Assessment of the relative reinforcing strength of cocaine in socially housed monkeys using a choice procedure.

Position in the social hierarchy can influence brain dopamine function and cocaine reinforcement in nonhuman primates during early cocaine exposure. With prolonged exposure, however, initial differences in rates of cocaine self-administration between dominant and subordinate monkeys dissipate. The present studies used a choice procedure to assess the relative reinforcing strength of cocaine in group-housed male cynomolgus monkeys with extensive cocaine self-administration histories. Responding was maintained under a concurrent fixed-ratio 50 schedule of food and cocaine (0.003-0.1 mg/kg per injection) presentation. Responding on the cocaine-associated lever increased as a function of cocaine dose in all monkeys. Although response distribution was similar across social rank when saline or relatively low or high cocaine doses were the alternative to food, planned t tests indicated that cocaine choice was significantly greater in subordinate monkeys when choice was between an intermediate dose (0.01 mg/kg) and food. When a between-session progressive-ratio procedure was used to increase response requirements for the preferred reinforcer (either cocaine or food), choice of that reinforcer decreased in all monkeys. The average response requirement that produced a shift in response allocation from the cocaine-associated lever to the food-associated lever was higher in subordinates across cocaine doses, an effect that trended toward significance (p = 0.053). These data indicate that despite an extensive history of cocaine self-administration, most subordinate monkeys were more sensitive to the relative reinforcing strength of cocaine than dominant monkeys.

Mapping power law distributions in digital health social networks: methods, interpretations, and practical implications

BACKGROUND: Social networks are common in digital health. A new stream of research is beginning to investigate the mechanisms of digital health social networks (DHSNs), how they are structured, how they function, and how their growth can be nurtured and managed. DHSNs increase in value when additional content is added, and the structure of networks may resemble the characteristics of power laws. Power laws are contrary to traditional Gaussian averages in that they demonstrate correlated phenomena. OBJECTIVES: The objective of this study is to investigate whether the distribution frequency in four DHSNs can be characterized as following a power law. A second objective is to describe the method used to determine the comparison. METHODS: Data from four DHSNs—Alcohol Help Center (AHC), Depression Center (DC), Panic Center (PC), and Stop Smoking Center (SSC)—were compared to power law distributions. To assist future researchers and managers, the 5-step methodology used to analyze and compare datasets is described. RESULTS: All four DHSNs were found to have right-skewed distributions, indicating the data were not normally distributed. When power trend lines were added to each frequency distribution, R(2) values indicated that, to a very high degree, the variance in post frequencies can be explained by actor rank (AHC .962, DC .975, PC .969, SSC .95). Spearman correlations provided further indication of the strength and statistical significance of the relationship (AHC .987. DC .967, PC .983, SSC .993, P<.001). CONCLUSIONS: This is the first study to investigate power distributions across multiple DHSNs, each addressing a unique condition. Results indicate that despite vast differences in theme, content, and length of existence, DHSNs follow properties of power laws. The structure of DHSNs is important as it gives insight to researchers and managers into the nature and mechanisms of network functionality. The 5-step process undertaken to compare actor contribution patterns can be replicated in networks that are managed by other organizations, and we conjecture that patterns observed in this study could be found in other DHSNs. Future research should analyze network growth over time and examine the characteristics and survival rates of superusers.