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. Author manuscript; available in PMC: 2025 Mar 1.
Published in final edited form as: Pharmacol Biochem Behav. 2024 Jan 23;236:173718. doi: 10.1016/j.pbb.2024.173718

Differential disruption of response alternation by precipitated Δ9-THC withdrawal and subsequent Δ9-THC abstinence in mice

ML Eckard 1,*, SG Kinsey 2
PMCID: PMC10955601  NIHMSID: NIHMS1963706  PMID: 38272272

Abstract

In addition to overt somatic symptoms, cannabinoid withdrawal can also manifest as disruptions in motivation and attention. Experimental animal models using operant-conditioning approaches reveal these differences, in either antagonist-precipitated or spontaneous withdrawal models. However, these processes have yet to be characterized in the same subjects simultaneously. To differentiate between motivational and attentional processes disrupted in cannabinoid withdrawal, the current study used a response alternation task in which a fixed-ratio (FR) schedule repeatedly alternated between two spatially distinct response options throughout daily training sessions. This task yielded traditional measures of motivation (e.g., response latency) as well as attention (e.g., responses to the incorrect side). After two weeks of training, male and female C57BL/6J mice either received vehicle or Δ9-THC (10 mg/kg, s.c.) twice daily for 5 days. On the 6th day, all mice received their final injection of vehicle or Δ9-THC followed 30 min later by injection of the CB1 receptor selective inverse agonist rimonabant (2 mg/kg, i.p.) to precipitate withdrawal. Testing continued for 3 days post-rimonabant to assess how THC abstinence impacted task performance. Whereas rimonabant decreased response rates to equal degrees in THC-treated and vehicle-treated mice, THC-treated mice showed longer session times, longer response latencies, and more errors per reinforcer. Only THC-treated mice showed a longer latency to switch after committing an error reflecting that precipitated withdrawal impacted measures of both motivation and attention. During the 3-day abstinence window, performance of vehicle-treated mice returned to baseline, but THC-treated mice continued to show disruptions in motivational measures. Importantly, attentional measures (errors and latency to switch after an error) were unaffected by THC abstinence. These data suggest that precipitated and “spontaneous” cannabinoid withdrawal may be qualitatively and quantitatively distinct withdrawal conditions with precipitated withdrawal disrupting both attentional and motivational processes, while abstinence may only affect motivation.

Keywords: Cannabinoid withdrawal, Cannabis use disorder, THC abstinence, attentional deficit, motivational deficit

1.0. Introduction

Cannabis remains the most widely used illicit drug in the world. Like other drugs of abuse, many people can use cannabis without becoming dependent, as indicated by approximately 20% of users developing Cannabis Use Disorder (CUD) (Hasin, 2018). Of federally illicit drugs, cannabis has the lowest rates of perceived harm and the highest perceived ease of access, social acceptance, and availability. Despite this low perceived addictive potential, dependent cannabis users often experience unsuccessful quit attempts without the aid of pharmacological and/or psychosocial treatment (Hughes et al., 2016), indicating a continued need for additional CUD treatment development. In humans, commonly reported withdrawal symptoms include sleep disruption, gastrointestinal discomfort, anxiety, irritability, depressed mood, and difficulty concentrating (Budney et al., 2003, 2008; Budney & Hughes, 2006; Schlienz et al., 2017). Although cannabis withdrawal symptoms are not harmful to physical health, they increase the probability of relapse (Allsop et al., 2012; Budney et al., 2008).

Animal models have been important in developing a mechanistic understanding of the neurobiological and behavioral symptom profile associated with the cannabinoid withdrawal syndrome (Lichtman & Martin, 2002; Ramesh et al., 2011; Wilson et al., 2006). Recent work has revealed that antagonist-precipitated or spontaneous cannabinoid withdrawal can increase anxiety-like behavior and decrease motivation to earn non-drug reinforcers in operant-conditioning paradigms (Beardsley & Martin, 2000; Eckard et al., 2020; Harte-Hargrove & Dow-Edwards, 2012; Kesner et al., 2022; Trexler et al., 2018). Disruptions in ongoing operant behavior are sensitive measures of drug withdrawal (Emmett-Oglesby et al., 1990). Simple reinforcement schedules have been used to quantify behavioral disruption during spontaneous or antagonist-precipitated cannabinoid withdrawal. Some examples include food-maintained responding on a single fixed-ratio schedule (Beardsley et al., 1986), variable-interval schedule (Beardsley & Martin, 2000), or progressive-ratio schedule (Eckard et al., 2020). Across these procedures, a common finding is that precipitated withdrawal or spontaneous withdrawal/abstinence decreases response rates and/or reinforcers earned, consistent with the decreased sensitivity to non-drug reinforcers observed during drug withdrawal generally (Negus & Banks, 2021).

While conditioning procedures may represent more sensitive measures of cannabinoid withdrawal than traditional observational measures, most studies using this approach have focused on single-operant preparations. That is, tasks often require responses to be allocated to only one response option. Despite their utility, a given performance metric from a single-operant task (e.g., response latency) may be influenced by multiple, dissociable processes. For example, if an animal displays long response latencies during drug withdrawal or some other putatively aversive state, it is unclear if the stimulus correlated with reinforcement lacks control over responding (i.e., attention/working memory), if the reinforcer itself has become less efficacious (i.e., motivation), or if the effect is due to motor effects or sedation (Lotfizadeh et al., 2012). It has been reported that cannabinoid withdrawal affects measures of attention or memory and motivation, but these processes have not been evaluated simultaneously. For example, precipitated cannabinoid withdrawal disrupts prepulse inhibition in addition to producing spatial and working memory impairment (Hampson et al., 2003; Marusich et al., 2014; Wise et al., 2011). Interestingly, spontaneous WIN55,212–2 withdrawal did not affect prepulse inhibition (Bortolato et al., 2005) suggesting that precipitated withdrawal may selectively impair attention or attentional gating. Motivational deficits have also been characterized during precipitated or spontaneous THC withdrawal in studies using simple operant-conditioning schedules outlined above (Beardsley et al., 1986; Beardsley & Martin, 2000; Eckard & Kinsey, 2021). However, attentional and motivational processes have not been evaluated concurrently to determine if THC withdrawal affects one process more so than the other within the same animal, particularly between precipitated and abstinent withdrawal states.

To determine the primary process affecting behavior, motivational and attentional processes should be parsed within a single procedure that requires both (1) continual or repeated stimulus attention and (2) some type of work requirement. This may be accomplished using a choice procedure that requires switching between two response alternatives with equal response requirements during a single session. A similar approach has recently been used to identify disruptions in cue-induced reward seeking in mice during spontaneous THC withdrawal (Kesner et al., 2022). This procedure showed high sensitivity in detecting general behavioral impairment induced by spontaneous THC withdrawal, although the lack of a response requirement limits its capacity to dissociate between motivational and attentional processes, per se.

To extend this previous work, the current study used an alternating fixed-ratio (FR) procedure to differentiate between behavioral disruption caused by precipitated THC withdrawal vs. post-withdrawal THC abstinence using a task that provides functional indices of attention and motivation. It was hypothesized that precipitated THC withdrawal would disrupt motivational measures to a greater extent than attentional measures and that both effects would be present but less robust during post-withdrawal THC abstinence.

2. Methods

2.1. Animals

Adult male and female C57BL/6J mice (Jackson Laboratories, Bar Harbor, ME) aged approximately 10 weeks at the start of testing were used as subjects. All mice were housed in the same AAALAC-accredited, climate-controlled (20–22C; 45–55% humidity) room operating on a 12:12 h light/dark cycle (lights on at 0600) in Polysulfone plastic cages (4–5 mice/cage) on corncob bedding, with water freely available. Food was restricted to 1.5–2g of chow/mouse/day and mice were weighed daily throughout testing. This feeding regimen produced consistent daily weights with individual mouse weights varying by less than 1g on a weekly basis. The Animal Care and Use Committee at West Virginia University, where all data collection occurred, approved all experimental protocols.

2.2. Drugs

Rimonabant (SR141716A) and Δ9-tetrahydrocannibinol (THC) were generously provided by the National Institute on Drug Abuse (NIDA) Drug Supply Program (Bethesda, MD). Both drugs were dissolved in a 1:1:18 parts vehicle consisting of ethanol:Kolliphor EL (Sigma-Aldrich, St. Louis, MO):saline (0.9% NaCl) (Eckard & Kinsey, 2021; Lichtman et al., 1998). When repeated injections began, THC (10 mg/kg, s.c.) or vehicle was injected 1 h prior to daily testing sessions and again approximately 12 h later that day. To precipitate withdrawal, rimonabant (2 mg/kg, i.p.) was administered 30 min prior to testing following the final THC or vehicle injection (see Pharmacological procedures, below). These doses and pretreatment times were based on prior experience and the published literature (Eckard et al., 2020; Trexler et al., 2018). All solutions were warmed to room temperature before being injected at a volume of 10 ul/g body mass.

2.3. Apparatus

Nine MED-Associates® operant-conditioning chambers for mice were used for data collection (17.8 cm L ×15.2 cm W ×18.4 cm H). The work panel of the chambers consisted of two nose-poke holes spaced 9 cm apart, both of which could be illuminated by a small yellow LED bulb. Head entries into the active nose-poke aperture were detected by breaks in an infrared photobeam. Sucrose water (15% wt/v; 20 μl/delivery) could be accessed from a dipper cup equidistant between both nose-poke holes. The floor of each chamber consisted of a stainless-steel grid of 19 horizontal bars. A houselight centered at the top of the back wall opposite the work panel illuminated the interior of the chamber during sessions. Chambers were enclosed in a sound-attenuating box with a wall-mounted fan that provided ventilation and white noise during sessions. All experimental events were controlled by a MED-Associates® interface and desktop computer in an adjacent room using MED-PC notation.

2.4. Behavioral training

2.4.1. Pre-training procedure

Nose-poking was established using an autoshaping procedure as previously described (Eckard et al., 2020), with minor adjustments. Briefly, sucrose water was made available according to a conjoint variable-time (VT) 90 s, fixed-ratio (FR) 1 schedule of reinforcement. That is, sucrose water was presented freely every 90 s on average or following a single response to either the left or right nose-poke aperture. Autoshaping sessions ended following 3 hr or 100 response-dependent reinforcers, whichever occurred first. All mice acquired nose-poking within 1–3 sessions. Once responding was reliable, an FR-1 schedule was in place for two sessions. The FR-1 schedule alternated between the left and right nose ports every five reinforcers, which was signaled by illumination of the nose port LED. Whether the left or right nose port was active at the start of the session was randomly determined (p = 50%). FR-1 sessions ended following 100 reinforcers or 90 minutes elapsing, whichever occurred first.

2.4.2. Response alternation procedure

Following two sessions of the alternating FR-1 schedule, the terminal alternating FR-3 schedule began. The procedure was identical to the FR-1 schedule except that three sequential responses to the correct nose port were required for reinforcement. Incorrect responses reset the response counter on the correct nose port. As with the FR-1 schedule, the correct nose port (left or right) was randomly selected (p = 50%) at the start of the session and alternated thereafter following every fifth reinforcer for a total of 60 reinforcers and 12 alternations per session (Fig. 1). Due to very low response rates expected during precipitated cannabinoid withdrawal (Eckard et al 2020; Beardsley & Martin, 2000), session duration was capped at 2.5 h. During baseline training, session times rarely exceeded 25 min (see Fig. 2). As an initial test to determine if motivational and attentional measures were dissociable on this task, two separate nose port signaling conditions were conducted: signaled and unsignaled nose ports, respectively. For the initial three sessions, the correct nose port was signaled via LED illumination while the incorrect nose port LED was extinguished. During the next five sessions, both nose ports were illuminated simultaneously (referred to as the unsignaled condition). Due to errors stabilizing at relatively high levels during the unsignaled condition, the signaled condition was reintroduced and remained in place for nine sessions until responding stabilized prior to repeated injections.

Figure 1.

Figure 1.

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Schematic representation of the alternating FR procedure. (A) At the start of a session, the left or right nose port is randomly selected as “correct” for each mouse. Mice earned a reinforcer for each FR-3 completed on the correct side. The “correct” nose port switched sides every five reinforcers earned for a total of 12 alternations per session. (B) Example pattern of six FR-3 alternations if the right nose port was selected first. Each grey box represents one FR-3 component.

Figure 2.

Figure 2.

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Rimonabant increased session time in THC- and vehicle-treated mice but to a greater extent in THC-treated mice. Session times remained longer in THC-treated mice during a 3-day abstinence window while vehicle-treated mice returned to baseline. Data represent mean ± SEM (n = 9; THC = 4F/5M; Vehicle = 5F/4M). BL 1 = pre-THC baseline. BL 2 = post-THC abstinence. * p < 0.05 vs Session 3 (THC phase) or vehicle (BL 2 phase); # p < 0.05 vs Session 3 & 8 and vehicle; ~ p < 0.05 vs. Session 8 vehicle.

2.5. Pharmacological procedures

Prior to drug treatment, mice were pseudo-randomly assigned to two groups (n = 9) to receive either THC or vehicle with both groups matched for response rate and sex (see Section 2.6 for group matching description). Groups received twice-daily injections of either THC (10 mg/kg, s.c., b.i.d.; n = 9) or vehicle (1:1:18, s.c., b.i.d.; n = 9) for 5 days. Morning injections occurred 1 h prior to operant testing sessions, and evening injections occurred approximately 12 h later. The final THC or vehicle injection occurred on the 6th day 1 h prior to the withdrawal testing session followed 30 min later by rimonabant (2 mg/kg, i.p.) administration, which occurred 30 min before testing (Eckard et al., 2020). Rimonabant was administered to all mice.

2.6. Dependent measures and grouping

Several dependent measures were used to quantify response alternation performance during baseline testing, repeated THC or vehicle injections, withdrawal, and subsequent abstinence following withdrawal. Overall response rates (i.e., responses per sec) were calculated by dividing total responses (correct + error) by total session time (responses / session time). Latency to initiate responding following a reinforcer was also measured and used to calculate run rate, which was calculated by dividing total responses by session time minus cumulative FR latency (responses / [session time – cumulative latency]). Session time in minutes was also recorded as an indication of overall task efficiency. Responses to the inactive nose port were recorded as errors and are expressed as relative errors or errors per reinforcer (error / total reinforcers) to control for decreases in total reinforcers, and thus opportunities to commit errors, during acute THC administration and withdrawal. When a single error or string of errors was committed following a reinforcer, the time between the last error and the next correct response was recorded as the latency to switch. Finally, in addition to total errors per reinforcer for each session, errors were also analyzed with respect to within-block FR sequence between alternations to assess perseverative responding. For example, it was expected that errors would be greatest just after a transition (FR #1) and would decrease thereafter to near-zero levels for the remaining ratios (FR #2–5). It is possible that repeated THC administration and/or THC withdrawal could selectively affect FR #1 error or produce erratic error patterns throughout the remaining FR’s within a block.

After responding stabilized during the final signaled condition, mice were assigned to receive repeated THC or vehicle injections. Groups were matched with respect to response rate with the added constraint that sex be balanced as best as possible using matched random assignment. Specifically, mice were rank-ordered highest to lowest on response rate and mice from each descending pair were randomly assigned to vehicle or THC groups. This resulted in approximately equal baseline values for each measure between groups (see Figures and Supplemental Figures) and as closely matched sex as possible (Vehicle: 5 F, 4 M; THC: 4 F, 5 M).

2.7. Statistical analyses

All statistical tests were initially conducted with sex as a between-subjects factor. However, no drug effects in this dataset were dependent on sex (all interaction p’s > 0.17). Thus, sex was dropped as a factor to increase statistical power.

To characterize effects of signaling on performance, the average of the final two sessions in each signal condition (signaled – unsignaled – signaled) were analyzed using a one-way repeated-measures analysis of variance (RMANOVA). Due to a programming error during these phases, latency to switch and run rate data were not available. Errors and response rate serve as surrogate measures of attention and motivation, respectively. Significant effects of condition were followed by Dunnett’s post-hoc test using the initial signal condition for comparison.

Three separate windows of sessions were analyzed to assess (1) acute effects of and tolerance to repeated THC administration (Sessions 3–7), (2) effects of rimonabant relative to the final session of baseline and repeated injections (Sessions 3, 8, and 9) and (3) effects of THC abstinence following withdrawal (Sessions 10–12). Data across sessions were analyzed using RMANOVA. Specifically, acute and repeated THC administration (1) was analyzed using 2 (Group) x 6 (Session) RMANOVA, and effects of rimonabant (2) and subsequent THC abstinence (3) were analyzed via 2 (Group) x 3 (Session) RMANOVA. Data from the single rimonabant session were also analyzed using independent-samples t tests.

Effects of rimonabant on error distribution across within-block ratios were analyzed in two ways. First, errors were analyzed on the day withdrawal was precipitated across ratio order using 2 (Group) × 5 (ratio order) RMANOVA. Second, withdrawal-day errors within each ratio order (i.e., separate RMANOVAs for each ratio order) were analyzed relative to the final day of baseline and final day of repeated injections within each group using a one-way RMANOVA with Session as the within-subjects factor.

All significant interactions from factorial RMANOVAs were followed by Tukey’s HSD post hoc test. Effect sizes were estimated using partial-eta squared. If violations of sphericity were detected with Mauchley’s test, Greenhouse-Geisser adjustments were applied. Tests were considered significant if p < 0.05. All data are expressed as average raw values +/− SEM.

3.0. Results

3.1. Nose port signaling affects errors but not response rate

As a preliminary validation of the FR alternation task to dissociate motivational from attentional measures, nose ports were signaled or unsignaled across conditions as outlined above. A main effect of condition was present for both total errors (F(2,34) = 102.81, p < 0.001, ηp2 = 0.86; Suppl. Fig 1A) and response rate (F(2,34) = 11.40, p < 0.001, ηp2 = 0.40; Suppl. Fig 1B). Errors during the unsignaled condition were higher than the initial signaled condition (p < 0.001) and reversed to near-baseline levels when signals were reintroduced but were still slightly higher than baseline (p = 0.03). Response rate increased across conditions irrespective of signaling with the unsignaled (p = 0.005) and subsequent signaled phases (p < 0.001) showing higher response rates than the initial signaled condition. Error distributions were also affected by signaling with a significant Phase × Within-block trial interaction (F(8,204) = 14.91, p < 0.001, ηp2 = 0.37) revealing that unsignaled errors were higher in each trial while error distributions were similar between the initial and final signaled conditions (Suppl. Fig. 1C).

3.2. Precipitated THC withdrawal and/or subsequent abstinence slows task engagement

Session time was initially analyzed to assess how repeated THC administration and subsequent withdrawal/abstinence affected overall task engagement (Fig. 2). THC administration increased session time overall (Group: F(1,16) = 9.04, p = 0.008, ηp2 = 0.36). A significant Session × Group interaction (F(5,80) = 4.25, p = 0.02, ηp2 = 0.21) revealed that THC specifically increased session time during the first two repeated THC sessions (p’s < 0.02) and approximated baseline thereafter (p’s > 0.14). Vehicle administration did not affect session time (p’s = 0.97). Analysis of rimonabant administration showed that THC-treated mice had longer session times overall (Group: F(1,16) = 8.79, p = 0.009, ηp2 = 0.35), but this effect also depended on the session observed (Group × Session: F(2,32) = 5.01, p = 0.03, ηp2 = 0.23). Specifically, rimonabant administration produced longer session times in THC-treated mice relative to vehicle-treated mice (p = 0.003) and all other session comparisons (p’s < 0.001). Importantly, rimonabant also increased session times in vehicle-treated mice relative to vehicle administration and baseline (p < 0.001). Following precipitated withdrawal, THC-treated mice continued to show prolonged session times during the 3-day abstinence window relative to vehicle-treated mice (Group: F(1,16) = 6.34, p = 0.02, ηp2 = 0.28; Fig. 2), whereas session times for vehicle-treated mice immediately returned to baseline levels.

Increased session times were likely influenced by prolonged latencies to initiate FR responding in THC-treated mice (Fig. 3A). Similar to session time, THC administration increased the average latency to initiate responding following reinforcement (Group: F(1,16) = 6.65, p = 0.02, ηp2 = 0.29). Latencies gradually decreased across repeated THC administration sessions (Group × Session: F(5,80) = 5.34 p < 0.001, ηp2 = 0.25) with Sessions 5 and 8 approximating baseline values (p’s > 0.09). Vehicle administration did not affect FR latency (p’s > 0.98). Rimonabant administration increased FR latency regardless of treatment group (Session: F(2,32) = 64.05, p < 0.0001, ηp2 = 0.80), but THC-treated mice showed longer latencies across sessions (Group: F(1,16) = 7.20, p = 0.02, ηp2 = 0.31) and on withdrawal day when compared to vehicle-treated mice (t(16) = 2.28, p = 0.036). During the abstinence window, THC-treated mice continued to show longer FR latencies (Group: F(1,16) = 6.01, p = 0.03, ηp2 = 0.27) relative to vehicle-treated mice with latencies of vehicle-treated mice returning to baseline following rimonabant administration.

Figure 3.

Figure 3.

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Precipitated THC withdrawal or THC abstinence slows task engagement. Rimonabant increased FR latency (A) in THC- and vehicle-treated mice but to a greater extent in THC-treated mice but decreased run rate (B) equally in both groups. FR latency (A) and run rate (B) remained disrupted in THC-treated mice during the 3-day abstinence window. Data represent mean ± SEM (n = 9; THC = 4F/5M; Vehicle = 5F/4M). BL 1 = pre-THC baseline. BL 2 = post-THC abstinence. * p < 0.05 vs Session 3 (THC phase) or vehicle (BL 2 phase); # p < 0.05 vs Session 3 & 8 and vehicle; ~ p < 0.05 vs. Session 8 vehicle; @ p < 0.05 vs Session 8.

As expected, THC administration initially decreased run rates (Group: F(1,16) = 5.03, p < 0.04, ηp2 = 0.24; Fig. 3B). Tolerance to THC’s suppressive effects were also observed (Group × Session: F(5,80) = 2.64, p < 0.03, ηp2 = 0.15) with only the first two repeated THC sessions differing from baseline (p’s < 0.004). Interestingly, tolerance to THC was not observed in overall response rate (all post hoc p’s < 0.02 relative to baseline; Suppl. Fig. 2). Rimonabant administration decreased run rate regardless of group (Session: F(2,32) = 22.86, p < 0.0001, ηp2 = 0.59) with no difference between THC- or vehicle-treated mice (p = 0.20). However, during THC abstinence following rimonabant, run rates of THC-treated mice remained low (Group: F(1,16) = 4.72, p = 0.04, ηp2 = 0.23) relative to run rates of vehicle-treated, which again returned to baseline levels.

3.3. Precipitated THC withdrawal impairs response efficiency and error correction

Measures of response efficiency and error correction were selectively impacted by precipitated THC withdrawal rather than THC abstinence. THC administration initially disrupted response efficiency via increases in errors per reinforcer (Group: F(1,16) = 6.05, p = 0.02, ηp2 = 0.27); however response efficiency improved with repeated THC administration (Group × Session: F(5,80) = 2.73, p = 0.03, ηp2 = 0.15) with only the 2nd day of repeated THC differing from baseline (p < 0.001; Fig. 4A). Vehicle administration did not affect errors per reinforcer (p’s > 0.99). Rimonabant did not differentially affect relative errors in THC-treated mice relative to previous sessions (Group & Session p’s > 0.12; Fig. 4A). Lastly, relative error was not impacted by THC abstinence (p = 0.83; Fig 4A).

Figure 4.

Figure 4.

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Precipitated THC withdrawal selectively disrupts response efficiency and error correction. THC-treated mice showed increased errors per reinforcer (A) and longer latency to switch (B) following rimonabant administration. THC abstinence had no effect on these measures. Data represent mean ± SEM (n = 9; THC = 4F/5M; Vehicle = 5F/4M). BL 1 = pre-THC baseline. BL 2 = post-THC abstinence. * p < 0.05 vs Session 3; @ p < 0.05 vs vehicle; # p < 0.05 vs Session 3 & 8 and vehicle.

Latency to switch after committing an error was transiently increased by repeated THC administration (Group × Session: F(5,80) = 2.93, p = 0.043, ηp2 = 0.15; Fig 4B). Only the 1st session of THC administration increased latency to switch (p = 0.001; all other p’s > 0.47). Rimonabant selectively increased latency to switch in THC-treated mice (Group × Session: F(2,32) = 6.27, p = 0.005, ηp2 = 0.28) without affecting switch latency in vehicle-treated mice (p = 0.78; Fig 4B).

During the precipitated withdrawal session, rimonabant selectively decreased reinforcers earned in THC-treated mice (M = 50.2, SEM = 2.98) relative to vehicle-treated mice (M = 60, SEM = 0) (t(16) = 3.27, p = 0.004; Fig. 5A). Additionally, THC-treated mice showed greater relative error compared to vehicle-treated mice during the precipitated withdrawal session (t(16) = 2.70, p = 0.02; Fig. 5B). Because rimonabant increased overall errors per reinforcer, an analysis was conducted to determine if that increase was uniform across within-block FR sequences or was specific to FR alternations (i.e., the first FR in a block). Reflecting the analysis in Fig. 5B, THC-treated mice showed higher overall errors per reinforcer across within-block trials following rimonabant administration (F(1,16) = 5.25, p = 0.035, ηp2 = 0.25; Fig 5C). However, the Group × FR order interaction was not significant (p = 0.15). Exploratory post-hoc tests showed that relative error in the first ratio was increased in THC-treated mice relative to vehicle-treated mice (p = 0.048) with no other differences observed across within-block ratios (p’s > 0.22; Fig. 5C). Supplementary analyses of error distributions were conducted to compare rimonabant effects to the final day of baseline and repeated injections within treatment groups. Importantly, rimonabant did not differentially affect error distributions in THC-treated mice relative to previous sessions (p’s > 0.32; Suppl. Fig. 3).

Figure 5.

Figure 5.

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Precipitated THC withdrawal produces inefficient, perseverative responding. Rimonabant administration reduced reinforcers earned (A) and increased errors per reinforcer (B) in THC-treated mice. The increase in relative errors was concentrated in the first ratio after a transition (C). Data represent mean ± SEM (n = 9; THC = 4F/5M; Vehicle = 5F/4M). * p < 0.05 vs Vehicle; # p < 0.05 vs Vehicle ratio 1.

4.0. Discussion

The present study was designed to test the hypothesis that precipitated THC withdrawal produces disruption in attentional and motivational measures, whereas post-withdrawal abstinence produces smaller effects on both sets of measures. To differentiate between these processes, we used a response-alternation task that allowed for simultaneous measurement of attentional and motivational processes. Our findings support the initial hypothesis that precipitated THC withdrawal disrupts attentional and motivational measures of FR-alternation performance. However, our second hypothesis was not fully supported as post-withdrawal THC abstinence was selective in disrupting motivational measures.

The outcomes reported here are consistent with previous findings of precipitated or spontaneous THC withdrawal. Either precipitated THC withdrawal in rodents (Beardsley & Martin, 2000; Eckard et al., 2020; Freedland et al., 2003) or spontaneous THC withdrawal in monkeys (Beardsley et al., 1986) reduces operant response rates and slows overall task engagement. While rimonabant administration unexpectedly decreased run rate and overall response rate in the current study in both THC-treated and vehicle-treated mice to a similar degree, THC-treated mice showed longer session times during precipitated withdrawal as well as longer latencies to initiate responding after receiving a reinforcer and fewer reinforcers overall during the rimonabant treatment session. Despite the rate-decreasing effects of rimonabant, longer sessions times were likely obtained in THC-treated mice through the additive effects of longer FR latencies and longer latencies to switch after committing an error. Historically, longer response latencies in the absence of motor suppression are interpreted as reduced motivation to seek reinforcers (Fouriezos et al., 1978; Wise, 2004). Importantly, these motivational disruptions were present for up to 3 days post-rimonabant administration in THC-treated, but not vehicle-treated mice. Although, prolonged response rate reductions during THC abstinence could be interpreted as possible continued hypolocomotor effects from repeated THC administration. Two pieces of evidence argue against this interpretation. First, across most measures of task performance, mice became tolerant to repeated THC (10 mg/kg, s.c.) resembling baseline performance by day 5 of THC treatment. Thus, if any circulating THC, or rimonabant for that matter, remained during abstinence, then rates would likely have been higher than observed during withdrawal or abstinence. Second, pharmacokinetic data from mice show that plasma and brain half-life of THC (5 mg/kg, i.p.) and its active metabolite 11-OH-THC is approximately 2 hrs (Torrens et al., 2020). Additionally, similar pharmacokinetic studies in rats have shown rimonabant (3 mg/kg, i.p.) plasma half-life to be around 5 hrs (Ravula et al., 2018). Post-injection data from vehicle-treated mice in the current study support these pharmacokinetic data in that all measures of performance returned to baseline in the sessions immediately following rimonabant administration. Thus, it is likely that the effects reported here are due to withdrawal and subsequent abstinence rather than continued effects of repeated THC or acute rimonabant.

Interestingly, measures that may reflect cognitive or attentional aspects of this FR alternation task were selectively affected by rimonabant administration in THC-treated mice, but not vehicle-treated mice, despite rimonabant decreasing vehicle response rates. During the precipitated withdrawal session, THC-treated mice showed increased errors per reinforcer as well as increased latency to switch after committing an error. Additionally, exploratory analyses showed that increased errors during the rimonabant session in THC-treated mice were concentrated in the first FR component following a side alternation. However, the interpretation of this exploratory analysis is tempered by assessing within-block errors across baseline, repeated THC, and withdrawal in which THC-treated mice do not show detectable increases from baseline (See Supplemental Fig. 3). These data support a tentative conclusion that precipitated cannabinoid withdrawal produces disruption in goal-directed behavior, whereas rimonabant administration in cannabinoid-naïve mice is selective in disrupting appetitive behavior (Marusich & Wiley, 2012; Rademacher & Hillard, 2007; Rasmussen & Huskinson, 2008). Rimonabant administration in cannabinoid-treated, relative to vehicle-treated, rats produces greater reductions in the expression of various CB1 G-protein mRNA (Rubino et al., 1998) and glucose utilization (Freedland et al., 2003) across various brain regions related to motivation, cognitive flexibility, and consummatory behavior. These anatomical findings are consistent with the robust behavioral disruption observed during precipitated withdrawal reported here. However, withdrawal-specific effects of rimonabant may be called into question given that some measures of performance (e.g., FR latency, run rate, session time) were nearly identical between acute THC and acute rimonabant/withdrawal. It is important to note that the dose of THC used in this study (10 mg/kg) is known to induce robust sedative effects in mice whereas the current dose of rimonabant (2 mg/kg) does not (Marshell et al., 2014). The profile of acute THC effects in the current study are consistent with an initial sedative effect that dissipated as tolerance developed whereas rimonabant appeared to reduce motivation for food specifically as noted above. Additionally, in cannabinoid-naïve mice, rimonabant shows consistent and robust antagonism of cannabimimetic effects in tetrad testing without showing effects when administered alone (Marshell et al., 2014; Vanegas et al., 2022; Wiley & Martin, 2003). In THC-treated mice in the current study, rimonabant did not reverse behavior back to baseline levels, as would be predicted in cannabinoid-naïve mice. The profile of effects observed here are consistent with an interpretation of precipitated withdrawal. Future studies using this procedure may consider conducting dose-response curves of THC alone and in combination with different doses of rimonabant to determine how THC + rimonabant affects responding in cannabinoid-naïve subjects in this task.

As noted above, the current findings may suggest that precipitated cannabinoid withdrawal affects attentional processes in addition to motivation. Similar findings have been reported in a prepulse inhibition model of attentional gating in which precipitated THC withdrawal reduced prepulse inhibition whereas spontaneous WIN55,212–2 withdrawal does not (Bortolato et al., 2005; Marusich et al., 2014). However, differences in agonist treatment and rodent strains may be partially responsible for these previous effects. Additionally, given that the FR alternation task was not fully validated using behavioral and pharmacological manipulations known to influence motivation and attention separately, other interpretations of the current findings are possible. For example, it could be that rimonabant administration in THC-treated mice produced somatic withdrawal signs that interfered with the ability to engage in the nose-poking task. While somatic signs were not quantified in this study, other studies using the same treatment regimen as used here suggest that somatic signs are not reliably induced in mice until rimonabant doses are 3 mg/kg or higher (Trexler et al., 2018; Wise et al., 2011). These studies also demonstrate that functional behavioral deficits (e.g., Morris water maze performance or marble burying) are detected at lower precipitated withdrawal magnitudes than somatic signs. It is also possible that precipitated withdrawal in the current study produced a memory impairment rather than strictly an attentional deficit. Indeed, precipitated THC withdrawal impairs performance in spatial navigation memory tasks as well as declarative memory tasks like delayed non-match to sample (Hampson et al., 2003; Wise et al., 2011). While this is a possible interpretation, the signaling manipulation in the current study selectively affected error distributions without affecting response rates. The unsignaled condition is somewhat similar to delayed recall tasks in that the subject must “remember” what side it most recently received reinforcement whereas the signaled condition provides explicit information about what side offers reinforcement. Errors committed during signaled performance like reflect a deficit in attending rather than remembering. However, additional manipulations that target motivational processes (e.g., pre-feeding or dopaminergic agonists) or attentional processes (e.g., cholinergic antagonists) will be necessary to fully validate measures of FR alternation performance.

It is a common critique of cannabinoid withdrawal studies to rely on precipitated models rather than spontaneous models. In agreement with these critiques, reports of spontaneous withdrawal in which no antagonist is administered are becoming more common (Kesner et al., 2022; Navarrete et al., 2018; Paronis et al., 2022; Trexler et al., 2018). However, these withdrawal signs are often limited to overt signs such as somatic behaviors, which are often very subtle changes (Trexler et al., 2018) or detected more robustly using higher efficacy agonists (e.g., AM2389; Paronis et al., 2022). These studies largely reflect that antagonist-precipitated withdrawal increases the magnitude of overtly observable withdrawal-related behaviors that may be too subtle to quantify during spontaneous withdrawal or abstinence. In contrast, behavioral assays involving choice of freely available concurrent food options (e.g., sucrose preference) or operant tasks involving choice or decision-making reveal more consistent effects of spontaneous THC withdrawal specifically (Kesner et al., 2022) and may be helpful in differentiating withdrawal states induced by precipitated vs. spontaneous withdrawal. Although the current study did not use a purely spontaneous model, the response alternation task was able to detect continued motivational disruption during abstinence following precipitated withdrawal. This finding contrasts with previous data in which performance in a single-operant task (PR schedule) immediately returned to baseline levels in the session following precipitated withdrawal using identical THC and rimonabant treatment regimens (Eckard et al., 2020). The current data are in line with those of Kesner et al. (2022) showing that spontaneous THC withdrawal increases latency to approach a conditioned stimulus signaling sucrose availability. The selectivity of THC abstinence in the current study for impacting motivational measures rather than attentional measures may suggest that spontaneous THC withdrawal produces an amotivational state rather than attentional or general cognitive disruption.

The findings in the current study should be interpreted considering several limitations. First, no rimonabant dose-response was conducted prior to THC administration to determine an ineffective dose. The 2 mg/kg dose was selected based on prior data that it did not significantly affect behavior maintained by a low step-size PR schedule (Eckard et al., 2020). Indeed, the sizeable reduction in response rates in vehicle-treated mice in current study supports the conclusion that choice-based tasks are more sensitive to cannabinoid augmentation relative to single-operant tasks. Second, data collection was terminated before abstinence performance in THC-treated mice resembled that of vehicle-treated mice. Previous data suggest that rimonabant administration does not differentially affect withdrawal-related behavior following four days of abstinence from CP55,940 administration in rats (Rubino et al., 1998). Behavior of THC-treated mice during abstinence in the current study began approximating vehicle levels by Day 3 of abstinence, so it is likely that this Day 4 target would have been beneficial to include in the current design. Third, although this study included both female and male mice, it was not powered to detect sex-specific effects of withdrawal. Prior data suggests that male mice may be more sensitive to THC withdrawal specifically when using operant choice tasks to detect spontaneous withdrawal (Kesner et al., 2022). Thus, future studies may consider including sex as a biological factor. Fourth, because a THC treatment group that did not receive rimonabant was not included in this study, purely spontaneous THC withdrawal cannot be dissociated from possible carryover effects of rimonabant in THC-tolerant mice. As noted above, pharmacokinetic data suggest that lingering effects of rimonabant are unlikely, particularly during Days 2–3 of abstinence. However, this additional treatment group will be critical in future studies to differentiate between effects of spontaneous withdrawal and post-precipitated withdrawal abstinence. Fifth, THC was administered involuntarily to mice in this study rather than self-administered. This approach is very common in cannabinoid dependence studies often due to relatively low consumption of THC in self-administration studies particularly at intermediate-to-high THC doses (Smoker et al., 2019; Spencer et al., 2018; Takahashi & Singer, 1979). Additionally, when THC self-administration is demonstrated, it is often sustained at relatively low work requirements and results in relatively low doses being self-administered (e.g., FR1-FR10, Garcia-Keller et al., 2023; Schindler et al., 2016). While cannabinoid self-administration is critical in understanding the abuse liability and reinforcing properties of cannabinoids, it is unclear whether THC is self-administered in animals to such an extent as to produce withdrawal following cessation of self-administration representing a potential fruitful area for future studies.

In light of these limitations, the current study provides tentative evidence that 1) antagonist-precipitated THC withdrawal and post-withdrawal THC abstinence are qualitatively distinct conditions in the context of goal-directed behavior, and 2) that THC abstinence appears to selectively affect motivational processes rather than cognitive/attentional processes. It will be important for future studies to validate and replicate these findings using additional controls and performance measures. Overall, these findings provide an initial basis to use operant choice tasks to improve models of spontaneous cannabinoid withdrawal that may be used to identify possible treatments for cannabinoid withdrawal frequently reported in humans with CUD.

Supplementary Material

MMC1

Highlights.

  • Operant-conditioning tasks are sensitive measures of cannabinoid withdrawal in rodents.

  • Choice-based tasks can dissociate motivational and attentional effects of withdrawal.

  • Precipitated cannabinoid withdrawal caused motivational and attentional disruption.

  • Post-withdrawal abstinence only affected motivational measures of task performance.

  • Choice tasks may be more sensitive and selective in detecting spontaneous cannabinoid withdrawal.

Acknowledgements:

We thank Brian Kotson, James Jordan, and Kristyn Campbell for assistance with data collection and the WVU OLAR staff for animal husbandry. These data were presented in part at the 2022 Carolina Cannabinoid Collaborative conference. This project was supported by NIH grant DA039335 (SGK).

Footnotes

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