Abstract

Genetically tractable animal models provide needed strategies to resolve the biological basis of drug addiction. Intravenous self-administration (IVSA) is the gold standard for modeling psychostimulant and opioid addiction in animals, but technical limitations have precluded the widespread use of IVSA in mice. Here, we describe IVSA paradigms for mice that capture the multi-stage nature of the disorder and permit predictive modeling. In these paradigms, C57BL/6J mice with long-standing indwelling jugular catheters engaged in cocaine- or remifentanil-associated lever responding that was fixed ratio-dependent, dose-dependent, extinguished by withholding the drug, and reinstated by the presentation of drug-paired cues. The application of multivariate analysis suggested that drug taking in both paradigms was a function of two latent variables we termed incentive motivation and discriminative control. Machine learning revealed that vulnerability to drug seeking and relapse were predicted by a mouse’s a priori response to novelty, sensitivity to drug-induced locomotion, and drug-taking behavior. The application of these behavioral and statistical-analysis approaches to genetically-engineered mice will facilitate the identification of neural circuits driving addiction susceptibility and relapse and focused therapeutic development.

Details

Title
Establishment of multi-stage intravenous self-administration paradigms in mice
Author
Slosky, Lauren M. 1 ; Pires, Andrea 2 ; Bai, Yushi 2 ; Clark, Nicholas B. 2 ; Hauser, Elizabeth R. 3 ; Gross, Joshua D. 2 ; Porkka, Fiona 2 ; Zhou, Yang 2 ; Chen, Xiaoxiao 4 ; Pogorelov, Vladimir M. 5 ; Toth, Krisztian 6 ; Wetsel, William C. 7 ; Barak, Lawrence S. 2 ; Caron, Marc G. 8 

 Duke University, Department of Cell Biology, Durham, USA (GRID:grid.26009.3d) (ISNI:0000 0004 1936 7961); University of Minnesota, Department of Pharmacology, Minneapolis, USA (GRID:grid.17635.36) (ISNI:0000000419368657) 
 Duke University, Department of Cell Biology, Durham, USA (GRID:grid.26009.3d) (ISNI:0000 0004 1936 7961) 
 Duke University, Department of Biostatistics and Bioinformatics, Durham, USA (GRID:grid.26009.3d) (ISNI:0000 0004 1936 7961) 
 Georgia Institute of Technology, School of Industrial and Systems Engineering, Atlanta, USA (GRID:grid.213917.f) (ISNI:0000 0001 2097 4943) 
 Duke University, Department of Psychiatry and Behavioral Sciences, Durham, USA (GRID:grid.26009.3d) (ISNI:0000 0004 1936 7961) 
 Campbell University, Department of Pharmaceutical Sciences, Buies Creek, USA (GRID:grid.253606.4) (ISNI:0000000097011136) 
 Duke University, Department of Cell Biology, Durham, USA (GRID:grid.26009.3d) (ISNI:0000 0004 1936 7961); Duke University, Department of Psychiatry and Behavioral Sciences, Durham, USA (GRID:grid.26009.3d) (ISNI:0000 0004 1936 7961); Duke University, Department of Neurobiology, Durham, USA (GRID:grid.26009.3d) (ISNI:0000 0004 1936 7961); Duke University, Mouse Behavioral and Neuroendocrine Analysis Core Facility, Durham, USA (GRID:grid.26009.3d) (ISNI:0000 0004 1936 7961) 
 Duke University, Department of Cell Biology, Durham, USA (GRID:grid.26009.3d) (ISNI:0000 0004 1936 7961); Duke University, Department of Neurobiology, Durham, USA (GRID:grid.26009.3d) (ISNI:0000 0004 1936 7961); Duke University, Department of Medicine, Durham, USA (GRID:grid.26009.3d) (ISNI:0000 0004 1936 7961) 
Publication year
2022
Publication date
2022
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2749295505
Copyright
© The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.