Progress in brain research, volume 224

Serial Editor

Vincent Walsh
Institute of Cognitive Neuroscience
University College London
17 Queen Square
London WC1N 3AR UK

Editorial Board
Mark Bear, Cambridge, USA.
Medicine & Translational Neuroscience
Hamed Ekhtiari, Tehran, Iran.
Addiction
Hajime Hirase, Wako, Japan.
Neuronal Microcircuitry
Freda Miller, Toronto, Canada.
Developmental Neurobiology
Shane O’Mara, Dublin, Ireland.
Systems Neuroscience
Susan Rossell, Swinburne, Australia.
Clinical Psychology & Neuropsychiatry
Nathalie Rouach, Paris, France.
Neuroglia
Barbara Sahakian, Cambridge, UK.
Cognition & Neuroethics
Bettina Studer, Dusseldorf, Germany.
Neurorehabilitation
Xiao-Jing Wang, New York, USA.
Computational Neuroscience

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Notices
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ISBN: 978-0-444-63716-1
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Contributors
Woo Young Ahn
Department of Psychiatry, Institute for Drug and Alcohol Studies, Virginia
Commonwealth University, Richmond, VA, and Department of Psychology,
The Ohio State University, Columbus, OH, USA
Nelly Alia-Klein
Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York,
NY, USA
Albert Batalla
Department of Psychiatry and Psychology, Hospital Clı´nic, IDIBAPS, CIBERSAM,
University of Barcelona, Barcelona, Spain, and Department of Psychiatry,
Radboud University Medical Centre, Nijmegen, The Netherlands
Samantha Brooks
Department of Psychiatry and MRC Unit on Anxiety & Stress Disorders, University
of Cape Town, Cape Town, South Africa
Gregory G. Brown
Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
Jerome R. Busemeyer
Department of Psychological and Brain Sciences, Indiana University,
Bloomington, IN, USA
Elizabeth Cabrera
National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health,
Bethesda, MD, USA
Salvatore Campanella
Laboratoire de Psychologie M
edicale et d’Addictologie, ULB Neuroscience
Institute (UNI), CHU Brugmann-Universite Libre de Bruxelles (U.L.B.), Brussels,
Belgium
Nazzareno Cannella

Institute of Psychopharmacology, Central Institute of Mental Health, Medical
Faculty Mannheim/Heidelberg University, Mannheim, Germany
Daniele Caprioli
Behavioral Neuroscience Research Branch, Intramural Research Program,
NIDA, NIH, Baltimore, MD, USA
Sandra Carvalho
Department of Physical Medicine and Rehabilitation, Laboratory of
Neuromodulation, Spaulding Rehabilitation Hospital and Massachusetts General
Hospital, Harvard Medical School, Boston, MA, USA, and
Neuropsychophysiology Laboratory, CIPsi, School of Psychology (EPsi),
University of Minho, Braga, Portugal

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Contributors

Bader Chaarani
Departments of Psychiatry and Psychology, University of Vermont, Burlington,
VT, USA
Roberto Ciccocioppo
School of Pharmacy, Pharmacology Unit, University of Camerino, Camerino, Italy
Patricia Conrod
Department of Psychiatry, Universite de Montreal, CHU Ste Justine Hospital,
Montreal, QC, Canada
Janna Cousijn
Department of Psychiatry and MRC Unit on Anxiety & Stress Disorders, University
of Cape Town, Cape Town, South Africa

Alain Dagher
Montreal Neurological Institute, McGill University, Montreal, QC, Canada
Junyi Dai
Centre for Adaptive Rationality, Max Planck Institute for Human Development,
Berlin, Germany
Michiel de Ruiter
Department of Psychosocial Research and Epidemiology, Netherlands Cancer
Institute, Amsterdam, The Netherlands
Sylvane Desrivieres
Institute of Psychiatry, King’s College London, London, UK
Elise E. DeVito
Department of Psychiatry, School of Medicine, Yale University, New Haven, CT,
USA
Hamed Ekhtiari
Research Center for Molecular and Cellular Imaging; Neurocognitive Laboratory,
Iranian National Center for Addiction Studies (INCAS); Translational
Neuroscience Program, Institute for Cognitive Sciences Studies (ICSS), and
Neuroimaging and Analysis Group, Research Center for Molecular and Cellular
Imaging (RCMCI), Tehran University of Medical Sciences, Tehran, Iran
Ashkan Faghiri
Research Center for Molecular and Cellular Imaging, Tehran University of
Medical Sciences, and Department of Electrical Engineering, Sharif University of
Technology, Tehran, Iran
Sarah W. Feldstein Ewing
Department of Psychiatry, Oregon Health & Science University, Portland, OR,
USA
Felipe Fregni
Department of Physical Medicine and Rehabilitation, Laboratory of
Neuromodulation, Spaulding Rehabilitation Hospital and Massachusetts General
Hospital, Harvard Medical School, Boston, MA, USA

Contributors

Hugh Garavan
Departments of Psychiatry and Psychology, University of Vermont, Burlington,
VT, USA
Thomas E. Gladwin
Addiction Development and Psychopathology (ADAPT) Lab, Department
of Psychology, University of Amsterdam, Amsterdam, and Research
Centre—Military Mental Health, Ministry of Defense, Utrecht, The Netherlands
David C. Glahn
Department of Psychiatry, Yale University School of Medicine, New Haven, CT,
USA
Rita Z. Goldstein
Department of Psychiatry, and Department of Psychiatry & Neuroscience, Icahn
School of Medicine at Mount Sinai, New York, NY, USA
Anna E. Goudriaan
Department of Psychiatry and MRC Unit on Anxiety & Stress Disorders, University
of Cape Town, Cape Town, South Africa, and Department of Psychiatry,
University of Amsterdam, Amsterdam, The Netherlands
Joshua L. Gowin
Section on Human Psychopharmacology, Intramural Research Program,
National Institute on Alcohol Abuse and Addiction, National Institutes of Health,
Bethesda, MD, USA
Markus Heilig
Center for Social and Affective Neuroscience, Department of Clinical and
Experimental Medicine, Link€
oping University, Link€oping, Sweden
Mary M. Heitzeg

Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
Marcus Herdener
Center for Addictive Disorders, Department of Psychiatry, Psychotherapy and
Psychosomatics, Psychiatric Hospital, University of Zurich, Zurich, Switzerland
Derrek P. Hibar
Department of Neurology, Imaging Genetics Center, Keck School of Medicine,
University of Southern California, Marina del Rey, CA, USA
Kent Hutchison
Department of Psychology and Neuroscience, University of Colorado Boulder,
Boulder, CO, USA
Joanna Jacobus
Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
Neda Jahanshad
Department of Neurology, Imaging Genetics Center, Keck School of Medicine,
University of Southern California, Marina del Rey, CA, USA

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Contributors

Kees-Jan Kan
Departments of Psychiatry and Psychology, University of Vermont, Burlington,
VT, USA
Bernard Le Foll
Translational Addiction Research Laboratory, Campbell Family Mental Health
Research Institute; Addiction Medicine Service, Ambulatory Care and Structured
Treatments, Centre for Addiction and Mental Health, and Department of Family

and Community Medicine, Pharmacology and Toxicology, Psychiatry, Institute of
Medical Sciences, University of Toronto, Toronto, ON, Canada
Lorenzo Leggio
Section on Clinical Psychoneuroendocrinology and Neuropsychopharmacology,
Laboratory of Clinical and Translational Studies, National Institute on Alcohol
Abuse and Alcoholism, and Intramural Research Program, National Institute on
Drug Abuse, Bethesda, MD, USA
Jorge Leite
Department of Physical Medicine and Rehabilitation, Laboratory of
Neuromodulation, Spaulding Rehabilitation Hospital and Massachusetts General
Hospital, Harvard Medical School, Boston, MA, USA, and
Neuropsychophysiology Laboratory, CIPsi, School of Psychology (EPsi),
University of Minho, Braga, Portugal
Chiang-Shan R. Li
Department of Psychiatry, Yale University School of Medicine, New Haven, CT,
USA
Edythe D. London
Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York,
NY, and David Geffen School of Medicine, University of California at Los Angeles,
Los Angeles, CA, USA
Valentina Lorenzetti
School of Psychological Sciences, Monash Institute of Cognitive and Clinical
Neurosciences and Monash Biomedical Imaging, Monash University,
Melbourne, Australia
Maartje Luijten
Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands
Scott Mackey
Departments of Psychiatry and Psychology, University of Vermont, Burlington,
VT, USA
Rocio Martin-Santos

Department of Psychiatry and Psychology, Hospital Clı´nic, IDIBAPS, CIBERSAM,
University of Barcelona, Barcelona, Spain
April C. May
Department of Psychiatry, University of California, San Diego, CA, USA


Contributors

Benjamin McKenna
Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
Scott J. Moeller
Departments of Psychiatry & Neuroscience, Icahn School of Medicine at Mount
Sinai, NY, USA
Reza Momenan
Section on Brain Electrophysiology and Imaging, Institute on Alcohol Abuse and
Alcoholism, Bethesda, USA
Angelica M. Morales
David Geffen School of Medicine, University of California at Los Angeles,
Los Angeles, CA, USA
Michael A. Nader
Department of Physiology and Pharmacology, Wake Forest School of Medicine,
Winston-Salem, NC, USA
Mohammad-Ali Oghabian
Research Center for Molecular and Cellular Imaging, and Advanced Diagnostic
and Interventional Radiology Research Center, Tehran University of Medical
Sciences, Tehran, Iran
Vani Pariyadath
Neuroimaging Research Branch, Intramural Research Program, National
Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, USA
Muhammad A. Parvaz

Departments of Psychiatry & Neuroscience, Icahn School of Medicine at Mount
Sinai, NY, USA
Martin P. Paulus
VA San Diego Healthcare System and Department of Psychiatry, University of
California San Diego, La Jolla, CA, and Laureate Institute for Brain Research,
Tulsa, OK, USA
Tomas Paus
Rotman Research Institute, University of Toronto, Toronto, ON, Canada
Godfrey Pearlson
Department of Psychiatry, Yale University School of Medicine, New Haven, CT,
USA
Boris B. Quednow
Experimental and Clinical Pharmacopsychology, Department of Psychiatry,
Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zurich,
and Neuroscience Centre Zurich, University of Zurich and Swiss Federal Institute
of Technology (ETH), Zurich, Switzerland

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Contributors

Tara Rezapour
Research Center for Molecular and Cellular Imaging, Tehran University of
Medical Sciences, and Translational Neuroscience Program, Institute for
Cognitive Science Studies, Tehran, Iran
Ren
ee Schluter

Department of Psychiatry, University of Amsterdam, Amsterdam, The Netherlands
Lianne Schmaal
Department of Psychiatry, VU University Medical Center, Amsterdam,
The Netherlands
Gunter Schumann
Institute of Psychiatry, King’s College London, London, UK
Yavin Shaham
Behavioral Neuroscience Research Branch, Intramural Research Program,
NIDA, NIH, Baltimore, MD, USA
Alireza Shahbabaie
Neurocognitive Laboratory, Iranian National Center for Addiction Studies
(INCAS); Translational Neuroscience Program, Institute for Cognitive Science
Studies (ICSS), and Neuroimaging and Analysis Group, Research Center for
Molecular and Cellular Imaging (RCMCI), Tehran University of Medical Sciences,
Tehran, Iran
Rajita Sinha
Department of Psychiatry, Yale University School of Medicine, New Haven, CT,
USA
Zsuzsika Sjoerds
Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
Emily Skarda
National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health,
Bethesda, MD, USA
Mehmet Sofuoglu
Department of Psychiatry, School of Medicine, Yale University, New Haven, and
VA Connecticut Healthcare System, West Haven, CT, USA
Nadia Solowij
School of Psychology, University of Wollongong, Wollongong, NSW, Australia
Dan J. Stein
Department of Psychiatry and MRC Unit on Anxiety & Stress Disorders, University

of Cape Town, Cape Town, South Africa
Elliot A. Stein
Intramural Research Program—Neuroimaging Research Branch, National
Institute on Drug Abuse, and Neuroimaging Research Branch, Intramural
Research Program, National Institute on Drug Abuse, National Institutes of
Health, Baltimore, MD, USA


Contributors

Jennifer L. Stewart
Department of Psychology, Queens College, City University of New York, NY, USA
Julie C. Stout
School of Psychological Sciences and Monash Institute of Cognitive and Clinical
Neuroscience, Monash University, Clayton, VIC, Australia
Susan Tapert
Laureate Institute for Brain Research, Tulsa, OK, USA
Rachel E. Thayer
Department of Psychology & Neuroscience, University of Colorado Boulder,
Boulder, CO, USA
Paul M. Thompson
Department of Neurology, Imaging Genetics Center, Keck School of Medicine,
University of Southern California, Marina del Rey, CA, USA
Massimo Ubaldi
School of Pharmacy, Pharmacology Unit, University of Camerino, Camerino, Italy
Anne Uhlmann
Department of Psychiatry and MRC Unit on Anxiety & Stress Disorders, University
of Cape Town, Cape Town, South Africa
Ruth van Holst
Department of Psychiatry, University of Amsterdam, Amsterdam, The

Netherlands
Jasmin Vassileva
Department of Psychiatry, Institute for Drug and Alcohol Studies, Virginia
Commonwealth University, Richmond, VA, USA
Dick Veltman
Department of Psychiatry, VU University Medical Center, Amsterdam, The
Netherlands
Marco Venniro
Behavioral Neuroscience Research Branch, Intramural Research Program,
NIDA, NIH, Baltimore, MD, USA, and Department of Public Health and
Community Medicine, Neuropsychopharmacology Laboratory, Section of
Pharmacology, University of Verona, Verona, Italy
Nora D. Volkow
National Institute on Alcohol Abuse and Alcoholism, and National Institute on
Drug Abuse, National Institutes of Health, Bethesda, MD, USA
Henrik Walter
Department of Psychiatry and Psychotherapy, Charite Universitatsmedizin,
Berlin, Germany
Gene-Jack Wang
National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health,
Bethesda, MD, USA

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Contributors

Corinde E. Wiers

Department of Psychiatry and Psychotherapy, Charite—Universita¨tsmedizin;
Berlin School of Mind and Brain, Humboldt-Universita¨t zu, Berlin, Germany, and
National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health,
Bethesda, MD, USA
Reinout W. Wiers
Addiction Development and Psychopathology (ADAPT) Lab, Department of
Psychology, University of Amsterdam, Amsterdam, The Netherlands
Margaret J. Wright
QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
Fatemeh Yavari
Neurocognitive Laboratory, Iranian National Center for Addiction Studies
(INCAS), Tehran University of Medical Sciences, Tehran, Iran
Murat Yucel
School of Psychological Sciences, Monash Institute of Cognitive and Clinical
Neurosciences and Monash Biomedical Imaging, Monash University,
Melbourne, Australia
Deborah Yurgelun-Todd
Department of Psychiatry, University of Utah School of Medicine, Salt Lake City,
UT, USA
Anna Zilverstand
Departments of Psychiatry & Neuroscience, Icahn School of Medicine at Mount
Sinai, NY, USA


Preface: Neuroscience for Addiction
Medicine: From Prevention to
Rehabilitation
It is estimated that a total of 246 million people, i.e., over 5% of the world’s adult
population, have used an illicit drug during the last year. Meanwhile, more than 10%
of these drug users are suffering from drug use disorders and the number of drugrelated deaths is estimated to be over 187,000 annually (UN Office of Drugs and

Crime, 2015). Adding disorders related to the nonpharmacologic or behavioral addictions such as pathological gambling, Internet and gaming addictions, overeating
and obesity, and compulsive sexual behaviors to the drug addictions comprises a
group of brain disorders that contribute as one of the major challenges for humankind
in the current millennium.
Addiction medicine has been regarded as a stand-alone specialty among other
medical professions in several countries; however, there are still serious concerns
regarding the availability and effectiveness of interventions in a wide range from prevention to rehabilitation in addiction medicine. Accumulating pathophysiological
evidences for “Addiction as a Brain Disorder” during last 20 years is extending expectations from neuroscience to contribute more seriously in the routine clinical
practices during prevention, assessment, treatment, and rehabilitation of addictive
disorders. Neuroscience has made tremendous progress toward understanding basic
neural processes; however, there is still a lot of progress needed to be made in utilizing neuroscience approaches in clinical medicine in general and addiction medicine in particular.
The basic idea of a book to provide the current status of the field of neuroscience
of addiction with particular emphasis on potential applications in a clinical setting
was jumped out during meetings in the 2nd Basic and Clinical Neuroscience Congress in October 2013 in Tehran with Professor Vincent Walsh, the Progress in Brain
Research, PBR, Editor in Chief. We, Martin and Hamed, started to work together for
a proposal to the PBR advisory board to compile a volume of reviews in June 2014 in
the Laureate Institute for Brain Research, Tulsa, OK. After receiving the green lights
from the PBR office, the invitations went out to the senior scholars in the field from
October 2014. We received overwhelming positive feedbacks from over 120 contributors from 90 institutes in 14 countries that ended up with 36 chapters in two volumes
in October 2015. During this 1 year of intensive efforts, all the chapters were peer
reviewed and revised accordingly to meet high-quality standards of the PBR and our
vision for the whole concept of the volumes. The first volume, PBR Vol. 223, is
mainly focused on the basic neurocognitive constructs contributing to pathophysiological basis of pharmacological and behavioral addictions, and the second volume,

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Preface: Neuroscience for addiction medicine

PBR Vol. 224, depicts the contribution of neuroscience methods and interventions in
the future of clinical practices in addiction medicine.
The goal of these two volumes is to provide readers with insights into current
gaps and possible directions of research that would address impactful questions.
The fundamental question that is addressed in these volumes is “how can neuroscience be used to make a real difference in addiction medicine”? To that end, we asked
the contributors to:
(1) review the recent literature with a time horizon of approximately 5–10 years,
(2) identify current gaps in our knowledge that contribute to the limited impact of
the area of research to clinical practice, and
(3) provide a perspective where the field is heading and how impactful questions can
be addressed to change the practice of addiction medicine.
We envision that both neuroscientists and clinical investigators will be the primary
audience of these two volumes. Moreover, the common interest of these individuals
will be the application of neuroscience approaches in studies to assess or treat individuals with addictive disorders. We think that these PBR volumes will provide the
audiences with most recent evidences from different disciplines in brain studies on
the wide range of addictive disorders in an integrative way toward “Neuroscience for
Addiction Medicine: From Prevention to Rehabilitation.” The hope is that the information provided in the series of chapters in these two volumes will trigger new researches that will help to connect basic neuroscience to clinical addiction medicine.
The Editors
Hamed Ekhtiari, MD,
Iranian National Center for Addiction Studies
Martin Paulus, MD,
Laureate Institute for Brain Research

REFERENCE
UN Office of Drugs and Crime, 2015. World Drug Report 2015. United Nation Publication,
Vienna.


CHAPTER

Animal models for addiction
medicine: From vulnerable
phenotypes to addicted
individuals

1

Michael A. Nader1
Department of Physiology and Pharmacology, Wake Forest School of Medicine, Winston-Salem,
NC, USA
1
Corresponding author: Tel.: +336-713-7172; Fax: +336-713-7180,
e-mail address:

Abstract
This chapter highlights the use of several animal models of abuse liability. The overall goal is
to describe the most frequently used methods, unconditioned behaviors and conditioned behaviors, and how investigators can use these techniques to compare drugs and to understand
the mechanisms of action mediating abuse liability. Thus, for each type of animal model described, research will be highlighted on three general features related to the use of the model:
(1) determine abuse potential, (2) treatment efficacy, and (3) brain-related changes associated
with drug administration.

Keywords
Animal models, Unconditioned behavior, Conditioned behavior, Drug discrimination, Drug
self-administration, Conditioned place preference, PET imaging, Microdialysis

1 INTRODUCTION
In his brief history of behavioral pharmacology, Roy Pickens (1977) listed two
events in the 1940–1955 period that he considered the most significant advancements
for the field: the discovery of the antipsychotic effects of chlorpromazine and the

hallucinogenic effects of LSD. The former was significant primarily because it advanced the predictive nature of animal models, while the latter was significant for
increasing attention on the relationship between biochemistry and behavior and because it led to the study of preclinical models of drug self-administration. The focus
of this chapter will be on animal models of addiction and the foundation for these
Progress in Brain Research, Volume 224, ISSN 0079-6123, />© 2016 Elsevier B.V. All rights reserved.

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CHAPTER 1 Animal models for addiction medicine

studies can be traced back to the preclinical work on chlorpromazine and LSD. As a
minimum, animal models must be predictive of some outcome in people. This predictive nature could be related to models of abuse liability (i.e., is this novel drug
reinforcing?) or to potential treatment outcomes (i.e., does drug X decrease drug addiction?); methods for both types of models will be described. In this chapter, two
main strengths of animal models will be emphasized: (1) the ability to start with
drug–naı¨ve subjects and determine phenotypic/trait characteristics that are associated with addiction and (2) the ability to study the neurochemical, physiological,
and pharmacodynamic consequences of chronic drug exposure. Utilizing both of
these qualities of animal models is necessary to develop novel treatment strategies
for drug addiction.
In animal models of addiction, the primary dependent variable is some behavioral
endpoint—whether it is activity level, or time in a quadrant related to a conditioned
stimulus (CS), or lever pressing or total drug intake. These dependent variables will
be the focus of the studies described in this chapter. In addition, the relationship between behavior and brain will also be described. Although there are many methods
used in the literature, this review will highlight the use of in vivo microdialysis, magnetic resonance imaging, and positron emission tomography (PET) in the study of
brain–behavior relationships. There are excellent reviews on this topic that will
not be repeated in this chapter (see Howell and Murnane, 2011; Murnane and
Howell, 2011; Nader and Banks, 2014 for recent reviews). Thus, for each type of
animal model described, the goal of this chapter will be to highlight three general
features for the use of the model: (1) determine abuse potential, (2) treatment efficacy, and (3) brain-related changes associated with drug administration.

2 TYPES OF ANIMAL MODELS
When assessing animal models for addiction medicine, there are two general categories of models: those that utilize unconditioned behaviors and those that require
the study of conditioned responses. While the majority of the chapter will be on conditioned responses, it is important to briefly describe some unconditioned models in
order to give researchers a more thorough representation of the breadth of experimental techniques available.

2.1 UNCONDITIONED BEHAVIORS
Perhaps the simplest of behaviors to measure is overall activity in an enclosed environment. These measures can be used as trait markers for vulnerability or as an
initial screen for “stimulant-like” drug effects. The best example of using locomotor activity as a trait marker for vulnerability to drug abuse was a study by Piazza
et al. (1989) in which rats were first characterized as high responders (HRs) or low
responders (LRs) in an open field. When given access to cocaine under a fixedratio (FR) 1 schedule of reinforcement, the locomotor HRs were more likely to


2 Types of animal models

acquire cocaine self-administration compared to the LRs. This behavioral phenotype has been well characterized in relation to corticosterone (Piazza and Le Moal,
1998; Piazza et al., 1991) and to dopamine (DA) D2-like receptor availability
(Dalley et al., 2007). Major strengths of this unconditioned behavior are (1) it requires no training, (2) can utilize large numbers of animals, and (3) provides a
quantitative measure that can be used to compare animals on other endpoints including neurochemical and behavioral (related to drug reinforcement, for
example).
It has been hypothesized that for stimulant drugs (e.g., cocaine, amphetamine,
nicotine) increases in locomotor behavior represent an initial screen for potential
abuse liability. These simple procedures involving unconditioned behaviors can
be used to better understand the potential mechanisms of action related to drugs
of abuse, but they are not models of abuse potential. For example, within the DA
D2 receptor family, drugs that act at different subtypes have been identified and these
subtypes (D2, D3, and D4) have implications for drugs of abuse. Li et al. (2010) used
drug-elicited yawning and locomotor activity in mice to better understand the roles
of DA D3 versus D2 receptors, respectively, with the goal of identifying in vivo
screens for each receptor subtype that could ultimately lead to medications for drug

abuse. Interestingly, Collins et al. (2008) showed that food restriction altered these
unconditioned behaviors suggesting an interaction between diet composition and
brain function, which could lead to increased or decreased vulnerability to drug
abuse. With regard to the DA D4 receptor subtype, Katz et al. (2003) examined
the effects of cocaine (1.0–10 mg/kg, i.p.) administered to wild-type (WT) and
DA D4 receptor knockout (KO) mice in order to better understand the role of this
receptor subtype in the behavioral effects of cocaine. While the two groups did
not differ in baseline measures of activity, cocaine administration resulted in significantly larger increases in locomotor activity in the D4 KO mice compared to WT
animals. Katz et al. (2003) also found that D4 KO mice were more sensitive to
the discriminative stimulus effects of cocaine compared to WT littermates.
Typically, in studies that utilize locomotor activity, other behavioral or
physiological measures are examined to more thoroughly characterize the behavioral
effects of drugs. For example, Miller et al. (2013) used an immunotherapeutic approach to attenuate the behavioral effects of methamphetamine and examined
multiple dependent variables. These investigators reported that vaccination
against methamphetamine blocked the effects on locomotor activity, as well as wheel
running (another measure of activity) and changes in body temperature, suggesting
protection against physiological and behavioral disruptions induced by
methamphetamine.
In a recent study, Vanhille et al. (2015) characterized rats using two unconditioned behaviors, novelty-induced locomotor activity and open-arm access in an
elevated plus maze, and a conditioned behavior in which lever pressing and head
entries into the food magazine during presentation of the CS were used to assess
sign tracking and goal tracking, respectively. Interestingly, all the behaviors were
characterized as normally distributed but not correlated with each other, indicating

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CHAPTER 1 Animal models for addiction medicine

independent constructs being assessed. When used to phenotypically characterize
vulnerability to cocaine abuse, neither elevated plus maze (high vs. low anxiety)
nor sign tracking versus goal tracking (i.e., CS vs. food-maintained lever pressing)
was related to a rat’s propensity to acquire cocaine self-administration. However,
HRs in the locomotor assay were more likely to choose saccharin over cocaine
than LRs, who primarily chose cocaine over saccharin. This interesting finding
is at odds with earlier work showing HR rats more vulnerable to cocaine reinforcement when available under an FR 1 schedule of reinforcement (Piazza et al.,
1989). Vanhille et al. (2015) suggest that the difference is due to the importance
of environmental context in which drugs are self-administered; environmental context has been shown to influence the behavioral effects of drugs under many conditions (see Barrett and Katz, 1981 for review; e.g., Barrett and Stanley, 1980;
McKearney and Barrett, 1975). There were important methodological differences
in the Vanhille et al. (2015) study compared to earlier saccharin–cocaine choice
studies (see Ahmed, 2010) that may have biased initial choice toward cocaine
(see comments from Ahmed, 2014). Certainly, there needs to be standardization
of protocols in order to better compare between studies, as has been pointed
out earlier (Katz, 1990).
Other investigators have also used locomotor activity as a trait marker to identify
or “unmask” some other predisposition. For example, Hamilton et al. (2010, 2011)
studied two groups of adult rhesus monkeys—one group was prenatally exposed to
cocaine and the other group was control monkeys. When they were approximately
12–14 years old, each monkey was assessed in an open field for locomotor activity,
along with other unconditioned behaviors including approaching a novel object.
Hamilton et al. (2011) reported that there were no differences in locomotor activity
or approaching a novel object between prenatally cocaine-exposed and control monkeys, even though other behaviors (e.g., drug-elicited yawning, resistance to extinction, and cocaine self-administration) were different between groups (Brutcher and
Nader, 2012; Hamilton et al., 2010, 2011). This suggests that some characteristics
that are hypothesized to influence vulnerability to drug abuse (e.g., in utero cocaine
exposure) may not be amenable to the predictive validity of behavioral assays hypothesized to measure “anxiety-like” behaviors, like locomotor activity in an
open-field apparatus.
There are some limitations to the use of locomotor activity to understand factors
related to abuse liability. One major limitation is that while behavioral sensitization

to locomotor stimulation frequently occurs, this does not necessarily translate into
sensitization to the reinforcing effects of cocaine, and vice versa (e.g., Ahmed
and Koob, 1998; Lack et al., 2008). It is also the case that characterizing animals
as “high” and “low” responders does not necessarily translate into more or less vulnerable individuals, respectively (e.g., Dalley et al., 2007). Thus, while the behavior
is amenable to pharmacological manipulations, and the combination of other unconditioned behaviors allows for rapid screening, some caution should be used when
these are the primary behaviors under investigation.


2 Types of animal models

2.2 CONDITIONED PLACE PREFERENCE
Conditioned place preference (CPP) studies are most frequently conducted in rodents
and are said to involve “reward.” CPP involves classical conditioning in which stimuli associated with one quadrant are paired with a drug dose, while stimuli associated
with a distinctly different quadrant are paired with the drug vehicle (see Mucha et al.,
1982); the two compartments are separated by a neutral space. CPP (i.e., reward) is
said to occur if the animal spends more time in the drug-paired side compared to the
vehicle-paired side (e.g., Bali et al., 2015; see reviews by Wise, 1989 and by SanchisSegura and Spanagel, 2006). In training a CPP, many investigators use an unbiased
procedure in which the initial phase consists of giving the animal access to both compartments of the apparatus. If the animal spends significantly more time in one compartment over the other (e.g., some investigators use 80% vs. 20%, others 67% vs.
33% as criterion), then they are not used in the conditioning phase of the study. In the
conditioning phases, drug is paired with one compartment and drug vehicle with the
other compartment; these compartments and drug/vehicle are typically counterbalanced across subjects. Most drugs of abuse can produce CPP and recent literature
indicates that this methodology is frequently used to study drugs of abuse from
all classes, including stimulants (e.g., Aguilar et al., 2015), opiates (e.g., Wang
et al., 2015), alcohol (e.g., Gubner et al., 2015), and D9-THC, the active ingredient
in marijuana (e.g., Manwell et al., 2014). Time spent on the drug-paired side is typically represented as an inverted U-shaped function of dose; very high doses can induce a conditioned place aversion (e.g., Kirkpatrick and Bryant, 2015).
In addition to examining abuse liability, CPP can be used to better understand the
neurochemical and neuropharmacological mechanisms of action for drugs of abuse.
Two examples will be provided here, one involving systemic drug administration
and the other central administration. Northcutt et al. (2015) trained rats using an unbiased CPP protocol with 10 mg/kg cocaine and saline in the different compartments
over 4 training days. For one group, during conditioning they received 10 mg/kg cocaine plus (+)-naloxone. When place preference was determined on Day 5, 10 mg/kg

cocaine induced a CPP, but the group that was coadministered (+)-naloxone did not
show a preference. Through in silico computer modeling and in vitro assays, the investigators hypothesized that cocaine and (+)-naloxone were binding to the same
proinflammatory central immune signaling cascade; the CPP data suggested a functional consequence to these in vitro findings.
Using a slightly different version of CPP, Galaj et al. (2014) first trained the CPP
with cocaine (10 mg/kg) and then examined the effects of a DA D1 receptor antagonist, SCH23390, administered via microinjection directly into the ventral tegmental
area. The investigators found that SCH23390 (0, 2.0, 4.0, and 8.0 mg/0.5 ml) dose dependently reduced cocaine CPP. The difference between the results of this study and
the previous one is related to neurochemical mediation involving acquisition
(Northcutt et al., 2015) and expression (Galaj et al., 2014). In the latter case, the
model addresses issues related to treatment efficacy, since conditioning had already

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taken place, while in the former study, neuropharmacological considerations related
to vulnerability were addressed.
Most recently, CPP has been used to investigate environmental and social variables that influence vulnerability to drug abuse. One hypothesis is that when combined with social enrichment, lower drug doses induce a CPP (e.g., Thiel et al., 2008,
2009; see review by Trezza et al., 2010). For example, Watanabe (2011) studied
three groups of mice in a methamphetamine CPP study: (1) individually housed animals in standard CPP training with a low methamphetamine dose of 2.0 mg/kg; (2)
paired animals in which both mice received the exact treatment (i.e., saline on one
side and 2.0 mg/kg methamphetamine on the other); and (3) control pairs in which
CPP training was reversed such that when one animal received methamphetamine
the other received saline. The pair group, in which both animals received the identical treatment, resulted in greater CPP than the individually housed and control
pairs, indicating an enhancement of methamphetamine reward when the cage mate
also received methamphetamine. It is important to note that merely the presence of a
cage mate did not enhance CPP, but rather only when both animals received drug
together was there evidence of methamphetamine reward. Interestingly, when time

spent on the nondrug side was examined, the control pairs showed a profound place
aversion. That is, when one animal received methamphetamine and the partner received saline, there was a place aversion on the saline side, perhaps indicating a negative consequence on social behavior related to drug use.
One final example to close out this section involves using CPP in combination
with in vivo brain imaging to better understand the neurochemical consequences associated with drug use. Schiffer et al. (2009) first trained rats in CPP using 5.0 mg/kg
(i.v.) cocaine and saline. This dose of cocaine was chosen because this group had
previously shown, using in vivo microdialysis, that the cocaine-paired side would
elicit increases in extracellular DA in the ventral striatum (Gerasimov et al.,
2001). After the CPP was established, each rat underwent two PET scans using
[11C]raclopride. The investigators hypothesized that if the cocaine-paired side elicited DA release, the [11C]raclopride binding potential would be significantly reduced compared to the PET signal when rats were placed on the saline-paired
side. In fact, Schiffer et al. (2009) found an approximate 20% lower [11C]raclopride
binding potential in the dorsal and ventral striatum on the cocaine-paired side relative
to the saline-associated side and a direct relationship between changes in binding
potential and cocaine preference. These findings highlight the amenability of CPP
to in vivo imaging studies.
There are some limitations to the use of CPP as a model to understand factors
related to abuse liability. As mentioned above, CPP does not measure “drug seeking”
or “drug taking,” two hallmarks of addiction. A second limitation is the ability to
study multiple pharmacological manipulations—once the conditioning has been
established, any tests without the drug of abuse can decrease the effectiveness of
the CS, thereby making repeated, longitudinal studies more challenging. In general,
these models are good initial screens that can lead to follow-up studies involving
drug discrimination (DD) and drug self-administration procedures.


2 Types of animal models

2.3 DRUG DISCRIMINATION
By definition, a discriminative stimulus “sets the occasion” for responding by providing information related to the contingencies mediating stimulus–response relationships. In models of DD, the discriminative stimulus is the presence or absence
of the training drug. Training a discriminative stimulus in animal models typically
involves two operant responses in which responding on one manipulandum (e.g., lever, key, nose poke, finger poke) is reinforced following administration of the training drug while responding on the other manipulandum is reinforced following

administration of the drug vehicle. For example, when the subject is administered
a dose of 0.2 mg/kg cocaine (the training drug and dose), responding on the left lever
results in food reinforcement; responding on the right lever would have no scheduled
consequence (or may reset the FR value on the correct lever). When the subject is
administered saline, responding on the right lever would be reinforced, but left-lever
responding would not. It has been hypothesized that the “interoceptive” discriminative stimulus effects of a drug in an animal, model the subjective effects in humans.
A particular strength of DD procedures is that the behavioral effects of drugs are
thought to be mediated centrally (i.e., receptor changes in the brain; see Carter
and Griffiths, 2009 and Stolerman et al., 2011 for reviews). In addition to understanding the mechanisms of action mediating the discriminative stimulus effects of a drug,
substitution studies are also used as an index of the abuse liability of compounds and
impact the scheduling of drugs by the US Food and Drug Administration (FDA; see
Nader et al., 2015 for examples).
In DD studies, the two primary dependent variables are % responding on the
drug-associated lever and overall response rates. Most investigators operationally
define substitution as occurring when at least 80% of the total responses occurred
on the drug-appropriate lever. Including response rate data is important for several
reasons. If a test drug substitutes for a drug of abuse, but only at doses that result in
significant rate-decreasing effects, that may suggest less abuse liability because
doses that disrupt ongoing behavior are required to produce subjective-like effects
similar to the drug of abuse. Conversely, if a novel drug is studied and that drug does
not substitute for the training drug, it may not be clear that high enough doses were
tested unless response rates were altered. Related to both substitution and response
rate effects of test drugs is the dose of the drug used to train the discriminative stimulus. As pointed out by Stolerman et al. (2011), “… training dose may show an impact on qualitative aspects of a discrimination, as defined by changes in the drugs to
which generalization occurs, and sensitivity to antagonists” (p. 415). One example
will be given in order to demonstrate the types of questions that can be addressed by
manipulating the training dose.

2.3.1 Influence of training dose
Grant et al. (2000) trained male (n ¼ 8) and female (n ¼ 10) cynomolgus monkeys to
discriminate either 1.0 g/kg ethanol from water or 2.0 g/kg ethanol from water (all

solutions were administered intragastrically) in a two-lever, food-reinforced operant

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procedure. In addition to determining an ethanol dose–response curve, pentobarbital,
midazolam, muscimol, and morphine dose–response curves were determined. Not
surprisingly, the training dose influenced the ED50 values for ethanol substitution,
with ethanol being more potent in the 1.0 g/kg training groups compared to the
2.0 g/kg groups. Pentobarbital and midazolam, two GABAA agonists, substituted
for 1.0 g/kg and 2.0 g/kg ethanol, but only the potency of pentobarbital was influenced
by training dose. Grant et al. (2000) did not observe sex differences with regard to any
manipulation in the study. These findings were extended to N-methyl-D-aspartate
(NMDA) glutamate receptors by examining PCP, ketamine, and dizocilpine
(Vivian et al., 2002). At the low training dose condition (1.0 g/kg ethanol), all three
NMDA receptor compounds substituted for ethanol in both males and females. In
contrast, at the 2.0 g/kg ethanol training dose, PCP, ketamine, and dizocilpine did
not substitute for ethanol in the males. One possible mechanism for these sex differences was the greater sensitivity to the rate-decreasing effects of NMDA receptor
antagonists in males compared to females; these sex differences were only apparent
when the higher ethanol training dose was studied. Taken together, using different
ethanol training doses, Grant and colleagues concluded that the NMDA receptor
system is less prominent than the GABAA receptors in mediating the discriminative
stimulus effects of ethanol in nonhuman primates, especially with higher ethanol
training doses. Such mechanistic understanding of ethanol’s effects would not have
been obtained if only one training dose had been studied.

2.3.2 Other methodological considerations
In addition to the importance of training dose and sex, there are other independent
variables that have recently been identified that can impact the substitution profile of
drugs. In many DD studies, the subjects are modestly food restricted in order to study
food-maintained operant responding. Depending on the drugs under study, this may
influence the outcome of substitution studies (e.g., Baladi and France, 2010). For
example, the DA D2/D3 receptor agonist quinpirole can be trained as a discriminative stimulus, and this typically involves food-restricted animals (e.g., Katz and
Alling, 2000). Baladi et al. (2010) trained free-feeding rats to discriminate quinpirole
from saline under a schedule of stimulus–shock termination. DA D2/D3 receptor agonists apomorphine and lisuride substituted for quinpirole and, as reported by Baladi
et al. (2010), similar findings have been reported in food-restricted animals. However, using DA receptor antagonists, differences between free-feeding and foodrestricted animals became apparent. In free-feeding rats, a D2/D3 receptor antagonist
(raclopride) and a D3 receptor-selective antagonist (PG01037), but not a D2
receptor-selective antagonist (L-741,626), blocked the discriminative stimulus effects of quinpirole, shifting the quinpirole dose–response curve to the right. These
findings suggest that the discriminative stimulus effects of quinpirole in free-feeding
animals are primarily D3 receptor mediated, while in food-restricted animals, quinpirole’s discriminative stimulus effects are thought to be mediated by D2 receptors
(cf. Baladi et al., 2010).


2 Types of animal models

2.3.3 DD in combination with brain imaging
As mentioned above, it is believed that the discriminative stimulus effects of drugs
are centrally mediated. Studies have been conducted that combine DD techniques
with in vivo microdialysis to study how drugs that share discriminative stimulus effects influence neurotransmitter concentrations (e.g., Czoty et al., 2000; Kimmel
et al., 2012). In one study, Czoty et al. (2004) trained monkeys to discriminate
0.32 mg/kg methamphetamine from saline under an FR 10 schedule of stimulus–
shock termination. Monkeys were also implanted with guide cannulae above the caudate nucleus and microdialysis experiments were conducted in the same operant
chambers as the DD procedures. The investigators found that methamphetamine,
as well as cocaine and methylphenidate, resulted in dose-dependent increases in
methamphetamine-appropriate responding when studied in the DD protocol.
Doses that occasioned 100% methamphetamine responding produced similar increases in extracellular DA concentrations. Interestingly, the time course for elevations in DA and substitution in DD was not identical, indicating the involvement of

other neurotransmitter systems in mediating the discriminative stimulus effects of
methamphetamine.
There are some considerations regarding the use of DD that investigators should
address. In terms of scheduling of drugs, the FDA suggests that if a novel drug substitutes for a drug of abuse, it has abuse liability, but if it does not substitute it may
still have abuse liability. Considering the examples provided in this section on how
dose, environmental context, and sex can influence these profiles, the use of DD in
scheduling of drugs appears less than straightforward. Also of relevance for the development of treatment agents is the time course of substitution. The FDA does not
distinguish the importance of time course, so if a novel drug does not substitute for
cocaine (for example) until 2 h after administration, this information does not factor
into “abuse liability,” but it should. If pharmacological agonists become a treatment
strategy, a profile in which the subjective effects occur at a later time after administration and last longer than the drug of abuse, should positively impact compliance
and reduce drug taking.

2.4 DRUG SELF-ADMINISTRATION MODELS
There is probably no behavioral model that is more predictive of human disease than
animal drug self-administration models of abuse liability. Readers interested in the
history of drug self-administration are referred to the original pioneering studies of
Spragg (1940), Weeks (1962), Thompson and Schuster (1964), and Deneau et al.
(1969); see also Griffiths et al. (1980). The behavioral process mediating drug
self-administration is reinforcement, which can be either positive reinforcement
or negative reinforcement. Positive reinforcement is defined as response-contingent
presentation of a stimulus (e.g., drug) increases the probability of the response that
produced the stimulus. Negative reinforcement is also an increase in responding, but
in this case it is based on the response contingency of removing a stimulus (e.g., withdrawal symptoms). In the initial work (Spragg, 1940; Thompson and Schuster, 1964;

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Weeks, 1962), animals were made physically dependent on morphine and the drug
self-administration behavior was thought to be mediated by negative reinforcement.
That is, responding leading to morphine presentation was believed to be maintained
by removing withdrawal symptoms. However, for all drugs of abuse, there are positive reinforcing effects and, most likely, negative reinforcing effects (see Czoty
et al., 2015 for more discussion of this distinction). Drugs are self-administered
by animals using the same routes of administration as humans including oral
(e.g., Baker et al., 2014; Carroll and Meisch, 1978; Grant and Samson, 1985), inhalation (e.g., Carroll et al., 1990; Evans et al., 2003; Newman and Carroll, 2006;
Pickens et al., 1973), and intravenous (some examples provided below). For the purposes of this chapter, basic information regarding schedules of reinforcement will be
provided, as well as some examples involving different drug classes using the intravenous route. Because much work has been done with intravenous stimulants, especially cocaine, that will be the most frequently described drug in this section.

2.4.1 Use of simple schedules of reinforcement
Depending on whether the investigator is simply examining a drug for abuse liability
or wanting to compare it to other drugs, different schedules of reinforcement are
used. For example, answering the question “does the drug have reinforcing effects?”
most investigators use an FR schedule of reinforcement in which a particular number
of responses are required for each drug injection. For example, an FR 30 schedule,
the thirtieth response results in drug presentation. If it is a within-subject design, behavior is compared to when saline is self-administered and if it is significantly
higher, the drug has abuse potential. Less ideal is the use of an “inactive” lever in
the chamber—responding that is higher on the “active,” drug-contingent lever relative to the inactive lever also represents reinforcement. Some investigators may use a
fixed-interval (FI) schedule of reinforcement, in which a response after a specific
period of time has elapsed results in drug presentation. For example, under an FI
3-min schedule, the first response after 3 min results in drug presentation; responding
during the interval has no scheduled consequence. If the drug under investigation has
substantial response rate-decreasing effects, this may be a better schedule than FR
schedules because only one response is required after the interval has timed out. Irrespective of the schedule of reinforcement, behavior (response rates or number of injections) is represented as an inverted U-shaped function of dose (e.g., Pickens and
Thompson, 1968; Weeks, 1962). The shape of this curve is influenced by several
factors (Zernig et al., 2004), including reinforcing effects (increasing the probability
of future responding) and rate-decreasing effects (decreasing likely responding). For

this reason, it is not appropriate to compare drugs and rank them in terms of abuse
potential using simple schedules of reinforcement. Later in this section, measures of
reinforcing strength will be described; these models can be used to directly compare
and rank drugs.
The use of animals allows investigators to begin with drug–naı¨ve subjects and
study vulnerability to drug abuse. As described earlier with high and low locomotor
responders, phenotypic characteristics can be used to identify more or less vulnerable


2 Types of animal models

individuals. Others have shown that a particular drug history is needed for certain
drugs to function as reinforcers. For example, Nader and Mach (1996) and
Collins and Woods (2007) showed that monkeys and rats required a cocaine selfadministration history before DA D3 receptor agonists would function as reinforcers,
implying that a cocaine history alters DA D3 receptor function. Investigators frequently operationally define acquisition of some performance criterion (e.g., number
of sessions needed to earn 30 injections) or acquisition of reinforcement. The latter
implies a within-subject design and compares self-administration of a drug to
vehicle-contingent responding. In order to show reinforcement, responding contingent on administration of a drug dose needs to be higher than responding leading to
drug vehicle administration.
In a recent study, Gill et al. (2012) tested the hypothesis that adolescent exposure
to methylphenidate would increase vulnerability to cocaine abuse. For this experiment, adolescent rhesus monkeys (30 months old) were treated with extendedrelease methylphenidate or vehicle for 12 months. At the end of that treatment
period, monkeys were trained to respond under an FR 30 schedule of food presentation (methylphenidate treatment had terminated and there was a 3- to 5-month
washout). When responding was deemed stable, saline was substituted for the food
pellets until responding declined to less than 20% of baseline for three consecutive
sessions. There was a return to food-reinforced baseline and then ascending doses of
cocaine were made available for at least the same number of sessions as was required
for saline extinction, beginning at a very low cocaine dose (0.001 mg/kg per injection) and making half log unit higher doses available until cocaine functioned as reinforcer. There was a return to food-reinforced baseline before different cocaine
doses were tested. This procedure allowed for a quantitative measure of cocaine
acquisition—defined as the dose that maintained higher responding than when saline
was available. Survival curves were generated for both groups and compared statistically. Gill et al. (2012) did not find any differences in vulnerability (i.e., cocaine

acquisition) in the group treated with methylphenidate and controls. A similar procedure has been used and shown to differentiate female monkeys based on their social rank (Nader et al., 2012b).
As mentioned above, most drugs that humans abuse, animals will self-administer.
One drug class that has proven challenging is marijuana or THC, the nonselective
partial cannabinoid agonist. One of the first efforts to maintain THC selfadministration in monkeys was reported by Harris et al. (1974). In that study, rhesus
monkeys were given access to THC (0.025–0.3 mg/kg/injection over 10 s) under an
FR 1 schedule of reinforcement during daily 12 h sessions. No dose maintained
responding higher than vehicle-contingent behavior. Next, the investigators gave
monkeys noncontingent THC in an effort to make them physically dependent and
studied 0.025 mg/kg THC self-administration (perhaps as a negative reinforcer).
Again, the behavior was not maintained above response rates leading to vehicle injections. Others have also reported negative results (Li et al., 2012; Mansbach et al.,
1994). However, Tanda et al. (2000) and Justinova et al. (2003, 2008) reported THC
self-administration in squirrel monkeys responding under an FR 10 schedule of

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reinforcement. There are several possibilities for the different outcomes including
the species used (squirrel monkeys vs. rhesus monkeys), the drug vehicle, the pump
duration, and the schedule of reinforcement. Clearly, much additional work is required (see Panagis et al., 2008) since recreational marijuana use continues to increase across the world.
In addition to acquisition (vulnerability), simple schedules of drug selfadministration have also been modified so as to assess other phases of addiction including “loss of control,” by studying long-access conditions (e.g., Ahmed and Koob,
1998), long-term consequences during maintenance of drug self-administration (e.g.,
Nader et al., 2006), and relapse/reinstatement (e.g., Achat-Mendes et al., 2012; de Wit
and Stewart, 1981), including the study of “incubation” (see reviews by Lu et al.,
2004; Weiss, 2010). A recent series of studies have examined the powerful role of
environment on drug self-administration, including alternative physical activities
(e.g., Smith and Lynch, 2011) and social variables (e.g., Morgan et al., 2002;

Nader et al., 2012b; Smith, 2012; Yap et al., 2015; see also Smith et al., 2014). Finally,
it should be mentioned that the use of simple schedules of reinforcement and drug selfadministration has recently been used to better understand the role of specific brain
regions related to drug addiction, using in vivo imaging, such as PET, in vitro imaging
using receptor autoradiography, optogenetics, and DREADDS. A full description of
these protocols is beyond the scope of this chapter, but it is relevant that investigators
studying the neurochemistry associated with addiction utilize self-administration
models rather than noncontingent drug administration.

2.4.2 Use of complex schedules of reinforcement
Several investigators have suggested that more complex schedules of reinforcement
that measure reinforcing strength (efficacy) are a better model of the human condition than simple schedules of reinforcement (Ahmed, 2010; Ahmed et al., 2013;
Badiani, 2013; Banks and Negus, 2012; Banks et al., 2015). The two most frequently
used models of reinforcing strength are the progressive-ratio (PR) schedule and drug
choice procedures (either drug vs. drug or food vs. drug). For responding maintained
under PR schedules, the number of responses required for a drug injection increase
with each injection; this may occur within the same session (e.g., Czoty et al., 2010a;
Kimmel et al., 2008) or across sessions (e.g., Griffiths et al., 1978; see also Rowlett
et al., 1996). For these studies, the primary dependent variable is the final ratio completed, termed the break point (BP), when no injections have been received after a
specified period of time (termed the limited hold) or at the end of the session. As with
all schedules of drug self-administration involving reinforcement, the shape of the
dose–response curve is an inverted U-shaped function; for PR studies, BPs for different drugs can be compared statistically (see Stafford et al., 1998 for review).
PR schedules are quite amenable to examining the effects of treatments on drug
self-administration, including cocaine self-administration (e.g., Czoty et al., 2006,
2010b, 2013). As an example, the effects of d-amphetamine on cocaine BP will
be described. Amphetamine has been shown to have efficacy as a cocaine pharmacotherapy (Grabowski et al., 2001; Negus and Mello, 2003a,b). In one study, Czoty
et al. (2011) had monkeys self-administering cocaine under a PR schedule; the dose


2 Types of animal models

of cocaine was on the ascending limb of the dose–response curve. Monkeys received
a continuous infusion of d-amphetamine at a rate of 0.4 ml/h and every 7 days they
were given access to cocaine. If the amphetamine treatment decreased the cocaine
BP, they were retested 1 week later to examine for tolerance to these effects; if tolerance developed or if the initial amphetamine dose had no effect on cocaine BP, the
daily amphetamine dose was increased. In this study, d-amphetamine decreased the
BP for cocaine and, importantly from a clinical perspective, tolerance did not develop to these effects. Also of relevance is that different amphetamine doses produced optimal effects in monkeys, so if all animals had been tested with the same
doses and mean data presented, the effects would not have been statistically significant. Studies of this type highlight the importance of individual subject variability in
drug responses.
For studies involving drug choice, the primary dependent variable is percentage
of trials the drug is chosen. There are two general variations of the choice procedure:
drug versus drug choice and food versus drug choice. In one sense, if an investigator
wanted to directly compare the reinforcing strength of a novel drug with a known
drug of abuse, the drug–drug choice procedure is ideal (e.g., Johanson and
Schuster, 1975). For these studies, animals are implanted with double-lumen catheters in which drug A is available through one lumen and drug B through the other. For
example, Lile et al. (2002) compared the reinforcing strength of a novel DA transporter (DAT) blocker, PTT, with cocaine. When first studied under a PR schedule,
the BP for PTT was significantly lower than that for cocaine (Lile et al., 2002). However, when monkeys were given the opportunity to choose between cocaine and PTT,
at the highest dose of each, PTT and cocaine were chosen on 50% of the completed
trials. Interestingly though, cocaine intake was reduced by nearly 90% relative to
when choice was between cocaine and saline. That is, the monkeys did not complete
many trials when both drugs were available (although half the trials resulted in cocaine and the other half PTT), suggesting that perhaps a long-acting DAT blocker
would be an effective treatment for cocaine addiction in the context in which cocaine
is still being used (see Nader et al., 2015 for additional discussion).
The second variation of drug choice involves comparing self-administration in
the context of alternative nondrug reinforcers. However, the food–drug choice procedure is too labor intensive to use to directly compare novel drugs in terms of measures of reinforcing strength. That is, how different drugs dose–response curves
appear in the context of a nondrug alternative are difficult studies to interpret. For
example, Nader and Woolverton (1991) had different groups of monkeys, one choosing between cocaine and food the other between procaine and food. Under baseline
conditions, the shapes of the dose–response curves for both drugs appeared similar.
However, when the magnitude of the alternative was manipulated (i.e., increases in
the number of food pellets available as an alternative to drug), the procaine dose–
response curve became much flatter than the cocaine curve, suggesting that procaine

had weaker reinforcing strength than cocaine.
When only one drug is studied (e.g., cocaine), investigators can utilize a food–
drug choice procedure to compare different groups of subjects in terms of sensitivity
to environmental context and alternative reinforcers. For example, when monkeys

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