Risky decision-making is associated with impulsive action and sensitivity to first-time nicotine exposure

Accepted Manuscript Title: Risky decision-making is associated with impulsive action and sensitivity to first-time nicotine exposure Authors: Daniel B.K. Gabriel, Timothy G. Freels, Barry Setlow, Nicholas W. Simon PII: DOI: Reference:

S0166-4328(18)31011-8 https://doi.org/10.1016/j.bbr.2018.10.008 BBR 11591

To appear in:

Behavioural Brain Research

Received date: Revised date: Accepted date:

13-7-2018 4-10-2018 4-10-2018

Please cite this article as: Gabriel DBK, Freels TG, Setlow B, Simon NW, Risky decision-making is associated with impulsive action and sensitivity to first-time nicotine exposure, Behavioural Brain Research (2018), https://doi.org/10.1016/j.bbr.2018.10.008 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

SC RI PT

Gabriel et al Risky decision-making

Risky decision-making is associated with impulsive action and sensitivity to first-time nicotine exposure.

U

Daniel B.K. Gabriela, Timothy G. Freelsa, Barry Setlowb, & Nicholas W. Simona*

a

A

N

Department of Psychology, University of Memphis, 400 Innovation Drive, Memphis, TN 38112, USA b Department of Psychiatry, University of Florida College of Medicine, PO Box 100256, Gainesville, FL 32610, USA *

TE

D

M

Corresponding Author: Nicholas W. Simon Department of Psychology, University of Memphis, Psychology 416, 400 Innovation Drive, Memphis, TN 38112, USA [email protected]

The rat risky decision-making task reveals widespread variability in performance. Risk-taking rats exhibit elevated impulsive action, but not impulsive choice. Habit formation is not associated with risky decision-making. Risk-taking predicts locomotor sensitivity to first-time nicotine exposure. Risk-taking rats are less sensitive to nicotine’s anxiogenic properties.

A

CC

    

EP

Highlights

Gabriel et al Risky decision-making

Abstract

SC RI PT

Excessive risk-taking is common in multiple psychiatric conditions, including substance use disorders. The risky decision-making task (RDT) models addiction-relevant risk-taking in rats by measuring preference for a small food reward vs. a large food reward associated with systematically increasing risk of shock. Here, we examined the relationship between risk-taking in the RDT and multiple addiction-relevant phenotypes. Risk-taking was associated with elevated impulsive action, but not impulsive choice or habit formation. Furthermore, risk-taking predicted locomotor sensitivity to first-time nicotine exposure and resilience to nicotine-evoked anxiety. These data demonstrate that risk preference in the RDT predicts other traits associated with substance use disorder, and may have utility for identification of neurobiological and genetic biomarkers that engender addiction vulnerability.

A

CC

EP

TE

D

M

A

N

U

Key Words: Risky decision-making, Impulsivity, Nicotine, Anxiety, Habit formation, Substance use disorders

Gabriel et al Risky decision-making

A

CC

EP

TE

D

M

A

N

U

SC RI PT

1. Introduction Risky decision-making is the preference for rewards accompanied by the possibility of aversive outcomes over safer alternatives. Excessive risk-taking is prevalent in addiction, as individuals suffering from substance use disorder persistently seek drug reinforcement despite high risk of adverse consequences [1–3]. Accordingly, understanding the biological and behavioral basis of risky decision-making may have utility for substance use disorder treatment. The risky decision-making task (RDT)1 models risk-taking in a preclinical rat model by measuring choice between a small, safe reward and a large reward accompanied by the risk of mild foot shock [4]. On average, preference for the large, risky reward decreases as risk of punishment increases, but a subset of subjects consistently prefer the risky reward even under high risk of punishment, demonstrating an insensitivity to consequences comparable to that observed in some with substance use disorder [2,4–8]. These risk-taking rats exhibit distinct characteristics associated with substance use disorder, including elevated cocaine selfadministration, greater salience attributed to reward-predicting cues, and dopaminergic abnormalities that are also prevalent in humans [5,6,9,10]. Therefore, quantifying risk-taking in the RDT may serve as an effective method of identifying a subpopulation of rats with addiction vulnerability-like characteristics. Here, we seek to further verify the RDT as a model of addiction vulnerability by testing for coexpression of risk-taking with other behavioral and pharmacological characteristics often comorbid with substance use disorders: impulsivity, habit formation, and nicotine sensitivity [11–13]. Impulsivity is the tendency to perform actions rapidly and prematurely without appropriate forethought, despite the possibility of undesirable consequences, and can be fractioned into impulsive action and impulsive choice [14]. Impulsive action is operationalized as the inability to withhold a prepotent response during pursuit of reward [14]. Rats with high impulsive action and risk-preference on RDT both self-administer more cocaine [6,15], and D1 receptors in the nucleus accumbens (NAc)2 shell have been implicated in both impulsive action and risk-taking [5,16,17]. Therefore, we predicted that risk-taking on the RDT would be associated with increased impulsive action. The other aspect of impulsivity, impulsive choice, is defined as preference for immediate gratification over delayed rewards, or delay discounting [18–20]. Excessive discounting of delayed rewards is associated with substance use disorder in human and preclinical models, and therapy targeting delay discounting has shown promise for attenuating substance use [11,21–24]. Despite these commonalities, previous studies have observed that impulsive choice is unrelated to RDT performance [4,7]. In addition, while impulsive choice shares some neurobiological overlap with impulsive action, the two constructs are often uncorrelated and have distinct neuronal substrates [16,25,26]. Here, we assessed the relationship between risk taking and both forms of impulsivity. Habitual behavior is the inability to alter a behavior in response to changes in actionoutcome relationships [27], and elevated habit formation has been proposed to be a component of compulsive drug-seeking [28]. Risk-taking rats display a persistent lack of adaptation in response to changes in risk [5], suggesting that enhanced habit formation could contribute to risky decision-making. In addition, diminished expression of inhibitory D2 dopamine receptors has been observed in the dorsal striatum in risk-taking rats [5,29]. The dorsal 1 2

Risky Decision-making Task = RDT Nucleus Accumbens = NAc

Gabriel et al Risky decision-making

A

CC

EP

TE

D

M

A

N

U

SC RI PT

striatum is a functionally heterogeneous structure associated with response learning, and is typically divided into dorsomedial and dorsolateral components [30]. The dorsolateral striatum is commonly associated with the formation of habitual seeking of both natural and drug rewards, or stimulus-response learning, while the dorsomedial striatum plays a role in outcome-driven instrumental learning [11,31]. The aforementioned diminished expression of D2 receptors was later localized to the dorsolateral striatum in risk-taking rats [6], suggesting that rats may differ in habit formation as a function of risk preference. This pattern of dopamine receptor expression has also been observed in humans with substance use disorder [10], and may accelerate the shift toward habitual behavior [29,32]. Here, we tested the relationship between risk taking in the RDT and habit formation. Drug-evoked locomotion is a common measure of psychostimulant sensitivity, with enhanced locomotor activation indicative of increased sensitivity [13,33,34]. Nicotine is an addictive psychostimulant shown to enhance both locomotion and evoked dopamine release in the nucleus accumbens [33]. Furthermore, repeated exposure to nicotine has been shown to increase locomotor sensitivity to the drug [35]. This phenomenon, referred to as locomotor sensitization, is associated with greater drug seeking and craving. Sensitization is often more pronounced after a period of drug cessation, which may play a role in relapse in abstinent individuals with a history of substance use [36]. Sensitization is associated with D1 receptor super-sensitivity in the NAc [36], and risk-taking rats have elevated abundance of D1 receptors in NAc [5]. D1 receptor activation increases nicotine reinforcement and nicotine induces NAc dopaminergic activity, indicating that risk-taking rats may be particularly susceptible to nicotine’s addictive properties [33,37]. Thus, we predicted that risk-taking rats would be more sensitive to the locomotor activating effects of nicotine. Collectively, these experiments aimed to identify relationships between risky decisionmaking and other behavioral constructs associated with addiction vulnerability. In Experiment 1, we tested the relationship between risky decision-making and impulsive action, impulsive choice, and habit formation. Impulsive action was assessed using a differential reinforcement of low rates of responding (DRL) task, which measures the ability to withhold a prepotent response during a set time window [38]. Impulsive choice was measured using the delay discounting task, in which subjects choose between a small, immediately-delivered reward and a large, delayed reward [16,39]. Habit formation was measured using reinforcer devaluation, in which outcomespecific satiation was used to devalue a highly trained instrumental reinforcer, after which enhanced responding for reward was indicative of elevated habitual behavior [40,41]. In Experiment 2, we measured locomotor sensitivity to nicotine in open field chambers following an injection schedule previously shown to induce nicotine sensitization [42]. 2. Materials and Methods 2.1. Subjects Male Long-Evans rats were obtained from Envigo Corp and Charles River. Rats arrived pair-housed and were kept on a 12-hour light/dark cycle beginning with lights off at 8am. Rats had ad libitum access to water. All subjects arrived at approximately three months old, and after a week of acclimation were food restricted to 90% of free feeding baseline weight to increase motivation to pursue the reward in behavioral tasks. Pairs were separated if fighting or food

Gabriel et al Risky decision-making domination was observed. All studies were approved by the University of Memphis IACUC.

SC RI PT

2.2. Experiment 1: Relationship between risk-taking and cognition 2.2.1. Behavioral Apparatus Most behavior and decision-making processes were measured in MedAssociates (FairFax, VA) operant conditioning chambers equipped with one retractable lever on either side of an illuminable food trough and 2.54cm nose-poke apparatus both with .635cm recessed photobeams, pellet dispenser, and shock grate. A subset of rats measured in DRL were tested in Coulbourn (MA) operant chambers. Sugar pellets weighing 45mg, consisting of 62.6% glucose and 26.8% fructose, obtained from Bio-Serv (Flemington, NJ) were used as a behavioral reinforcer. Behavioral experiments were conducted in counterbalanced order, with different cohorts performing distinct task sequences. See Table 1 for details.

A

CC

EP

TE

D

M

A

N

U

2.2.2. Risky Decision-Making Task A total of n=68 rats were tested in the RDT to determine risk preference. The order of tasks in Experiment 1 was counterbalanced across subjects, with different groups of rats tested in the sequence of tasks in different orders, and some only tested in specific tasks. Critically, although some subjects were only tested in some tasks, all subjects were tested in the RDT (Table 1). Initial shaping was adapted from past experiments, and is described in detail in [43] . Briefly, rats were trained to associate the food trough with food delivery over a 64 minute session during which a total of 38 sugar pellets were dispensed to the food trough accompanied by trough illumination. Rats were then trained to enter the food trough when it was illuminated to initiate lever extension, and to press both levers to receive food reward. After completing this training to the extent of 35 presses on each lever in a session, one lever was changed to a large (3 pellet) reward. Once rats learned to discriminate between the large (3 pellet) and the small (1 pellet) reward levers, demonstrating consistent preference for the large reward (>75% of trials), they began training in the RDT. The RDT consisted of 5 blocks of 18 trials, totaling 90 trials per session. Each block began with 8 forced trials (single lever) followed by 10 choice trials (dual lever). These forced choice trials (on which only a single lever was presented) served to establish new risk contingencies upon initiation of each new block. During forced choice trials, lever presentation was pseudorandomized, with the same lever never extended more than twice in a row. After 4 forced choice trials on each lever to establish the risk contingencies for a block, both levers were extended upon trial initiation, offering rats a choice between the small, safe reward and the large, risky reward. Each trial began with simultaneous illumination of house and trough lights. Rats had 10 seconds to initiate a trial via food trough entry, which extinguished the food trough light and extended either one or both levers, depending on trial type (forced or free choice). A press of the safe lever resulted in the delivery of a single food pellet. A risky lever press delivered 3 food pellets with the risk of a 1 second footshock. Upon lever press and pellet delivery, the levers retracted and the food trough light illuminated. After food was collected or ten seconds passed, the food trough and house lights were extinguished and an intertrial interval (10s ± 4s) preceded the next trial. The risk of punishment upon choice of the large reward lever escalated across blocks (0%, 25%, 50%, 75%, 100%). Failure to initiate a trial or

Gabriel et al Risky decision-making

SC RI PT

press a lever within 10 seconds of lever presentation resulted in the trial being marked as an omission and proceeding to the ITI preceding the next trial (Figure 1). The RDT began with no shock, then once subjects demonstrated preference for the large reward, risk of shock was added to this reward. Shock intensity increased from 0.15mA to 0.35mA across multiple sessions, as using 0.35mA as the initial shock amplitude can induce an excessive number of omitted trials and a strong bias away from the large reward during task acquisition [43]. After reaching 0.35mA, rats continued to perform in the RDT until stability was achieved over five days, defined as no significant main effect of day in percent choice of risky reward via a 2-way repeated measures ANOVA, as well as no day x block interaction. Mean percent choice of the risky reward over the final 5 sessions was used to divide rats into “risk-taking” and “risk-averse” groups based on a median split of total percent choice of the risky reward.

EP

TE

D

M

A

N

U

2.2.3. Impulsive Action Rats (n=49) were first trained to perform a lever press for a pellet reinforcer. After lever press training, rats were trained in the differential reinforcement of low rates of responding (DRL) task to measure impulsive action [38]. During DRL, rats pressed a lever for food reinforcement, then were required to withhold the next lever press for a set number of seconds before another press would be reinforced. A “premature” lever press during this time period resulted in the timer being reset (Figure 2). Thus, rats were forced to withhold a prepotent response in order to receive reinforcement. This response withholding period began at 5 seconds. Subjects were tested until reaching stable performance, defined as no significant difference in correct responding as determined by repeated measures ANOVA across 3 consecutive sessions. After achieving stability, the task advanced to 10 seconds (DRL-10), then after again reaching stability advanced to 20 seconds (DRL-20). Rats performed DRL-20 until they achieved stable responding, quantified as no significant difference in behavior across 3 consecutive sessions in a repeated measures ANOVA. Each session was terminated after 45 minutes. The ratio of correct responses (responses performed after the 5, 10, or 20 second waiting period) to total responses on the final day of each schedule served as a measure of impulsive action. Therefore, a higher correct response ratio is associated with lower impulsivity.

A

CC

2.2.4. Impulsive Choice Impulsive choice was assessed using a delay-discounting task [20,44]. As with the RDT, rats (n=31) had an option to choose between a large (3 pellet) and small (1 pellet) reward. However, in the delay-discounting task, the large reward was delivered after a delay that increased in blocks of trials across the session. The session began with a 0 second delay preceding the large reward, which increased through 4, 8, 16, and 32 seconds over 5 blocks of 10 choice trials each. Each block was preceded by 2 forced choice trials (one on each lever). Stability was assessed using criteria identical to the RDT (2.2.2). The mean percent choice of the delayed reward (averaged across blocks) over the final 5 sessions served as a measure of impulsive choice, or willingness to wait for a larger reward. Note that the delay before large reward delivery was added to the ITI after small reward delivery to keep trial length consistent regardless of reward choice (Figure 3).

Gabriel et al Risky decision-making

N

U

SC RI PT

2.2.5. Habit Formation Rats (n=25) were then trained in a reinforcer devaluation protocol previously shown to elicit habitual responding [39]. To distinguish performance on this task from that in DRL, nosepokes into a lit port were used as the operant behavior. Rats were first trained to nose-poke for pellets under a fixed ratio 1 schedule. Then, rats ran one session on a variable ratio 5 schedule, on which an average of five nose-pokes resulted in food delivery. On the following session, rats shifted to a variable interval (VI) schedule, in which only a nose-poke emitted after variable delay resulted in food delivery. Rats trained for five days each on VI-5, -10, and -60, with each value indicating the average amount of time required before a response was reinforced. For one hour immediately prior to the following session, rats were given free access to their reward pellets, which is sufficient to produce outcome-specific devaluation of the food pellet reinforcer [39]. Then, rats performed 10 minutes of instrumental behavior during which no reinforcer was available (extinction), followed by 20 minutes of normal VI-60. Habit formation was quantified on this probe session using a “devaluation ratio”, which was equal to responses during extinction divided by the sum of responses during an equivalent time on the previous day’s VI-60 and during extinction. A larger devaluation ratio was indicative of ongoing reward seeking despite reinforcer devaluation, indicative of decreased sensitivity to outcome value and therefore greater habit formation.

EP

TE

D

M

A

2.2.6. Data Analysis Pearson’s correlations were used to search for linear relationships between risk preference in the RDT and the primary measure on each behavioral task. In addition, a median split of RDT performance was conducted to categorize rats as risk taking or risk averse. In addition, we used a more stringent criterion to divide rats into either high risk preference (>80% choice of risky reward) or risk avoidance (< 30% risky reward choice to assess differences between extreme cases of risk-taking or risk-aversion. These groups were compared using one-way ANOVA. This method has previously been shown to reveal differences in gene expression in specific brain regions between high and low risk-taking rats [5].

CC

2.3. Experiment 2: Relationship between risk-taking and nicotine sensitivity 2.3.1. Behavioral Apparatus Nicotine sensitization was measured using a MedAssociates open field chamber equipped with a grid of photobeams to capture locomotor activity. The open field chamber was a square 29.21cm on a side and enclosed by 30.48 cm high walls.

A

2.3.2. Nicotine Sensitization All subjects in experiment 2 (n=19) were characterized in the RDT prior to nicotine sensitization to preclude any long-term influence of nicotine exposure on choice behavior. Nicotine-evoked locomotion and sensitization were assessed using a previously established protocol [42]. Briefly, rats were placed in the open field chamber to measure locomotor activity, rearing, and stereotypy for 30 minutes. They then received a subcutaneous injection of 0.9% saline (Vedco, MO), followed by 30 minutes in the open field, then another injection of nicotine tartrate (0.2 mg/kg, measured as the free base) in saline vehicle, followed

Gabriel et al Risky decision-making by an hour in the open field. This protocol continued for 5 consecutive days, after which rats underwent an 8-day washout period in the absence of nicotine. A final probe day then consisted of another 0.2 mg/kg nicotine injection. Locomotion on the final day of nicotine exposure served as a measurement of locomotor sensitization after a period of nicotine abstinence.

U

SC RI PT

2.3.3. Data Analysis A repeated measures ANOVA was conducted across the initial 5 days of nicotine exposure to measure changes in nicotine sensitivity with repeated administration. Sensitization was confirmed using a paired t-test to compare nicotine-evoked locomotion on day 5 of nicotine administration and the post washout probe day. Pearson’s correlations were used to evaluate linear relationships between risk preference in the RDT and locomotion on each day of nicotine administration. A median split of RDT performance was conducted to categorize rats as risktaking or risk-averse. These groupings were used to further assess and visualize risk-taking and nicotine sensitivity through ANOVAs. To account for differences in response to subcutaneous injection, we calculated a ratio of nicotine-evoked locomotion to saline-evoked locomotion+1 (the +1 was added to avoid possible divide by zero errors).

EP

TE

D

M

A

N

3. Results 3.1. Experiment 1 Results 3.1.1. Risky Decision-making For all experiments, rats (n=68) were trained in the RDT to provide an index of baseline risky decision-making, with the order of tasks counterbalanced across subjects. On average, rats required 26.18 training sessions to achieve stable responding across five consecutive sessions. Percent choice of the risky reward across a 5-day average served as the measure of risk-taking propensity. A repeated measures ANOVA revealed a significant effect of punishment probability (F (4, 64) = 81.297, p < .001), with rats shifting away from the large, risky reward as risk of punishment increased across the session. This indicated that rats discount large rewards as a function of risk of punishment. There was a near significant increase in omissions as risk of punishment increased across the sessions (F (4, 63) = 2.356, p = .054). However, there were no differences in omissions between risk-taking and risk-averse groups based on either median (F (4, 66) = .594, p = .444) or extreme (F (1, 48) = 1.135, p = .292) splits.

A

CC

3.1.2. Impulsive Action For a subset of rats (n=49), RDT performance was compared with impulsive action, quantified as correct responses divided by total responses on the DRL task, with lower DRL ratios indicative of greater impulsivity. The distribution of RDT scores is displayed in Figure 4a. A Pearson’s correlation analysis found no relationship between risk preference and DRL-5 s correct ratio (r (47) = -.112, p = .444, Figure 4b); however, enhancing task difficulty by lengthening the DRL inter-response period revealed negative correlations between DRL ratios and RDT performance (DRL-10 s: r (47) = -.368, p = .009, Figure 4c; DRL-20 s: r (47) = -.340, p = .017, Figure 4d), indicating that greater preference for the large, risky reward is associated with greater impulsive action. To further explore differences in impulsive action as a function of risk-taking propensity, we conducted a median split of all subjects based on mean choice of the

Gabriel et al Risky decision-making

SC RI PT

large, risky reward on the RDT. After dividing subjects into “risk-taking” and “risk-averse” subgroups based on a median split (Figure 4e), a one-way ANOVA revealed no difference between groups on the DRL-5 ratio (F (1, 47) = .003, p = .956, Figure 4f). However, as observed with the correlational analyses, there were significant effects of risk group on DRL ratio during DRL-10 (F (1, 47) = 11.306, p = .002, Figure 4g) and DRL-20 (F (1, 47) = 7.028, p = .011, Figure 4h), such that rats categorized as risk-taking expressed higher levels of impulsive action than risk-averse. To selectively explore differences between subjects with extremely high levels of risk-taking or risk-aversion, we conducted a similar analysis comparing subjects with either >80% or <30% preference for the risky reward. Between these groups, there was again no difference in performance in the DRL-5 (F (1, 32) = .739, p = .396), while significant differences in the DRL-10 (F (1, 32) = 6.512, p = .016) and the DRL20 (F (1, 32) = 4.761, p = .037) persisted.

TE

D

M

A

N

U

3.1.3. Impulsive Choice Performance on the RDT was compared with performance on the delay discounting task. Mean percent choice of the large, delayed reward averaged across the final 5 days of training served as the measure of impulsive choice. Collectively, rats (n=31) demonstrated a repeated measures effect of delay (F (4, 27) = 112.352, p < .001), indicating discounting of the large reward as a function of delay. The distribution of risk-taking scores is depicted in Figure 5a. We observed no relationship between risk-taking and impulsive choice, reflected by a lack of significant correlation between measures (r (29) = -.074, p = .692, Figure 5b) and no group difference in impulsive choice between risk-taking and risk-averse rats based on a median split (F (1, 29) = .238, p = .629, Figure 5c-d). After regrouping subjects as risktaking or risk-averse based on the extreme split outlined above (>80 or <25 preference for risky reward), there remained no difference between groups (F (1, 21) = .017, p = .897). In addition, there were no correlations between delay discounting and any schedule of DRL (DRL-5/DD: r (29) = -.066, p = .725; DRL-10/DD: r = .121, p = .516; DRL-20/DD: r = .176, p = .345), indicating that impulsive action was not associated with impulsive choice.

A

CC

EP

3.1.4. Habit Formation RDT was also compared with habit formation, measured in the reinforcer devaluation task (n=25; Figure 6a). Responses during extinction following devaluation by satiation were significantly reduced from an equivalent 10 minute baseline on the previous session of VI-60 (t (24) = 7.673, p < .001). A paired t-test showed that this reduction from baseline persisted after the reinforcer was reintroduced 10 minutes into the probe session (t (24) = 2.853, p = .009). Habit formation, defined here as the ratio between the extinction session and baseline responding, was not correlated with RDT performance (r (23) = -.195, p = .351, Figure 6b), nor did habit formation differ between either median split (F (1, 23) = .308, p = .584, Figures 6c-d) or extreme split risk groups (F (1, 18) = 1.015, p = .327). 3.2.1. Experiment 2 Results 3.2.2. Comparison of Nicotine Sensitization and Risky Decision-making Rats (n=19) were first characterized in the RDT (Figure 7a), then tested for sensitivity to the locomotor stimulant effects of nicotine. Of these, 3 were removed from

Gabriel et al Risky decision-making

A

CC

EP

TE

D

M

A

N

U

SC RI PT

subsequent analysis due to inability to acquire RDT, equipment error, and excessively adverse reaction to nicotine, leaving 16 rats for all reported analyses. A repeated measures ANOVA across days revealed no significant change in nicotine-evoked locomotor activity across the five initial nicotine sessions (F (4, 12) = 1.238, p =.304; Figure 7b). However, all rats demonstrated an increase in nicotine-evoked locomotion on the final day of nicotine exposure, confirmed by a paired-sample t-test comparing locomotion between day 5 and the test session after nine days of abstinence (t (15) = -5.633, p < .001, Figure 7b). This indicated that the protocol was sufficient to induce locomotor sensitization to nicotine. Risk-taking and measures of nicotine sensitivity were compared both by Pearson’s correlation and by dividing rats into risk-taking and risk-averse subgroups as in experiment 1. A quartile split was not used for group assignment, because not enough subjects met criterion for a meaningful sample size. There was a significant correlation between risktaking and nicotine-evoked locomotion on the first day of nicotine treatment (r (14) = .500, p = .049, Figure 8a), suggesting that greater risk-taking is associated with greater sensitivity to the locomotor activating effects of nicotine. In addition, there was a near-significant group difference suggesting that risk-takers were more sensitive than risk-averse to first-time nicotine exposure (F (1, 14) = 3.398, p = .087, Figure 8b). Furthermore, paired t-tests revealed that risk-taking rats increased locomotor activity after the initial nicotine injection compared to saline (t (7) = -3.503, p = .010), whereas risk-averse rats exhibited a similar locomotor response to both treatments (t (7) = -1.505, p = .176). Therefore, risk-taking but not risk-averse rats demonstrated elevated locomotion upon first exposure to nicotine. Interestingly, predictive relationships between risk-taking phenotype and nicotine locomotion were only observed upon the first nicotine exposure, with no significant correlations observed between RDT and nicotine-evoked locomotion on any of the four subsequent sessions, and no difference between risk groups in locomotion (Table 1). Furthermore, risktaking was not predictive of nicotine locomotor sensitization following the nine day abstinence period (r (14) = .154, p = .569). As the relationship between risk-taking and nicotine-evoked locomotion was only evident during the initial exposure to nicotine, further statistical analyses were restricted to this session. To confirm that the greater locomotor sensitivity to first-time nicotine in risk-takers was not a product of differential sensitivity to the subcutaneous injection, we compared RDT performance with a ratio of nicotine-evoked to saline-evoked locomotion. The correlation between risk-taking and nicotine sensitivity was still present after saline-induced baseline locomotion was accounted for via this ratio measure (r (14) = .516, p = .041, Figure 8c). One possible explanation for risk-taking predicting higher levels of first-time nicotineevoked locomotion is a diminished response to the anxiogenic effects of nicotine [45]. To explore this possibility, we assessed total distance traveled in the center of the open field, which is associated with lower levels of anxiety [46]. We found a positive correlation between risktaking and distance traveled in the center after nicotine (r (14) = .589, p = .013, Figure 9a), suggesting that risk-preference on the RDT was associated with reduced susceptibility to the anxiogenic effects of first-time nicotine. This was supported further by the observation that risktaking rats showed greater activity in the center of the field than risk-averse rats (F (1, 14) = 13.117, p = .003, Figure 9b). To control for this effect being an artifact of greater overall locomotion, we compared risk-taking to a ratio of time in center to total locomotion and observed

Gabriel et al Risky decision-making

SC RI PT

a strong trend toward correlation between risk-taking and this ratio measure (r (14) = .437, p = .079, Figure 9c). Additionally, a one-way ANOVA revealed that risk-taking rats had significantly higher scores on this measure (F (1, 14) = 7.286, p = .017, Figure 9d). This is likely not driven by elevated baseline anxiety, as time in center under the effects of a saline injection was not associated with risk-taking (r (15) = .046, p =.866). Collectively, these data demonstrate that risk-taking is predictive of increased locomotion evoked by first-time nicotine exposure, and that this locomotor activation may be related to decreased sensitivity to the anxiogenic effects of first-time nicotine exposure.

N

U

4. Discussion Excessive risk taking is common in multiple psychiatric conditions, including substance use disorders [47]. In these studies, we examined addiction-relevant behavioral and neurochemical phenotypes associated with risk-taking as quantified by the RDT. In experiment 1, we observed that greater risk taking was associated with elevated impulsive action, but not impulsive choice or habit formation. In experiment 2, we found that greater risk taking was associated with greater locomotor sensitivity to first-time nicotine exposure, and that this is likely related to a reduction in nicotine-evoked anxiety. Collectively, these data provide evidence that risk-taking in the RDT is associated with behavioral phenotypes linked to addiction vulnerability.

A

CC

EP

TE

D

M

A

4.1. Experiment 1: Risky decision-making and cognition These data indicate that risk-taking is associated with deficient inhibitory control, a trait referred to as motor impulsivity which is predictive of substance use disorder in humans [48]. This relationship was only observed at higher DRL schedules (10 and 20 sec), suggesting that differences in impulsivity only manifest under conditions of greater cognitive demand. In addition, using the parameters from [5], we compared “extreme” risk-takers (preferred risky option over 80% of trials) with risk-averse (preferred risky option less than 30% of trials). This also revealed a difference in impulsive action at DRL-10 and DRL-20 schedule. The relationship between risk-taking and impulsive action suggests that persistent, inflexible risk-taking may be related to an inability to appropriately suppress a response despite increased risk of punishment. The comorbidity between impulsive action and risk-taking may arise from specific patterns of dopamine receptor expression. Risk-taking in the RDT is associated with diminished expression of D2 receptors in the NAc, whereas lowered receptor availability is associated with increased impulsivity in the 5-choice serial reaction time task, an alternative measure of impulsive action [6,15]. Therefore, D2 receptors in the NAc likely play a role in both cognitive constructs. Elevated D1 receptor expression in NAc shell has previously been identified as a biomarker of both risk-taking in the RDT [5] and impulsive action in DRL [16] suggesting NAc as a potential locus driving both behaviors. Indeed, both NAc lesions and inactivation have been shown to increase premature responding in the 5choice serial reaction time task [49,50]. Systemic blockade of D1 receptors causes decreased motivation for natural rewards [51], suggesting that comorbidity between excessive impulsivity and risk-taking may be driven at least in part by altered reward motivation. However, previous data revealed that risk-taking was uncorrelated with reward seeking or consumption

Gabriel et al Risky decision-making

A

CC

EP

TE

D

M

A

N

U

SC RI PT

[5], and that manipulating levels of motivation (via enhancing reward magnitude or inducing satiation) did not alter risk preference [4,7]. Thus, baseline risk-taking appears to be at least somewhat independent of motivation. Another possible explanation for the difference in impulsive action between risk groups is that risk-takers were unable to comprehend the parameters of the DRL task due to lower overall cognitive capacity, or insufficient engagement in the task. This is unlikely, as risk-taking was not associated with performance on the DRL-5, a less demanding version of DRL. This demonstrated that risk-takers were able to sufficiently learn the parameters of DRL, and only displayed differences when the “wait time” between responses was sufficient to drive impulsivity (10 or 20 sec). Additionally, previous research has found that poor set shifting is associated with risk aversion rather than preference in the RDT [7], suggesting that excessive risk-taking is unrelated to inability to change behavior with changing contingencies. Finally, in humans, risktaking is typically dissociable from measures of executive function and intelligence [52]. Therefore, it is unlikely that the comorbidity between risk-taking and impulsive action is driven by overall cognitive capacity or task comprehension. No relationship was observed between risk-taking in the RDT and impulsive choice, which is consistent with previous data in rat models using comparable measures [4,7]. While humans with substance use disorders commonly exhibit comorbid exacerbation of both risktaking and impulsive choice [1,3,21,53], these measures are often uncorrelated [54]. The results of this experiment support the argument that reduction in economic value during decisionmaking does not always follow a unitary system. Rather, the value of a reward varies differently based on the modality of the factor that devalues the reward (risk for RDT, delay for delay discounting) [55]. Furthermore, previous data have revealed differences in the neural substrates underlying the processing of punishment risk and delay during decision-making. For example, basolateral amygdala lesions reduce preference for the discounted reward in delay discounting, but enhance preference for the risky reward in the RDT [56,57]. Task-specific differences are also evident at the receptor level. Elevated risk-taking in the RDT has been shown to predict greater D1 receptor expression in the insular cortex [5]. Conversely, D1 receptors in this region appear to be associated with reduced impulsive choice, as their blockade induces impulsivity in delay discounting [58]. Habit formation, measured here via reinforcer devaluation, did not differ as a function of risk-taking phenotype. This implies that risk-taking rats shift from goal-directed to habitual responding at a rate comparable to risk-averse rats, indicating that perseverative selection of the large, risky reward is likely not related to an inability to adjust behavior in accordance with changing action-outcome contingencies. This contrasts with the previous finding of diminished D2 receptor population in the dorsal striatum of risk-takers, which was expected to predict elevated habit formation via striatal disinhibition [5,12,29]. It is important to note the functional heterogeneity of the dorsal striatum, with the dorsolateral striatum being associated with stimulus response, or habit, learning, and the dorsomedial striatum being more involved in action-outcome learning [12]. While the initial study comparing D2 expression in the dorsal striatum with RDT did not differentiate between these regions, a more recent study found that lower D2 receptor mRNA expression specifically in the dorsolateral striatum was associated with greater risk taking in the RDT [5,6]. Therefore, D2 receptor expression in the dorsolateral striatum may not serve as a reliable predictor of

Gabriel et al Risky decision-making habit formation. Alternatively, more training prior to devaluation may be necessary to produce a degree of habitual behavior necessary to reveal an association between risktaking and habit formation.

A

CC

EP

TE

D

M

A

N

U

SC RI PT

4.2. Experiment 2: Risky decision-making and nicotine sensitivity Risk-taking was associated with an elevated locomotor response to the first administration of nicotine, with no relationship between locomotor activation and risk-taking observed across subsequent nicotine exposures. Rats demonstrated locomotor sensitization to nicotine after a period of abstinence, which was anticipated based on the nicotine dose and schedule utilized here [42]. Contrary to our hypothesis, there was no relationship between locomotor sensitization and risk-taking, indicating that the predictive relationship between risktaking and nicotine sensitivity is limited to the initial exposure. Initial response to nicotine is a reliable predictor of prolonged nicotine abuse in humans [59–61]. Initial cigarette consumption is typically both anxiogenic and physically aversive [62,63], but chronic smokers report experiencing fewer aversive effects when first beginning to smoke, enhancing the likelihood of continued nicotine abuse [60]. Here, we observed that risktaking predicted sensitivity to the locomotor-activating effects of initial nicotine exposure. Furthermore, risk-taking was associated with reduced anxiety evoked by initial nicotine, as reflected by greater time spent in the center of the open field. This is likely not a result of lower baseline anxiety in risk-taking rats, as 1.) there was no relationship between RDT and salineinduced time in the center, and 2.) previous research has shown that risk-taking and riskaverse rats do not differ in measures of trait anxiety [5]. Collectively, these data suggest that risky decision-making predicts resistance to the anxiogenic effects of first time nicotine, which is associated with vulnerability to nicotine abuse [5]. The relationship between risky decision-making and enhanced nicotine-evoked locomotor activity may also be related to enhanced sensitivity to nicotine’s reinforcing properties, as locomotion is often predictive of nicotine reinforcement and dopamine release [64]. Nicotine’s reinforcing effects are mediated by D1 receptors in the mesolimbic pathway [65,66], and D1 receptor expression is elevated in the NAc of risk-taking rats [5]. Risk-taking rats also exhibit elevated D1 receptors in the insular cortex [5] and blockade of D1 receptors in the insular cortex has been shown to decrease nicotine self-administration [67]. These previous data in concert with the predictive relationship between greater risk taking and enhanced nicotine-evoked locomotion observed here indicate that risk-taking rats may experience elevated sensitivity to nicotine’s reinforcing properties. However, it is important to note that while nicotine-evoked locomotor sensitization is associated with mesolimbic dopaminergic abnormalities commonly associated with addiction and self-administration [64,68,69], locomotor response to a drug does not necessarily coincide with propensity to self-administer that drug. Therefore, further work is needed to explore this hypothesis, such as direct assessment of nicotine self-administration following RDT training. Conversely, it is possible that risk-averse subjects may be hyposensitive to nicotine’s initial locomotor effects rather than risk-taking subjects expressing hypersensitivity. Indeed, some patterns of behavior and gene expression are specific to risk-averse subjects relative to the rest of the population [5,7]. However, previous studies have reported that initial exposure to nicotine at doses comparable to that utilized here

Gabriel et al Risky decision-making

SC RI PT

does not typically cause an increase in locomotion relative to saline exposure [42, 70, 71]. Rather, nicotine-induced hyperlocomotion manifests after repeated exposure. While we observed this pattern in risk-averse rats (no significant locomotor elevation on day one of nicotine), risk-taking rats demonstrated enhanced locomotor sensitivity to nicotine relative to saline on day one. This distinction between risk-taking rats and previous nicotine studies suggests that risk-taking predicts hypersensitivity to initial nicotine exposure, rather than risk-aversion being associated with hyposensitivity.

M

A

N

U

4.3. Conclusions and Implications We observed comorbidity between risk-taking and 1.) impulsive action, 2.) locomotor sensitivity to first-time nicotine exposure, and 3.) reduced anxiety evoked by first-time nicotine exposure. Collectively, these data demonstrate that the RDT can serve as a powerful tool for identifying multiple addiction-relevant characteristics within a single behavioral assay. As only a subset of individuals who use substances develop substance use disorders, it is critical to identify factors that engender the transition from casual to disordered use. The RDT is a preclinical model that not only measures an addiction-relevant form of risk-taking, but also detects multiple other behavioral and biological addiction-relevant phenotypes. The ability to pinpoint a vulnerable population in rats will facilitate the identification of biomarkers and genetic influences that promote addiction vulnerability, with the goal of being able to detect vulnerable individuals prior to the onset of pathology. The RDT may have utility for development of precise treatments that attenuate pathological risk-taking as well as sensitivity to drugs of abuse.

TE

D

Funding: This work was supported by a Young Investigator Award from the Brain and Behavior Research Foundation and a Faculty Research Grant from the University of Memphis (NWS), GSCC Student Research Grant from the University of Memphis (DBKG).

A

CC

EP

5. Acknowledgements We thank Dr. Helen Sable for providing the use of her MedAssociates Activity Monitor Open Field Chambers. We also thank Anna L. Vongphrachanh, Samantha Morrison, Andrew Starnes, Alan Rasheed, Amber Woods, Haleigh Joyner, and Tiffany Chism for technical support.

Gabriel et al Risky decision-making References

[7] [8]

[9]

[10]

[11]

[12]

SC RI PT

U

CC

[13]

N

[6]

A

[5]

M

[4]

D

[3]

TE

[2]

M. Brand, M. Roth-Bauer, M. Driessen, H.J. Markowitsch, Executive functions and risky decision-making in patients with opiate dependence, Drug Alcohol Depend. 97 (2008) 64–72. D. Brevers, A. Bechara, A. Cleeremans, C. Kornreich, P. Verbanck, X. Noël, Impaired decision-making under risk in individuals with alcohol dependence, Alcohol. Clin. Exp. Res. 38 (2014) 1924–1931. S.D. Lane, D.R. Cherek, Analysis of risk taking in adults with a history of high risk behavior, Drug Alcohol Depend. 60 (2000) 179–187. N.W. Simon, R.J. Gilbert, J.D. Mayse, J.L. Bizon, B. Setlow, Balancing Risk and Reward: A Rat Model of Risky Decision Making, Neuropsychopharmacology. 34 (2009) 2208– 2217. N.W. Simon, K.S. Montgomery, B.S. Beas, M.R. Mitchell, C.L. LaSarge, I.A. Mendez, C. Banuelos, C.M. Vokes, A.B. Taylor, R.P. Haberman, J.L. Bizon, B. Setlow, Dopaminergic Modulation of Risky Decision-Making, J. Neurosci. 31 (2011) 17460–17470. M.R. Mitchell, V.G. Weiss, B.S. Beas, D. Morgan, J.L. Bizon, B. Setlow, Adolescent risk taking, cocaine self-administration, and striatal dopamine signaling, Neuropsychopharmacology. 39 (2014) 955–962. K.G. Shimp, M.R. Mitchell, B.S. Beas, J.L. Bizon, B. Setlow, Affective and cognitive mechanisms of risky decision making, Neurobiol. Learn. Mem. 117 (2015) 60–70. A. Verdejo-Garcia, T.T.-J. Chong, J.C. Stout, M. Yücel, E.D. London, Stages of dysfunctional decision-making in addiction, Pharmacol. Biochem. Behav. 164 (2016) 99– 105. M.E. Olshavsky, J. Shumake, A.A. Rosenthal, A. Kaddour-Djebbar, F. Gonzalez-Lima, B. Setlow, H.J. Lee, Impulsivity, risk-taking, and distractibility in rats exhibiting robust conditioned orienting behaviors, J. Exp. Anal. Behav. 102 (2014) 162–178. N.D. Volkow, J.S. Fowler, G.-J. Wang, J.M. Swanson, F. Telang, Dopamine in drug abuse and addiction: results from imaging studies and treatment implications., Arch. Neurol. 64 (2007) 1575–1579. B.J. Everitt, D. Belin, D. Economidou, Y. Pelloux, J.W. Dalley, T.W. Robbins, Neural mechanisms underlying the vulnerability to develop compulsive drug-seeking habits and addiction, Philos. Trans. R. Soc. B Biol. Sci. 363 (2008) 3125–3135. B.J. Everitt, T.W. Robbins, Drug Addiction: Updating Actions to Habits to Compulsions Ten Years On, Annu. Rev. Psychol. 67 (2016) 23–50. M. Nisell, G.G. Nomikos, P. Hertel, G. Panagis, T.H. Svensson, Condition-Independent Sensitization of Locomotor Stimulation and Mesocortical Dopamine Release Following Chronic Nicotine Treatment in the Rat, Synapse. 22 (1996) 369–381. J.W. Dalley, T.W. Robbins, Fractionating impulsivity: neuropsychiatric implications, Nat. Rev. Neurosci. 18 (2017) 158–171. J.W. Dalley, T.D. Fryer, L. Brichard, E.S.J. Robinson, D.E.H. Theobald, K. Laane, Y. Pena, E.R. Murphy, Y. Shah, K. Probst, I. Abakumova, F.I. Aigbirhio, H.K. Richards, Y. Hong, J.-C. Baron, B.J. Everitt, T.W. Robbins, Nucleus Accumbens D2/3 Receptors Predict Trait Impulsivity and Cocaine Reinforcement, Neuropsychopharmacology, 32 (2007) 273-282. N.W. Simon, B.S. Beas, K.S. Montgomery, R.P. Haberman, J.L. Bizon, B. Setlow, Prefrontal cortical–striatal dopamine receptor mRNA expression predicts distinct forms of impulsivity, Eur. J. Neurosci. 37 (2013) 1779–1788. T. Pattij, M.C.W. Janssen, L.J.M.J. Vanderschuren, A.N.M. Schoffelmeer, M.M. van

EP

[1]

[14]

A

[15]

[16]

[17]

Gabriel et al Risky decision-making

[25] [26]

[27] [28]

[29]

[30]

SC RI PT

CC

[31]

U

[24]

N

[23]

A

[22]

M

[21]

D

[20]

TE

[19]

EP

[18]

Gaalen, Involvement of dopamine D1 and D2 receptors in the nucleus accumbens core and shell in inhibitory response control, Psychopharmacology (Berl). 191 (2007) 587– 598. C.A. Winstanley, D.E.H. Theobald, J.W. Dalley, T.W. Robbins, Interactions between serotonin and dopamine in the control of impulsive choice in rats: Therapeutic implications for impulse control disorders., Neuropsychopharmacology. 30 (2005) 669– 82. W. Bickel, L. Marsch, Toward a Behavioral Economic Understanding of Drug Dependence: Delay Counting Process, Addiction. 96 (2001) 73–86. J. Evenden, The pharmacology of impulsive behaviour in rats V: The effects of drugs on responding under a discrimination task using unreliable visual stimuli, Psychopharmacology (Berl). 143 (1999) 111–122. J.G. Murphy, A.A. Dennhardt, J.R. Skidmore, B. Borsari, N.P. Barnett, S.M. Colby, M.P. Martens, A Randomized Controlled Trial of a Behavioral Economic Supplement to Brief Motivational Interventions for College Drinking, 80 (2012) 876–886. W.K. Bickel, M.W. Johnson, M.N. Koffarnus, J. MacKillop, J.G. Murphy, The Behavioral Economics of Substance Use Disorders: Reinforcement Pathologies and Their Repair, Annu. Rev. Clin. Psychol. 10 (2014) 641–677. J.L. Perry, M.E. Carroll, The role of impulsive behavior in drug abuse, Psychopharmacology (Berl). 200 (2008) 1–26. H. Garavan, R. Hester, The role of cognitive control in cocaine dependence, Neuropsychol. Rev. 17 (2007) 337–345. R.N. Cardinal, Neural systems implicated in delayed and probabilistic reinforcement, Neural Networks. 19 (2006) 1277–1301. C.A. Winstanley, D.M. Eagle, T.W. Robbins, Behavioral models of impulsivity in relation to ADHD: Translation between clinical and preclinical studies, Clin. Psychol. Rev. 26 (2006) 379–395. B.W. Balleine, A. Dickinson, Goal-directed instrumental action: Contingency and incentive learning and their cortical substrates, Neuropharmacology. 37 (1998) 407–419. L. Hogarth, B.W. Balleine, L.H. Corbit, S. Killcross, Associative learning mechanisms underpinning the transition from recreational drug use to addiction, Ann. N. Y. Acad. Sci. 1282 (2013) 12–24. J. Obadiah, T. Avidor-Reiss, C.S. Fishburn, S. Carmon, M. Bayewitch, Z. Vogel, S. Fuchs, B. Levavi-Sivan, Adenylyl cyclase interaction with the D2 dopamine receptor family; differential coupling to Gi, Gz, and Gs, Cell. Mol. Neurobiol. 19 (1999) 653–664. B.W. Balleine, M.R. Delgado, O. Hikosaka, The Role of the Dorsal Striatum in Reward and Decision-Making, J. Neurosci. 27 (2007) 8161–8165. T.W. Robbins, K.D. Ersche, B.J. Everitt, Drug addiction and the memory systems of the brain, Ann. N. Y. Acad. Sci. 1141 (2008) 1–21. R.A. Poldrack, M.G. Packard, Competition among multiple memory systems: Converging evidence from animal and human brain studies, Neuropsychologia. 41 (2003) 245–251. G.J. Panagis, M. Nisell, G.G. Nomikos, K. Chergui, T.H. Svensson, Nicotine injections into the ventral tegmental area increase locomotion and Fos-like immunoreactivity in the nucleus accumbens of the rat, Brain Res. 730 (1996) 133–142. M.E.M. Benwell, D.J.K. Balfour, The effects of acute and repeated nicotine treatment on nucleus accumbens dopamine and locomotor activity, Br. J. Pharmacol. 4 (1992) 849– 856. C. Cadoni, G. Di Chiara, Differential changes in accumbens shell and core dopamine in behavioral sensitization to nicotine, Eur. J. Pharmacol. 387 (2000) 1999–2001. Y. Shaham, U. Shalev, L. Lu, H. De Wit, J. Stewart, The reinstatement model of drug relapse: History, methodology and major findings, Psychopharmacology (Berl). 168

[32]

A

[33]

[34]

[35] [36]

Gabriel et al Risky decision-making

[40]

[41] [42]

[43] [44]

[48] [49]

D

CC

[50]

TE

[47]

EP

[46]

M

A

[45]

SC RI PT

[39]

U

[38]

(2003) 3–20. S.R. Laviolette, N.M. Lauzon, S.F. Bishop, N. Sun, H. Tan, Dopamine Signaling through D 1 -Like versus D 2 -Like Receptors in the Nucleus Accumbens Core versus Shell Differentially Modulates Nicotine Reward Sensitivity, 28 (2008) 8025–8033. E.R. Hankosky, J.M. Gulley, Performance on an impulse control task is altered in adult rats exposed to amphetamine during adolescence, Dev. Psychobiol. 55 (2013) 733–744. C.N. Evenden, J.N. & Ryan, The pharmacology of impulsive behavior in rats of drugs on response choice with variyng delays of reinforcement., Psychopharmacology (Berl). 128 (1996) 161–170. K.H. LeBlanc, N.T. Maidment, S.B. Ostlund, Repeated Cocaine Exposure Facilitates the Expression of Incentive Motivation and Induces Habitual Control in Rats, PLoS One. 8 (2013) e61355. C.D. Adams, A. Dickinson, Instrumental responding following reinforcer devaluation, Q. J. Exp. Psychol. Sect. B. 33 (1981) 109–121. L.S. Bueno-Junior, N.W. Simon, M.A. Wegener, B. Moghaddam, Repeated Nicotine Strengthens Gamma Oscillations in the Prefrontal Cortex and Improves Visual Attention, Neuropsychopharmacology. 42 (2017) 1590. N.W. Simon, B. Setlow, Modeling Risky Decision Making in Rodents, Methods Mol Biol. 829 (2012) 165–175. I.A. Mendez, R.J. Gilbert, J.L. Bizon, B. Setlow, Effects of acute administration of nicotinic and muscarinic cholinergic agonists and antagonists on performance in different costbenefit decision making tasks in rats, Psychopharmacology (Berl). 224 (2012) 489–499. M. Casarrubea, C. Davies, F. Faulisi, M. Pierucci, R. Colangeli, L. Partridge, S. Chambers, D. Cassar, M. Valentino, R. Muscat, A. Benigno, G. Crescimanno, G. Di Giovanni, Acute nicotine induces anxiety and disrupts temporal pattern organization of rat exploratory behavior in hole-board: a potential role for the lateral habenula, Front. Cell. Neurosci. 9 (2015) 1–17. P. Simon, R. Dupuis, J. Costentin, Thigmotaxis as an index of anxiety in mice. Influence of dopaminergic transmissions, Behav. Brain Res. 61 (1994) 59–64. A. Bechara, S. Dolan, N. Denburg, A. Hindes, S.W. Anderson, P.E. Nathan, Decisionmaking deficits, linked to a dysfunctional ventromedial prefrontal cortex, revealed in alcohol and stimulant abusers, Neuropsychologia. 39 (2001) 376–389. M.C. Olmstead, Animal models of drug addiction: Where do we go from here?, Q. J. Exp. Psychol. 59 (2006) 625–653. A. Christakou, Prefrontal Cortical-Ventral Striatal Interactions Involved in Affective Modulation of Attentional Performance: Implications for Corticostriatal Circuit Function, J. Neurosci. 24 (2004) 773–780. M. Feja, L. Hayn, M. Koch, Nucleus accumbens core and shell inactivation differentially affects impulsive behaviours in rats, Prog. Neuro-Psychopharmacology Biol. Psychiatry. 54 (2014) 31–42. I.N. Rusk, S.J. Cooper, Parametric studies of selective D1 or D2 antagonists: Effects on appetitive and feeding behaviour, Behav. Pharmacol. 5 (1994) 615–622. M.E. Toplak, G.B. Sorge, A. Benoit, R.F. West, K.E. Stanovich, Decision-making and cognitive abilities: A review of associations between Iowa Gambling Task performance, executive functions, and intelligence, Clin. Psychol. Rev. 30 (2010) 562–581. W.K. Bickel, M.N. Koffarnus, L. Moody, A.G. Wilson, The behavioral- and neuroeconomic process of temporal discounting: A candidate behavioral marker of addiction, Neuropharmacology. 76 (2014) 518–527. L.F. Andrade, N.M. Petry, Delay and probability discounting in pathological gamblers with and without a history of substance use problems, Psychopharmacology (Berl). 219 (2012) 491–499.

N

[37]

[51]

A

[52]

[53]

[54]

Gabriel et al Risky decision-making

[56] [57]

[58] [59]

[60] [61]

[66]

[67]

[68]

A

M

CC

[69]

D

[65]

TE

[64]

EP

[63]

N

U

[62]

L. Green, J. Myerson, A discounting framework for choice with delayed and probabilistic rewards, Psychol. Bull. 130 (2004) 769–792. C.A. Winstanley, Contrasting Roles of Basolateral Amygdala and Orbitofrontal Cortex in Impulsive Choice, J. Neurosci. 24 (2004) 4718–4722. C.A. Orsini, R.T. Trotta, J.L. Bizon, B. Setlow, Dissociable Roles for the Basolateral Amygdala and Orbitofrontal Cortex in Decision-Making under Risk of Punishment, J. Neurosci. 35 (2015) 1368–1379. T. Pattij, D. Schetters, A.N.M. Schoffelmeer, Dopaminergic modulation of impulsive decision making in the rat insular cortex, Behav. Brain Res. 270 (2014) 118–124. J.R. DiFranza, J.A. Savageau, K. Fletcher, J.K. Ockene, N.A. Rigotti, A.D. McNeill, M. Coleman, C. Wood, Recollections and repercussions of the first inhaled cigarette, Addict. Behav. 29 (2004) 261–272. T. Eissenberg, R.L. Balster, Initial tobacco use episodes in children and adolescents: current knowledge, future directions., Drug Alcohol Depend. 59 Suppl 1 (2000) S41–S60. M.C. Hu, M. Davies, D.B. Kandel, Epidemiology and correlates of daily smoking and nicotine dependence among young adults in the United States, Am. J. Public Health. 96 (2006) 299–308. P.A. Newhouse, T. Sunderland, P.K. Narang, A.M. Mellow, J.B. Fertig, B.A. Lawlor, D.L. Murphy, Neuroendocrine, physiologic, and behavioral responses following intravenous nicotine in nonsmoking healthy volunteers and in patients with Alzheimer’s disease, Psychoneuroendocrinology. 15 (1990) 471–484. B.R. Bewley, J.M. Bland, R. Harris, Factors associated with the starting of cigarette smoking by primary school children., Br. J. Prev. Soc. Med. 28 (1974) 37–44. G. Di Chiara, Role of dopamine in the behavioural actions of nicotine related to addiction, Eur. J. Pharmacol. 393 (2000) 295–314. W. A. Corrigall, K.B.J. Franklin, K.M. Coen, P.B.S. Clarke, The mesolimbic dopaminergic system is implicated in the reinforcing effects of nicotine., Psychopharmacology (Berl). 107 (1992) 285–289. B.J. Hall, S. Slade, C. Allenby, M.G. Kutlu, E.D. Levin, Neuro-anatomic mapping of dopamine D1 receptor involvement in nicotine self-administration in rats, Neuropharmacology. 99 (2015) 689–695. M.G. Kutlu, D. Burke, S. Slade, B.J. Hall, J.E. Rose, E.D. Levin, Role of insular cortex D₁ and D₂ dopamine receptors in nicotine self-administration in rats, Behav. Brain Res. 256 (2013) 273–8. V. Pascoli, J. Terrier, A. Hiver, C. Lüscher, Sufficiency of Mesolimbic Dopamine Neuron Stimulation for the Progression to Addiction, Neuron. 88 (2015) 1054–1066. S. Lammel, B.K. Lim, R.C. Malenka, Reward and aversion in a heterogeneous midbrain dopamine system, Neuropharmacology. 76 (2014) 351–359. A.L. Bracken, R.A. Chambers, S.A. Berg, Z.A. Rodd, W.J. McBride, Nicotine Exposure during Adolescence Enhances Behavioral Sensitivity to Nicotine during Adulthood in Wistar Rats, Pharmacol. Biochem. Behav. 99 (2011) 87–93. R.E. Bernardi, R. Spanagel, A two-injection protocol for nicotine sensitization, Behav. Brain Res. 275 (2014) 11–14.

SC RI PT

[55]

[70]

A

[71]

Gabriel et al Risky decision-making Figure Captions

SC RI PT

Figure 1. Risky Decision Making Task (RDT). Schematic shows the sequence of events in a trial in the RDT.

M

A

N

U

Figure 2. Differential Reinforcement of Low Rates of Desponding (DRL) Task. Schematic shows the sequence of events in a trial in the DRL task.

A

CC

EP

TE

D

Figure 3. Delay Discounting Task. Schematic shows the sequence of events in a trial in the delay discounting task.

Figure 4: Impulsive Action and Risk-Taking. a. Individual variability in RDT performance. b-d. Scatterplots depicting relationship between risk-taking and impulsive action. Lower DRL ratio scores (x-axis) indicate greater impulsivity. There was no correlation between risk-taking and DRL-5 accuracy (b), but there were significant negative correlations between DRL-10 and 20 accuracy and risk-taking, such that high risk-taking predicted high impulsive action (c-d). e. RDT means for risk-taking and risk-averse groups, obtained using a median split of RDT

Gabriel et al Risky decision-making

U

SC RI PT

performance. Where error bars are not visible, error was smaller than the symbol size. f-h. Group differences in DRL correct ratio. There was no difference in DRL-5 between groups (f), but risk-taking rats were more impulsive on both DRL-10 (g) and DRL-20 (h). * denotes p < .05.

A

CC

EP

TE

D

M

A

N

Figure 5: Impulsive Choice and Risk-Taking. a. Individual variability in RDT performance. b. Scatterplot depicting relationship between risk-taking and impulsive choice. Lower preference for the delayed reward (x-axis) indicates greater impulsivity. There was no correlation between risk-taking and delay discounting. c. RDT means for risk-taking and risk-averse groups. Where error bars are not visible, error was smaller than the symbol size. d. Group differences in delay discounting. There was no difference in delay discounting between risk groups.

TE

D

M

A

N

U

SC RI PT

Gabriel et al Risky decision-making

A

CC

EP

Figure 6: Habit Formation and Risk-Taking. a. Individual variability in RDT performance. b. Scatterplot depicting relationship between risk-taking and habit formation, with higher devaluation ratios (x-axis) indicating greater habit formation. There was no correlation between risk-taking and habit formation. c. RDT means for risk-taking and risk-averse groups. Where error bars are not visible, error was smaller than the symbol size. d. Group differences in habit formation. There was no difference in habit formation between groups.

TE

D

M

A

N

U

SC RI PT

Gabriel et al Risky decision-making

A

CC

EP

Figure 7: Risk-Taking and Nicotine Sensitization. a. Individual variability in RDT performance. b. Mean distance traveled for each nicotine exposure session. There was a significant difference in distance traveled between session 5 of nicotine and the sensitization session after a nine-day abstinence, indicative of locomotor sensitization to nicotine. * denotes p < .05.

SC RI PT

Gabriel et al Risky decision-making

EP

TE

D

M

A

N

U

Figure 8: Risk-taking and sensitivity to nicotine: a. There was a significant positive correlation between risk-taking and nicotine-evoked locomotion on session one. b. There was a near significant difference in locomotion evoked by the first nicotine exposure, but no differences or trends between groups in subsequent sessions. c. There was a significant positive relationship between risk-taking and the ratio between nicotine- and saline-evoked locomotion. * denotes p < .05. + denotes near significant differences (p between .1 and .05).

A

CC

Figure 9: Risk-Taking and First-Time Nicotine Exposure: a. There was a significant positive correlation between risk-taking and distance traveled in center, a measure of reduced anxiety. b. Risk-taking rats showed greater time in the center of the field after nicotine compared to riskaverse. c. There was a near significant positive correlation between risk-taking and the ratio between time in center and total nicotine-evoked locomotion. d. Risk-taking rats demonstrated a higher ratio between time in center and total locomotion compared to risk-averse. * denotes p < .05. + denotes near significant differences (p between .1 and .05).

A

CC

EP

TE

D

M

A

N

U

SC RI PT

Gabriel et al Risky decision-making

Gabriel et al Risky decision-making Table Table 1. Sequence of Behavioral Tasks and Group Sizes in Experiment 1. The first column depicts tasks performed by each cohort in chronological order. The second column depicts sample sizes for each cohort. Sample Size

Delay Discounting, RDT, DRL

n=9

Delay Discounting, RDT, DRL, Reinforcer Devaluation n=6 n=16

Reinforcer Devaluation, RDT

n=19

DRL, RDT

n=18

A

N

U

DRL, RDT, Delay Discounting

SC RI PT

Task Sequence

Correlation

Day 1 Nicotine

Median Risk Group Difference

r = .500, p = .049*

F (1, 14) = 3.398, p = .087+

r = -.079, p = .770

F (1, 14) = .124, p = .730

r = -.267, p = .318

F (1, 14) = .693, p = .419

Day 4 Nicotine

r = -.185, p = .493

F (1, 14) = .933, p = .350

Day 5 Nicotine

r = -.060, p = .826

F (1, 14) = .073, p = .791

Sensitization Nicotine

r = .154, p = .569

F (1, 14) = .034, p = .856

A

Day 3 Nicotine

CC

EP

Day 2 Nicotine

TE

D

Nicotine session

M

Table 2: Statistical relationships between nicotine-induced locomotion and risk-taking.