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Updated: Feb 7, 2026

Author Spotlight: An Efficient Methodology to Confidently Differentiate and Characterize Fentanyl Analogs
Published on: November 8, 2024
Dopamine signatures of excessive and compulsive cocaine and fentanyl use
Ke Chen1,2, Hao Zheng1,2,3, Gabrielle Sevrain1,2
1Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology; Cambridge, 02139, USA.
Abstract:
Excessive and compulsive drug use despite adverse consequences is a hallmark of substance use disorder, yet individuals differ markedly in their vulnerability to develop these behaviors. Drugs of abuse are long known to alter endogenous dopamine (DA) signaling, but shared principles for how DA dynamics impact compulsive use among individuals and across drug classes are lacking. Here, we monitored DA release in the medial shell of nucleus accumbens (NAc) during cocaine and fentanyl self-administration, with or without coincident punishment, in large cohorts of mice. Contingent cocaine and fentanyl self-administration evoked complex and individually distinct DA dynamics; nevertheless, a robust negative correlation held across both drugs, such that high takers exhibited lower drug-evoked DA signals. During punished drug taking, cocaine and fentanyl cases were associated with distinct DA signatures of compulsivity. For cocaine, punishment-resistant mice showed lower sustained DA responses during the post-shock, drug-associated cue period, whereas for fentanyl, punishment-resistant mice displayed larger phasic DA at the co-occurrence of footshock and drug infusion. To identify common principles underlying these observations, we developed a computational model grounded in an Actor-Critic temporal-difference (TD) learning framework that incorporates internal states, agent's uncertainty, and drug-specific effects. Remarkably, this model captures the observed diversity in DA dynamics across drug classes and among mice with variable drug taking propensities, hereby providing a unified interpretation of NAc DA signals as encoding TD reward prediction errors.
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