed no effect of drug use on switch − read [F(2,91) = 2.46; MSE = 107.08, p = .091], and a significant effect on foc − read [F(2,91) = 6.27; MSE = 35.51, p = .003]. Table 3 displays mean values and SDs for the two measures under consideration. Post-hoc LSD showed that heroin users and controls did not differ between them on the foc − read measure, and the two of them were significantly faster than cocaine users (p = .004 and .003). In spite of the fact that the main effect of drug consumption on the switch − read did not reach significance, it was analysed nonetheless. In this case, cocaine users, but not heroin users, were significantly slower than controls (p = .034). Cocaine users did not differ from heroine users on this measure. Participants committed virtually no errors in the reading and counting subtests. Therefore, only the focusing and switching subtests were considered for error-score analyses (in this case there was no

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Table 3 Average number of errors (errors) in the two relevant 2 sub-measures of the 5DT (Focusing, Switching), and mean difference latencies (time score) for the two composite measures (Focusing − reading, Switching − reading) Group

Focusing

Switching

Number of errors Control Cocaine Heroin

.60 (.86) 1.64 (1.63) .88 (1.24)

Foc − read

Switch − read

Time score (difference) .67 (.84) 2.38 (2.56) 2.20 (1.85)

12.40 (4.78) 16.59 (6.64) 12.32 (5.08)

20.37 (7.16) 25.77 (11.34) 24.76 (11.85)

Values in parentheses stand for standard deviations (S.D.) for the corresponding conditions.

apparent effect of reading ability and speed of processing to discount). Drug use had a detrimental effect on both the focusing and the switching measures [F(2,91) = 10.06, 27.92; MSE = 1.75, 3.87; p = 004, .001]. Cocaine users committed more errors than heroin users (p = .027) and controls (p = .020) on the focusing measure, whereas both cocaine (p = .001) and heroin (p = .005) users significantly differed from controls in the switching measure. Mean error scores are also displayed in Table 3. 3.3. IGT We conducted a 5 (block) × 3 (Group: Control, Cocaine, Heroin) mixed-design ANOVA on the net scores of the IGT. Results showed a significant effect of Block, F(4,84) = 17.74, p = .000; Group, F(2,87) = 3.81, p = .026, and the Block × Group interaction, F(8,170) = 2.23, p = .025. Univariate ANOVAs showed significant differences between groups on blocks 4 (p = .015) and 5 (p = .042). Post-hoc LSD tests showed significant differences between performance of cocaine PSA and healthy participants (p = .005; p = .019); and between performance of heroin PSA and healthy participants (p = .045; p = .046) on blocks 4 and 5, respectively. However, we found no significant differences between cocaine and heroin PSA on any of these blocks (see Fig. 1).

Fig. 1. IGT performance across blocks, measured as the difference between C and D decks choices and A and B decks choices, for cocaine PSA, heroin PSA, and matched controls. ⁎p < .05.

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3.4. Go–No Go task The total number of errors, the rate of commission errors f calculated as [# commission errors / (# commission errors + # correct rejections)] and the hit rate h [# hits / (# hits + # omission errors)] in the two phases (pre-switch and post-switch) of the Go–No Go task were analysed separately. The total number of errors (omission errors + commission errors) for each task phase and each group is displayed in Fig. 2 (Upper panel). The two-way ANOVA (Group: Control, Cocaine, Heroin × Phase: Preswitch, Post-switch) yielded significant effects of the two factors [F(1,84) = 4.35; MSE = 3.20; p = .040;

Fig. 2. Mean number of errors (upper panel) and mean hit rate (lower panel) across the two Go/No-Go task phases for controls, cocaine users, and heroin users. Error bars represent the corresponding standard errors (S.E.).

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and F(2,84) = 3.72; MSE = 7.82; p = .028, for Phase and Group, respectively]. Specifically, the number of errors decreased from the first to the second phase. With regard to the effect of drug consumption, LSD post-hoc tests showed controls to perform better (commit less errors) than cocaine users (p = .030), but did not yield significant differences between controls and heroin users or between heroin and cocaine users. Drug use also had a significant detrimental effect on the hit rate, h, F(2,84) = 3.93; MSE = .42; p = .023. Again, cocaine users (p = .030), but not heroin users, significantly differed from controls. Mean hit rates for each group and each phase are displayed in Fig. 2 (lower panel). Finally, drug use did not have any significant effect on the commission error rate. Seemingly, the bulk of the difference between drug users and controls is mostly attributable to omission errors. In order to assess the potential effect of criterion switch, an extra analysis was carried out. The median number of errors in the first phase was computed, and participants from the three groups were first pooled together and then classified as good or bad performers (considering the first phase only) according to a median split. Only good performers were selected for further analyses. This selection procedure ensured that the performance level of all groups was equally good in the first phase, before the criterion change had taken place. After selection, n = 20, 18, and 10, for the control, cocaine users', and heroin users' group, respectively. As displayed in Fig. 3 (upper panel), despite the fact that the three groups showed similar performance levels in the first phase, a between-group difference in the second phase remained. A two-way ANOVA (Phase × Group) yielded a significant effect of Phase, F(1,45) = 9.15; MSE = 1.79; p = .004. Neither the effect of Group nor the interaction reached significance. Despite the nonsignificant interaction, we carried out exploratory LSD tests for each group. These analyses showed a significant effect of Phase in the cocaine users' group (p = .022), but not in the heroin users' group (despite the larger between-phases difference observed in the latter). However, assuming sphericity, and using the same global error term (MSE = 1.79) for both comparisons, independent ANOVAs showed the two of them to reach significance [F(1,17) = 4.49, p = .050, and F(1,13) = 5.47, p = .039 for the cocaine and the heroin groups, respectively]. On the contrary, the same effect remained far from significance in the control group. In other words, the bulk of the effect of task switch in the pre-selected sample of high-performance participants was attributable to the two PSA groups. If only the commission error rate is analysed a very interesting pattern emerges. The two-way ANOVA (Fig. 3, middle panel) showed f to be higher in the second phase than in the first one, F(1,45) = 7.99; MSE = .005; p = .007. However, that effect is mostly attributable to the cocaine group, F(1,17) = 9.06; MSE = .005; p = .008. In the two other groups, the possible effect of task switch was very far from significance. The complementary pattern emerged when the hit rate was analysed. In this case, the two-way ANOVA (Fig. 3, bottom panel) showed h to be marginally lower after the task switch, F(2,45) = 3.54; MSE = .013; p = .067. The effect of task switch was significant in the heroin group, F(1,13) = 4.69; MSE = .013; p = .02, whereas in the other two groups, the possible effect of task switch was very far from significance. It must be taken into account, however, that the differential effect of task-switch across groups (or, to be precise, the interaction on which that effect should be reflected) did not reach significance. The analyses in the interaction, being illustrative and probably worthy to take into account, must be considered as exploratory, and interpreted cautiously.

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Fig. 3. Mean number of errors (upper panel), mean commission error rate f (middle panel), and mean hit rate h (bottom panel) across the two Go/No-Go task phases for controls, cocaine users, and heroin users. Error bars represent the corresponding standard errors (S.E.).

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4. Discussion Cocaine and heroin PSA showed evident signs of neuropsychological impairment, when compared to matched controls. In this study we were maximally interested in analysing different measures of impulsivity. Consistently with initial hypotheses, cocaine and heroin PSA showed differential patterns of performance on specific measures of response inhibition. In the 5DT, cocaine PSA deviated from controls significantly more than heroin users, in terms of both latency and error scores. In the Go/No Go task, cocaine PSA also showed increased error scores and decreased hit rates when compared to controls, and this effect was seemingly larger than for heroin PSA. However, global analyses from the Go/No Go task do not allow studying the possible detrimental effect of task switch, and we conducted a post-hoc analysis of pre-switch good-performance individuals. This analysis showed an increased task-switch cost in PSA, compared against controls; but, at the same time, specific analyses of hit and commission error rates revealed that cocaine PSA sub-optimal performance was mostly attributable to a high number of commission errors, whereas heroin PSA poor performance was mostly attributable to an increased number of omission errors. In other words, in this specific sub-sample of drug users, only cocaine users showed evident signs of motor impulsivity. In contrast, both cocaine and heroin PSA showed a similar pattern of impaired performance on the Stroop test and the IGT, if compared to control participants. In the former case, this is probably attributable to the higher dependency of Stroop scores on reading abilities. In the latter case, however, the absence of differences between cocaine and heroin PSA can be due to the fact that the IGT taxes a different form of impulsivity than the 5DT, defined as the lack of ability to make decisions in accordance with long-term goals rather than short-term rewards. These results thus show a general pattern of poorer performance in cocaine PSA, compared to heroin PSA and healthy participants in tests of perceptual (5DT) impulsivity, and compatible trends in detailed analyses of motor (Go/No Go) impulsivity. These results are consistent with previous studies showing that stimulant users usually present a poorer ability to inhibit pre-potent responses, and require more time to inhibit these responses, as measured by several response inhibition paradigms (Fillmore & Rush, 2002; Monterosso et al., 2005; Salo et al., 2002). Accordingly, functional imaging studies have shown that deficits of inhibitory control are related to decreased activity in the ACC during time-pressured inhibition of pre-potent responses in chronic cocaine users (Bolla et al., 2004; Kaufman, Ross, Stein, & Garavan, 2003). Furthermore, cocaine users present structural abnormalities on the ACC and related PFC regions (Franklin et al., 2002; Matochik, London, Eldreth, Cadet, & Bolla, 2003). In contrast, evidence of impaired inhibitory control in abstinent opioid/heroin abusers is rather limited. Only two studies have shown subtle deficits in heroin users performing the Porteus Maze test (Lee & Pau, 2002; Pau, Lee, & Chan, 2002), requiring planning and reflection impulsivity skills. However, response inhibition deficits have been revealed in active methadone maintenance patients (Mintzer & Stitzer, 2002; Verdejo, Toribio, Orozco, Puente, & Pérez-García, 2005). Therefore, it is possible that impulse control deficits in opioid users depend critically on if they are actually “on the drug” or abstinent. Our results for response inhibition tasks (5DT/Go–No Go) support previous direct comparisons of neuropsychological performance between stimulant and opioid PSA. It is noteworthy that cocaine but not heroin PSA with high performance levels in the pre-switch phase presented a significantly larger number of commission errors in the post-switch phase of the Go/No Go; whereas heroin but not cocaine PSA (again, with high performance levels in the pre-switch phase) presented increased omission errors in the post-switch phase. These results resemble the double dissociation reported by Ornstein et al. (2000)

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between performance of opioid users (impaired in pre-switch IDS stage) and amphetamine users (impaired in post-switch EDS) using a set-shifting task. Although exploratory, these data could indicate that performance of cocaine PSA becomes more impulsive in response to changes in task contingencies, reflecting an inability to inhibit previously reinforced responses. In contrast, heroin users become more conservative, or have more difficulties to engage in the task after switch. We are however aware that error pattern differences were not large enough to allow the Error type × Group interaction to be significant. Thus, these data must then be taken cautiously. Still, they indicate that analysing hit and commission error rates from the Go/ No Go task separately can be useful to identify differential cognitive deficits in different populations of drug users. Studies by Ersche et al. (2006) and Rogers et al. (1999) also showed decreased performance of stimulants (amphetamines), with regard to opioid users, in tasks measuring different aspects of impulsivity. The greater extent of impairment in cocaine PSA is supported as well by imaging studies showing greater white matter abnormalities in the PFC of cocaine, as compared to heroin users (Lyoo et al., 2004). Against the main findings on response inhibition skills, the interference score of the Stroop test revealed no differences between the two groups of drug users. As noted above, a possible reason for this absence of differences between the two drug using groups is the high level of dependence of the Stroop performance on reading and speed of processing abilities (which were significantly decreased in both drug using groups – data not shown). In accordance with this hypothesis, two related measures (the focusing and focusing–reading measures of the 5DT) showed little signs of impairment in heroin users and significant signs of impairment in cocaine users. This probably indicates that these are purer measures of interference and control than the Stroop IS. A compatible pattern of results emerged in the decision-making measure (IGT) with both drug using groups performing at a similar level, but significantly below that of healthy controls. This task assesses a different aspect of impulsivity, associated with the ability to choose according to long-term goals rather than short-term rewards (Bechara et al., 2001; Petry et al., 1998). Psychometric studies have associated drug users' performance on the IGT with their performance in delay-discounting tasks (Monterosso, Ehrman, Napier, O'Brien, & Childress, 2001), supporting the notion that the IGT taxes a different form of impulsivity not equivalent to response inhibition measures. Thus, it is not surprising that both cocaine and heroin users performed disadvantageously in this task, since multiple studies have shown that users of these drugs have a higher rate of discount associated to delay (Coffey et al., 2003; Kirby & Petry, 2004) and poorer ability to decide according to long-term outcomes (Rotheram-Fuller et al., 2004; Stout et al., 2004; Stout, Rock, Campbell, Busemeyer, & Finn, 2005). The dissociation between the differential performance of users of different substances on response inhibition measures and their similar performance on decision-making is opened to several interpretations. First, it is possible that consumption of different drugs is somehow related to different aspects of the PFC circuitry. According to this hypothesis, opioid use may correlate with the functions of ventromedial PFC (a critical neural substrate for decision-making), while stimulant use may correlate with different aspects of PFC function in a more generalised fashion. Second, since the IGT is a very complex task in which several neurocognitive process are involved (including apathy, planning, inhibition, working memory or sensitivity to rewards/punishments) it is more likely that opioid users present deficits in any of these components than in the more specific process of response inhibition measured by the Stroop or Go/No Go tasks. And third, and related to the previous one, it is possible that the dependent measures used for the IGT in our study are not sensitive enough for

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grasping possible discrepancies between opioid and cocaine users performance. For example, postswitch analyses of the Go/No Go revealed a trend of a differential pattern of commission/omission errors in the two PSA groups. Similarly, a detailed analysis of different aspects involved in IGT performance (for example, differences in sensitivity to reward vs. punishment) may provide further dissociations between drug users groups in this task (see Ersche, Roiser, et al., 2005; Stout et al., 2004, 2005). Taken together, these marked impairments in measures of cognitive impulsivity may have important implications for the clinical functioning and rehabilitation of drug users. It has been argued that executive control deficits may contribute to drug use and negatively affect the rehabilitation in two ways (Rogers & Robbins, 2001). First, they could affect the ability of drug users to participate in the activities of the rehabilitation programs. Second, they could increase the probability of the occurrence of behaviors of searching for and using drugs. For example, it has been shown that response inhibition deficits are related to difficulties in controlling attentional biases and impulsive responses related to drug use stimuli (Hester et al., 2006; Noel et al., 2005). Similarly, alterations in decision-making can contribute significantly to relapses, by influencing the selection of courses of action that involve immediate compensation resulting in important long-term negative consequences (Bechara et al., 2001; Paulus et al., 2005). Several limitations complicate interpretation of our findings. First, both cocaine and heroin users were polysubstance users who had co-abused other multiple drugs in the past. Although this is an obvious limitation of our study, and we emphasize that the deficits revealed cannot be considered as the one-to-one consequence of the use of one particular substance, we should also note that both drug using groups showed similar levels of polysubstance abuse, and that in any instance, the less impaired group (the opioid group) was the one with higher polysubstance involvement. Second, an obvious limitation of this study is that it cannot address the issue of causation of the impulsivity deficits revealed in drug users. Although there is evidence of detrimental effects of dose-related measures of drug abuse on ECF/impulsivity measures, a growing line of evidence from animal and human studies indicate that these deficits may predate the onset of drug use and constitute vulnerability markers for liability to addiction (Perry, Larson, German, Madden, & Carroll, 2005; Vanyukov et al., 2003). Similarly, other premorbid variables, including personality traits related to impulsivity or basal levels of anxiety/depression may as well contribute to explain testing differences. Finally, it should be noted that post-switch analyses in the Go/No Go were conducted on a reduced subsample of participants who performed well on the pre-switch phase, complicating generalisation of the results obtained. The limited size of this subsample could probably have contributed to the lack of interaction effects for the pattern of omission/commission errors. Thus, these results may be interpreted cautiously and considered exploratory. Furthermore, future research should confirm and extend this pattern analysing larger sample sizes or increasing the number of trials in the pre-switch phase to make sure that all subgroups achieve an acceptable learning of initial contingencies. Acknowledgement This study has been supported with funds from BSO2003-07169 (to the first and third authors) and BSO2003-03723 (to the second author) from the Spanish Ministerio de Ciencia y Tecnología (MCYT). This study complies with the current laws of Spain and all international ethical guidelines for human studies.

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