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Acute effects of oral cocaine on inhibitory control of behavior in humans
Drug and Alcohol Dependence 67 (2002) 157 /167 www.elsevier.com/locate/drugalcdep
Acute effects of oral cocaine on inhibitory control of behavior in humans Mark T. Fillmore a,*, Craig R. Rush a,b,c, Lon Hays c a
b
Department of Psychology, University of Kentucky, Lexington, KY 40506-0044, USA Department of Behavioral Science, University of Kentucky, Lexington, KY 40539-0086, USA c Department of Psychiatry, University of Kentucky, Lexington, KY 40539-0086, USA
Received 7 March 2001; received in revised form 24 January 2002; accepted 5 March 2002
Abstract Studies of humans show that individuals with histories of cocaine abuse display reduced inhibitory control over behavioral impulses. The present study tested the effects of oral cocaine on the ability to inhibit behavior in humans. Eight adult volunteers (seven men and one woman) with a history of cocaine abuse participated as in-patient volunteers. Response inhibition and response execution were measured by a stop-signal paradigm using a choice reaction time task that engaged subjects in responding to gosignals when stop-signals occasionally informed them to inhibit the response. Subjects’ performance on the task was tested just before and 1 h after a randomized, double-blind administration of 0 mg (placebo), 50, 100, and 150 mg of oral cocaine HCl. Cocaine reduced subjects’ ability to inhibit responses to stop-signals. By contrast, no effect of cocaine was observed on the ability to execute responses in terms of their speed and accuracy. Subjective and physiological effects of cocaine were also observed. Together, the findings indicate that acute administration of cocaine can impair the ability to inhibit behavioral responses at doses that do not affect the ability to respond. These findings are important because they identify a specific disinhibiting effect of cocaine that could help explain the documented association between long-term cocaine use and poor impulse control. # 2002 Elsevier Science Ireland Ltd. All rights reserved. Keywords: Cocaine; Response inhibition; Human
1. Introduction There is growing evidence that long-term habitual cocaine users display neuropsychological impairments and have neuroanatomical abnormalities (e.g. Caine, 1998; Jentsch and Taylor, 1999). Several studies have found that long-term cocaine use is associated with performance deficits on neuropsychological tasks, including tests of attention, memory (e.g. Ardila et al., 1991), intellectual functioning (e.g. O’Malley et al., 1992), learning, problem solving, and perceptual motor speed (e.g. Beatty et al., 1995) (for a review, see Strickland and Stein, 1995). The neuroanatomical basis for these neuropsychological deficits is supported by evidence from neuroimaging studies which show im-
* Corresponding author. Tel.: /1-859-257-4728; fax: /1-859-3231979 E-mail address: [email protected] (M.T. Fillmore).
pairments of frontal lobe functions that are thought to be important in the control and regulation of behavior of these individuals (e.g. Volkow et al., 1996). A principal function of this brain region is to control behavior via inhibitory processes that normally serve to regulate behavior by suppressing or terminating prepotent (i.e. environmentally-triggered) responses (for reviews see Caine, 1998; Jentsch and Taylor, 1999). Some investigators have suggested that dopaminergic activation of the neural circuits by chronic cocaine use could impair inhibitory functions, leading to a loss of control over behavioral impulses (e.g. Lyvers, 2000; Volkow et al., 1996). Several lines of research have pointed to an association between long-term cocaine use and impairments of inhibitory processes (e.g. Ardila et al., 1991; Biggins et al., 1997; Horner et al., 1996; Volkow et al., 1996). Studies of animals and humans provide converging evidence that repeated cocaine use produces persistent cognitive deficits that often involve
03765-8716/02/$ - see front matter # 2002 Elsevier Science Ireland Ltd. All rights reserved. PII: S 0 3 7 6 - 8 7 1 6 ( 0 2 ) 0 0 0 6 2 - 5
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inhibitory processes of attention and behavior. For example, rats show long-term deficits in sensory inhibition following repeated cocaine administration (Boutros et al., 1994, 1997). Studies of humans show that cocaine users are more likely to report symptoms of attention deficit/hyperactivity disorder (ADHD) and other behavioral self-regulation disorders than are individuals with no history of cocaine abuse (Horner et al., 1996; Levin et al., 1998). Evidence for impaired inhibitory processes in cocaine users has been obtained by recent laboratory studies that found individuals with histories of cocaine use display patterns of premature responding (Bauer, 2001) and behavioral perseveration (Lane et al., 1998). Fillmore and Rush (in press) recently showed that cocaine users displayed a reduced ability to inhibit pre-potent responses. That study examined the ability to inhibit and execute behavioral responses in adult cocaine users and in an aged-matched sample with no history of cocaine use. Response inhibition and response execution were measured by a stop-signal choice reaction time (RT) task that required subjects to quickly respond to go-signals and suddenly inhibit responses when a stopsignal was presented. Compared with controls, cocaine users displayed poorer ability to inhibit their behavioral responses, however, no group differences were found in the ability to execute responses. The findings are important because they identify a specific deficit involving behavioral inhibition that could explain the association of cocaine use with disorders of self-regulation, such as ADHD. Despite mounting evidence that chronic cocaine abuse is associated with an impaired ability to inhibit behavior, no research has directly tested the effects of acute cocaine administration on inhibitory control of behavior in humans. Behavioral tasks have been developed to identify the specific inhibitory processes that underlie disorders of self-control. In particular, the development of a cognitive ‘stop-signal paradigm’ has generated considerable research on inhibitory processes and the assessment of specific deficits in the ability to inhibit behavioral responses (Logan and Cowan, 1984; Logan et al., 1984; Schachar et al., 1995). The paradigm is based on a cognitive model of control which asserts that the ability to inhibit an action is determined by the outcome of competitive activating and inhibiting processes elicited by cues to activate or inhibit a response. The time in which each competing process is completed determines the behavioral outcome. If the inhibiting processes are completed first, the response is withheld. If the activating processes finish first, the response is executed. The model is tested by a stop-signal task which is essentially a dual task that elicits conflicting goand stop-processes in individuals by engaging them in responding to go-signals, but occasionally requiring them to inhibit the response when a stop-signal occurs.
The stop-signal task directly measures the subject’s ability to inhibit (i.e. suppress) a pre-potent behavioral response in the presence of conflicting go- and stopsignals. The task is unique because of its direct assessment of the ability to inhibit a pre-potent action, and thus, it is thought to provide a more direct assessment of inhibitory control than other methods and tests which claim to assess inhibition (Quay, 1997). There is evidence showing that stop-signal tasks are sensitive to impairments of behavioral inhibition that underlie self-control. Studies using the stop-signal paradigm have found that behavioral inhibition was reduced in individuals with self-control disorders, such as Oppositional/Defiant Disorder and ADHD (e.g. Oosterlaan and Sergeant, 1996; Schachar et al., 1995). Recent studies have used the stop-signal paradigm to show that behavioral inhibition is reduced in response to acute doses of alcohol and the sedative-hypnotic drug, triazolam (Fillmore and Vogel-Sprott, 1999, 2000; Fillmore et al., 2001). The hypothesis that acute cocaine administration might also impair the ability to inhibit behavior has not been directly tested. However, such evidence would be important because it could provide some explanation for observations of poor impulse control in individuals with a history of cocaine abuse. The present study was designed to test the hypothesis that acute cocaine administration impairs the basic ability to inhibit behavior. The study examined adult volunteers with a history of cocaine use. The stop-signal paradigm was used to measure the acute effects of three doses of oral cocaine HCL (50, 100, and 150 mg) on inhibitory control in a double-blind, placebo-controlled study.
2. Method 2.1. Subjects Eight adult volunteers (seven men and one woman) with histories of cocaine use participated in the study. Subjects’ ages ranged between 25 and 45 years (mean/ 35.2, S.D. /8.2). The racial make-up of the sample was African American (n/7) and Caucasian (n /1). The sample was drawn from adults in the Lexington, KY metropolitan area who volunteered to participate in studies of behavioral pharmacology. Participants were recruited via notices posted on community bulletin boards and by word of mouth. The study was approved by the University of Kentucky Medical Institutional Review Board, and subjects provided their written, informed consent prior to participating. Volunteers had to have: a minimum of grade 8 education, demonstrated reading ability, no uncorrected vision or auditory problems, and no self-reported major Axis I psychiatric disorder, head trauma, or other CNS
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injury. Volunteers also had to meet all of the following criteria: (1) a score of at least four on the drug abuse screening test (Skinner, 1982); (2) self-report of past week cocaine use; (3) positive test for the presence of cocaine or benzoylecgonine in urine-analysis (ONTRAK Abusscreens, Roche Diagnostic Systems, Nutley, NJ). All subjects smoked cocaine in the form of crack. No volunteer was currently in, or seeking, treatment for their substance use. The sample reported an average frequency of cocaine use of 3.2 days in the past week (S.D. /2.4), and 14.8 days in the past month (S.D./9.1). The sample reported smoking a mean daily amount of 2.4 rocks of crack cocaine per day (S.D. / 2.3) and spending an average of $24.0 per day on cocaine (S.D./21.1). The sample reported using cocaine for an average period of 9.2 years (S.D. /5.6). All volunteers also reported using nicotine, alcohol, and caffeine. The mean total weekly alcohol consumption for this sample was 44.6 standard drinks (S.D. /35.3). The sample reported smoking an average of 19.2 (S.D./9.6) cigarettes per day. The sample reported an average, total daily caffeine consumption of 161.4 mg (S.D./241.6). Most of the sample also reported some past month use of marijuana (n/7), with an average monthly frequency of 11.0 times (S.D. /13.9). No other drug-use, including prescription medication, was reported by the sample. Prior to participation, all potential volunteers were examined by a psychiatrist and completed a comprehensive medical history questionnaire, drug-use questionnaire, mini-mental status examination (Folstein et al., 1975), and vital sign assessment. Routine, clinical laboratory blood chemistry tests and an electrocardiogram were conducted on all potential volunteers. Potential volunteers with histories of serious physical disease, current physical disease, impaired cardiovascular functioning, chronic obstructive pulmonary disease, seizure, head trauma, CNS tumors, or past histories of psychiatric disorder (i.e. Axis I, DSM IV) other than substance use or dependence were excluded from participation. Female volunteers had to have an effective form of birth control in use. All subjects were in good health with no contraindications to stimulant drugs.
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required to respond to each letter as quickly as possible by pressing one of two adjacent keys on the computer keyboard using the index and middle fingers of the preferred hand. One key (the period key) was pressed to indicate that either ‘A’ or ‘C’ appeared, and the adjacent key (the forward slash key) was pressed to indicate that either ‘B’ or ‘D’ appeared. A letter was displayed for 500 ms and the computer screen was blank for a 2.5-s interstimulus interval before the next letter was displayed. This provided a 3-s period in which the subject could respond to the letter. A single test consisted of 176 trials in which each of the four letter stimuli were presented equally often. A stop-signal occurred on 27% of the 176 trials (i.e. 48 trials) during a test. The stop-signal was a 500-ms, 900Hz tone generated by the computer at a comfortable listening level. Subjects were required to withhold (i.e. inhibit) any response on trials in which a stop-signal sounded. Stop-signals were presented 12 times, at each of four delays after the onset of a letter: 50, 150, 250, and 350 ms. These delays were based on previous research using this task that showed the probability of inhibiting decreased in an orderly, linear fashion as the stop-signal delays increased from 50 to 350 ms (Fillmore et al., 2001; Fillmore and Rush, in press). The order of letters, stop-signals, and delays was random. Trials always began with a 500-ms preparation interval in which a fixation point (#) appeared in the center of the computer screen. A test was completed in approximately 10 min. Response inhibition was determined by the proportion of responses that an individual inhibited on stopsignal trials, and by the estimated mean latency to inhibit a response (Section 2.4). Response execution to go-signals was measured by the mean RT to the gosignals during a test (i.e. the average time from the onset of letter presentation until a computer key press). Shorter RTs (i.e. faster responding) indicated greater response execution. Choice response errors to go-signals were also recorded. The inhibition and execution measures are highly reliable across trials (alpha coefficients /0.90) and stable over sessions (test /retest reliabilities /0.85) (Mulvihill et al., 1997). The task was operated by Micro Experimental Laboratory Software (Psychology Software Tools Inc., Pittsburgh, PA).
2.2. Apparatus and materials 2.2.1. Stop-signal task Response inhibition and response execution were measured by a stop-signal choice RT task performed on a PC computer. The task requires subjects to make quick, choice key press responses to visually-presented go-signals and to inhibit any response when an auditory stop-signal is suddenly sounded. The go-signals were four 1.5-cm letters (A, B, C, and D), presented one at a time in the center of a computer monitor. Subjects were
2.2.2. Addiction research center inventory The short form of the addiction research center inventory (ARCI) consisted of 49 true/false questions and contained five major subscales: morphine /benzedrine group (a measure of euphoria); pentobarbital, chlorpromazine, alcohol group (a measure of sedation); lysergic acid diethylamide (a measure of dysphoria); benzedrine group (BG); and amphetamine (A) scales (empirically-derived amphetamine-sensitive scales) (Martin et al., 1971; Jasinski, 1977). The inventory
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items were presented individually on a computer and subjects used the computer mouse to endorse each item as either true or false. The computer generated total scores for each subscale. 2.2.3. Drug effect questionnaire This questionnaire consisted of 20 items that were presented individually on the computer monitor. Subjects rated each item using the computer mouse to select among one of five response options: not at all, a little bit, moderately, quite a bit, and very much (scored numerically from 0 to 4, respectively). The items rated were: any effects, active/alert/energetic, bad effects, bad mood, confused-dazed/spaced out, crave cocaine, good effects, good mood, high, irregular heart beat/racing, like, anxious/nervous, pay for this drug, relaxed/carefree, rush, shaky/jittery, stimulated, suspicious, take this drug again, and dry mouth. 2.3. General procedures Subjects resided on the General Clinical Research Center (GCRC) at the University of Kentucky Hospital while they participated in the study. The GCRC unit is a fully-supported NIH-funded medical facility for research requiring medical support. Subjects resided in private rooms during the study. Subjects were informed that the study examined the effects of various doses of cocaine on mood and behavior. They were given no information about the exact doses examined in the study, or the hypotheses concerning effects of the drug. Subjects resided on the unit for 7 days (Day 1, admission; Day 2, acclimation/practice; Days 3/6, dose administration test sessions; and Day 7, discharge). 2.3.1. Admission and acclimation/practice sessions On the day of admission to the unit, subjects provided a urine sample which was screened for the presence of cocaine/benzoylecgonine, benzodiazepines, barbiturates, tetrahydrocannabinol, amphetamine, and opiates. A breathalyser obtained a breath sample from subjects to ensure a zero blood alcohol concentration prior to admission. A psychiatrist reviewed the subject’s record and conducted a physical examination. During the acclimation/practice day, subjects were acquainted with the general testing procedure. All testing occurred in the subject’s room. The acclimation/practice session required approximately 5 h to complete. The experimenter explained the requirements for the stop-signal task. Subjects were instructed to respond to the letters (i.e. go-signals) as quickly and as accurately as possible, and to try to withhold their response if they heard a tone sound. Subjects were told to always respond as quickly as possible and not to wait for the possibility that a tone might sound (for verbatim instructions, see Fillmore et al., 2001). The subject then
performed practice tests on the stop-signal task. Subjects were also familiarized with the ARCI, drug effect questionnaire (DEQ), and physiological monitoring equipment. Subjects completed the questionnaires and had their heart rate and blood pressure measured by an automated blood pressure monitor at intermittent intervals over the 5-h period. 2.3.2. Dose administration sessions The four dose administration sessions were conducted on consecutive days, usually beginning early in the week (Monday, Tuesday, or Wednesday). Prior to beginning a session, subjects ate a low-fat hospital-prepared breakfast at 7:00 am. Those who smoked tobacco cigarettes, were allowed to smoke one cigarette between 7:30 am and 8:00 am. Subjects were not allowed to smoke during the sessions. Subjects provided a urine sample prior to each session that was screened for the presence of benzodiazepines, barbiturates, tetrahydrocannabinol, amphetamine, and opiates. A breathalyser was used to obtain a breath sample from subjects to ensure a zero blood alcohol concentration. 2.3.2.1. Baseline testing and dose administration. The testing began at 9:00 am with the baseline test (i.e. precapsule) of subjects’ stop-signal performance. After the stop-signal test, subjects completed the ARCI, DEQ, and then had their physiological measures recorded. Immediately after baseline testing, subjects received one of four doses of cocaine HCl: 0 (placebo), 50, 100, and 150 mg. All doses were administered orally in a doubleblind fashion. Drug doses were prepared by encapsulating 50-mg cocaine HCl USP (Mallinckrodt, St. Louis, MO) in a size 00 green opaque capsule combined with lactose filler. Placebo capsules contained only lactose. Subjects ingested three capsules during each dose administration session. The combination of active and placebo capsules determined the dose. Capsules were taken orally with 150 ml of water. Dose order across the four dose administration sessions was random. An oral administration route was chosen for this initial study because it provides reliable double-blind control over drug delivery, and avoids potential side-effects that can arise from nasal administrations. All doses were prepared by the University of Kentucky Investigational Pharmacy. 2.3.2.2. Post-capsule testing. Subjects performed the stop-signal task at 50 min post-capsule. Because the test required 10 min to complete, the assessment spanned 50/60 min post-capsule. This time interval was chosen to maximize the likelihood of observing a drug effect on inhibitory control. Time-course analyses of oral cocaine dose effects on physiological and subjective measures typically show peak effects approximately 1 h following administration (for review, see
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Bigelow and Walsh, 1998). Subjects completed the ARCI and DEQ at 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 4.0, and 5.0 h post-capsule. Physiological measures were recorded at 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, and 5.0 h post-capsule. No volunteers experienced any adverse reaction to the drug or any procedure. Volunteers were paid $240 for their participation. 2.4. Criterion measures and data analyses of stop-signal performance 2.4.1. Measures of response inhibition The ability to inhibit a response can be impaired in two important ways (Logan, 1994; Tannock et al., 1995): (1) The initiation of the inhibitory process might fail to occur occasionally, thus, resulting in less stopsignal inhibitions; (2) The completion of the inhibitory process might be slowed, so that it takes longer to inhibit a response to a stop-signal. The stop-signal paradigm provides a method for assessing these two criterion measures of response inhibition. 2.4.1.1. Probability of inhibition (p-inhibition). The proportion of successfully inhibited responses on the 48 stop-signal trials for a test provided a general measure of the ability to inhibit responses for a subject. Smaller proportions indicated a lower probability of inhibiting a response to a stop-signal (poorer inhibitory control of behavior). The proportion of successfully inhibited responses was also examined at each stopsignal delay (i.e. proportion of inhibited responses to the 12 stop-signals presented at each of the four delays). 2.4.1.2. Stop-signal reaction time. Deficits in inhibitory control might arise because more time is required to inhibit a response. The estimated mean time (in ms) required for a subject to inhibit responses to stop-signals (stop-signal reaction time, SSRT) was measured in the present research. The method for calculating SSRT was based on the subject’s probability of inhibiting responses to stop-signals and the distribution of RTs to go-signal trials. SSRT is the time needed to inhibit the pre-potent response once the stop-signal occurs. The measure is based on an estimate of how long the stop-signal can be delayed after the go-signal before the subject can no longer inhibit the response. As a general observation, the time required to inhibit is less than the time required to respond (Logan and Cowan, 1984). This is evident by the fact that individuals can successfully inhibit a response to a stop-signal that does not occur until some time after the go-signal. SSRTs are generally related to the number of successfully inhibited responses on a test, with shorter SSRTs associated with a greater number of inhibited responses. However, the SSRT also provides latency information concerning the speed of the inhibitory processes. Longer SSRTs, resulting from
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a general slowing of the inhibitory processes, indicate weak inhibitory control over behavior. The method for calculating SSRT was based on a ‘race’ model of inhibitory control. The method is summarized here and is explained in detail elsewhere (Logan, 1994). The SSRT measure is based on the notion that acts of control (like inhibiting an inappropriate response) take time to complete. Inhibiting an action is an internal response and is not observable. Although the latency to inhibit a response cannot be directly observed, the mean time required to complete the process during a test can be estimated using the distribution of observable RTs to go-signal trials and the probability of inhibiting to stop-signals as a function of their delay from the onset of the go-signal. SSRT is the difference between: (1) the point at which the stop-signal is presented; and (2) the point at which the inhibitory process is completed. The point at which the inhibitory process is completed was calculated using the distribution of observable RTs to the 128 go-signal trials (i.e. when no stop-signals were presented). After removing any outlier RTs that were 2.5 SDs of a subject’s mean RT for a test, the distribution of the remaining RTs scores for that participant’s test was rank-ordered from shortest to longest. To estimate the SSRT, certain RT scores from this ranked distribution were selected based on the subjects’ probability of failing to inhibit at each stop-signal delay. The n th RTs scores were chosen by multiplying the number of responses to go-signals by the probability of failure to inhibit at each of the four stop-signal delays. This produced four RTs that represented the times at which the stopping process was completed relative to the onset of the go-signal. The SSRT was determined by subtracting the appropriate stop-signal delay from each nth RT. The four resulting values were averaged to yield a measure of the participant’s mean SSRT for the test. 2.4.2. Measures of response execution The stop-signal task also provides measures of response execution. Responses displayed during gosignal trials (i.e. when no stop-signal occurs) yielded two important criterion measures of response execution: go-signal reaction time (RTgo) and go-signal response accuracy. 2.4.2.1. Go-signal reaction time. The strength of response execution to go-signals was measured by a subject’s mean RT (ms) to the 128 go-signal trials presented during a test (i.e. the average time from the onset of go-signals until a computer key press). This produced a mean RTgo score for a participant for each test. All analyses involving RTgo scores were performed on observed mean RTgo scores, and on the mean RTgo scores obtained after removing outlier trial RTs that were 2.5 SDs from the mean RTgo score on a test. On
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average, less than 2% of trials on a test had outlier RT values. Analyses based on actual RTgo scores and trimmed RTgo scores yielded identical conclusions. The Results section reports analyses based on trimmed RTgo scores. No RT data were recorded for any nonresponses to the 128 go-signal trials (i.e. failing to respond to a letter on a go-signal trial). Non-responses were infrequent and, on average, subjects responded to 98.5% of go-signal trials. 2.4.2.2. Go-signal response accuracy. The percentage of choice response errors on go-signal trials was also recorded. These errors occur when a subject presses the incorrect key in response to a letter presentation during a go-signal trial. The errors generally reflect failures of attention or information processing. Choice response errors during a test are rare and typically occur on less than 5% of all go-signal responses (Fillmore and Vogel-Sprott, 1999, 2000; Logan, 1994). 2.4.3. Data analyses of stop-signal performance The treatment effects on the four criterion measures of stop-signal performance were analyzed by separate 2 Test (Baseline Test vs. Post-capsule Test) /4 Dose (0, 50, 100, and 150 mg) analyses of variance (ANOVA). Treatment effects on p-inhibition were also examined by stop-signal delay. Predicted effects on inhibitions were analyzed by simple-effects comparisons of the omnibus ANOVA. All multiple comparisons were based on twotailed probabilities. 2.5. Criterion measures and data analyses of physiological and subjective effects Two sets of analyses examined physiological and subjective effects. First, peak effect data were calculated and analyzed by one-factor repeated measures ANOVA with Dose (0, 50, 100, and 150 mg cocaine) as the repeated measures factor. Peak effect was defined as the maximum value from 0.5 to 5.0 h after drug administration. Second, time-course data were analyzed by twofactor, repeated measures ANOVA with Dose (0, 50, 100, and 150 mg) and Time (pre-capsule, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, and 5.0 h for the physiological measures; pre-capsule, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 4.0, and 5.0 h for the subjective effect measures).
Table 1 Mean stop-signal task performance measures of subjects at baseline and test under four doses: 0, 50, 100, and 150 mg cacaine HCL Dose Baseline
Test
P-inhibition
0 50 100 150
0.55 0.60 0.59 0.62
0.57 0.53 0.51 0.50
SSRT (ms)
0 50 100 150
284.0 279.5 292.4 279.7
(41.6) (47.5) (73.6) (41.1)
311.7 330.5 312.3 321.5
(59.4) (86.8) (77.4) (81.1)
RTgo (ms)
0 50 100 150
499.4 528.2 532.5 517.4
(68.3) (50.8) (95.0) (84.9)
519.7 526.4 513.3 518.2
(55.6) (78.9) (77.6) (109.3)
Percentage choice response errors
0 50 100 150
4.1 3.9 4.9 3.9
(0.24) (0.19) (0.25) (0.18)
(3.0) (2.9) (4.3) (4.4)
4.0 3.6 3.5 3.8
(0.22) (0.21) (0.21) (0.16)
(3.2) (3.4) (3.2) (2.6)
Standard deviation is in parentheses.
dose interaction (F3,21 /5.0, P /0.009). Table 1 presents the mean p-inhibition scores for the interaction which is illustrated in Fig. 1. The figure shows that the probability of inhibiting a response at test decreased compared with baseline under all three active doses, with the largest decrement observed under the 150 mg dose. Four simple-effects tests examined the interaction. The tests compared the mean p-inhibition score at postcapsule test with the mean score at baseline under each dose condition. These simple-effect comparisons were chosen to provide the most information concerning the hypotheses while minimizing the family-wise error rate (Howell, 1987). Significant differences from baseline to test were obtained under the 150 mg (F1,21 /21.1, P /
3. Results 3.1. Dose effects on stop-signal measures of response Inhibition 3.1.1. Probability of inhibition A 2 (Test) /4 (Delay) /4 (Dose) ANOVA of subjects’ p-inhibition scores obtained a significant test by
Fig. 1. Mean probability of inhibiting a response (p-inhibition) to 48 stop-signals during baseline (pre-capsule) and at test (post-capsule) under four doses of oral cocaine HCl: placebo (PL), 50, 100, and 150 mg. Vertical capped bars indicate standard error of the mean.
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0.002) and 100 mg dose conditions (F1,21 /9.0, P / 0.020). The baseline-versus-test difference under the 50 mg dose approached significance (F1,21 /5.0, P / 0.060). No significant difference was observed under placebo (F1,21 /1.2, P /0.317). Thus, based on this measure of response inhibition, the findings support the hypothesis that acute cocaine administration (100 and 150 mg) can reduce inhibitory control. The ANOVA also obtained a significant main effect of stop-signal delay (Delay) on subjects’ p-inhibition scores (F3,21 /37.0, P B/0.001). There was no significant interactions of delay with dose or test (P s/0.479). To explore the main effect of delay on subjects’ p-inhibition, their scores were plotted as a function of stopsignal delay. Research shows that the probability of inhibiting a response diminishes as the stop-signal delay increases because less time is available to inhibit the prepotent response (Logan, 1994). Fig. 2 plots the mean p-
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inhibition score at each stop-signal delay for baseline and at test under each of the four dose conditions. As expected, the probability of inhibiting diminished as a function of increasing stop-signal delay. This function was evident at baseline and at test under all dose conditions. With respect to cocaine effects on inhibition, the figure shows that the baseline-to-test reduction in pinhibition scores under active cocaine doses was fairly similar at all stop-signal delays. Supplemental analyses examined the reliability of the p-inhibition measure over the four test sessions. Previous research has shown this measure to be reliable over time in non-drug-abusing populations (Mulvihill et al., 1997). However, no research had examined the reliability of response inhibition in a drug-abusing population. The mean p-inhibition scores on the baseline test ranged from 0.552 to 0.617. Thus, on average, subjects successfully inhibited responses on approxi-
Fig. 2. Mean probability of inhibiting a response (p-inhibition) to 12 stop-signals at each stop-signal delay at baseline and at test under four doses of oral cocaine HCl: placebo (PL), 50, 100, and 150 mg. Vertical capped bars indicate standard error of the mean.
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mately one half of the 48 stop-signal trials. Reliability of response inhibitions over the four dose conditions was determined by examining subjects’ p-inhibition at baseline over the four sessions. Reliability was estimated by Coefficient Alpha, based on the intraclass correlation among baseline p-inhibition scores across dose conditions (McGraw and Wong, 1996). The analysis obtained a coefficient of 0.96, indicating that individual differences among subjects’ baseline p-inhibition scores showed a high degree of consistency over the four dose conditions. 3.1.2. Stop-signal reaction time A 2 (Test) /4 (Dose) ANOVA of subjects’ SSRT scores obtained a significant main effect of Test (F1,7 / 17.5, P /0.004). No significant main effect of dose (F3,21 /0.1, P /0.985), or test by dose interaction (F3,21 /0.8, P /0.524), was observed. The mean SSRT scores at baseline and test are presented in Table 1. The table shows that the main effect of test was due to an increase in subjects’ SSRT from baseline to test under each dose condition. 3.2. Dose effects on stop-signal measures of response execution 3.2.1. Go-signal reaction time A 2 (Test)/4 (Dose) ANOVA of subjects’ RTgo scores obtained no significant main effects or interaction (P s/0.397). Table 1 presents the mean RTgo scores and shows no consistent change in the mean time to respond to go-signals over the four dose conditions. 3.2.2. Go-signal response accuracy A 2 (Test) /4 (Dose) ANOVA of subjects’ percent choice response errors to go-signal trials obtained no significant main effects or interaction (P s /0.171). Table 1 shows that the mean percent of choice response errors remained fairly consistent from baseline to test across all dose conditions. The table also shows that the percentage of response errors was low, indicating a high degree of response accuracy. 3.3. Physiological measures Fig. 3 shows that heart rate (F3,21 /6.6, P /0.003) and systolic blood pressure (F3,21 /4.1, P /0.020) increased under active dose conditions. Measures of diastolic blood pressure showed no significant effect of dose (P /0.504). Mean (SD) peak diastolic blood pressure measures across 0, 50, 100, and 150 mg dose conditions were: 79.3 (6.5), 79.8 (6.1), 81.4 (4.2), and 82.1(4.5) mm Hg, respectively. Dose by time ANOVAs of heart rate and blood pressure (systolic and diastolic) obtained no significant dose by time interactions (P s/ 0.193).
3.4. Subjective effect measures Fig. 4 shows that the DEQ ratings of the items, Any Effect (F3,18 /4.4, P /0.018) and Rush (F3,18 /3.3, P /0.045), increased under active dose conditions. No other DEQ or ARCI items showed significant dose effects (P s/0.05). Dose by time ANOVAs of the ARCI subscales obtained significant dose by time interactions for two subscales: amphetamine (F24,144 /1.8, P / 0.021) and BG (F24,144 /1.6, P /0.040). These timecourse effects are illustrated in Fig. 5. The figure shows generally similar ratings under all four doses. However, increases in subjective ratings from pre-capsule levels to 60 min post-capsule are most apparent under the active dose conditions. No such increases were observed under placebo.
4. Discussion This study used a choice RT stop-signal task to test the effects of oral cocaine on the inhibitory control of behavior in adult cocaine abusers. The results showed that the drug reduced the proportion of successfully inhibited responses to stop-signals. No effect of cocaine was observed on the estimated time required to inhibit a response. The drug also had no effect on subjects’ ability to execute responses, in terms of their speed and accuracy. Together, these findings indicate that acute administration of cocaine can impair the ability to inhibit behavioral responses at doses that do not affect the ability to execute responses. Other studies have shown that CNS depressant drugs, such as alcohol and the sedative/hypnotic, triazolam, also reduce response inhibitions on the stop-signal task (e.g. Fillmore and Vogel-Sprott, 1999; Fillmore et al., 2001). These drugs also increased the estimated time to inhibit a response (SSRT), and this effect could account for the reduced inhibitions. Slowing the inhibitory processes makes it less likely that they can be completed before response execution occurs (Logan and Cowan, 1984). Cocaine did not increase SSRT in the present study. This suggests that the drug might reduce inhibitions by some other mechanism that does not involve slowing of inhibitory processes. Instead cocaine might disrupt the initiation of the inhibitory process so that it fails to occur occasionally, resulting in less inhibitions to stop-signals. This assumption appears consistent with the evidence that cocaine reduced inhibitions to a similar degree regardless of stop-signal delay. Any slowing of the inhibitory processes might be expected to produce a greater reduction of inhibitions at the longer delays when there is the least time to inhibit responses. This was not observed in the present study. This study examined the effects of low doses of oral cocaine (i.e. 50, 100, 150 mg). These doses had little
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Fig. 3. Mean peak heart rate and systolic blood pressure under four doses of oral cocaine HCl: placebo (PL), 50, 100, and 150 mg. Vertical capped bars indicate standard error of the mean.
effect on subjective ratings, and produced only moderate increases in heart rate and blood pressure. Other research has examined higher doses (e.g. 300 mg) and found more robust effects on subjective ratings and physiological indices (e.g. Rush et al., 1999). However, there are a couple of important reasons for examining low dose effects of cocaine on inhibitory control. The low doses used in the study allowed effects on inhibitory control to be examined without any concomitant speeding effects on reaction time (RTgo), which likely occur at higher doses when the stimulant effects of cocaine become more pronounced (Farre´ et al., 1993; Higgins et al., 1990). Evidence that inhibitory control can be reduced by low doses indicates that this behavioral function might be particularly sensitive to the CNS effects of cocaine, and that it can be impaired independent of any changes in response execution. The research also examined some important measurement issues regarding the assessment of inhibitory
control. First, the study showed that subjects displayed a high degree of consistency over sessions in their baseline ability to inhibit responses. Coefficient Alpha showed a high degree of reliability of subjects’ baseline p-inhibition scores over daily sessions. This finding is consistent with the assumption that the task assesses a characteristic ability to inhibit and control behavior. Second, the range of baseline p-inhibition scores (0.55/ 0.62) is comparable to the mean p-inhibition of a larger sample of cocaine abusing subjects observed in another recent study in our lab (Fillmore and Rush, in press). Third, the study also showed that response inhibition diminished as a function of increasing the stop-signal delay. Fig. 2 shows the negative slope functions that relate the probability of inhibiting a response to the stop-signal delays. The slopes are consistent with previous studies showing that stop-signal latencies affect the probability of response inhibition (Fillmore and Rush, in press, Logan and Cowan, 1984). Moreover, the
Fig. 4. Mean peak DEQ ratings of items, Any Effects and Rush under four doses of oral cocaine HCl: placebo (PL), 50, 100, and 150 mg. Vertical capped bars indicate standard error of the mean.
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Fig. 5. Mean ARCI ratings for Amphetamine and BG subscales pre-capsule (Pre) and at intervals following the administration of four doses of oral cocaine HCl: placebo (PL), 50, 100, and 150 mg.
presence of these slopes provides verification that subjects understood the task requirements and followed instructions. In studies of other populations, such as young children, task performance can be affected by inattention and by random response strategies, owing to a lack of motivation or interest on the part of the subject (e.g. Schachar et al., 1995; Tannock et al., 1995). Such response styles are detected by the slope function. Randomly inhibiting and executing responses generates a flat slope function because inhibitions are equally likely to occur at all stop-signal delays under such a strategy. This was not observed in the present study. Rather, the negative slopes demonstrate that response inhibition during baseline and test was under some degree of stimulus control of the stop-signals in all dose conditions. The present findings point to promising new performance measures for understanding cocaine effects on behavior. Evidence that cocaine administration can directly reduce inhibitory control could help explain the association between long-term cocaine use and impairments of inhibitory processes (e.g. Ardila et al., 1991; Biggins et al., 1997; Fillmore and Rush, in press, Horner et al., 1996; Volkow et al., 1996). The evidence appears consistent with the suggestion that repeated dopaminergic activation by chronic cocaine use can impair inhibitory functions and lead to a loss of control over behavioral impulses (e.g. Lyvers, 2000; Volkow et al., 1996). However, the present findings are new and raise many questions concerning behavioral control and cocaine use. Higher doses must be examined to determine how inhibitory control is affected when stimulant
effects on behavior are also present. In addition, the effects of cocaine via other administration routes (e.g. intranasal) should be examined to test behavioral effects following more rapid drug absorption. These and other issues await to be explored.
Acknowledgements This research was supported by grants DA14079 and DA10325 from the National Institute on Drug Abuse.
References Ardila, A., Rosselli, M., Strumwasser, S. 1991. Neuropsychological deficits in chronic cocaine abusers. Int. J. Neurosci. 57, 73 /79. Bauer, L.O. 2001. Antisocial personality disorder and cocaine dependence: their effects on behavioral and electroencephalographic measures of time estimation. Drug Alcohol Depend. 63, 87 /95. Beatty, W.W., Katzung, V.M., Moreland, V.J., Nixon, S.J. 1995. Neuropsychological performance of recently abstinent alcoholics and cocaine abusers. Drug Alcohol Depend. 37, 247 /253. Bigelow, G.E., Walsh, S.L. 1998. Evaluation of potential pharmacotherapies: response to cocaine challenge in the human laboratory. In: Higgins, S.T., Katz, J.L. (Eds.), Cocaine Abuse: Behavior, Pharmacology, and Clinical Applications. Academic Press, New York, pp. 209 /238. Biggins, C.A., MacKay, S., Clark, W., Fein, G. 1997. Event-related potential evidence for frontal cortex effects of chronic cocaine dependence. Soc. Biol. Psychiat. 42, 472 /485. Boutros, N.N., Uretsky, N., Bernston, G., Bornstein, R. 1994. Effects of cocaine on sensory inhibition in rats: preliminary data. Soc. Biol. Psychiat. 36, 242 /248.
M.T. Fillmore et al. / Drug and Alcohol Dependence 67 (2002) 157 /167 Boutros, N.N., Uretsky, N., Lui, J.J., Millana, R.B. 1997. Effects of repeated cocaine administration on sensory inhibition in rats: preliminary data. Soc. Biol. Psychiat. 41, 461 /466. Caine, S.B. 1998. Neuroanatomical basis of the reinforcing stimulus effects of cocaine. In: Higgins, S.T., Katz, J.L. (Eds.), Cocaine Abuse: Behavior, Pharmacology, and Clinical Applications. Academic Press, New York, pp. 21 /50. Farre´, M., De Le Torre, R., Llorente, M., Lamas, X., Ugena, B., Segura, J., Camı´, J. 1993. Alcohol and cocaine interactions in humans. J. Pharmacol. Exp. Ther. 266, 1364 /1373. Fillmore, M.T., Rush, C.R., in press. Impaired inhibitory control in chronic cocaine abusers. Drug Alcohol Depend. Fillmore, M.T., Rush, C.R., Kelly, H.K., Hays, L. 2001. Triazolam impairs inhibitory control of behavior in humans. Exp. Clin. Psychopharmacol. 7, 363 /371. Fillmore, M.T., Vogel-Sprott, M. 1999. An alcohol model of impaired inhibitory control and its treatment in humans. Exp. Clin. Psychopharmacol. 7, 49 /55. Fillmore, M.T., Vogel-Sprott, M. 2000. Response inhibition under alcohol: effects of cognitive and motivational conflict. J. Stud. Alcohol 61, 239 /246. Folstein, M., Folstein, S., Hugh, P. 1975. ‘‘Mini-Mental State’’ */a practical method for grading the cognitive state of patients for the clinician. J. Psychiat. Res. 12, 189 /198. Higgins, S.T., Bickel, W.K., Hughes, J.R., Lynn, M., Capeless, M.A., Fenwick, J.W. 1990. Effects of intranasal cocaine on human learning, performance and physiology. Psychopharmacology 102, 451 /458. Horner, B., Scheibe, K., Stine, S. 1996. Cocaine abuse and attentiondeficit hyperactivity disorder: implications of adult symptomatology. Psych. Addict. Behav. 10, 55 /60. Howell, D.C. 1987. Statistical Methods for Psychology, Second ed.. Duxbury Press, Boston. Jasinski, D. 1977. Assessment of the abuse potentiality of morphinelike drugs (methods used in man). In: Morton, W.R. (Ed.), Drug Addiction I. Springer, New York, pp. 197 /258. Jentsch, J.D., Taylor, J.R. 1999. Impulsivity resulting from frontostriatal dysfunction in drug abuse: implication for the control of behavior by reward-related stimuli. Psychopharmacology 146, 373 /390. Lane, S.D., Cherek, D.R., Dougherty, D.M., Moeller, F.G. 1998. Laboratory measurement of adaptive behavior change in humans with a history of substance dependence. Drug Alcohol Depend. 51, 239 /252. Levin, F.R., Evans, S.M., Kleber, H.D. 1998. Prevalence of adult attention-deficit hyperactivity disorder among cocaine abusers seeking treatment. Drug Alcohol Depend. 52, 15 /25.
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Logan, G.D. 1994. On the ability to inhibit thought and action: a users’ guide to the stop-signal paradigm. In: Dagenbach, D., Carr, T.H. (Eds.), Inhibitory Processes in Attention, Memory, and Language. Academic Press, San Diego, CA, pp. 189 /239. Logan, G.D., Cowan, W.B. 1984. On the ability to inhibit thought and action: a theory of an act of control. Psychol. Rev. 91, 295 /327. Logan, G.D., Cowan, W.B., Davis, K.A. 1984. On the ability to inhibit simple and choice reaction time responses: a model and a method. J. Exp. Psychol: Hum. Percept. Perform. 10, 276 /291. Lyvers, M. 2000. ‘‘Loss of control’’ in alcoholism and drug addiction: a neuroscientific interpretation. Exp. Clin. Psychopharmacol. 8, 225 /249. Martin, W.R., Sloan, J.W., Sapiro, J.D., Jasinski, D.R. 1971. Physiologic, subjective, and behavioral effects of amphetamine, methamphetamine, ephedrine, phenmetrazine, and methylphenidate in man. Clin. Pharmacol. Ther. 12, 245 /258. McGraw, K., Wong, S.P. 1996. Forming inferences about some intraclass correlation coefficients. Psychol. Methods 1, 30 /46. Mulvihill, L.E., Skilling, T.A., Vogel-Sprott, M. 1997. Alcohol and the ability to inhibit behavior in men and women. J. Stud. Alcohol. 58, 600 /605. O’Malley, S., Adamse, M., Heaton, R.K., Gawin, F.H. 1992. Neuropsychological impairment in chronic cocaine abusers. Am. J. Drug Alcohol Abuse 18, 131 /144. Oosterlaan, J., Sergeant, J.A. 1996. Inhibition in ADHD, aggressive, and anxious children: a biologically based model of child psychopathology. J. Abnorm. Child Psychol. 24, 19 /37. Quay, H.C. 1997. Inhibition and attention deficit hyperactivity disorder. J. Abnorm. Child Psychol. 25, 7 /13. Rush, C.R., Baker, R.W., Wright, K. 1999. Acute physiological and behavioral effects of oral cocaine in humans: a dose /response analysis. Drug Alcohol Depend. 55, 1 /12. Schachar, R., Tannock, R., Marriott, M. 1995. Deficient inhibitory control in attention deficit hyperactivity disorder. J. Abnorm. Child Psychol. 23, 411 /437. Skinner, H.A. 1982. The drug abuse screening test. Addict. Behav. 7, 363 /371. Strickland, T.L., Stein, R. 1995. Cocaine-induced cerebrovascular impairment: challenges to neuropsychological assessment. Neuropsychol. Rev. 5, 69 /79. Tannock, R., Schachar, R., Logan, G. 1995. Methylphenidate and cognitive flexibility: dissociated dose effects in hyperactive children. J. Abnorm. Child Psychol. 23, 235 /267. Volkow, N.D., Ding, Y., Fowler, J.S., Wang, G. 1996. Cocaine addiction: hypothesis derived from imaging studies with PET. J. Addict. Diseases 15, 55 /71.
Acute effects of oral cocaine on inhibitory control of behavior in humans Mark T. Fillmore a,*, Craig R. Rush a,b,c, Lon Hays c a
b
Department of Psychology, University of Kentucky, Lexington, KY 40506-0044, USA Department of Behavioral Science, University of Kentucky, Lexington, KY 40539-0086, USA c Department of Psychiatry, University of Kentucky, Lexington, KY 40539-0086, USA
Received 7 March 2001; received in revised form 24 January 2002; accepted 5 March 2002
Abstract Studies of humans show that individuals with histories of cocaine abuse display reduced inhibitory control over behavioral impulses. The present study tested the effects of oral cocaine on the ability to inhibit behavior in humans. Eight adult volunteers (seven men and one woman) with a history of cocaine abuse participated as in-patient volunteers. Response inhibition and response execution were measured by a stop-signal paradigm using a choice reaction time task that engaged subjects in responding to gosignals when stop-signals occasionally informed them to inhibit the response. Subjects’ performance on the task was tested just before and 1 h after a randomized, double-blind administration of 0 mg (placebo), 50, 100, and 150 mg of oral cocaine HCl. Cocaine reduced subjects’ ability to inhibit responses to stop-signals. By contrast, no effect of cocaine was observed on the ability to execute responses in terms of their speed and accuracy. Subjective and physiological effects of cocaine were also observed. Together, the findings indicate that acute administration of cocaine can impair the ability to inhibit behavioral responses at doses that do not affect the ability to respond. These findings are important because they identify a specific disinhibiting effect of cocaine that could help explain the documented association between long-term cocaine use and poor impulse control. # 2002 Elsevier Science Ireland Ltd. All rights reserved. Keywords: Cocaine; Response inhibition; Human
1. Introduction There is growing evidence that long-term habitual cocaine users display neuropsychological impairments and have neuroanatomical abnormalities (e.g. Caine, 1998; Jentsch and Taylor, 1999). Several studies have found that long-term cocaine use is associated with performance deficits on neuropsychological tasks, including tests of attention, memory (e.g. Ardila et al., 1991), intellectual functioning (e.g. O’Malley et al., 1992), learning, problem solving, and perceptual motor speed (e.g. Beatty et al., 1995) (for a review, see Strickland and Stein, 1995). The neuroanatomical basis for these neuropsychological deficits is supported by evidence from neuroimaging studies which show im-
* Corresponding author. Tel.: /1-859-257-4728; fax: /1-859-3231979 E-mail address: [email protected] (M.T. Fillmore).
pairments of frontal lobe functions that are thought to be important in the control and regulation of behavior of these individuals (e.g. Volkow et al., 1996). A principal function of this brain region is to control behavior via inhibitory processes that normally serve to regulate behavior by suppressing or terminating prepotent (i.e. environmentally-triggered) responses (for reviews see Caine, 1998; Jentsch and Taylor, 1999). Some investigators have suggested that dopaminergic activation of the neural circuits by chronic cocaine use could impair inhibitory functions, leading to a loss of control over behavioral impulses (e.g. Lyvers, 2000; Volkow et al., 1996). Several lines of research have pointed to an association between long-term cocaine use and impairments of inhibitory processes (e.g. Ardila et al., 1991; Biggins et al., 1997; Horner et al., 1996; Volkow et al., 1996). Studies of animals and humans provide converging evidence that repeated cocaine use produces persistent cognitive deficits that often involve
03765-8716/02/$ - see front matter # 2002 Elsevier Science Ireland Ltd. All rights reserved. PII: S 0 3 7 6 - 8 7 1 6 ( 0 2 ) 0 0 0 6 2 - 5
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inhibitory processes of attention and behavior. For example, rats show long-term deficits in sensory inhibition following repeated cocaine administration (Boutros et al., 1994, 1997). Studies of humans show that cocaine users are more likely to report symptoms of attention deficit/hyperactivity disorder (ADHD) and other behavioral self-regulation disorders than are individuals with no history of cocaine abuse (Horner et al., 1996; Levin et al., 1998). Evidence for impaired inhibitory processes in cocaine users has been obtained by recent laboratory studies that found individuals with histories of cocaine use display patterns of premature responding (Bauer, 2001) and behavioral perseveration (Lane et al., 1998). Fillmore and Rush (in press) recently showed that cocaine users displayed a reduced ability to inhibit pre-potent responses. That study examined the ability to inhibit and execute behavioral responses in adult cocaine users and in an aged-matched sample with no history of cocaine use. Response inhibition and response execution were measured by a stop-signal choice reaction time (RT) task that required subjects to quickly respond to go-signals and suddenly inhibit responses when a stopsignal was presented. Compared with controls, cocaine users displayed poorer ability to inhibit their behavioral responses, however, no group differences were found in the ability to execute responses. The findings are important because they identify a specific deficit involving behavioral inhibition that could explain the association of cocaine use with disorders of self-regulation, such as ADHD. Despite mounting evidence that chronic cocaine abuse is associated with an impaired ability to inhibit behavior, no research has directly tested the effects of acute cocaine administration on inhibitory control of behavior in humans. Behavioral tasks have been developed to identify the specific inhibitory processes that underlie disorders of self-control. In particular, the development of a cognitive ‘stop-signal paradigm’ has generated considerable research on inhibitory processes and the assessment of specific deficits in the ability to inhibit behavioral responses (Logan and Cowan, 1984; Logan et al., 1984; Schachar et al., 1995). The paradigm is based on a cognitive model of control which asserts that the ability to inhibit an action is determined by the outcome of competitive activating and inhibiting processes elicited by cues to activate or inhibit a response. The time in which each competing process is completed determines the behavioral outcome. If the inhibiting processes are completed first, the response is withheld. If the activating processes finish first, the response is executed. The model is tested by a stop-signal task which is essentially a dual task that elicits conflicting goand stop-processes in individuals by engaging them in responding to go-signals, but occasionally requiring them to inhibit the response when a stop-signal occurs.
The stop-signal task directly measures the subject’s ability to inhibit (i.e. suppress) a pre-potent behavioral response in the presence of conflicting go- and stopsignals. The task is unique because of its direct assessment of the ability to inhibit a pre-potent action, and thus, it is thought to provide a more direct assessment of inhibitory control than other methods and tests which claim to assess inhibition (Quay, 1997). There is evidence showing that stop-signal tasks are sensitive to impairments of behavioral inhibition that underlie self-control. Studies using the stop-signal paradigm have found that behavioral inhibition was reduced in individuals with self-control disorders, such as Oppositional/Defiant Disorder and ADHD (e.g. Oosterlaan and Sergeant, 1996; Schachar et al., 1995). Recent studies have used the stop-signal paradigm to show that behavioral inhibition is reduced in response to acute doses of alcohol and the sedative-hypnotic drug, triazolam (Fillmore and Vogel-Sprott, 1999, 2000; Fillmore et al., 2001). The hypothesis that acute cocaine administration might also impair the ability to inhibit behavior has not been directly tested. However, such evidence would be important because it could provide some explanation for observations of poor impulse control in individuals with a history of cocaine abuse. The present study was designed to test the hypothesis that acute cocaine administration impairs the basic ability to inhibit behavior. The study examined adult volunteers with a history of cocaine use. The stop-signal paradigm was used to measure the acute effects of three doses of oral cocaine HCL (50, 100, and 150 mg) on inhibitory control in a double-blind, placebo-controlled study.
2. Method 2.1. Subjects Eight adult volunteers (seven men and one woman) with histories of cocaine use participated in the study. Subjects’ ages ranged between 25 and 45 years (mean/ 35.2, S.D. /8.2). The racial make-up of the sample was African American (n/7) and Caucasian (n /1). The sample was drawn from adults in the Lexington, KY metropolitan area who volunteered to participate in studies of behavioral pharmacology. Participants were recruited via notices posted on community bulletin boards and by word of mouth. The study was approved by the University of Kentucky Medical Institutional Review Board, and subjects provided their written, informed consent prior to participating. Volunteers had to have: a minimum of grade 8 education, demonstrated reading ability, no uncorrected vision or auditory problems, and no self-reported major Axis I psychiatric disorder, head trauma, or other CNS
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injury. Volunteers also had to meet all of the following criteria: (1) a score of at least four on the drug abuse screening test (Skinner, 1982); (2) self-report of past week cocaine use; (3) positive test for the presence of cocaine or benzoylecgonine in urine-analysis (ONTRAK Abusscreens, Roche Diagnostic Systems, Nutley, NJ). All subjects smoked cocaine in the form of crack. No volunteer was currently in, or seeking, treatment for their substance use. The sample reported an average frequency of cocaine use of 3.2 days in the past week (S.D. /2.4), and 14.8 days in the past month (S.D./9.1). The sample reported smoking a mean daily amount of 2.4 rocks of crack cocaine per day (S.D. / 2.3) and spending an average of $24.0 per day on cocaine (S.D./21.1). The sample reported using cocaine for an average period of 9.2 years (S.D. /5.6). All volunteers also reported using nicotine, alcohol, and caffeine. The mean total weekly alcohol consumption for this sample was 44.6 standard drinks (S.D. /35.3). The sample reported smoking an average of 19.2 (S.D./9.6) cigarettes per day. The sample reported an average, total daily caffeine consumption of 161.4 mg (S.D./241.6). Most of the sample also reported some past month use of marijuana (n/7), with an average monthly frequency of 11.0 times (S.D. /13.9). No other drug-use, including prescription medication, was reported by the sample. Prior to participation, all potential volunteers were examined by a psychiatrist and completed a comprehensive medical history questionnaire, drug-use questionnaire, mini-mental status examination (Folstein et al., 1975), and vital sign assessment. Routine, clinical laboratory blood chemistry tests and an electrocardiogram were conducted on all potential volunteers. Potential volunteers with histories of serious physical disease, current physical disease, impaired cardiovascular functioning, chronic obstructive pulmonary disease, seizure, head trauma, CNS tumors, or past histories of psychiatric disorder (i.e. Axis I, DSM IV) other than substance use or dependence were excluded from participation. Female volunteers had to have an effective form of birth control in use. All subjects were in good health with no contraindications to stimulant drugs.
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required to respond to each letter as quickly as possible by pressing one of two adjacent keys on the computer keyboard using the index and middle fingers of the preferred hand. One key (the period key) was pressed to indicate that either ‘A’ or ‘C’ appeared, and the adjacent key (the forward slash key) was pressed to indicate that either ‘B’ or ‘D’ appeared. A letter was displayed for 500 ms and the computer screen was blank for a 2.5-s interstimulus interval before the next letter was displayed. This provided a 3-s period in which the subject could respond to the letter. A single test consisted of 176 trials in which each of the four letter stimuli were presented equally often. A stop-signal occurred on 27% of the 176 trials (i.e. 48 trials) during a test. The stop-signal was a 500-ms, 900Hz tone generated by the computer at a comfortable listening level. Subjects were required to withhold (i.e. inhibit) any response on trials in which a stop-signal sounded. Stop-signals were presented 12 times, at each of four delays after the onset of a letter: 50, 150, 250, and 350 ms. These delays were based on previous research using this task that showed the probability of inhibiting decreased in an orderly, linear fashion as the stop-signal delays increased from 50 to 350 ms (Fillmore et al., 2001; Fillmore and Rush, in press). The order of letters, stop-signals, and delays was random. Trials always began with a 500-ms preparation interval in which a fixation point (#) appeared in the center of the computer screen. A test was completed in approximately 10 min. Response inhibition was determined by the proportion of responses that an individual inhibited on stopsignal trials, and by the estimated mean latency to inhibit a response (Section 2.4). Response execution to go-signals was measured by the mean RT to the gosignals during a test (i.e. the average time from the onset of letter presentation until a computer key press). Shorter RTs (i.e. faster responding) indicated greater response execution. Choice response errors to go-signals were also recorded. The inhibition and execution measures are highly reliable across trials (alpha coefficients /0.90) and stable over sessions (test /retest reliabilities /0.85) (Mulvihill et al., 1997). The task was operated by Micro Experimental Laboratory Software (Psychology Software Tools Inc., Pittsburgh, PA).
2.2. Apparatus and materials 2.2.1. Stop-signal task Response inhibition and response execution were measured by a stop-signal choice RT task performed on a PC computer. The task requires subjects to make quick, choice key press responses to visually-presented go-signals and to inhibit any response when an auditory stop-signal is suddenly sounded. The go-signals were four 1.5-cm letters (A, B, C, and D), presented one at a time in the center of a computer monitor. Subjects were
2.2.2. Addiction research center inventory The short form of the addiction research center inventory (ARCI) consisted of 49 true/false questions and contained five major subscales: morphine /benzedrine group (a measure of euphoria); pentobarbital, chlorpromazine, alcohol group (a measure of sedation); lysergic acid diethylamide (a measure of dysphoria); benzedrine group (BG); and amphetamine (A) scales (empirically-derived amphetamine-sensitive scales) (Martin et al., 1971; Jasinski, 1977). The inventory
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items were presented individually on a computer and subjects used the computer mouse to endorse each item as either true or false. The computer generated total scores for each subscale. 2.2.3. Drug effect questionnaire This questionnaire consisted of 20 items that were presented individually on the computer monitor. Subjects rated each item using the computer mouse to select among one of five response options: not at all, a little bit, moderately, quite a bit, and very much (scored numerically from 0 to 4, respectively). The items rated were: any effects, active/alert/energetic, bad effects, bad mood, confused-dazed/spaced out, crave cocaine, good effects, good mood, high, irregular heart beat/racing, like, anxious/nervous, pay for this drug, relaxed/carefree, rush, shaky/jittery, stimulated, suspicious, take this drug again, and dry mouth. 2.3. General procedures Subjects resided on the General Clinical Research Center (GCRC) at the University of Kentucky Hospital while they participated in the study. The GCRC unit is a fully-supported NIH-funded medical facility for research requiring medical support. Subjects resided in private rooms during the study. Subjects were informed that the study examined the effects of various doses of cocaine on mood and behavior. They were given no information about the exact doses examined in the study, or the hypotheses concerning effects of the drug. Subjects resided on the unit for 7 days (Day 1, admission; Day 2, acclimation/practice; Days 3/6, dose administration test sessions; and Day 7, discharge). 2.3.1. Admission and acclimation/practice sessions On the day of admission to the unit, subjects provided a urine sample which was screened for the presence of cocaine/benzoylecgonine, benzodiazepines, barbiturates, tetrahydrocannabinol, amphetamine, and opiates. A breathalyser obtained a breath sample from subjects to ensure a zero blood alcohol concentration prior to admission. A psychiatrist reviewed the subject’s record and conducted a physical examination. During the acclimation/practice day, subjects were acquainted with the general testing procedure. All testing occurred in the subject’s room. The acclimation/practice session required approximately 5 h to complete. The experimenter explained the requirements for the stop-signal task. Subjects were instructed to respond to the letters (i.e. go-signals) as quickly and as accurately as possible, and to try to withhold their response if they heard a tone sound. Subjects were told to always respond as quickly as possible and not to wait for the possibility that a tone might sound (for verbatim instructions, see Fillmore et al., 2001). The subject then
performed practice tests on the stop-signal task. Subjects were also familiarized with the ARCI, drug effect questionnaire (DEQ), and physiological monitoring equipment. Subjects completed the questionnaires and had their heart rate and blood pressure measured by an automated blood pressure monitor at intermittent intervals over the 5-h period. 2.3.2. Dose administration sessions The four dose administration sessions were conducted on consecutive days, usually beginning early in the week (Monday, Tuesday, or Wednesday). Prior to beginning a session, subjects ate a low-fat hospital-prepared breakfast at 7:00 am. Those who smoked tobacco cigarettes, were allowed to smoke one cigarette between 7:30 am and 8:00 am. Subjects were not allowed to smoke during the sessions. Subjects provided a urine sample prior to each session that was screened for the presence of benzodiazepines, barbiturates, tetrahydrocannabinol, amphetamine, and opiates. A breathalyser was used to obtain a breath sample from subjects to ensure a zero blood alcohol concentration. 2.3.2.1. Baseline testing and dose administration. The testing began at 9:00 am with the baseline test (i.e. precapsule) of subjects’ stop-signal performance. After the stop-signal test, subjects completed the ARCI, DEQ, and then had their physiological measures recorded. Immediately after baseline testing, subjects received one of four doses of cocaine HCl: 0 (placebo), 50, 100, and 150 mg. All doses were administered orally in a doubleblind fashion. Drug doses were prepared by encapsulating 50-mg cocaine HCl USP (Mallinckrodt, St. Louis, MO) in a size 00 green opaque capsule combined with lactose filler. Placebo capsules contained only lactose. Subjects ingested three capsules during each dose administration session. The combination of active and placebo capsules determined the dose. Capsules were taken orally with 150 ml of water. Dose order across the four dose administration sessions was random. An oral administration route was chosen for this initial study because it provides reliable double-blind control over drug delivery, and avoids potential side-effects that can arise from nasal administrations. All doses were prepared by the University of Kentucky Investigational Pharmacy. 2.3.2.2. Post-capsule testing. Subjects performed the stop-signal task at 50 min post-capsule. Because the test required 10 min to complete, the assessment spanned 50/60 min post-capsule. This time interval was chosen to maximize the likelihood of observing a drug effect on inhibitory control. Time-course analyses of oral cocaine dose effects on physiological and subjective measures typically show peak effects approximately 1 h following administration (for review, see
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Bigelow and Walsh, 1998). Subjects completed the ARCI and DEQ at 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 4.0, and 5.0 h post-capsule. Physiological measures were recorded at 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, and 5.0 h post-capsule. No volunteers experienced any adverse reaction to the drug or any procedure. Volunteers were paid $240 for their participation. 2.4. Criterion measures and data analyses of stop-signal performance 2.4.1. Measures of response inhibition The ability to inhibit a response can be impaired in two important ways (Logan, 1994; Tannock et al., 1995): (1) The initiation of the inhibitory process might fail to occur occasionally, thus, resulting in less stopsignal inhibitions; (2) The completion of the inhibitory process might be slowed, so that it takes longer to inhibit a response to a stop-signal. The stop-signal paradigm provides a method for assessing these two criterion measures of response inhibition. 2.4.1.1. Probability of inhibition (p-inhibition). The proportion of successfully inhibited responses on the 48 stop-signal trials for a test provided a general measure of the ability to inhibit responses for a subject. Smaller proportions indicated a lower probability of inhibiting a response to a stop-signal (poorer inhibitory control of behavior). The proportion of successfully inhibited responses was also examined at each stopsignal delay (i.e. proportion of inhibited responses to the 12 stop-signals presented at each of the four delays). 2.4.1.2. Stop-signal reaction time. Deficits in inhibitory control might arise because more time is required to inhibit a response. The estimated mean time (in ms) required for a subject to inhibit responses to stop-signals (stop-signal reaction time, SSRT) was measured in the present research. The method for calculating SSRT was based on the subject’s probability of inhibiting responses to stop-signals and the distribution of RTs to go-signal trials. SSRT is the time needed to inhibit the pre-potent response once the stop-signal occurs. The measure is based on an estimate of how long the stop-signal can be delayed after the go-signal before the subject can no longer inhibit the response. As a general observation, the time required to inhibit is less than the time required to respond (Logan and Cowan, 1984). This is evident by the fact that individuals can successfully inhibit a response to a stop-signal that does not occur until some time after the go-signal. SSRTs are generally related to the number of successfully inhibited responses on a test, with shorter SSRTs associated with a greater number of inhibited responses. However, the SSRT also provides latency information concerning the speed of the inhibitory processes. Longer SSRTs, resulting from
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a general slowing of the inhibitory processes, indicate weak inhibitory control over behavior. The method for calculating SSRT was based on a ‘race’ model of inhibitory control. The method is summarized here and is explained in detail elsewhere (Logan, 1994). The SSRT measure is based on the notion that acts of control (like inhibiting an inappropriate response) take time to complete. Inhibiting an action is an internal response and is not observable. Although the latency to inhibit a response cannot be directly observed, the mean time required to complete the process during a test can be estimated using the distribution of observable RTs to go-signal trials and the probability of inhibiting to stop-signals as a function of their delay from the onset of the go-signal. SSRT is the difference between: (1) the point at which the stop-signal is presented; and (2) the point at which the inhibitory process is completed. The point at which the inhibitory process is completed was calculated using the distribution of observable RTs to the 128 go-signal trials (i.e. when no stop-signals were presented). After removing any outlier RTs that were 2.5 SDs of a subject’s mean RT for a test, the distribution of the remaining RTs scores for that participant’s test was rank-ordered from shortest to longest. To estimate the SSRT, certain RT scores from this ranked distribution were selected based on the subjects’ probability of failing to inhibit at each stop-signal delay. The n th RTs scores were chosen by multiplying the number of responses to go-signals by the probability of failure to inhibit at each of the four stop-signal delays. This produced four RTs that represented the times at which the stopping process was completed relative to the onset of the go-signal. The SSRT was determined by subtracting the appropriate stop-signal delay from each nth RT. The four resulting values were averaged to yield a measure of the participant’s mean SSRT for the test. 2.4.2. Measures of response execution The stop-signal task also provides measures of response execution. Responses displayed during gosignal trials (i.e. when no stop-signal occurs) yielded two important criterion measures of response execution: go-signal reaction time (RTgo) and go-signal response accuracy. 2.4.2.1. Go-signal reaction time. The strength of response execution to go-signals was measured by a subject’s mean RT (ms) to the 128 go-signal trials presented during a test (i.e. the average time from the onset of go-signals until a computer key press). This produced a mean RTgo score for a participant for each test. All analyses involving RTgo scores were performed on observed mean RTgo scores, and on the mean RTgo scores obtained after removing outlier trial RTs that were 2.5 SDs from the mean RTgo score on a test. On
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average, less than 2% of trials on a test had outlier RT values. Analyses based on actual RTgo scores and trimmed RTgo scores yielded identical conclusions. The Results section reports analyses based on trimmed RTgo scores. No RT data were recorded for any nonresponses to the 128 go-signal trials (i.e. failing to respond to a letter on a go-signal trial). Non-responses were infrequent and, on average, subjects responded to 98.5% of go-signal trials. 2.4.2.2. Go-signal response accuracy. The percentage of choice response errors on go-signal trials was also recorded. These errors occur when a subject presses the incorrect key in response to a letter presentation during a go-signal trial. The errors generally reflect failures of attention or information processing. Choice response errors during a test are rare and typically occur on less than 5% of all go-signal responses (Fillmore and Vogel-Sprott, 1999, 2000; Logan, 1994). 2.4.3. Data analyses of stop-signal performance The treatment effects on the four criterion measures of stop-signal performance were analyzed by separate 2 Test (Baseline Test vs. Post-capsule Test) /4 Dose (0, 50, 100, and 150 mg) analyses of variance (ANOVA). Treatment effects on p-inhibition were also examined by stop-signal delay. Predicted effects on inhibitions were analyzed by simple-effects comparisons of the omnibus ANOVA. All multiple comparisons were based on twotailed probabilities. 2.5. Criterion measures and data analyses of physiological and subjective effects Two sets of analyses examined physiological and subjective effects. First, peak effect data were calculated and analyzed by one-factor repeated measures ANOVA with Dose (0, 50, 100, and 150 mg cocaine) as the repeated measures factor. Peak effect was defined as the maximum value from 0.5 to 5.0 h after drug administration. Second, time-course data were analyzed by twofactor, repeated measures ANOVA with Dose (0, 50, 100, and 150 mg) and Time (pre-capsule, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, and 5.0 h for the physiological measures; pre-capsule, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 4.0, and 5.0 h for the subjective effect measures).
Table 1 Mean stop-signal task performance measures of subjects at baseline and test under four doses: 0, 50, 100, and 150 mg cacaine HCL Dose Baseline
Test
P-inhibition
0 50 100 150
0.55 0.60 0.59 0.62
0.57 0.53 0.51 0.50
SSRT (ms)
0 50 100 150
284.0 279.5 292.4 279.7
(41.6) (47.5) (73.6) (41.1)
311.7 330.5 312.3 321.5
(59.4) (86.8) (77.4) (81.1)
RTgo (ms)
0 50 100 150
499.4 528.2 532.5 517.4
(68.3) (50.8) (95.0) (84.9)
519.7 526.4 513.3 518.2
(55.6) (78.9) (77.6) (109.3)
Percentage choice response errors
0 50 100 150
4.1 3.9 4.9 3.9
(0.24) (0.19) (0.25) (0.18)
(3.0) (2.9) (4.3) (4.4)
4.0 3.6 3.5 3.8
(0.22) (0.21) (0.21) (0.16)
(3.2) (3.4) (3.2) (2.6)
Standard deviation is in parentheses.
dose interaction (F3,21 /5.0, P /0.009). Table 1 presents the mean p-inhibition scores for the interaction which is illustrated in Fig. 1. The figure shows that the probability of inhibiting a response at test decreased compared with baseline under all three active doses, with the largest decrement observed under the 150 mg dose. Four simple-effects tests examined the interaction. The tests compared the mean p-inhibition score at postcapsule test with the mean score at baseline under each dose condition. These simple-effect comparisons were chosen to provide the most information concerning the hypotheses while minimizing the family-wise error rate (Howell, 1987). Significant differences from baseline to test were obtained under the 150 mg (F1,21 /21.1, P /
3. Results 3.1. Dose effects on stop-signal measures of response Inhibition 3.1.1. Probability of inhibition A 2 (Test) /4 (Delay) /4 (Dose) ANOVA of subjects’ p-inhibition scores obtained a significant test by
Fig. 1. Mean probability of inhibiting a response (p-inhibition) to 48 stop-signals during baseline (pre-capsule) and at test (post-capsule) under four doses of oral cocaine HCl: placebo (PL), 50, 100, and 150 mg. Vertical capped bars indicate standard error of the mean.
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0.002) and 100 mg dose conditions (F1,21 /9.0, P / 0.020). The baseline-versus-test difference under the 50 mg dose approached significance (F1,21 /5.0, P / 0.060). No significant difference was observed under placebo (F1,21 /1.2, P /0.317). Thus, based on this measure of response inhibition, the findings support the hypothesis that acute cocaine administration (100 and 150 mg) can reduce inhibitory control. The ANOVA also obtained a significant main effect of stop-signal delay (Delay) on subjects’ p-inhibition scores (F3,21 /37.0, P B/0.001). There was no significant interactions of delay with dose or test (P s/0.479). To explore the main effect of delay on subjects’ p-inhibition, their scores were plotted as a function of stopsignal delay. Research shows that the probability of inhibiting a response diminishes as the stop-signal delay increases because less time is available to inhibit the prepotent response (Logan, 1994). Fig. 2 plots the mean p-
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inhibition score at each stop-signal delay for baseline and at test under each of the four dose conditions. As expected, the probability of inhibiting diminished as a function of increasing stop-signal delay. This function was evident at baseline and at test under all dose conditions. With respect to cocaine effects on inhibition, the figure shows that the baseline-to-test reduction in pinhibition scores under active cocaine doses was fairly similar at all stop-signal delays. Supplemental analyses examined the reliability of the p-inhibition measure over the four test sessions. Previous research has shown this measure to be reliable over time in non-drug-abusing populations (Mulvihill et al., 1997). However, no research had examined the reliability of response inhibition in a drug-abusing population. The mean p-inhibition scores on the baseline test ranged from 0.552 to 0.617. Thus, on average, subjects successfully inhibited responses on approxi-
Fig. 2. Mean probability of inhibiting a response (p-inhibition) to 12 stop-signals at each stop-signal delay at baseline and at test under four doses of oral cocaine HCl: placebo (PL), 50, 100, and 150 mg. Vertical capped bars indicate standard error of the mean.
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mately one half of the 48 stop-signal trials. Reliability of response inhibitions over the four dose conditions was determined by examining subjects’ p-inhibition at baseline over the four sessions. Reliability was estimated by Coefficient Alpha, based on the intraclass correlation among baseline p-inhibition scores across dose conditions (McGraw and Wong, 1996). The analysis obtained a coefficient of 0.96, indicating that individual differences among subjects’ baseline p-inhibition scores showed a high degree of consistency over the four dose conditions. 3.1.2. Stop-signal reaction time A 2 (Test) /4 (Dose) ANOVA of subjects’ SSRT scores obtained a significant main effect of Test (F1,7 / 17.5, P /0.004). No significant main effect of dose (F3,21 /0.1, P /0.985), or test by dose interaction (F3,21 /0.8, P /0.524), was observed. The mean SSRT scores at baseline and test are presented in Table 1. The table shows that the main effect of test was due to an increase in subjects’ SSRT from baseline to test under each dose condition. 3.2. Dose effects on stop-signal measures of response execution 3.2.1. Go-signal reaction time A 2 (Test)/4 (Dose) ANOVA of subjects’ RTgo scores obtained no significant main effects or interaction (P s/0.397). Table 1 presents the mean RTgo scores and shows no consistent change in the mean time to respond to go-signals over the four dose conditions. 3.2.2. Go-signal response accuracy A 2 (Test) /4 (Dose) ANOVA of subjects’ percent choice response errors to go-signal trials obtained no significant main effects or interaction (P s /0.171). Table 1 shows that the mean percent of choice response errors remained fairly consistent from baseline to test across all dose conditions. The table also shows that the percentage of response errors was low, indicating a high degree of response accuracy. 3.3. Physiological measures Fig. 3 shows that heart rate (F3,21 /6.6, P /0.003) and systolic blood pressure (F3,21 /4.1, P /0.020) increased under active dose conditions. Measures of diastolic blood pressure showed no significant effect of dose (P /0.504). Mean (SD) peak diastolic blood pressure measures across 0, 50, 100, and 150 mg dose conditions were: 79.3 (6.5), 79.8 (6.1), 81.4 (4.2), and 82.1(4.5) mm Hg, respectively. Dose by time ANOVAs of heart rate and blood pressure (systolic and diastolic) obtained no significant dose by time interactions (P s/ 0.193).
3.4. Subjective effect measures Fig. 4 shows that the DEQ ratings of the items, Any Effect (F3,18 /4.4, P /0.018) and Rush (F3,18 /3.3, P /0.045), increased under active dose conditions. No other DEQ or ARCI items showed significant dose effects (P s/0.05). Dose by time ANOVAs of the ARCI subscales obtained significant dose by time interactions for two subscales: amphetamine (F24,144 /1.8, P / 0.021) and BG (F24,144 /1.6, P /0.040). These timecourse effects are illustrated in Fig. 5. The figure shows generally similar ratings under all four doses. However, increases in subjective ratings from pre-capsule levels to 60 min post-capsule are most apparent under the active dose conditions. No such increases were observed under placebo.
4. Discussion This study used a choice RT stop-signal task to test the effects of oral cocaine on the inhibitory control of behavior in adult cocaine abusers. The results showed that the drug reduced the proportion of successfully inhibited responses to stop-signals. No effect of cocaine was observed on the estimated time required to inhibit a response. The drug also had no effect on subjects’ ability to execute responses, in terms of their speed and accuracy. Together, these findings indicate that acute administration of cocaine can impair the ability to inhibit behavioral responses at doses that do not affect the ability to execute responses. Other studies have shown that CNS depressant drugs, such as alcohol and the sedative/hypnotic, triazolam, also reduce response inhibitions on the stop-signal task (e.g. Fillmore and Vogel-Sprott, 1999; Fillmore et al., 2001). These drugs also increased the estimated time to inhibit a response (SSRT), and this effect could account for the reduced inhibitions. Slowing the inhibitory processes makes it less likely that they can be completed before response execution occurs (Logan and Cowan, 1984). Cocaine did not increase SSRT in the present study. This suggests that the drug might reduce inhibitions by some other mechanism that does not involve slowing of inhibitory processes. Instead cocaine might disrupt the initiation of the inhibitory process so that it fails to occur occasionally, resulting in less inhibitions to stop-signals. This assumption appears consistent with the evidence that cocaine reduced inhibitions to a similar degree regardless of stop-signal delay. Any slowing of the inhibitory processes might be expected to produce a greater reduction of inhibitions at the longer delays when there is the least time to inhibit responses. This was not observed in the present study. This study examined the effects of low doses of oral cocaine (i.e. 50, 100, 150 mg). These doses had little
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Fig. 3. Mean peak heart rate and systolic blood pressure under four doses of oral cocaine HCl: placebo (PL), 50, 100, and 150 mg. Vertical capped bars indicate standard error of the mean.
effect on subjective ratings, and produced only moderate increases in heart rate and blood pressure. Other research has examined higher doses (e.g. 300 mg) and found more robust effects on subjective ratings and physiological indices (e.g. Rush et al., 1999). However, there are a couple of important reasons for examining low dose effects of cocaine on inhibitory control. The low doses used in the study allowed effects on inhibitory control to be examined without any concomitant speeding effects on reaction time (RTgo), which likely occur at higher doses when the stimulant effects of cocaine become more pronounced (Farre´ et al., 1993; Higgins et al., 1990). Evidence that inhibitory control can be reduced by low doses indicates that this behavioral function might be particularly sensitive to the CNS effects of cocaine, and that it can be impaired independent of any changes in response execution. The research also examined some important measurement issues regarding the assessment of inhibitory
control. First, the study showed that subjects displayed a high degree of consistency over sessions in their baseline ability to inhibit responses. Coefficient Alpha showed a high degree of reliability of subjects’ baseline p-inhibition scores over daily sessions. This finding is consistent with the assumption that the task assesses a characteristic ability to inhibit and control behavior. Second, the range of baseline p-inhibition scores (0.55/ 0.62) is comparable to the mean p-inhibition of a larger sample of cocaine abusing subjects observed in another recent study in our lab (Fillmore and Rush, in press). Third, the study also showed that response inhibition diminished as a function of increasing the stop-signal delay. Fig. 2 shows the negative slope functions that relate the probability of inhibiting a response to the stop-signal delays. The slopes are consistent with previous studies showing that stop-signal latencies affect the probability of response inhibition (Fillmore and Rush, in press, Logan and Cowan, 1984). Moreover, the
Fig. 4. Mean peak DEQ ratings of items, Any Effects and Rush under four doses of oral cocaine HCl: placebo (PL), 50, 100, and 150 mg. Vertical capped bars indicate standard error of the mean.
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Fig. 5. Mean ARCI ratings for Amphetamine and BG subscales pre-capsule (Pre) and at intervals following the administration of four doses of oral cocaine HCl: placebo (PL), 50, 100, and 150 mg.
presence of these slopes provides verification that subjects understood the task requirements and followed instructions. In studies of other populations, such as young children, task performance can be affected by inattention and by random response strategies, owing to a lack of motivation or interest on the part of the subject (e.g. Schachar et al., 1995; Tannock et al., 1995). Such response styles are detected by the slope function. Randomly inhibiting and executing responses generates a flat slope function because inhibitions are equally likely to occur at all stop-signal delays under such a strategy. This was not observed in the present study. Rather, the negative slopes demonstrate that response inhibition during baseline and test was under some degree of stimulus control of the stop-signals in all dose conditions. The present findings point to promising new performance measures for understanding cocaine effects on behavior. Evidence that cocaine administration can directly reduce inhibitory control could help explain the association between long-term cocaine use and impairments of inhibitory processes (e.g. Ardila et al., 1991; Biggins et al., 1997; Fillmore and Rush, in press, Horner et al., 1996; Volkow et al., 1996). The evidence appears consistent with the suggestion that repeated dopaminergic activation by chronic cocaine use can impair inhibitory functions and lead to a loss of control over behavioral impulses (e.g. Lyvers, 2000; Volkow et al., 1996). However, the present findings are new and raise many questions concerning behavioral control and cocaine use. Higher doses must be examined to determine how inhibitory control is affected when stimulant
effects on behavior are also present. In addition, the effects of cocaine via other administration routes (e.g. intranasal) should be examined to test behavioral effects following more rapid drug absorption. These and other issues await to be explored.
Acknowledgements This research was supported by grants DA14079 and DA10325 from the National Institute on Drug Abuse.
References Ardila, A., Rosselli, M., Strumwasser, S. 1991. Neuropsychological deficits in chronic cocaine abusers. Int. J. Neurosci. 57, 73 /79. Bauer, L.O. 2001. Antisocial personality disorder and cocaine dependence: their effects on behavioral and electroencephalographic measures of time estimation. Drug Alcohol Depend. 63, 87 /95. Beatty, W.W., Katzung, V.M., Moreland, V.J., Nixon, S.J. 1995. Neuropsychological performance of recently abstinent alcoholics and cocaine abusers. Drug Alcohol Depend. 37, 247 /253. Bigelow, G.E., Walsh, S.L. 1998. Evaluation of potential pharmacotherapies: response to cocaine challenge in the human laboratory. In: Higgins, S.T., Katz, J.L. (Eds.), Cocaine Abuse: Behavior, Pharmacology, and Clinical Applications. Academic Press, New York, pp. 209 /238. Biggins, C.A., MacKay, S., Clark, W., Fein, G. 1997. Event-related potential evidence for frontal cortex effects of chronic cocaine dependence. Soc. Biol. Psychiat. 42, 472 /485. Boutros, N.N., Uretsky, N., Bernston, G., Bornstein, R. 1994. Effects of cocaine on sensory inhibition in rats: preliminary data. Soc. Biol. Psychiat. 36, 242 /248.
M.T. Fillmore et al. / Drug and Alcohol Dependence 67 (2002) 157 /167 Boutros, N.N., Uretsky, N., Lui, J.J., Millana, R.B. 1997. Effects of repeated cocaine administration on sensory inhibition in rats: preliminary data. Soc. Biol. Psychiat. 41, 461 /466. Caine, S.B. 1998. Neuroanatomical basis of the reinforcing stimulus effects of cocaine. In: Higgins, S.T., Katz, J.L. (Eds.), Cocaine Abuse: Behavior, Pharmacology, and Clinical Applications. Academic Press, New York, pp. 21 /50. Farre´, M., De Le Torre, R., Llorente, M., Lamas, X., Ugena, B., Segura, J., Camı´, J. 1993. Alcohol and cocaine interactions in humans. J. Pharmacol. Exp. Ther. 266, 1364 /1373. Fillmore, M.T., Rush, C.R., in press. Impaired inhibitory control in chronic cocaine abusers. Drug Alcohol Depend. Fillmore, M.T., Rush, C.R., Kelly, H.K., Hays, L. 2001. Triazolam impairs inhibitory control of behavior in humans. Exp. Clin. Psychopharmacol. 7, 363 /371. Fillmore, M.T., Vogel-Sprott, M. 1999. An alcohol model of impaired inhibitory control and its treatment in humans. Exp. Clin. Psychopharmacol. 7, 49 /55. Fillmore, M.T., Vogel-Sprott, M. 2000. Response inhibition under alcohol: effects of cognitive and motivational conflict. J. Stud. Alcohol 61, 239 /246. Folstein, M., Folstein, S., Hugh, P. 1975. ‘‘Mini-Mental State’’ */a practical method for grading the cognitive state of patients for the clinician. J. Psychiat. Res. 12, 189 /198. Higgins, S.T., Bickel, W.K., Hughes, J.R., Lynn, M., Capeless, M.A., Fenwick, J.W. 1990. Effects of intranasal cocaine on human learning, performance and physiology. Psychopharmacology 102, 451 /458. Horner, B., Scheibe, K., Stine, S. 1996. Cocaine abuse and attentiondeficit hyperactivity disorder: implications of adult symptomatology. Psych. Addict. Behav. 10, 55 /60. Howell, D.C. 1987. Statistical Methods for Psychology, Second ed.. Duxbury Press, Boston. Jasinski, D. 1977. Assessment of the abuse potentiality of morphinelike drugs (methods used in man). In: Morton, W.R. (Ed.), Drug Addiction I. Springer, New York, pp. 197 /258. Jentsch, J.D., Taylor, J.R. 1999. Impulsivity resulting from frontostriatal dysfunction in drug abuse: implication for the control of behavior by reward-related stimuli. Psychopharmacology 146, 373 /390. Lane, S.D., Cherek, D.R., Dougherty, D.M., Moeller, F.G. 1998. Laboratory measurement of adaptive behavior change in humans with a history of substance dependence. Drug Alcohol Depend. 51, 239 /252. Levin, F.R., Evans, S.M., Kleber, H.D. 1998. Prevalence of adult attention-deficit hyperactivity disorder among cocaine abusers seeking treatment. Drug Alcohol Depend. 52, 15 /25.
167
Logan, G.D. 1994. On the ability to inhibit thought and action: a users’ guide to the stop-signal paradigm. In: Dagenbach, D., Carr, T.H. (Eds.), Inhibitory Processes in Attention, Memory, and Language. Academic Press, San Diego, CA, pp. 189 /239. Logan, G.D., Cowan, W.B. 1984. On the ability to inhibit thought and action: a theory of an act of control. Psychol. Rev. 91, 295 /327. Logan, G.D., Cowan, W.B., Davis, K.A. 1984. On the ability to inhibit simple and choice reaction time responses: a model and a method. J. Exp. Psychol: Hum. Percept. Perform. 10, 276 /291. Lyvers, M. 2000. ‘‘Loss of control’’ in alcoholism and drug addiction: a neuroscientific interpretation. Exp. Clin. Psychopharmacol. 8, 225 /249. Martin, W.R., Sloan, J.W., Sapiro, J.D., Jasinski, D.R. 1971. Physiologic, subjective, and behavioral effects of amphetamine, methamphetamine, ephedrine, phenmetrazine, and methylphenidate in man. Clin. Pharmacol. Ther. 12, 245 /258. McGraw, K., Wong, S.P. 1996. Forming inferences about some intraclass correlation coefficients. Psychol. Methods 1, 30 /46. Mulvihill, L.E., Skilling, T.A., Vogel-Sprott, M. 1997. Alcohol and the ability to inhibit behavior in men and women. J. Stud. Alcohol. 58, 600 /605. O’Malley, S., Adamse, M., Heaton, R.K., Gawin, F.H. 1992. Neuropsychological impairment in chronic cocaine abusers. Am. J. Drug Alcohol Abuse 18, 131 /144. Oosterlaan, J., Sergeant, J.A. 1996. Inhibition in ADHD, aggressive, and anxious children: a biologically based model of child psychopathology. J. Abnorm. Child Psychol. 24, 19 /37. Quay, H.C. 1997. Inhibition and attention deficit hyperactivity disorder. J. Abnorm. Child Psychol. 25, 7 /13. Rush, C.R., Baker, R.W., Wright, K. 1999. Acute physiological and behavioral effects of oral cocaine in humans: a dose /response analysis. Drug Alcohol Depend. 55, 1 /12. Schachar, R., Tannock, R., Marriott, M. 1995. Deficient inhibitory control in attention deficit hyperactivity disorder. J. Abnorm. Child Psychol. 23, 411 /437. Skinner, H.A. 1982. The drug abuse screening test. Addict. Behav. 7, 363 /371. Strickland, T.L., Stein, R. 1995. Cocaine-induced cerebrovascular impairment: challenges to neuropsychological assessment. Neuropsychol. Rev. 5, 69 /79. Tannock, R., Schachar, R., Logan, G. 1995. Methylphenidate and cognitive flexibility: dissociated dose effects in hyperactive children. J. Abnorm. Child Psychol. 23, 235 /267. Volkow, N.D., Ding, Y., Fowler, J.S., Wang, G. 1996. Cocaine addiction: hypothesis derived from imaging studies with PET. J. Addict. Diseases 15, 55 /71.