Differences between males and females in psychostimulant/sucrose self-administration (when observed) may not necessarily be driven by biological sex.

BackgroundSex differences in psychostimulant-related behaviors are often attributed to biological sex; however, individual variability may also strongly influence behavioral outcomes. The new MISSING (Mapping Intrinsic Sex Similarities as an Integral quality of Normalized Groups) model identifies mixed-sex behavioral groups in which differences are driven primarily by individual variability rather than sex. The goal of this study was to validate the MISSING model for psychostimulant/sucrose self-administration. MethodsLong Evans rats self-administered methamphetamine (METH, male n = 25, female n = 32, 0.1 mg/kg/infusion, FR1, 6h per day for 20 days), sucrose (male n = 20, female n = 22, one-20 mg pellet/delivery, all other conditions being equal) and saline (male n = 3, female n = 10, other things being equal). We developed a new Quantitative Structure of Curve Analytical (QSCAn) model (using exponential-plateau and linear fit) for the assessment of individual drug self-administration time curve profiles irrespective of biological sex. We analyzed our data using regression analysis and ANOVA. ResultsQSCAn identified three distinct self-administration profiles (consisting of both sexes), which we named exponential-plateau negative (EP-), exponential-plateau positive (EP+), and undefined (EP0). There were no differences in self-administration profiles when we compared males and females within the same group. Differences between sexes (when observed) were due to mismatched comparisons (males from one group versus females from a different group). ConclusionsOur study reinforces the MISSING model for psychostimulant and sucrose self-administration by indicating that differences between males and females (when observed) may not necessarily be driven by biological sex. Significance StatementCurrent approaches often interpret variability in psychostimulant self-administration between males and females primarily through the lens of biological sex. However, this framework may overlook meaningful behavioral phenotypes shared across sexes. The present quantitative model suggests that individual patterns of behavior may better account for variability than sex alone, particularly in behaviors not strongly driven by sex-hormone-dependent mechanisms such as drug self-administration. By classifying animals according to behavioral profiles rather than biological sex, this approach may foster the identification of clinically and biologically relevant phenotypes underlying psychostimulant reinforcement.