Subjects

Four adult male squirrel monkeys (Saimiri boliviensis) weighing between 800 and 1300 g and between 11 and 16 years of age were used in all studies. Subjects were group housed in a 1.4 × 1.8 × 0.7 m3 cage with access to swings and perches. The colony and laboratory were kept at ~23 ° C. Subjects were fed twice daily (monkey chow: Harlan Teklad, Madison, WI; fresh fruits and vegetables), had ad libitum access to water, and received daily enrichment (ie, foraging opportunities and different toys that were changed daily). All subjects had previous exposure to drugs acting on monoaminergic and/or glutamatergic systems (eg, Cooper et al, 2014). However, the last drug exposure for all animals was at least 2 years before the beginning of this study. All studies were conducted in accordance with the National Institute of Health’s Guide for the Care and Use of Laboratory Animals, the Association for Accreditation of Laboratory Animals Care, and were approved by the Institutional Animal Care and Use Committee of Emory University.

Experimental Protocol

For experimental sessions, the home cage was moved to a laboratory room separated from the colony. Subjects were left alone in the laboratory for 2 h before drug administration to habituate to the environment. All subjects were given the same dose of racemic MDMA (0.03–1.0 mg/kg, i.m.), S(+) or R(−) MDMA (0.3–3.0 mg/kg, i.m.), methamphetamine (0.01–0.3 mg/kg, i.m.), or sterile saline. Doses of drugs were given in a randomized order with at least 2 days in between drug administrations. Pretreatments with M100907 (M100) (0.1 and 0.3 mg/kg, i.m.), a selective 5-HT 2A receptor antagonist (Table 1), were administered 1 h prior to MDMA administration. Pretreatments with WAY163909 (WAY163) (0.03 and 0.3 mg/kg, i.m.), a selective 5-HT 2c receptor agonist (Table 1), were administered 45 min prior to MDMA administration. These doses and time frames were chosen because they have been shown previously to affect behavior, neuroendocrine response, and neurotransmitter release following stimulant administration (eg, Fantegrossi et al, 2009). Pretreatments with WAY100635 (WAY100) (0.1 and 0.3 mg/kg, i.m.), a selective 5-HT 1A receptor antagonist (Table 1), were administered 20 min prior to MDMA administration. Dose and timing were based on previous studies in marmosets (Harder and Ridley, 2000) and rodents (Thompson et al, 2007). Saline (sterile 0.9%, i.m.) controls were performed in between drug administration days. Experiments were broken into two testing phases, with baselines collected at the beginning of the experiment and before WAY163 and WAY100 testing. For 1 h following drug administration, subjects were videotaped (Samsung F90BN HD camcorder, Suwon, South Korea) and vocalizations were recorded (Seinheiser K6 microphone (Wedemark, Germany) on a Focusrite Scarlett 2i audio interface (High Wycombe, UK) using the Ableton live lite 8 software (Berlin, Germany).

Table 1 Binding Affinity (Ki) for 5-HT Receptor Ligands at Various 5-HT, Dopamine, and Adrenergic Receptors Full size table

For behavioral outcomes, a reviewer blinded to drug condition watched the video recordings and used a behavioral ethogram to score duration of behaviors (J-Watcher v1.0 software; Sydney, Australia). A single rater, trained to high inter-rater reliability across multiple training videos, scored all videos being compared statistically. The behavioral ethogram used huddling as the main affiliative behavior (squirrel monkeys, unlike other nonhuman primates, do not groom socially; Baldwin and Baldwin, 1981). The ethogram also included duration of activity, aggression (eg, chasing and head grasping), and residual (ie, not performing other scored behaviors) (Hopf et al, 1974). The focal animal scoring technique (Altmann, 1974) was used to assess duration of behaviors. Each monkey was assessed for 5 min within each of three, 20-min blocks across the hour-long observation period (ie, each monkey was scored for 15 total minutes across the hour). The order of scoring was randomized across trials but kept consistent across the three blocks within a single hour session.

Auditory files of vocalizations for the entire group were converted to spectrogram files in the MATLAB software (The MathWorks, Natick, MA) using software custom-written by Sober and Brainard (2009). Vocalizations were distinguished based on shape of spectrogram and classified into one of the three categories. Vocalizations categorized as affiliative were chucks, purrs, and pulsed calls. These call types are associated with huddling, soliciting contact from a partner, or providing important information to the troop, respectively (Jurgens, 1979; Smith et al, 1982). The other two vocalizations were growls, calls commonly observed in connection with threat displays and aggression, and peeps, observed during exploration and after changes in the environment (Winter, 1968; Jurgens, 1979).

Drugs

Racemic, S(+), and R(−) MDMA HCl, methamphetamine HCl (National Institute on Drug Abuse, Research Technology Branch, Research Triangle Park, NC), and WAY100635 HCl (N-[2-[4-(2-Methoxyphenyl)-1-piperazinyl]ethyl]-N-2-pyridinylcyclohexanecarboxamide maleate salt) (Abcam Biochemicals, Cambridge, MA) were dissolved in 0.9% sterile physiological saline. M100907 HCl ((R)-(+)-α-(2,3-dimethoxyphenyl)-1-[2-(4-fluorophenyl)ethyl]-4-pipidinemethanol) was a generous gift from Kenner C Rice, PhD and was synthesized at the Molecular Targets and Medications Discovery Branch (National Institute on Drug Abuse and National Institute on Alcohol Abuse and Alcoholism at the National Institutes of Health). M100 was dissolved in sterile saline and 1.0 N hydrochloric acid and returned to a pH of 5–6. WAY163909 HCl ((7b-R,10a-R)-1,2,3,4,8,9,10,10a-octahydro-7bH-cyclopenta[b][1,4] diazepino [6,7,1hi] indole) was a generous gift from Pfizer Incorporated (New York, NY) and was dissolved in a 10 mg/ml solution of beta cyclodextrin. Doses were calculated from salt weights.

Data Analysis

The behavioral and vocalization data were analyzed using linear mixed-effects models (LMMs) following log(y+1)-transformation of the dependent variables. This method reliably controls type I error rates and is more parsimonious than generalized linear mixed-effects models (GLMMs) that are often applied to non-Gaussian data when testing for significance of regression coefficients (Warton et al, 2016). We conducted all statistical analyses in R statistical software (R Core Team, 2014), and LMMs were computed using the lme4 package (Bates et al, 2012). To evaluate the possibility that more complex GLMMs better describe the relationships between dose, behavior, and vocalizations, we also implemented GLMMs with a log-link function (ie, Poisson regression) and compared pseudo-R-squared values between the GLMM and corresponding LMM using the MuMIn package (Barton, 2015). GLMM results did not qualitatively differ from the linear model results and are therefore reported in Supplementary Table S1 instead of the main text.

Behavioral data was modeled in seconds and included dose and bin (within the 1 h observation period) as fixed effects and controlled for random effects of study subject and testing day. Vocalization data (group-wide frequency) were summed into 6, 10-min bins across the 1 h observation period and were modeled as frequencies (ie, counts), with dose and bin as fixed effects and the random effect of testing day.

As dose–response curves are sometimes non-linear, we also tested for polynomial relationships between drug dosage and behavioral and vocalization responses by re-running each LMM with an orthogonal, second-order polynomial (ie, quadratic) dosage term as a fixed effect. We tested for improvements in fit over the simpler monomial linear models using chi-squared statistics implemented in lme4. When results of the likelihood-ratio test suggested an improved fit for the polynomial models, we tested for significance of the fixed effects and report regression coefficients and t-statistics from the polynomial models (Supplementary Table S1).

Model residuals were visually inspected for homoscedasticity, and normality was assessed using the one-sample Komlogorov–Smirnov test to examine deviation of standardized residuals from a theoretical standard normal distribution. Model degrees of freedom (df), t-statistics, and p-values for fixed effects in LMMs were obtained by using residual maximum likelihood tests with Satterthwaite approximations of df using the lmerTest package (Kuznetsova et al, 2015).