Fig. 7. Genes associated with addiction-relevant behavior enrich for processes related to energetic utilization and extracellular matrix function across multiple regions.

(A) Approach to identify genes associated with addiction-relevant behavioral outcomes of IVSA. Factor analysis was used to collapse all behavioral variables collected during IVSA into three primary latent variables related to selectivity of responding for heroin, total heroin intake, and response vigor. Factors were then combined to generate a single latent variable, termed the AI, for each animal individually, and these scores were higher in animals that self-administered heroin (middle right, blue symbols, shaded by relative AI score) compared to saline (middle right, gray symbols). Linear regression was then used to identify genes in each brain region that were significantly positively or negatively associated with the AI (slope > 20%, nominal P < 0.05). (B) Heatmaps showing AI-associated genes (top row; positive in red, negative in gray) ranked by most negative to most positive, and log₂ FC of corresponding gene from DEG analysis from all experimental conditions [subsequent rows; down-regulated in blue, up-regulated in yellow; heroin 24 hours (H24), saline-heroin (SH), heroin-saline (HS), and heroin-heroin (HH)] across all brain regions. (C) Genes identified to be positively (red) or negatively (gray) associated with the AI across ≥3 brain regions, showing strong coordinated patterns throughout the reward circuit. (D) GO analysis on positively associated (red, top graphs) and negatively associated (gray, bottom graphs) AI genes identifies enrichment of biological processes related to extracellular matrix function and metabolic activity across multiple brain regions.