Individual variations in motives for nicotine self-administration in male rats: evidence in support for a precision psychopharmacology.

The significant heterogeneity in smoking behavior among smokers, coupled with the inconsistent efficacy of approved smoking cessation therapies, supports the presence of individual variations in the mechanisms underlying smoking. This emphasizes the need to shift from standardized to personalized smoking cessation therapies. However, informed precision medicine demands precision fundamental research. Tobacco smoking is influenced and sustained by diverse psychopharmacological interactions between nicotine and environmental stimuli. In the classical experimental rodent model for studying tobacco dependence, namely intravenous self-administration of nicotine, seeking behavior is reinforced by the combined delivery of nicotine and a discrete cue (nicotine+cue). Whether self-administration behavior is driven by the same psychopharmacological mechanisms across individual rats remains unknown and unexplored. To address this, we employed behavioral pharmacology and unbiased cluster analysis to investigate individual differences in the mechanisms supporting classical intravenous nicotine self-administration (0.04 mg/kg/infusion) in male outbred Sprague-Dawley rats. Our analysis identified two clusters: one subset of rats sought nicotine primarily for its reinforcing effects, while the second subset sought nicotine to enhance the reinforcing effects of the discrete cue. Varenicline (1 mg/kg i.p.) reduced seeking behavior in the former group, whereas it tended to increase in the latter group. Crucially, despite this fundamental qualitative difference revealed by behavioral manipulation, the two clusters exhibited quantitatively identical nicotine+cue self-administration behavior. The traditional application of rodent models to study the reinforcing and addictive effects of nicotine may mask individual variability in the underlying motivational mechanisms. Accounting for this variability could significantly enhance the predictive validity of translational research.

Integrating operant behavior and fiber photometry with the open-source python library Pyfiber.

Despite the popularity of fiber photometry (FP), its integration with operant behavior paradigms is progressing slowly. This can be attributed to the complex protocols in operant behavior - resulting in a combination of diverse non-predictable behavioral responses and scheduled events, thereby complicating data analysis. To overcome this, we developed Pyfiber, an open-source python library which facilitates the merge of FP with operant behavior by relating changes in fluorescent signals within a neuronal population to behavioral responses and events. Pyfiber helps to 1. Extract events and responses that occur in operant behavior, 2. Extract and process the FP signals, 3. Select events of interest and align them to the corresponding FP signals, 4. Apply appropriate signal normalization and analysis according to the type of events, 5. Run analysis on multiple individuals and sessions, 6. Collect results in an easily readable format. Pyfiber is suitable for use with many different fluorescent sensors and operant behavior protocols. It was developed using Doric lenses FP systems and Imetronic behavioral systems, but it possesses the capability to process data from alternative systems. This work sets a solid foundation for analyzing the relationship between different dimensions of complex behavioral paradigms with fluorescent signals from brain regions of interest.